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Zhang ZY, Yang LT, Yue Q, Kang KJ, Li YJ, An HP, C G, Chang JP, Chen YH, Cheng JP, Dai WH, Deng Z, Fang CH, Geng XP, Gong H, Guo QJ, Guo T, Guo XY, He L, He SM, Hu JW, Huang HX, Huang TC, Jiang L, Karmakar S, Li HB, Li HY, Li JM, Li J, Li QY, Li RMJ, Li XQ, Li YL, Liang YF, Liao B, Lin FK, Lin ST, Liu JX, Liu SK, Liu YD, Liu Y, Liu YY, Ma H, Mao YC, Nie QY, Ning JH, Pan H, Qi NC, Ren J, Ruan XC, Singh MK, Sun TX, Tang CJ, Tian Y, Wang GF, Wang JZ, Wang L, Wang Q, Wang YF, Wang YX, Wong HT, Wu SY, Wu YC, Xing HY, Xu R, Xu Y, Xue T, Yan YL, Yi N, Yu CX, Yu HJ, Yue JF, Zeng M, Zeng Z, Zhang BT, Zhang FS, Zhang L, Zhang ZH, Zhao JZ, Zhao KK, Zhao MG, Zhou JF, Zhou ZY, Zhu JJ. Experimental Limits on Solar Reflected Dark Matter with a New Approach on Accelerated-Dark-Matter-Electron Analysis in Semiconductors. Phys Rev Lett 2024; 132:171001. [PMID: 38728703 DOI: 10.1103/physrevlett.132.171001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/22/2024] [Accepted: 03/19/2024] [Indexed: 05/12/2024]
Abstract
Recently a dark matter-electron (DM-electron) paradigm has drawn much attention. Models beyond the standard halo model describing DM accelerated by high energy celestial bodies are under intense examination as well. In this Letter, a velocity components analysis (VCA) method dedicated to swift analysis of accelerated DM-electron interactions via semiconductor detectors is proposed and the first HPGe detector-based accelerated DM-electron analysis is realized. Utilizing the method, the first germanium based constraint on sub-GeV solar reflected DM-electron interaction is presented with the 205.4 kg·day dataset from the CDEX-10 experiment. In the heavy mediator scenario, our result excels in the mass range of 5-15 keV/c^{2}, achieving a 3 orders of magnitude improvement comparing with previous semiconductor experiments. In the light mediator scenario, the strongest laboratory constraint for DM lighter than 0.1 MeV/c^{2} is presented. The result proves the feasibility and demonstrates the vast potential of the VCA technique in future accelerated DM-electron analyses with semiconductor detectors.
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Affiliation(s)
- Z Y Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - L T Yang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q Yue
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - K J Kang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y J Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H P An
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- Department of Physics, Tsinghua University, Beijing 100084
| | - Greeshma C
- Institute of Physics, Academia Sinica, Taipei 11529
| | | | - Y H Chen
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J P Cheng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - W H Dai
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Z Deng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - C H Fang
- College of Physics, Sichuan University, Chengdu 610065
| | - X P Geng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H Gong
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q J Guo
- School of Physics, Peking University, Beijing 100871
| | - T Guo
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - X Y Guo
- YaLong River Hydropower Development Company, Chengdu 610051
| | - L He
- NUCTECH Company, Beijing 100084
| | - S M He
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J W Hu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H X Huang
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - T C Huang
- Sino-French Institute of Nuclear and Technology, Sun Yat-sen University, Zhuhai 519082
| | - L Jiang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - S Karmakar
- Institute of Physics, Academia Sinica, Taipei 11529
| | - H B Li
- Institute of Physics, Academia Sinica, Taipei 11529
| | - H Y Li
- College of Physics, Sichuan University, Chengdu 610065
| | - J M Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - J Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q Y Li
- College of Physics, Sichuan University, Chengdu 610065
| | - R M J Li
- College of Physics, Sichuan University, Chengdu 610065
| | - X Q Li
- School of Physics, Nankai University, Tianjin 300071
| | - Y L Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y F Liang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - B Liao
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - F K Lin
- Institute of Physics, Academia Sinica, Taipei 11529
| | - S T Lin
- College of Physics, Sichuan University, Chengdu 610065
| | - J X Liu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - S K Liu
- College of Physics, Sichuan University, Chengdu 610065
| | - Y D Liu
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - Y Liu
- College of Physics, Sichuan University, Chengdu 610065
| | - Y Y Liu
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - H Ma
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y C Mao
- School of Physics, Peking University, Beijing 100871
| | - Q Y Nie
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - J H Ning
- YaLong River Hydropower Development Company, Chengdu 610051
| | - H Pan
- NUCTECH Company, Beijing 100084
| | - N C Qi
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J Ren
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - X C Ruan
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - M K Singh
- Institute of Physics, Academia Sinica, Taipei 11529
- Department of Physics, Banaras Hindu University, Varanasi 221005
| | - T X Sun
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - C J Tang
- College of Physics, Sichuan University, Chengdu 610065
| | - Y Tian
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - G F Wang
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - J Z Wang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - L Wang
- Department of Physics, Beijing Normal University, Beijing 100875
| | - Q Wang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- Department of Physics, Tsinghua University, Beijing 100084
| | - Y F Wang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y X Wang
- School of Physics, Peking University, Beijing 100871
| | - H T Wong
- Institute of Physics, Academia Sinica, Taipei 11529
| | - S Y Wu
- YaLong River Hydropower Development Company, Chengdu 610051
| | - Y C Wu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H Y Xing
- College of Physics, Sichuan University, Chengdu 610065
| | - R Xu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y Xu
- School of Physics, Nankai University, Tianjin 300071
| | - T Xue
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y L Yan
- College of Physics, Sichuan University, Chengdu 610065
| | - N Yi
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - C X Yu
- School of Physics, Nankai University, Tianjin 300071
| | - H J Yu
- NUCTECH Company, Beijing 100084
| | - J F Yue
- YaLong River Hydropower Development Company, Chengdu 610051
| | - M Zeng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Z Zeng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - B T Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - F S Zhang
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - L Zhang
- College of Physics, Sichuan University, Chengdu 610065
| | - Z H Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - J Z Zhao
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - K K Zhao
- College of Physics, Sichuan University, Chengdu 610065
| | - M G Zhao
- School of Physics, Nankai University, Tianjin 300071
| | - J F Zhou
- YaLong River Hydropower Development Company, Chengdu 610051
| | - Z Y Zhou
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - J J Zhu
- College of Physics, Sichuan University, Chengdu 610065
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Li WY, Liu Y, Zhang YM, Dou LZ, He S, Ke Y, Liu XD, Liu YM, Wu HR, Wang GQ. [Therapeutic efficacy analysis of endoscopic combined with serological diagnosis strategy and endoscopic in G1 and G2 gastric neuroendocrine neoplasms]. Zhonghua Zhong Liu Za Zhi 2024; 46:326-334. [PMID: 38644268 DOI: 10.3760/cma.j.cn112152-20231219-00368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Objective: To investigate the endoscopic combined serological diagnosis strategy for G1 and G2 gastric neuroendocrine neoplasms (G-NENs), and to evaluate the safety, short-term, and long-term efficacy of two endoscopic treatment procedures: endoscopic mucosal resection (EMR) and endoscopic submucosal dissection (ESD). Methods: This study retrospectively analyzed the clinical data of 100 consecutive patients with G-NENs who were hospitalized at the Cancer Hospital of the Chinese Academy of Medical Sciences from January 2011 to October 2023. These patients underwent endoscopic treatment, and propensity score matching (PSM) was used to compare clinicopathological characteristics, as well as short-term and long-term efficacy of lesions in the EMR group and ESD group before and after treatment. Results: Among the 100 patients with G-NENs, the median age was 54 years old. Before surgery, 29 cases underwent endoscopic combined serological examination, and 24 of them (82.2%) had abnormally elevated plasma chromogranin A. The combined diagnostic strategy for autoimmune atrophic gastritis (AIG) achieved a diagnostic accuracy of 100%(22/22). A total of 235 G-NEN lesions were included, with 84 in the ESD group and 151 in the EMR group. The median size of the lesions in the ESD group (5.0 mm) was significantly larger than that in the EMR group (2.0 mm, P<0.001). Additionally, the ESD group had significantly more lesions with pathological grade G2[23.8%(20/84) vs. 1.3%(2/151), P<0.001], infiltration depth reaching the submucosal layer [78.6%(66/84) vs. 51.0%(77/151), P<0.001], and more T2 stage compared to the EMR group[15.5%(13/84) vs. 0.7%(1/151), P<0.001]. After PSM, 49 pairs of lesions were successfully matched between the two groups. Following PSM, there were no significant differences in the en bloc resection rate [100.0%(49/49) vs. 100.0%(49/49)], complete resection rate [93.9%(46/49) vs. 100.0%(49/49)], and complication rate [0(0/49) vs. 4.1%(2/49)] between the two groups. During the follow-up period, no recurrence or distant metastasis was observed in any of the lesions in both groups. Conclusions: The combination of endoscopy and serology diagnostic strategy has the potential to enhance the accuracy of diagnosing G1 and G2 stage G-NENs and their background mucosa. Endoscopic resection surgery (EMR, ESD) is a proven and safe treatment approach for G1 and G2 stage G-NENs.
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Affiliation(s)
- W Y Li
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y M Zhang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Z Dou
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S He
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Ke
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - X D Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y M Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - H R Wu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - G Q Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Huang Y, Ge R, Qian J, Lu J, Qiao D, Chen R, Jiang H, Cui D, Zhang T, Wang N, He S, Wang M, Yan F. Lacticaseibacillus rhamnosus GG Improves Periodontal Bone Repair via Gut-Blood Axis in Hyperlipidemia. J Dent Res 2024; 103:253-262. [PMID: 38197171 DOI: 10.1177/00220345231217402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024] Open
Abstract
Periodontal bone regeneration remains a clinical challenge, and hyperlipidemia can aggravate alveolar bone resorption. Probiotics have recently been reported to improve bone mass. We aimed to determine the role of Lacticaseibacillus rhamnosus GG (LGG) in periodontal bone regeneration improvement within the context of periodontitis with hyperlipidemia. A Sprague Dawley rat model for periodontitis, hyperlipidemia, and periodontal fenestration defect was constructed (n = 36) and administered LGG gavage for 6 wk (the rats were subsequently sacrificed). Fecal microbiota from donor rats 3 wk after LGG gavage was transplanted into recipient rats to evaluate the role of LGG-modulated gut microbiota in periodontal bone regeneration. Regenerated bone mass was detected using micro-computerized tomography and hematoxylin and eosin stain. Gut microbiota was analyzed using 16S ribosomal RNA sequencing. Serum metabolites were detected by liquid chromatography-mass spectrometry (6 wk after LGG gavage). The pro-osteogenic effects of screened serum metabolite were verified in vitro on bone marrow mesenchymal stem cells (BMMSCs). We found that the bone mineral density, bone volume (BV), trabecular bone volume fraction (BV/TV), and trabecular thickness of the regenerated periodontal bone increased after LGG gavage (P < 0.05) but had little effect on oral flora. After LGG gavage, Staphylococcus, Corynebacterium, and Collinsella in the gut of donors were significantly changed, and these differences were maintained in recipients, who also showed increased trabecular thickness of the regenerated periodontal bone (P < 0.05). These key genera were correlated with BV/TV and BV (P < 0.05). In addition, LGG gavage significantly regulated bone-related blood metabolites, of which selenomethionine promoted BMMSC osteogenesis. Notably, selenomethionine was associated with key gut genera (P < 0.05). Collectively, LGG improved periodontal bone regeneration in the context of periodontitis with hyperlipidemia by modulating gut microbiota and increasing pro-osteogenic metabolites in the blood. These results reveal new insights into the use of probiotics to promote periodontal bone regeneration via the gut-blood-bone axis.
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Affiliation(s)
- Y Huang
- Department of Periodontology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
- Department of Periodontology, Stomatological Hospital and Dental School of Tongji University, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Shanghai, China
| | - R Ge
- School of Stomatology, Zunyi Medical University, Zunyi, China
| | - J Qian
- Department of Periodontology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - J Lu
- Department of Periodontology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - D Qiao
- Department of Periodontology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - R Chen
- Department of Periodontology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - H Jiang
- Department of Periodontology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
- Department of Stomatology, Dushu Lake Hospital Affiliated to Soochow University, Medical Center of Soochow University, Suzhou Dushu Lake Hospital, Suzhou, China
| | - D Cui
- Department of Periodontology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - T Zhang
- Department of Periodontology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - N Wang
- Department of Periodontology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - S He
- Department of Periodontology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - M Wang
- Department of Periodontology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - F Yan
- Department of Periodontology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
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Liu J, Lin P, Xu HF, Yang F, Fu XB, Yao ZL, Xie SL, He SM, Li JR, Pan SY, Li Y. [High-risk sexual behaviors of HIV/AIDS and related factors in young students in Guangzhou]. Zhonghua Liu Xing Bing Xue Za Zhi 2024; 45:265-272. [PMID: 38413067 DOI: 10.3760/cma.j.cn112338-20230617-00383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Objective: To explore high-risk sexual behaviors of HIV/AIDS and related factors in young students in Guangzhou. Methods: A cross-sectional survey was conducted in 5 different types of Guangzhou colleges by convenience sampling with minimum number of classes per grade and 600 samples per school from September to November 2021. The R 4.2.2 software was used to consolidate databases. Simultaneously, a logistic regression model and a decision tree algorithm model, stratifying by whether sexual behaviors had occurred before, were constructed. In each layer, the prediction performance of the two models was evaluated through area under receiver operating characteristic and the confusion matrix, and then the model with high prediction performance was retained. Results: A total of 7 346 students were surveyed. The proportion of the respondents reporting sexual experience were 9.08% (667/7 346), in whom 26.24% (175/667) had risky sexual activity in the past year. The decision tree algorithm model performs well in predicting whether high-risk sexual behaviors have occurred in the past year. When the complexity parameter value is 0.018, and nsplit reaches 4, which means there are 5 leaf nodes in the model, the cross error of the tree will be the smallest. The first best grouping variable in the decision tree was whether to use condoms throughout the first sexual behavior. If condoms were used at their sexual debut, but homosexual practices have occurred in the past year, the probability of risky sexual behavior will increase. If homosexual practices have not occurred in the past year, but the age of sexual debut was below 18 years old while the period of HIV education was after high school, the probability of risk sexual behavior will also increase. Conclusions: AIDS-related risky behaviors of young students still deserved attention. The experience of sexual debut and whether AIDS-related health education has been received before the sexual debut were significant predictors for the occurrence of high-risk sexual behavior. The decision tree algorithm model has particular applicability for predicting and screening potential risk populations.
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Affiliation(s)
- J Liu
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention, Guangzhou 511430, China
| | - P Lin
- Guangdong Association of STD & AIDS Prevention and Control, Guangzhou 511430, China
| | - H F Xu
- Guangdong Association of STD & AIDS Prevention and Control, Guangzhou 511430, China
| | - F Yang
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention, Guangzhou 511430, China
| | - X B Fu
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Z L Yao
- Guangdong Association of STD & AIDS Prevention and Control, Guangzhou 511430, China
| | - S L Xie
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention, Guangzhou 511430, China
| | - S M He
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention, Guangzhou 511430, China
| | - J R Li
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention, Guangzhou 511430, China
| | - S Y Pan
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Y Li
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention, Guangzhou 511430, China
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Ma X, Wang L, Li J, Guo Y, He S. The pathogenicity and immune effects of different generations of Mycoplasma synoviae on chicken embryos. Br Poult Sci 2024; 65:19-27. [PMID: 38018666 DOI: 10.1080/00071668.2023.2287733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 11/05/2023] [Indexed: 11/30/2023]
Abstract
1. Mycoplasma synoviae (MS) is the primary causative agent of synovitis in avian species. In order to investigate the pathogenicity and immunological responses associated with MS in specific pathogen-free chicken embryos, a series of generations (F1, F95, F120, F160 and F200) of MS were introduced into 7-day-old SPF chicken embryos and subsequent mortality rates were recorded and analysed2. Reverse transcription-quantitative polymerase chain reaction was performed to detect expression of heat shock proteins HSP27, HSP40, HSP60, HSP70 and HSP90 and inflammatory factors interleukin (IL)-1β, caspase-1 and IL-18 in the tracheal tissue.3. The results showed that the mortality rate of SPF chicken embryos decreased with an increase in the number of passages, with the highest being 80% (8/10) for F1 generation and the lowest being 10% (1/10) for F200. The expression of HSP27, IL-1β, HSP40, caspase-1, HSP70 and HSP90 showed a significant downregulation trend with an increase in the generation (except IL-18; P < 0.05). The HSP60 expression was significantly upregulated with increasing generations (P < 0.05).4. A relationship between pathogenicity and the number of passages was observed and the decrease in pathogenicity appeared to be associated with HSP and genes related to inflammatory factors. The present work offers a scientific foundation for screening potential MS strains that might be employed to develop attenuated vaccines.
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Affiliation(s)
- X Ma
- School of Animal Science and Technology, Ningxia University, Yinchuan, Ningxia, China
| | - L Wang
- School of Animal Science and Technology, Ningxia University, Yinchuan, Ningxia, China
| | - J Li
- School of Animal Science and Technology, Ningxia University, Yinchuan, Ningxia, China
| | - Y Guo
- Ningxia Academy of Agricultural and Forestry Science's Yinchuan, Institute of Animal Science, Yinchuan, Ningxia, China
| | - S He
- School of Animal Science and Technology, Ningxia University, Yinchuan, Ningxia, China
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Chen X, He S, Wang Z, Zhai Y, Guo W, Li X. Production of transgenic periclinal chimeras in pumpkin - a tool for revealing cell fates of L1 meristem. Plant Biol (Stuttg) 2024; 26:126-139. [PMID: 37975550 DOI: 10.1111/plb.13593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023]
Abstract
Genetic engineering is commonly used to improve the agronomic traits of crops. However, genetic transformation in pumpkin remains a challenge. Conducting transformation trials, we accidentally created transgenic L1 periclinal chimeras in pumpkins. Using our modified Agrobacterium-mediated transformation, we generated transgenic L1 periclinal chimeras which have high value in research on development of the meristem. Fluorescence observations of transformed L1 cells enabled us to reveal cell fates. These L1 cells can develop into stomata, epidermal hairs, seed coat, and epidermis of the root, stem, leaf, flower, and fruit. These periclinal chimeras can be propagated vegetatively with minimal risk of transgene flow. This study offers new perspectives on development of the meristem and a promising technique for creating transgenic periclinal chimeras in plants.
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Affiliation(s)
- X Chen
- School of Horticulture and Landscape Architecture, Henan Institute of Science and Technology, Xinxiang, Henan, China
- Henan Province Engineering Research Center of Horticultural Plant Resource Utilization and Germplasm Enhancement, Xinxiang, Henan, China
| | - S He
- School of Horticulture and Landscape Architecture, Henan Institute of Science and Technology, Xinxiang, Henan, China
| | - Z Wang
- School of Horticulture and Landscape Architecture, Henan Institute of Science and Technology, Xinxiang, Henan, China
| | - Y Zhai
- School of Horticulture and Landscape Architecture, Henan Institute of Science and Technology, Xinxiang, Henan, China
- Henan Province Engineering Research Center of Horticultural Plant Resource Utilization and Germplasm Enhancement, Xinxiang, Henan, China
| | - W Guo
- School of Horticulture and Landscape Architecture, Henan Institute of Science and Technology, Xinxiang, Henan, China
- Henan Province Engineering Research Center of Horticultural Plant Resource Utilization and Germplasm Enhancement, Xinxiang, Henan, China
| | - X Li
- School of Horticulture and Landscape Architecture, Henan Institute of Science and Technology, Xinxiang, Henan, China
- Henan Province Engineering Research Center of Horticultural Plant Resource Utilization and Germplasm Enhancement, Xinxiang, Henan, China
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Liu J, Lin P, Xu HF, Li Y, Fu XB, Yao ZL, Xie SL, He SM, Li JR, Pan SY, Yang F. [Perception of HIV-related behavior and influencing factors among young students in Guangzhou]. Zhonghua Liu Xing Bing Xue Za Zhi 2023; 44:1956-1962. [PMID: 38129153 DOI: 10.3760/cma.j.cn112338-20230617-00384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Objective: To investigate the risk perception for risky behavior of HIV/AIDS infection among young students and to analyze the related influencing factors. Methods: A cross-sectional survey was conducted in 5 different types of Guangzhou colleges from September to November 2021, in which convenience sampling and a minimum number of classes per grade and 600 samples per school were used according to the national unity program. Disordered multi-classification logistic regression was used to construct a risk perception model and analyze influencing factors in different risk perception levels. Results: A total of 7 346 young students were surveyed, and most rated themselves at low risk of HIV/AIDS infections (90.58%, 6 654/7 346). A total of 89.10% (6 545/7 346) of subjects' perception of their HIV/AIDS infection risk was consistent with their risk behavior, while 10.90% (801/7 346) was inconsistent. Among those inconsistent subjects, 19.10% (153/801) showed underestimating their risk , while 80.90% (648/801) seen overestimating their risk. Disordered multi-classification logistic regression analysis showed that, after controlling for other factors, compared with the non-sexual group, respondents whose first sex age under 18 had a higher rate of underestimating their risk of infection (OR=129.39, 95%CI: 73.28-228.48), as well as a higher rate of overestimated their risk of infection (OR=1.76, 95%CI: 1.04-2.99). First sexual intercourse at age 18 or older was a risk factor for underestimating risk (OR=70.56, 95%CI: 42.72-116.53), but was not statistically associated with overestimating risk. Being female, other school type, non-heterosexual orientation, and self-rated HIV-related knowledge as fair or no knowledge were risk factors for overestimating risk but were not statistically associated with underestimating risk. Conclusions: Overall, young students in universities of Guangzhou have a good risk perception of HIV/AIDS infection. Individual factors, education factors and sexual experience will influence students' risk perception of HIV/AIDS infection. Raising the awareness rate of HIV/AIDS knowledge and delaying the age of first sexual intercourse will improve the risk perception ability of young students.
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Affiliation(s)
- J Liu
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention,Guangzhou 511430, China
| | - P Lin
- Guangdong Association of STD/AIDS Prevention and Control, Guangzhou 511430, China
| | - H F Xu
- Guangdong Association of STD/AIDS Prevention and Control, Guangzhou 511430, China
| | - Y Li
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention,Guangzhou 511430, China
| | - X B Fu
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention,Guangzhou 511430, China
| | - Z L Yao
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention,Guangzhou 511430, China
| | - S L Xie
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention,Guangzhou 511430, China
| | - S M He
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention,Guangzhou 511430, China
| | - J R Li
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention,Guangzhou 511430, China
| | - S Y Pan
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention,Guangzhou 511430, China
| | - F Yang
- Department for HIV/AIDS Control and Prevention, Guangdong Center for Disease Control and Prevention,Guangzhou 511430, China
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8
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He S, Liu Y, Wu S. Suicidal ideation and associated risk factors among COVID-19 patients. QJM 2023; 116:966-967. [PMID: 37632781 DOI: 10.1093/qjmed/hcad196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Indexed: 08/28/2023] Open
Affiliation(s)
- S He
- Department of Geriatrics, The Second Affiliated Hospital of Soochow University, No.1055, Sanxiang Road, Gusu District, Suzhou 215004, China
| | - Y Liu
- Department of Geriatrics, The Second Affiliated Hospital of Soochow University, No.1055, Sanxiang Road, Gusu District, Suzhou 215004, China
| | - S Wu
- Department of Geriatrics, The Second Affiliated Hospital of Soochow University, No.1055, Sanxiang Road, Gusu District, Suzhou 215004, China
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9
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Lin L, Mo Z, Xiao J, Kou J, Guo C, He SM, Zhang W, Sun Y. Identification and Automated Delineation of Radioresistant Biological Tumor Volume in Nasopharyngeal Carcinoma Based on Magnetic Resonance Imaging Radiomics. Int J Radiat Oncol Biol Phys 2023; 117:e598-e599. [PMID: 37785804 DOI: 10.1016/j.ijrobp.2023.06.1958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Widespread use of intensity modulated radiotherapy (IMRT) has improved the tumor control rate of nasopharyngeal carcinoma (NPC). However, nearly 20% of the patients with local-advanced NPC would relapse after precise irradiation and 80% of the recurrent lesions occur within the high dose field, suggesting that there are radiation-resistant cancer cell subsets within the tumor. In this context, identification and contouring of radiation resistance region of NPC for dose escalation at primary IMRT could be advantageous. In this work, we proposed a two-step radiomics workflow to predict local relapse and the recurrent region of NPC before primary IMRT. MATERIALS/METHODS In this single-center, retrospective study, pre-treatment magnetic resonance (MR) sequences of T1-weighted imaging (T1-w) and contrast-enhanced T1-weighted imaging (CET1-w) were collected from 800 patients of newly diagnosed and non-metastatic NPC between April 2009 and December 2015. The primary gross tumor volume (GTVp) of all patients and the actual recurrent lesion (GTVr) of patients who suffered from local recurrence were manually contoured for further analysis. A two-step complete radiomics workflow was designed to predict tumor recurrence and segment the region. First, least absolute shrinkage and selection operator (LASSO) was utilized for radiomics features selection of GTVp and support vector machine (SVM) was adopted to predict the recurrence. If the model predicts a recurrence, then the workflow utilizes an improved 3D U-Net to segment the recurrent region. Area under receiver operating characteristic curve (ROC-AUC) was used to evaluate the performance of tumor recurrence prediction, and Dice similarity coefficient (DSC) was used to assess the consistence between the actual and predicted GTVr. RESULTS Of 800 NPC patients, 95 (11.9%) patients developed in-field local recurrence. For recurrence risk prediction, the SVM ensemble model (T1-w+CET1-w) was selected for further application with higher sensitivity. The average ROC-AUC, specificity, sensitivity of the SVM ensemble model in a 5-fold cross-validation and in the independent test set of 160 patients were 0.922, 0.922, 0.777 and 0.928, 0.915, 0.737, respectively. Moreover, for recurrent region segmentation, the multi-modality (T1-w+CET1-w) model was superior to the single-modality (T1-w or CET1-w) model. In an independent test set of 15 patients, the DSC, sensitivity and 95% Hausdorff Distance between actual and predicted GTVr was 0.549±0.176, 0.696±0.118 and 9.813±4.788 which was superior to 0.444±0.188, 0.497±0.218 and 12.047±5.361 of original 3D U-Net. CONCLUSION The proposed two-step radiomics workflow showed a good performance in predicting tumor recurrence of NPC. The predicted location of the recurrence lesion was all accurate, but there was still a certain difference between the volume of the automated delineated and actual GTVr, which needed to be further optimized to be used as biological tumor volume.
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Affiliation(s)
- L Lin
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Guangzhou, China
| | - Z Mo
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - J Xiao
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - J Kou
- Sun Yat-sen University Cancer Center, Guangzhou, China
| | - C Guo
- First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - S M He
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - W Zhang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Y Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
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Zhang R, Liu Y, Yang R, Chen C, Fu C, Pan Z, Cai W, He SM, Zhang W. Deep Learning for Automated Contouring of Primary Gross Tumor Volumes by MRI for Radiation Therapy of Brain Metastasis. Int J Radiat Oncol Biol Phys 2023; 117:e496. [PMID: 37785562 DOI: 10.1016/j.ijrobp.2023.06.1734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Radiotherapy is one of the most effective methods for the treatment of brain metastases (BMs). Traditional manual delineation of primary gross tumor volumes (GTV) of multiple BMs (especially small metastases) in radiotherapy practice is extremely labor intensive and highly dependent on oncologists' experience, achieving the precise and efficient automatic delineation of BMs is of great significance for efficient and homogeneous one-stop adaptive radiotherapy. MATERIALS/METHODS We retrospectively collected 62 MRI (non-enhanced T1-weighted sequences) sequences of 50 patients with BMs from January 2020 to July 2021. An automatic model (BUC-Net) for automatic delineation BMs was proposed in this work, which was based on deep learning by combining 3D bottler layer module and the cascade architecture to improve the accuracy and efficient of BMs' automatic delineation, especially for small metastases with tiny size and relatively low contrast. The prosed method was compared with the existing 3D U-Net (U-Net) and 3D U-Net Cascade (U-Net Cascade). The performance of our proposed method was evaluated by Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD) with human experts. RESULTS The automatic segmentation results of BUC-Net evaluated with 310 BMs in 13 test patients was summarized in Table 1. These BMs in each test patient were automatically delineated by two types of contours: as a whole tumor contour (Whole-delineation) and the multiple tumor contours (Multiple-delineation). BUC-Net performed the best mean DSC and HD95, which is significantly outperformed U-Net (Whole-delineation: 0.911 & 0.894 of DSC, Multiple-delineation: 0.794 & 0.754 of DSC, P < 0.05 for both) and U-Net cascade (Whole-delineation: 0.947 & 7.141 of HD95, Multiple-delineation: 0.902 & 1.171 of HD95, P < 0.05 for both); Additionally, BUC-Net achieved the best mean ASD for Whole-delineation and comparable ASD (0.290 & 0.277, P > 0) for Multiple-delineation with U-Net Cascade. CONCLUSION Our results showed that the proposed approach is promising for the automatic delineation of BMs in MRI, which can be integrated into a radiotherapy workflow to significantly shorten segmentation time.
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Affiliation(s)
- R Zhang
- Department of Radiation Oncology, The First Hospital of Tsinghua University, Beijing, China
| | - Y Liu
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - R Yang
- Department of Radiation Oncology, The First Hospital of Tsinghua University, Beijing, China
| | - C Chen
- Department of Radiation Oncology, The First Hospital of Tsinghua University, Beijing, China
| | - C Fu
- Department of Radiation Oncology, The First Hospital of Tsinghua University, Beijing, China
| | - Z Pan
- Department of Radiation Oncology, The First Hospital of Tsinghua University, Beijing, China
| | - W Cai
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - S M He
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - W Zhang
- Shanghai United Imaging Healthcare Technology Co., Ltd, Shanghai, China
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Peng J, Liu Y, Jiang D, Wang X, Peng P, He SM, Zhang W, Zhou F. Deep Learning and GAN-Synthesis for Auto-Segmentation of Pancreatic Cancer by Non-Enhanced CT for Adaptive Radiotherapy. Int J Radiat Oncol Biol Phys 2023; 117:e499-e500. [PMID: 37785569 DOI: 10.1016/j.ijrobp.2023.06.1742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) In conventional adaptive radiotherapy (ART) for pancreatic cancer, contrast-enhanced CT (CECT) helps to more precisely delineate primary gross tumor volume (GTV) than non-enhanced CT (NECT). However, frequent use of contrast medium can damage kidneys and prolong treatment time. Moreover, traditional manual delineation is labor-intensive and highly dependent on the experience of oncologists. Currently, automatic delineation based on deep learning with Generative Adversarial Networks (GAN)-based CT synthesis is one of the most feasible solutions to these problems. MATERIALS/METHODS A dataset of 35 pancreatic cancer patients was retrospectively collected from May 2021 to December 2022. All patients consist of a pair of NECT and CECT. We designed and developed an automatic delineation framework (Proposed) for GTV of pancreatic cancer based on Trans-cycleGAN and a modified 3D U-Net. TranscycleGAN can not only synthesize CECT from NECT, but can also augment the amount of CT images; then all real and synthesized CT images were used to train the modified 3D U-Net for automatic delineation of GTV; finally, our framework was able to automatically delineate GTV by NECT, but not only by CECT. Our framework was evaluated by dice similarity coefficient (DSC), 95% Harsdorff distance (95HD) and average surface distance (ASD) with oncologists' manual delineation ("gold standard"). RESULTS The evaluation results were summarized in Table 1. The proposed framework achieved the best automatic delineation results by NECT, which was superior to that of CECT: 0.917 & 0.903 of DSC, 2.498mm & 3.029mm of HD95, 0.481mm & 0.534mm of ASD, p < 0.05 for DSC and HD95. Specifically, it is significantly superior to the automatic delineation results using U-Net by CECT 0.917 & 0.818 of DSC, 2.498mm & 13.228mm of HD95, 0.481mm & 3.633mm of ASD, p < 0.05 for DSC. CONCLUSION We proposed an automatic delineation framework for contouring GTV in ART of pancreatic cancer based on deep learning and Trans-cycleGAN network. This framework could automatically delineate GTV and achieve better performance with NECT compared to CECT. Our method could not only reduce the use of contrast medium, but also increase the precision and effectiveness of tumor delineation, which could have a positive impact on precision radiotherapy.
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Affiliation(s)
- J Peng
- Department of Radiation and Medical Oncology, Hubei Key Laboratory of Tumor Biological Behaviors, Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Y Liu
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - D Jiang
- Department of Radiation and Medical Oncology, Hubei Key Laboratory of Tumor Biological Behaviors, Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - X Wang
- Department of Radiation and Medical Oncology, Hubei Key Laboratory of Tumor Biological Behaviors, Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - P Peng
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - S M He
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - W Zhang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
| | - F Zhou
- Department of Radiation and Medical Oncology, Hubei Key Laboratory of Tumor Biological Behaviors, Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, China
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12
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Zhou GQ, Yang YX, Yang X, Jia LC, Jiang X, Zhou J, Chen AQ, Diao WC, Liu L, Li H, Zhang K, He SM, Zhang W, Lin L, Sun Y. All-in-One Online Radiotherapy for Nasopharyngeal Carcinoma: Preliminary Results of Treatment Time, Contouring Accuracy, Treatment Plan Quality and Patient Compliance. Int J Radiat Oncol Biol Phys 2023; 117:e636-e637. [PMID: 37785898 DOI: 10.1016/j.ijrobp.2023.06.2040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) To explore the feasibility of Fan-beam CT (FBCT)-based all in one (AIO) online workflow for nasopharyngeal carcinoma (NPC) in radical radiotherapy setting, and to preliminarily describe the timing of different steps in the process, contouring accuracy of regions of interest (ROIs), target coverage, organs at risk (OARs) dose and patient compliance. MATERIALS/METHODS From March 16, 2022 to January 04, 2023, 25 NPC patients (22/25 diagnosed as phase III/IV disease according to 8th edition of the AJCC/UICC staging system) consecutively treated with AIO radiotherapy were prospectively enrolled. All patients received mask fixation and MRI simulation scan in advance. Primary gross tumor volume (GTVp) of nasopharynx was automatically delineated by AI and edited manually on MRI images. AIO online workflow started with an integrated KV-level CT in a CT-integrated linear accelerator. After that GTVp was registrated to CT images and other ROIs was contoured automatically and then modified manually as needed. Subsequently automatic treatment plan was calculated and optimized until the dose of target and OARs was evaluated satisfactory by physicians and physicists. Finally, treatment was delivered using volumetric modulated arc treatment (VMAT), with prescribed dose of 6996 cGy/ 33 fractions to the GTVp. RESULTS Twenty-four patients (24/25, 96%) completed the AIO radiotherapy workflow successfully, with average treatment time of 28.3 min (range: 19.9-42.4 min). the AI-assisted ROIs automatically contouring took 1.55 min in average (range: 1.32-1.77 min), with an average DICE of 97.7% compared with modified contouring, and the average DICE was 95.7% for clinical tumor volume 1 (CTV1), 88.6% for CTV2, 73.6% for GTVn (cervical lymph node), 99.3% for 30 OARs. The automatic treatment plan averagely needed 3.5 min, and the pass rate of radiotherapy planning was 91.7% (22/24). The target coverage for PTVs for GTVp, CTV1, and CTV2 was 99.3%, 99.8%, 98.0% respectively. As for the dose of OARs, the average Dmax of brainstem was 5,583cGy; the Dmax of spinal cord was 3,467cGy; the Dmean of parotid was 3,285 cGy. The average monitor units of all patients was 643 MU and the delivery took 2.93 min. Patient compliance with respect to AIO workflow and total treatment time was excellent. CONCLUSION The AIO online radiotherapy was promising for NPC patients, with clinically acceptable AI assisted ROIs contouring and treatment planning, as well as favorable patient compliance to the AIO online workflow.
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Affiliation(s)
- G Q Zhou
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Y X Yang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Guangzhou, China
| | - X Yang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Guangzhou, China
| | - L C Jia
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - X Jiang
- Sun Yat-sen University Cancer Center, Guangzhou, China
| | - J Zhou
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
| | - A Q Chen
- Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W C Diao
- Sun Yat-sen University Cancer Center, Guangzhou, China
| | - L Liu
- Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H Li
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - K Zhang
- Shanghai United Imaging Healthcare (UIH) Co., Ltd, Shanghai, 201807, China, Shanghai, China
| | - S M He
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - W Zhang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
| | - L Lin
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Guangzhou, China
| | - Y Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
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13
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Lin L, Wei Z, Jia LC, Guo C, Zhou GQ, Yang YX, He SM, Zhang W, Sun Y. Automated Contouring of Cervical Lymph Nodes and Clinical Target Volumes for Nasopharyngeal Carcinoma Based on Deep Learning and Experience Constraints. Int J Radiat Oncol Biol Phys 2023; 117:e598. [PMID: 37785805 DOI: 10.1016/j.ijrobp.2023.06.1957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Application of artificial intelligence (AI) for automated contouring of tumor volumes and organs at risk (OARs) for radiotherapy of nasopharyngeal carcinoma (NPC) leads to improved contouring accuracy and efficiency. However, few studies have involved the automated contouring of gross tumor volume of cervical lymph nodes (GTVn) and clinical target volumes (CTVs). In this work, we proposed an AI automated contouring tool for GTVn and CTVs for radiotherapy of NPC on the plain scans of planning compute tomography (CT). MATERIALS/METHODS In this retrospective study, plain scan datasets of planning CT covering the nasopharynx and neck from 139 patients with NPC between March 2022 and December 2022 were collected and divided into training, validation, and testing cohorts of 95, 24, and 20 patients, respectively. Ground truth contours of primary gross tumor volume (GTVp), GTVn (divided into GTVn_L in left neck and GTVn_R in right neck), CTVs (including high risk CTV1 contains GTVp and low risk CTV2 contains GTVp and cervical nodal levels) and OARs were delineated and were defined by consensus of two experts. We first proposed a three-dimensional (3D) U-net using GTVp and OARs as experience constrains to guide the automated delineation of GTVn and CTVs. The average Dice similarity coefficient (DSC) and average surface distance (ASD) were used to quantify the performance of the AI tool. Next, five prospective patients were enrolled for clinical evaluation of our AI tool. DSC between automated contours and radiation oncologist-revised contours and time consuming of the revision were record. RESULTS Clinical characteristics of 139 retrospective and 5 prospective patients are list in Table 1. In the independent testing set of 20 patients, our AI tool showed high performance in GTVn and CTVs contouring when compared with the ground truth contours. The mean DSC were 0.73 ± 0.07, 0.74 ± 0.05, 0.93 ± 0.03, and 0.88 ± 0.03, and the mean ASD were 1.01 ± 0.43 mm, 1.14 ± 0.61 mm, 0.51 ± 0.13 mm, 1.17 ± 0.43 mm for GTVn_L, GTVn_R, CTV1 and CTV2, respectively. In the five prospective patients, mean DSC were 0.74 ± 0.07, 0.74 ± 0.10, 0.95 ± 0.01 and 0.89 ± 0.04, respectively. The median time consuming for GTVn and CTVs revision was 2minutes and 10 seconds (range, 1 minutes to 3 minutes). CONCLUSION The proposed AI tool integrating clinical experience as constrains showed high accuracy for contouring GTVn and CTVs of NPC. With the assistance of AI contours, contouring efficiency could be probably increased, which is promising in online adaptive radiotherapy of NPC.
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Affiliation(s)
- L Lin
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Guangzhou, China
| | - Z Wei
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - L C Jia
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - C Guo
- First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - G Q Zhou
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Guangzhou, China
| | - Y X Yang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Guangzhou, China
| | - S M He
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - W Zhang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Y Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
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Lin L, Zhou GQ, Yang X, Yang YX, Jiang X, Li B, Chen AQ, Diao WC, Liu L, He SM, Li H, Jia LC, Zhang W, Zhou J, Sun Y. First Implementation of Full-Workflow Automation for Online Adaptive Radiotherapy of Nasopharyngeal Carcinoma. Int J Radiat Oncol Biol Phys 2023; 117:e687. [PMID: 37786019 DOI: 10.1016/j.ijrobp.2023.06.2156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The aim of this work is to established the technical characteristics and implementation procedures of an artificial intelligence (AI)-powered radiotherapy workflow that enables full-process automation for online adaptive radiotherapy (ART); and evaluate its feasibility and performance implemented for ART of nasopharyngeal carcinoma (NPC). MATERIALS/METHODS This single center, prospective study has been approved by the ethical committee of the institution. The online ART workflow was developed based on a CT-integrated linear accelerator. During the course of radiotherapy, the patient underwent daily pre-treatment fan-beam CT (FBCT) scan. Then the FBCT was automatically registered to the original planning CT and used to assess the need for the patient to implement ART according to radiation oncologist's discretionary. The online ART workflow incorporates critical radiotherapy procedures from re-simulation, auto-segmentation by integrating image fusion and deep learning method, auto-replanning, beam delivery, and in vivo quality assurance (QA) into one scheme, while the patient is on the treatment couch during the whole process. RESULTS From 2th April 2022 to 5th January 2023, 20 patients with newly-diagnosed, non-metastatic NPC were enrolled in this study. Only one-time online ART was performed for each patient, because that the appropriate timing for triggering online ART was explored in parallel with this study. According to radiation oncologists' discretionary, the median fraction for performing online ART was at 21 fractions (interquartile range, 19-24 fractions). All patients were well tolerated and successfully completed the treatment. For tumor targets contouring, minor revisions were required for automated contours of the primary gross tumor volume (GTVp) and clinical target volumes (CTVs, including CTV1 and CTV2), with the mean DSC between before and after revision of 0.91±0.042, 0.94 ± 0.042 and 0.91 ± 0.061, respectively; and much more revisions for the automated contours of cervical lymph nodes GTV (GTVn), with the mean DSC of 0.74 ± 0.28. The automated contours of normal tissues were clinically acceptable with little modifications. Median time consuming for auto-segmentation and revision was 9.5 minutes (min). For treatment planning, 18 automated plans (90%) were passed at their first auto-optimization and two plans (10%) were passed after further optimization of the dose coverage of CTVs by physicist; and the median time consuming for auto-planning was 6.2 min. Time consuming for other procedures were as follows: re-simulation, 2.3 min; plan evaluation, 3.3 min; beam delivery, 4.6 min; and the duration of the entire process was 25.9 min, range from 19.4 min to 32.5 min. CONCLUSION We successfully established an AI-powered online ART workflow for adaptive radiotherapy of NPC, and confirmed that current auto-segmentation and auto-replanning methods are powered enough to support the clinical application of its online ART.
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Affiliation(s)
- L Lin
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, Guangzhou, China
| | - G Q Zhou
- Sun Yat-sen University Cancer Center, Guangzhou, China
| | - X Yang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, Guangzhou, China
| | - Y X Yang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, Guangzhou, China
| | - X Jiang
- Sun Yat-sen University Cancer Center, Guangzhou, China
| | - B Li
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
| | - A Q Chen
- Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W C Diao
- Sun Yat-sen University Cancer Center, Guangzhou, China
| | - L Liu
- Sun Yat-sen University Cancer Center, Guangzhou, China
| | - S M He
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - H Li
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - L C Jia
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - W Zhang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
| | - J Zhou
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Y Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
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Li X, Lin FY, Jia LC, Liu T, He SM, Zhang W, Zhang M, Wang Y. Preserving Structural Consistency in the Generation of Synthetic CT in Pelvic MR-Only Radiation Treatment Planning. Int J Radiat Oncol Biol Phys 2023; 117:e686. [PMID: 37786017 DOI: 10.1016/j.ijrobp.2023.06.2154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) MR-based synthetic CT (sCT) generation is necessary for MR-only radiotherapy to assist in radiation dose calculation, owing to no electronic density information in MR images. This study investigated the feasibility of synthesizing CT images from magnetic resonance (MR) images using generation antagonism networks (GANs) for MR radiotherapy of rectal cancer. Meanwhile, the transformer module and the contrast learning loss were introduced to improve the sCT. MATERIALS/METHODS The data set used in this study was the T2-weighted MR and CT image data of 108 patients with rectal cancer. Three-fold cross-validation was performed on all data sets. The transformer module was introduced into the plain CycleGAN, and the improved Patch Noise Contrastive Estimation (PatchNCE) loss was used as the loss function. The improved PatchNCE loss maintained the structural consistency of the MR and the synthetic CT by ensuring the consistency of the distribution of image patches on the MR-sCT image pair. The 2.5D images were taken as the input of our model, which refers to taking two consecutive adjacent layers in a specific layer. The CT-to-sCT image similarity was evaluated by metrics of mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and Structure Similarity Index Measure (SSIM). The sCT dosimetric accuracy was verified against CT-based dose distributions for the photon plan. Relative dose differences in the planning target volume and organs at risk were computed. RESULTS The evaluation indicators of sCT images generated by our model were superior to the plain CycleGAN in the results of the three-fold cross-validation. MAE, PSNR and SSIM of our model were 42.850HU, 26.486 and 0.988, respectively, which were superior to 47.129HU, 25.167 and 0.978 of the plain CycleGAN. In addition, sCT generated by our model exhibited good continuity in the axial direction compared with plain CycleGAN. Furthermore, most of the relative differences in the DVH indicators were less than 1%. CONCLUSION The accuracy of sCT can be effectively improved by introducing a transformer module and comparative learning loss function. Moreover, all dosimetric differences were within clinically acceptable criteria for photon radiotherapy, demonstrating the feasibility of the MRI-only workflow for patients with rectal cancer.
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Affiliation(s)
- X Li
- Peking University People's Hospital, Beijing, China
| | - F Y Lin
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - L C Jia
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, Guangdong, China
| | - T Liu
- Peking University People's Hospital, Beijing, China
| | - S M He
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - W Zhang
- Shanghai United Imaging Healthcare Technology Co., Ltd, Shanghai, China
| | - M Zhang
- Department of Radiation Oncology, Peking University People's Hospital, Beijing, China
| | - Y Wang
- Peking University People's Hospital, Beijing, China
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Tian S, Liu Y, Mao X, Xu X, Wang C, Han G, Yang Y, Wang J, He SM, Zhang W. A Multicenter Study on Deep Learning for Glioblastoma Auto-Segmentation with Prior Knowledge in Multimodal Imaging. Int J Radiat Oncol Biol Phys 2023; 117:e488. [PMID: 37785541 DOI: 10.1016/j.ijrobp.2023.06.2299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) A precise radiotherapy plan is required to ensure accurate delineation of gross tumor volumes (GTV) and clinical target volumes (CTV1 and CTV2) of glioblastomas (GBMs). However, traditional manual delineation is labor intensive and highly dependent on oncologists' experience. To construct and evaluate a deep learning-based automatic delineation method using prior knowledge in multimodal medical imaging to automate precise GTV, CTV1 and CTV2 contouring in GBM patients. MATERIALS/METHODS We retrospectively collected the CT and MRI scans of 55 eligible patients with histologically proven high-grade glioma (HGG) from an institute, these scans were performed with non-enhanced CT (CT), contrast-enhanced T1-weighted (T1C) and T2-FLAIR (T2F) sequences. We proposed a two-stage automatic segmentation framework (PKMI-Net) for GTV, CTV1 and CTV2 based on deep learning using prior knowledge in multimodal medical imaging, and its segmentation performance was evaluated with dice similarity coefficient (DSC), 95% Harsdorff distance (HD95), average surface distance (ASD) and relative volume difference (RVD). To further investigate the generalizability of our method, we designed and conducted two evaluation strategies (Mix and Cross) on four multicenter datasets (including 55 patients, 37 patients, 21 patients and 35 patients). RESULTS The evaluation results with an 11-patient test set from the single institute were summarized in Table 1, the proposed method demonstrated the best accuracy in segmenting, respectively, GTV, CTV1 and CTV, achieving a DSC of 0.94, 0.95 and 0.92; HD95 of 2.07 mm, 1.18 mm and 3.80 mm; ASD of 0.69 mm, 0.39 mm and 1.13 mm and RVE of 5.50%, 3.97% and 9.68%. In the multicenter evaluation, the segmentation performance of our method implemented with the Cross strategy was comparable to that with the Mix strategy, demonstrating that our method had high and stable generalizability across multicenter datasets in automatically segmenting GTV, CTV1 and CTV2. CONCLUSION Our proposed method achieved promising results in automatically segmenting gliomas across various datasets, which could improve the quality and efficiency of glioblastoma radiotherapy.
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Affiliation(s)
- S Tian
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Y Liu
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - X Mao
- Radiotherapy Center, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China
| | - X Xu
- Department of Radiation Oncology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, China
| | - C Wang
- Department of Oncology, Sanya Central Hospital, Sanya, China
| | - G Han
- Department of Radiation Oncology, Hubei Cancer Hospital, Wuhan, China
| | - Y Yang
- Department of Radiation Oncology, Peking University International Hospital, Beijing, China
| | - J Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - S M He
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - W Zhang
- Shanghai United Imaging Healthcare Technology Co., Ltd, ShangHai, China
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Jia KY, Chen F, Peng Y, Wei JF, He S, Wei X, Tang H, Meng W, Feng Y, Chen M. Multidetector CT-derived tricuspid annulus measurements predict tricuspid regurgitation reduction after transcatheter aortic valve replacement. Clin Radiol 2023; 78:779-788. [PMID: 37574402 DOI: 10.1016/j.crad.2023.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/13/2023] [Accepted: 07/10/2023] [Indexed: 08/15/2023]
Abstract
AIM To use multidetector row computed tomography (MDCT)-derived tricuspid annulus (TA) measurements to identify predictors for tricuspid regurgitation (TR) reduction after transcatheter aortic valve replacement (TAVR), and to investigate the impact of TR change on prognosis. MATERIALS AND METHODS A retrospective, single-centre study was conducted on consecutive patients who underwent TAVR with concomitant baseline mild or more severe TR from April 2012 to April 2022. TA parameters were measured using MDCT. RESULTS The study comprised 266 patients (mean age 74.2 ± 7.6 years, 147 men) and 45.1% had more than one grade of TR reduction at follow-up. Independent predictors of TR reduction at follow-up were distance between TA centroid and antero-septal commissure (odd ratio [OR] 0.776; 95% confidence interval [CI]: 0.672-0.896, p=0.001), baseline TR of moderate or worse (OR 4.599; 95% CI: 2.193-9.648, p<0.001), systolic pulmonary artery pressure (OR 1.018; 95% CI: 1.002-1.035, p=0.027), age (OR 0.955; 95% CI: 0.920-0.993, p=0.019), and pre-existing atrial fibrillation (OR 0.209; 95% CI: 0.101-0.433, p<0.001). Patients without TR reduction had higher rates of rehospitalisation (hazard ratio [HR] 0.642; 95% CI: 0.413-0.998, p=0.049). CONCLUSIONS The MDCT-derived TA parameter was predictive of TR reduction after TAVR. Persistent TR after TAVR was associated with higher rates of rehospitalisation.
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Affiliation(s)
- K-Y Jia
- Department of Cardiology, West China Hospital, Sichuan University, 37 Guoxue Road, 610041 Chengdu, China
| | - F Chen
- Department of Cardiology, West China Hospital, Sichuan University, 37 Guoxue Road, 610041 Chengdu, China
| | - Y Peng
- Department of Cardiology, West China Hospital, Sichuan University, 37 Guoxue Road, 610041 Chengdu, China
| | - J-F Wei
- Department of Cardiology, West China Hospital, Sichuan University, 37 Guoxue Road, 610041 Chengdu, China
| | - S He
- Department of Cardiology, West China Hospital, Sichuan University, 37 Guoxue Road, 610041 Chengdu, China
| | - X Wei
- Department of Cardiology, Section of Cardiac Ultrasound, West China Hospital, Sichuan University, 37 Guoxue Road, 610041 Chengdu, China
| | - H Tang
- Department of Cardiology, Section of Cardiac Ultrasound, West China Hospital, Sichuan University, 37 Guoxue Road, 610041 Chengdu, China
| | - W Meng
- Department of Cardiovascular Surgery, West China Hospital, Sichuan University, 37 Guoxue Road, 610041 Chengdu, China.
| | - Y Feng
- Department of Cardiology, West China Hospital, Sichuan University, 37 Guoxue Road, 610041 Chengdu, China.
| | - M Chen
- Department of Cardiology, West China Hospital, Sichuan University, 37 Guoxue Road, 610041 Chengdu, China.
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Qi W, Li S, Xiao J, Zhang W, Mo Z, He SM, Li H, Chen J, Zhao S. Prediction of Response to Neoadjuvant Chemoradiotherapy Combined with Pembrolizumab in Esophageal Squamous Cell Carcinoma with CT/FDG PET Radiomic Signatures Based on Machine Learning Classification. Int J Radiat Oncol Biol Phys 2023; 117:e358-e359. [PMID: 37785233 DOI: 10.1016/j.ijrobp.2023.06.2443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) PALACE-1 trial has confirm that the addition of pembrolizumab to neoadjuvant chemoradiotherapy (NCRT) improves the pathological complete response(pCR) for esophageal squamous cell carcinoma (ESCC), which might be a novel treatment strategy for ESCC. In the present study, we aim to establish a machine learning model to predict the local response to NCRT+ pembrolizumab for ESCC by using pretreatment 18-fluorodeoxyglucose positron emission tomography (FDG PET) and contrast-enhanced plan CT images. MATERIALS/METHODS A total of 65 cases treated with NCRT+ pembrolizumab followed by surgery were prospectively enrolled for analysis from 2019-2022. Each patient contains a contrast-enhanced plan CT and FDG PET images. 52 patients were randomly divided into training set and 13 patients were used as test set. The Extraction of radiomics features was performed using an open-source Python library PyRadiomics automatically. Features were computed according to the radiologist-drawn ROIs on both CT and PET images. In the feature selection stage least absolute shrinkage and selection operator (LASSO) was utilized on CT features and PET features separately. Four different machine learning models were implemented: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) and XGBoost (XGB). The features selected by LASSO regression were used as model input and the output of the model is "pCR" or "non-pCR". To find the optimal parameter, the 5-fold cross-validation method was used in the training stage. In this study, we use accuracy, sensitivity and specificity as the metrics to evaluate the performance of the model on the testing cohort. The predictive performance of the model was assessed using the area under curve (AUC) of the receiver operating characteristics curve (ROC). RESULTS Of the 65 cases treated with NCRT+pembrolizumab, 35 patients archived pCR (53.8%), and 30 archived non-pCR. 1684 radiomics features were extracted from each case, and half of them (842 features) were from CT and others were from PET. Among the machine learning models mentioned above SVM achieves the most promising performance on the evaluation metrics. Accuracy, sensitivity, specificity and AUC score on test set were 0.692, 0.833, 0.571 and 0.786 for CT features and 0.615, 0.667, 0.571 and 0.762 for PET features, respectively. For CT+FDG PET fused features accuracy, sensitivity, specificity and AUC score on test set were 0.769, 0.667, 0.857 and 0.833. CONCLUSION In this study, we performed several different machine learning models to predict the response to NCRT+ pembrolizumab among ESCC based on the extracted radiomics features from CT and FDG PET images. The best-performing model based on radiomics features of CT and PET images could identify non-pCR to NCRT + pembrolizumab in EC patients.
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Affiliation(s)
- W Qi
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - S Li
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - J Xiao
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - W Zhang
- Shanghai United Imaging Healthcare Technology Co., Ltd, Shanghai, China
| | - Z Mo
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - S M He
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - H Li
- Department of Thoracic Surgery Ruijin Hospital affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - J Chen
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - S Zhao
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Yang YX, Zhou GQ, Lin L, Jiang X, Yang X, Cai W, He SM, Li H, Jia LC, Zhang W, Zhou J, Sun Y. Dosimetric Benefits of Online Adaptive Radiotherapy in Nasopharyngeal Carcinoma. Int J Radiat Oncol Biol Phys 2023; 117:e635-e636. [PMID: 37785896 DOI: 10.1016/j.ijrobp.2023.06.2038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Online adaptive radiotherapy (ART) has the advantage of compensating for potential underdosing to targets and overdosing to organs-at-risk (OARs) caused by variations in patient anatomy and tumor geometry. Artificial intelligence (AI)-assisted rapid generation of new plans makes online ART possible. We aimed to evaluate the dosimetric benefits of online ART on tumor coverage and OARs sparing in nasopharyngeal carcinoma (NPC). MATERIALS/METHODS Twenty patients diagnosed with NPC (19 with stage III and 1 with stage II according to the 8th edition of the AJCC/UICC staging system) who underwent definitive radiotherapy or concurrent chemoradiotherapy and received online ART on CT-Linac between April 2022 and December 2022 were included in this study, consisting of 14 males and 6 females with a median age of 48 years (range: 29-68 years). The prescription dose was 6996 cGy/33 fractions for primary gross tumor volume (GTVp), 6600-6996 cGy/33 fractions for gross tumor volume of nodes (GTVn), 6006 cGy/33 fractions for high-risk clinical tumor volume (CTV1), 5412 cGy/33 fractions for low-risk clinical tumor volume (CTV2). The majority of the patients (15/20) received online ART during the fourth to fifth week of their radiotherapy treatment The auto-segmented contours and auto-plan generated by AI were manually reviewed and edited by radiotherapists and physicists. The paired samples t-test was used to compare the dose and volumes metrics of targets and OARs between scheduled plan and online ART plan. RESULTS The results of this study showed that compared to the scheduled plan, the online ART plan resulted in significant reductions in the volumes of all targets and 8/12 OARs (temporal lobes, optic nerves, lenses, eyes, parotids, submandibulars, mandibles, and thyroid) (P<0.05). The online ART plan also improved target coverage, with D98% for GTVp in the scheduled plan compared to the online ART plan being 7063.4 ± 76.1 cGy and 7096.1 ± 53.9 cGy (P = 0.1), CTV1 being 6266.7 ± 114.9 cGy and 6208.7 ± 54.7 cGy (P<0.05), and CTV2 being 4142.5 ± 1700.9 cGy and 5416.4 ± 23.8 cGy (P<0.01), respectively. The dose to all 12 OARs was reduced with the use of online ART, with 5/12 OARs showing statistical significance. The D0.03cm3 for the spinal cord in the scheduled plan and online ART plan were 3630.9 ± 197.6 and 3454.1 ± 132.0 cGy; for the temporal lobes were 7075.2 ± 303.0 and 6994.2 ± 345.1 cGy; and 4396.0 ± 2575.0 and for the pituitary were 4214.5 ± 2499.2 cGy. Meanwhile the Dmean for the eyes in the scheduled plan and online ART plan was 769.0 ± 232.0 and 714.8 ± 200.1 cGy; and for the mandibles were 3187.7 ± 211.5 and 3066.0 ± 152.1 cGy. CONCLUSION Online ART was effective in protecting most of the OARs in NPC patients, while simultaneously indicating a trend towards enhancing target coverage. This study demonstrated the promising potential of online ART for patients with NPC. This approach will be tested in an upcoming phase III trial.
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Affiliation(s)
- Y X Yang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Guangzhou, China
| | - G Q Zhou
- Sun Yat-sen University Cancer Center, Guangzhou, China
| | - L Lin
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Guangzhou, China
| | - X Jiang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Guangzhou, China; Sun Yat-sen University Cancer Center, Guangzhou, China
| | - X Yang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Guangzhou, China
| | - W Cai
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - S M He
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - H Li
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - L C Jia
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - W Zhang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
| | - J Zhou
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Y Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
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Li X, Jia LC, Lin FY, Liu T, He SM, Zhang W, Zhang M, Wang Y. Small Samples and Low-Cost Auto-Segmentation Method for Pelvic Organ-at-Risk Segmentation in Magnetic Resonance Images Using Deep-Learning. Int J Radiat Oncol Biol Phys 2023; 117:e685-e686. [PMID: 37786015 DOI: 10.1016/j.ijrobp.2023.06.2153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) In radiotherapy, magnetic resonance (MR) imaging has higher contrast of soft tissue, and no radiation compared with computed tomography (CT) scanning. Due to the high-cost of manual annotation, the deep-learning based automatic organ-at-risk (OAR) and target delineation algorithms are in high-demand, but the collecting of large amounts of high-quality annotated datasets remains difficulty. In this paper, we proposed a low-cost OAR segmentation method with semi-supervised annotation using small annotation samples of pelvic MR images. MATERIALS/METHODS This study consisted of 94 patients diagnosed with rectal cancer from April 2018 to March 2021 at Peking University People's Hospital. We used 17 slices of MR images with annotation and 78 slices without annotation to train a deep-learning based segmentation model. The bladder, femoral heads, rectum and small intestine were selected as OAR. Semi-supervised method and ensemble learning were used for generating training set using small sample with annotation. Post-processing algorithm was used to correct the self-annotation data. Two of 14 annotation samples were set as test set. As for un-labeled images, 40 of them were set as semi-supervised annotation train set, the rest were test set. Besides, both 2D and 3D auto-segmentation networks were evaluated. RESULTS The dice of bladder, femoral head left and right, rectum and small intestine between segmentation results and reference masks is 0.947, 0.983, 0.981, 0.900, 0.845 only using self-annotation and post-processing method of 2D segmentation model. And the dice of corresponding OAR is 0.871, 0.975, 0.975, 0.783, 0.724 using 3D segmentation network, 0.885,0.982, 0.982, 0.882, 0,814 using 2D segmentation network with supervised method (nnUNet). The 2D model outperformed 3D model with better segmentation performance, shorter inference time and fewer parameters. CONCLUSION The results proved that we can train a multi-OAR segmentation model only using small annotation samples and other unlabeled samples. Ensemble learning and post-processing methods are necessary for semi-supervised data annotation. For anisotropy data, 2D model shows better performance than 3D models.
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Affiliation(s)
- X Li
- Peking University People's Hospital, Beijing, China
| | - L C Jia
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - F Y Lin
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - T Liu
- Peking University People's Hospital, Beijing, China
| | - S M He
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - W Zhang
- Shanghai United Imaging Healthcare Technology Co., Ltd, Shanghai, China
| | - M Zhang
- Department of Radiation Oncology, Peking University People's Hospital, Beijing, China
| | - Y Wang
- Peking University People's Hospital, Beijing, China
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Shiau C, Cao J, Gregory M, Kim Y, He S, Reeves J, Wang S, Lester NA, Su J, Wang PL, Beechem J, Hong TS, Wo JY, Ting D, Hemberg M, Hwang WL. Intercellular Mechanisms of Therapeutic Resistance at the Tumor-Stromal Interface Using Ultra High-Plex Single-Cell Spatial Transcriptomics and Genetically-Engineered Tumoroids. Int J Radiat Oncol Biol Phys 2023; 117:S101-S102. [PMID: 37784270 DOI: 10.1016/j.ijrobp.2023.06.056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) There is a major gap in knowledge regarding how intercellular interactions in the tumor microenvironment (TME) mediate therapeutic resistance. Achievement of this goal has been limited by a lack of (1) spatial context in dissociated single-cell methods; (2) single-cell resolution in spatial profiling approaches; (3) high quality data and yield with FFPE patient specimens; and (4) computational methods for ligand-receptor analyses that consider both gene expression and spatial coordinates. MATERIALS/METHODS We developed an innovative spatial biology paradigm that combines cutting-edge experimental and computational methods to enable high-resolution, spatially-guided discovery of critical mediators of therapeutic resistance. We applied this approach to dissect the single-cell spatial transcriptomic landscape of untreated vs. chemoradiotherapy-treated primary human pancreatic ductal adenocarcinoma (PDAC; n = 21) using ultra-high plex spatial molecular imaging (SMI) optimized for high-sensitivity, subcellular detection of up to 6000 gene transcripts in FFPE sections-an order of magnitude greater than contemporary methods. RESULTS We recovered over 1,000,000 high-quality single cells in situ representing more than 20 distinct cell types, including epithelial, immune, endothelial, endocrine, and diverse stromal cells. We developed an optimal transport-based computational method to infer cell-cell communication at the cancer-stromal interface. Treatment with chemoradiotherapy was associated with the largest increase in fibroblast-malignant interactions. Comparing the SMI data with orthogonal single-nucleus RNA-sequencing and digital spatial profiling data, we identified CLCF1-CNTFR as the fibroblast-malignant interaction most associated with resistance to chemoradiotherapy in PDAC. CLCF1 is a gp130-family cytokine that activates Jak-STAT signaling and acts as a potent neurotrophic factor. Notably, the CLCF1-CNTRF (fibroblast-malignant) interaction has prominent pro-oncogenic effects in lung adenocarcinoma and an engineered CNTFR decoy receptor with therapeutic potential has been developed. To functionally validate the role of the CLCF1-CNTFR (fibroblast-malignant) interaction in mediating resistance to cytotoxic therapy, we created CRISPR-engineered cancer-fibroblast tumoroids and modulated expression of this ligand-receptor pair. Pancreatic cancer cell viability in the presence of 5-fluorouracil was better maintained with increased CLCF1-CNTFR signaling. CONCLUSION In this study, we integrated ultra high-plex single-cell spatial transcriptomics, optimal transport ligand-receptor predictions, and genetically-engineered stromal tumoroids to identify and validate CLCF1-CNTFR as an important intercellular mechanism of resistance to chemoradiotherapy in PDAC-pioneering a paradigm for translating single-cell spatial biology to clinical oncology.
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Affiliation(s)
- C Shiau
- Massachusetts General Hospital, Boston, MA
| | - J Cao
- Brigham and Women's Hospital, Boston, MA
| | - M Gregory
- Nanostring Technologies, Seattle, WA
| | - Y Kim
- Nanostring Technologies, Seattle, WA
| | - S He
- Nanostring Technologies, Seattle, WA
| | - J Reeves
- Nanostring Technologies, Seattle, WA
| | - S Wang
- Columbia University, New York, NY
| | - N A Lester
- Massaschusetts General Hospital, Boston, MA
| | - J Su
- Massachusetts General Hospital, BOSTON, MA
| | - P L Wang
- Massaschusetts General Hospital, Boston, MA
| | - J Beechem
- Nanostring Technologies, Seattle, WA
| | - T S Hong
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - J Y Wo
- Newton-Wellesley Hospital, Newton, MA
| | - D Ting
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA
| | - M Hemberg
- Brigham and Women's Hospital, Boston, MA
| | - W L Hwang
- Broad Institute of MIT and Harvard, Cambridge, MA
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Lin L, Peng P, Zhou GQ, Huang SM, Hu J, Liu Y, He SM, Sun Y, Zhang W. Deep Learning-Based Synthesis of Contrast-Enhanced MRI for Automated Delineation of Primary Gross Tumor Volume in Radiotherapy of Nasopharyngeal Carcinoma. Int J Radiat Oncol Biol Phys 2023; 117:e475. [PMID: 37785507 DOI: 10.1016/j.ijrobp.2023.06.1687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Contrast-enhanced MRIs are necessary to delineate the primary gross tumor volume (GTVp) in radiotherapy of nasopharyngeal carcinoma (NPC). However, using contrast agents to scan contrast-enhanced MRIs is not applicable to some patients due to metal implants or their allergy, and it increases the treatment cost of patients. To address these problems, this work aims at synthesizing contrast-enhance MRIs from unenhanced MRIs by implementing generative adversarial network (GAN). MATERIALS/METHODS In this work, 324 MRI datasets of patients with NPC were retrospectively collected between September 2016 and September 2017 from a single institute. MRI examinations were performed with un-enhanced T1-weighted (T1) and T2-weighted (T2) sequences, and contrast-enhanced T1-weighted (T1C) and fat-suppressed T1-weighted (T1FSC) sequences. We designed and developed a modified pix2pix network to synthesize T1C (sT1C) and T1FSC (sT1FSC) from real T1. The end of the generator in this network was assembled with multiple heads (the classification head and gradient head) to learn more representation information and features from real images, the discriminator in this network distinguished whether the synthesized image is real and fake and supervised that the generator outputs more realistic synthesized image. We verified the performance of the synthesized images for automated delineation of GTVp. In an independent testing set of 11 patients, the synthesized sT1C and sT1FSC were inputted into the segmentation deep learning network along with their corresponding T1 and T2 sequences to generate GTVp contours. Delineation performance of the synthesized images and real images for automated delineation were evaluated by dice similarity coefficient (DSC), and average surface distance (ASD), using human expert contours as the ground truth. RESULTS In automated contouring of GTVp for NPC, the segmentation deep learning network using one or two synthesized MRIs showed equivalent performance when compared with the automated contours which generated from four real MRI sequences. Mean DSCs between automated contours by sT1C-replaced or sT1C and sT1FSC-replaced network and ground truth contours were 0.726 ± 0.143 and 0.711 ± 0.157, respectively, slightly inferior to that of contours generated from four real MRI sequences (0.740 ± 0.154, both P >0.05). In terms of mean ASD, there was also no significant difference between automated contours generated from synthesized images and real images (3.056 ± 4.216 mm and 3.537 ± 4.793 mm vs. 3.124 ± 4.637 mm; both P > 0.05). CONCLUSION We proposed an MRI-synthesis method based on GAN and the synthesized contrast-enhanced MRIs performed equivalent as the real contrast-enhanced MRIs in the automated delineation of gross tumor volume for radiotherapy of NPC.
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Affiliation(s)
- L Lin
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Guangzhou, China
| | - P Peng
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - G Q Zhou
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Guangzhou, China
| | - S M Huang
- Sun Yat-sen University Cancer Center, Guangzhou, China
| | - J Hu
- Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y Liu
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - S M He
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Y Sun
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China, Guangzhou, China
| | - W Zhang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
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23
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Sun S, Sun X, Liang Y, Wang J, Sun Y, Wang Y, Liang H, Hu K, Zhang F, Lin FY, Liu Y, He SM, Zhang W. Clinical prior Knowledge-Based One-Shot Learning for Automatic Delineation of Clinical Target Volumes in Adaptation Radiotherapy of Cervical Cancer. Int J Radiat Oncol Biol Phys 2023; 117:e488. [PMID: 37785540 DOI: 10.1016/j.ijrobp.2023.06.2298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Rapid and accurate delineation of clinical target volumes (CTV) of cervical cancer is the crux to ensure the efficiency and benefits of adaptation radiotherapy (ART). However, contour propagation using deformation image registration (DIR) is difficult to ensure the accuracy of CTV contours due to the significant tumor recession in next fraction, and the tumor progress in each fraction is not considered by conventional automatic delineation methods based on deep learning (DL). Currently, one-shot learning (OSL) is feasible to learn the tumor progress from former fractions to improve the accuracy of automatically delineating CTV. MATERIALS/METHODS We retrospectively collected 45 patients with cervical cancer from January 2021 to May 2022 in our department. All patients consist of a pair of planning CT and daily CT in ART. A personalized automatic delineation method based on one-shot learning was developed to delineate CTV in daily CT by learning the clinical prior knowledge from the CTV contours and images of planning CT. The performance of our proposed method was evaluated by dice similarity coefficient (DSC), 95% Harsdorff distance (95HD) and average surface distance (ASD) with human experts, and its automatic delineation performance were compared with DIR and DL in daily CT. RESULTS Our automatic delineation method OSL performed the best results in all evaluation metrics (denoted by mean ± standard deviation) as shown in Table 1, it is superior to method DL: 0.92 & 0.90 of DSC, 2.33 mm & 2.68 mm of HD95, 0.68 mm & 0.82 mm of ASD, P < 0.05 for DSC and ASD. Specifically, our method is significantly superior to the automatic delineation results by method DIR: 0.92 & 0.84 of DSC, 2.33 mm & 4.11 mm of HD95, 0.68 mm & 1.52 mm of ASD, P < 0.05 for all. In addition, OSL can significantly overcome the delineation problems in fuzzy boundary and delineation missing and perform better generalization for some unusual images, compared with DIR and DL. CONCLUSION We proposed an automatic delineation method based on one-shot learning for CTV of cervical cancer in ART, the results demonstrated that the proposed method could improve the precision and generalization of automatically delineating CTV compared against current popular methods. Therefore, it is potential to improve the quality and efficiency of ART for personalized patients and have a positive impact on tumor control and patient survival.
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Affiliation(s)
- S Sun
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - X Sun
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - Y Liang
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - J Wang
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Y Sun
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Y Wang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - H Liang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - K Hu
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - F Zhang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - F Y Lin
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - Y Liu
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - S M He
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - W Zhang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
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24
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Wei M, He S, Meng D, Yang G, Wang Z. Erratum to: Hybrid Exercise Program Enhances Physical Fitness and Reverses Frailty in Older Adults: Insights and Predictions from Machine Learning. J Nutr Health Aging 2023; 27:903. [PMID: 38216223 DOI: 10.1007/s12603-023-2004-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Affiliation(s)
- M Wei
- Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, 130024, Jilin, Changchun, China
| | - S He
- Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, 130024, Jilin, Changchun, China
| | - D Meng
- Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, 130024, Jilin, Changchun, China
| | - Guang Yang
- Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, 130024, Jilin, Changchun, China.
| | - Ziheng Wang
- Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, 130024, Jilin, Changchun, China; AI Group, Intelligent Lancet LLC, 95816, Sacramento, CA, USA.
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25
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Adam J, Adamczyk L, Adams JR, Adkins JK, Agakishiev G, Aggarwal MM, Ahammed Z, Alekseev I, Anderson DM, Aparin A, Aschenauer EC, Ashraf MU, Atetalla FG, Attri A, Averichev GS, Bairathi V, Barish K, Behera A, Bellwied R, Bhasin A, Bielcik J, Bielcikova J, Bland LC, Bordyuzhin IG, Brandenburg JD, Brandin AV, Butterworth J, Caines H, Calderón de la Barca Sánchez M, Cebra D, Chakaberia I, Chaloupka P, Chan BK, Chang FH, Chang Z, Chankova-Bunzarova N, Chatterjee A, Chen D, Chen J, Chen JH, Chen X, Chen Z, Cheng J, Cherney M, Chevalier M, Choudhury S, Christie W, Chu X, Crawford HJ, Csanád M, Daugherity M, Dedovich TG, Deppner IM, Derevschikov AA, Didenko L, Dong X, Drachenberg JL, Dunlop JC, Edmonds T, Elsey N, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fazio S, Federic P, Fedorisin J, Feng CJ, Feng Y, Filip P, Finch E, Fisyak Y, Francisco A, Fulek L, Gagliardi CA, Galatyuk T, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Guryn W, Hamad AI, Hamed A, Harabasz S, Harris JW, He S, He W, He XH, He Y, Heppelmann S, Heppelmann S, Herrmann N, Hoffman E, Holub L, Hong Y, Horvat S, Hu Y, Huang HZ, Huang SL, Huang T, Huang X, Humanic TJ, Huo P, Igo G, Isenhower D, Jacobs WW, Jena C, Jentsch A, Ji Y, Jia J, Jiang K, Jowzaee S, Ju X, Judd EG, Kabana S, Kabir ML, Kagamaster S, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Ke HW, Keane D, Kechechyan A, Kelsey M, Khyzhniak YV, Kikoła DP, Kim C, Kimelman B, Kincses D, Kinghorn TA, Kisel I, Kiselev A, Kocan M, Kochenda L, Kosarzewski LK, Kramarik L, Kravtsov P, Krueger K, Kulathunga Mudiyanselage N, Kumar L, Kumar S, Kunnawalkam Elayavalli R, Kwasizur JH, Lacey R, Lan S, Landgraf JM, Lauret J, Lebedev A, Lednicky R, Lee JH, Leung YH, Li C, Li C, Li W, Li W, Li X, Li Y, Liang Y, Licenik R, Lin T, Lin Y, Lisa MA, Liu F, Liu H, Liu P, Liu P, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Longacre RS, Lukow NS, Luo S, Luo X, Ma GL, Ma L, Ma R, Ma YG, Magdy N, Majka R, Mallick D, Margetis S, Markert C, Matis HS, Mazer JA, Minaev NG, Mioduszewski S, Mohanty B, Mooney I, Moravcova Z, Morozov DA, Nagy M, Nam JD, Nasim M, Nayak K, Neff D, Nelson JM, Nemes DB, Nie M, Nigmatkulov G, Niida T, Nogach LV, Nonaka T, Nunes AS, Odyniec G, Ogawa A, Oh S, Okorokov VA, Page BS, Pak R, Pandav A, Panebratsev Y, Pawlik B, Pawlowska D, Pei H, Perkins C, Pinsky L, Pintér RL, Pluta J, Pokhrel BR, Porter J, Posik M, Pruthi NK, Przybycien M, Putschke J, Qiu H, Quintero A, Radhakrishnan SK, Ramachandran S, Ray RL, Reed R, Ritter HG, Rogachevskiy OV, Romero JL, Ruan L, Rusnak J, Sahoo NR, Sako H, Salur S, Sandweiss J, Sato S, Schmidke WB, Schmitz N, Schweid BR, Seck F, Seger J, Sergeeva M, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao M, Sheikh AI, Shen WQ, Shi SS, Shi Y, Shou QY, Sichtermann EP, Sikora R, Simko M, Singh J, Singha S, Smirnov N, Solyst W, Sorensen P, Spinka HM, Srivastava B, Stanislaus TDS, Stefaniak M, Stewart DJ, Strikhanov M, Stringfellow B, Suaide AAP, Sumbera M, Summa B, Sun XM, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Szymanski P, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Timmins AR, Tlusty D, Tokarev M, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tripathy SK, Tsai OD, Tu Z, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vanek J, Vasiliev AN, Vassiliev I, Videbæk F, Vokal S, Voloshin SA, Wang F, Wang G, Wang JS, Wang P, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Wen L, Westfall GD, Wieman H, Wissink SW, Witt R, Wu Y, Xiao ZG, Xie G, Xie W, Xu H, Xu N, Xu QH, Xu YF, Xu Y, Xu Z, Xu Z, Yang C, Yang Q, Yang S, Yang Y, Yang Z, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zbroszczyk H, Zha W, Zhang C, Zhang D, Zhang S, Zhang S, Zhang XP, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao J, Zhong C, Zhou C, Zhu X, Zhu Z, Zurek M, Zyzak M. Erratum: Global Polarization of Ξ and Ω Hyperons in Au+Au Collisions at sqrt[s_{NN}]=200 GeV [Phys. Rev. Lett. 126, 162301 (2021)]. Phys Rev Lett 2023; 131:089901. [PMID: 37683178 DOI: 10.1103/physrevlett.131.089901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Indexed: 09/10/2023]
Abstract
This corrects the article DOI: 10.1103/PhysRevLett.126.162301.
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Zhao ZG, Li RT, Wei X, Peng Y, Wei JF, He S, Li Q, Li X, Li YJ, Li X, Zhou X, Zheng MX, Chen G, An Q, Chen M, Feng Y. [Preliminary experience of transcatheter pulmonary valve replacement using domestic balloon-expandable valve]. Zhonghua Xin Xue Guan Bing Za Zhi 2023; 51:825-831. [PMID: 37583330 DOI: 10.3760/cma.j.cn112148-20230608-00336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
Objectives: To evaluate the feasibility and preliminary clinical results of transcatheter pulmonary valve replacement (TPVR) with the domestically-produced balloon-expandable Prizvalve system. Methods: This is a prospective single-center observational study. Patients with postoperative right ventricular outflow tract (RVOT) dysfunction, who were admitted to West China Hospital of Sichuan University from September 2021 to March 2023 and deemed anatomically suitable for TPVR with balloon-expandable valve, were included. Clinical, imaging, procedural and follow-up data were analyzed. The immediate procedural results were evaluated by clinical implant success rate, which is defined as successful valve implantation with echocardiography-assessed pulmonary regurgitation
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Affiliation(s)
- Z G Zhao
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - R T Li
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - X Wei
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Y Peng
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - J F Wei
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - S He
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Q Li
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - X Li
- Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Y J Li
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - X Li
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - X Zhou
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - M X Zheng
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - G Chen
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Q An
- Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - M Chen
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Y Feng
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
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27
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Song SB, Dou LZ, Liu Y, Zhang YM, He S, Wang GQ. [Endoscopic hand-suturing combined with titanium clips for rectal defects closure after endoscopic submucosal dissection: a pilot study]. Zhonghua Zhong Liu Za Zhi 2023; 45:697-703. [PMID: 37580276 DOI: 10.3760/cma.j.cn112152-20230216-00064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
Objective: To explore the feasibility of endoscopic hand-suturing (EHS) for rectal defects closure after endoscopic submucosal dissection (ESD), and the clinical practicability of EHS combined with titanium clips. Methods: This is a prospective study performed by two experienced endoscopists from the Cancer Hospital, Chinese Academy of Medical Sciences who had received EHS training in sixporcine gastric ESD defects in vivo before the study. From December 2022 to February 2022, 20 patients with rectal mucosal lesions or submucosal diseases underwent ESD. Then EHS combined with titanium clips was adopted to close the rectal ESD defects. Specifically, we first sutured the defects as much as possible through EHS, then use titanium clips to fix the tail of the suture, and finally use additional titanium clips to close the residual parts of the defects that cannot be sutured. The main observational indicators were complete closure of the wound and delayed bleeding within one month after surgery. Results: In the 20 rectal cases, the size of defects ranged from 2.2 to 3.6 cm, with a median of 2.7 cm. All cases achieved complete closure without delayed bleeding, of which 12 (60.0%) were completely sutured with EHS and 8 (40.0%) required additional titanium clips to achieve complete closure after suturing. Conclusion: EHS technique is feasible and safe for rectum. EHS combined with titanium clips can also effectively close the rectal ESD defects, prevent postoperative delayed bleeding, and may be easier to be implemented in clinical practice.
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Affiliation(s)
- S B Song
- Department of Endoscopy, National Cancer Center/National Clinical Research Center forCancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Z Dou
- Department of Endoscopy, National Cancer Center/National Clinical Research Center forCancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center forCancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y M Zhang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center forCancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S He
- Department of Endoscopy, National Cancer Center/National Clinical Research Center forCancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - G Q Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center forCancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Chen ZH, Dou LZ, Zhang YM, Liu Y, He S, Ke Y, Liu XD, Liu YM, Wu HR, Zou SM, Wang GQ. [Risk factors analysis and prediction model construction of submucosal deep infiltration of early colorectal tumor]. Zhonghua Zhong Liu Za Zhi 2023; 45:613-620. [PMID: 37462018 DOI: 10.3760/cma.j.cn112152-20211201-00886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Objective: To investigate the risk factors for the development of deep infiltration in early colorectal tumors (ECT) and to construct a prediction model to predict the development of deep infiltration in patients with ECT. Methods: The clinicopathological data of ECT patients who underwent endoscopic treatment or surgical treatment at the Cancer Hospital, Chinese Academy of Medical Sciences from August 2010 to December 2020 were retrospectively analyzed. The independent risk factors were analyzed by multifactorial regression analysis, and the prediction models were constructed and validated by nomogram. Results: Among the 717 ECT patients, 590 patients were divided in the within superficial infiltration 1 (SM1) group (infiltration depth within SM1) and 127 patients in the exceeding SM1 group (infiltration depth more than SM1). There were no statistically significant differences in gender, age, and lesion location between the two groups (P>0.05). The statistically significant differences were observed in tumor morphological staging, preoperative endoscopic assessment performance, vascular tumor emboli and nerve infiltration, and degree of tumor differentiation (P<0.05). Multivariate regression analysis showed that only erosion or rupture (OR=4.028, 95% CI: 1.468, 11.050, P=0.007), localized depression (OR=3.105, 95% CI: 1.584, 6.088, P=0.001), infiltrative JNET staging (OR=5.622, 95% CI: 3.029, 10.434, P<0.001), and infiltrative Pit pattern (OR=2.722, 95% CI: 1.347, 5.702, P=0.006) were independent risk factors for the development of deep submucosal infiltration in ECT. Nomogram was constructed with the included independent risk factors, and the nomogram was well distinguished and calibrated in predicting the occurrence of deep submucosal infiltration in ECT, with a C-index and area under the curve of 0.920 (95% CI: 0.811, 0.929). Conclusion: The nomogram prediction model constructed based on only erosion or rupture, local depression, infiltrative JNET typing, and infiltrative Pit pattern has a good predictive efficacy in the occurrence of deep submucosal infiltration in ECT.
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Affiliation(s)
- Z H Chen
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Z Dou
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y M Zhang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S He
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Ke
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - X D Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y M Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - H R Wu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S M Zou
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - G Q Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Zhou JS, Xu RZ, Yu XQ, Cheng FJ, Zhao WX, Du X, Wang SZ, Zhang QQ, Gu X, He SM, Li YD, Ren MQ, Ma XC, Xue QK, Chen YL, Song CL, Yang LX. Evidence for Band Renormalizations in Strong-Coupling Superconducting Alkali-Fulleride Films. Phys Rev Lett 2023; 130:216004. [PMID: 37295091 DOI: 10.1103/physrevlett.130.216004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 02/06/2023] [Accepted: 04/17/2023] [Indexed: 06/12/2023]
Abstract
There has been a long-standing debate about the mechanism of the unusual superconductivity in alkali-intercalated fullerides. In this Letter, using high-resolution angle-resolved photoemission spectroscopy, we systematically investigate the electronic structures of superconducting K_{3}C_{60} thin films. We observe a dispersive energy band crossing the Fermi level with the occupied bandwidth of about 130 meV. The measured band structure shows prominent quasiparticle kinks and a replica band involving the Jahn-Teller active phonon modes, which reflects strong electron-phonon coupling in the system. The electron-phonon coupling constant is estimated to be about 1.2, which dominates the quasiparticle mass renormalization. Moreover, we observe an isotropic nodeless superconducting gap beyond the mean-field estimation (2Δ/k_{B}T_{c}≈5). Both the large electron-phonon coupling constant and large reduced superconducting gap suggest a strong-coupling superconductivity in K_{3}C_{60}, while the electronic correlation effect is suggested by the observation of a waterfall-like band dispersion and the small bandwidth compared with the effective Coulomb interaction. Our results not only directly visualize the crucial band structure but also provide important insights into the mechanism of the unusual superconductivity of fulleride compounds.
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Affiliation(s)
- J S Zhou
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
| | - R Z Xu
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
| | - X Q Yu
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
| | - F J Cheng
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
| | - W X Zhao
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
| | - X Du
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
| | - S Z Wang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
| | - Q Q Zhang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
| | - X Gu
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
| | - S M He
- Department of Physics, Clarendon Laboratory, University of Oxford, Parks Road, Oxford OX1 3PU, United Kingdom
| | - Y D Li
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
| | - M Q Ren
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
| | - X C Ma
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
- Collaborative Innovation Center of Quantum Matter, Beijing 100084, China
| | - Q K Xue
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
| | - Y L Chen
- Department of Physics, Clarendon Laboratory, University of Oxford, Parks Road, Oxford OX1 3PU, United Kingdom
- School of Physical Science and Technology, ShanghaiTech University and CAS-Shanghai Science Research Center, Shanghai 201210, China
- ShanghaiTech Laboratory for Topological Physics, Shanghai 200031, China
| | - C L Song
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
- Collaborative Innovation Center of Quantum Matter, Beijing 100084, China
| | - L X Yang
- State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
- Collaborative Innovation Center of Quantum Matter, Beijing 100084, China
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Shen X, He S, Wang J, Qian X, Wang H, Zhang B, Chen Y, Li H, An Y, Gong Q, Li G. Modifiable predictors of type 2 diabetes mellitus and roles of insulin resistance and β-cell function over a 6-year study and 30-year follow-up. J Endocrinol Invest 2023; 46:883-891. [PMID: 36219314 DOI: 10.1007/s40618-022-01932-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 09/29/2022] [Indexed: 04/17/2023]
Abstract
PURPOSE This study aimed to examine the modifiable predictors of T2DM and the roles of insulin resistance (IR) and β-cell function over a 6-year study and 30-year follow-up. METHODS A total of 462 non-diabetic participants, 282 with impaired glucose tolerance (IGT), and 180 with normal glucose tolerance (NGT) were enrolled in this analysis. The Matsuda IR index and area under the curve of insulin-to-glucose ratio (AUCI/G-R) were used as IR and β-cell function indices in the analysis. RESULTS In all participants, multivariable analysis showed that BMI, glucose status, Matsuda IR index and systolic blood pressure (SBP) at baseline were independently associated with an increased risk of T2DM over 30 years, whereas lifestyle intervention and AUCI/G-R were inversely associated with this risk. The predictive effect of the Matsuda IR index and AUCI/G-R in participants with IGT was consistent with the results of all participants, whereas in those with NGT, only the Matsuda IR index, not the AUCI/G-R, predicted the development of T2DM (HR = 1.42, 95% CI 1.07-1.89 vs HR = 1.09, 95% CI 0.76-1.56). The predictive effect of the Matsuda IR index on T2DM existed even in participants with BMI < 25 (p = 0.049). CONCLUSION The modifiable predictors of T2DM in Chinese adults were high BMI, hypertension, mild hyperglycaemia, IR, and β-cell dysfunction. Both IR and β-cell function contributed to the development of T2DM in the long term; however, IR remains the initial and long-standing key risk factor for T2DM.
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Affiliation(s)
- X Shen
- Center of Endocrinology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, China
| | - S He
- Center of Endocrinology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, China
| | - J Wang
- Department of Cardiology, Da Qing First Hospital, No. 9 Zhongkang Street, Saltu District, Da Qing, 163411, Heilongjiang, China
| | - X Qian
- Center of Endocrinology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, China
| | - H Wang
- Center of Endocrinology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, China
| | - B Zhang
- Department of Endocrinology, China-Japan Friendship Hospital, No 2, East Yinghua Road, Chaoyang District, Beijing, 100029, China
| | - Y Chen
- Center of Endocrinology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, China
| | - H Li
- Department of Cardiology, Da Qing First Hospital, No. 9 Zhongkang Street, Saltu District, Da Qing, 163411, Heilongjiang, China
| | - Y An
- Center of Endocrinology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, China
| | - Q Gong
- Center of Endocrinology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, China.
| | - G Li
- Center of Endocrinology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, China.
- Department of Endocrinology, China-Japan Friendship Hospital, No 2, East Yinghua Road, Chaoyang District, Beijing, 100029, China.
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Zhao X, Dou LZ, Zhang YM, Liu Y, He S, Ke Y, Liu XD, Liu YM, Wu HR, Li ZQ, Chen ZH, Wang GQ. [Risk factors for residual cancer or lymph node metastasis after endoscopic noncurable resection of early colorectal cancer]. Zhonghua Zhong Liu Za Zhi 2023; 45:335-339. [PMID: 37078215 DOI: 10.3760/cma.j.cn112152-20210126-00082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
Objective: Risk factors related to residual cancer or lymph node metastasis after endoscopic non-curative resection of early colorectal cancer were analyzed to predict the risk of residual cancer or lymph node metastasis, optimize the indications of radical surgical surgery, and avoid excessive additional surgical operations. Methods: Clinical data of 81 patients who received endoscopic treatment for early colorectal cancer in the Department of Endoscopy, Cancer Hospital, Chinese Academy of Medical Sciences from 2009 to 2019 and received additional radical surgical surgery after endoscopic resection with pathological indication of non-curative resection were collected to analyze the relationship between various factors and the risk of residual cancer or lymph node metastasis after endoscopic resection. Results: Of the 81 patients, 17 (21.0%) were positive for residual cancer or lymph node metastasis, while 64 (79.0%) were negative. Among 17 patients with residual cancer or positive lymph node metastasis, 3 patients had only residual cancer (2 patients with positive vertical cutting edge). 11 patients had only lymph node metastasis, and 3 patients had both residual cancer and lymph node metastasis. Lesion location, poorly differentiated cancer, depth of submucosal invasion ≥2 000 μm, venous invasion were associated with residual cancer or lymph node metastasis after endoscopic (P<0.05). Logistic multivariate regression analysis showed that poorly differentiated cancer (OR=5.513, 95% CI: 1.423, 21.352, P=0.013) was an independent risk factor for residual cancer or lymph node metastasis after endoscopic non-curative resection of early colorectal cancer. Conclusions: For early colorectal cancer after endoscopic non-curable resection, residual cancer or lymph node metastasis is associated with poorly differentiated cancer, depth of submucosal invasion ≥2 000 μm, venous invasion and the lesions are located in the descending colon, transverse colon, ascending colon and cecum with the postoperative mucosal pathology result. For early colorectal cancer, poorly differentiated cancer is an independent risk factor for residual cancer or lymph node metastasis after endoscopic non-curative resection, which is suggested that radical surgery should be added after endoscopic treatment.
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Affiliation(s)
- X Zhao
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Z Dou
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y M Zhang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S He
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Ke
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - X D Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y M Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - H R Wu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Z Q Li
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Z H Chen
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - G Q Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Zhang J, Yang R, He S, Yuan P. [Spatial clustering analysis of scarlet fever incidence in China from 2016 to 2020]. Nan Fang Yi Ke Da Xue Xue Bao 2023; 43:644-648. [PMID: 37202202 DOI: 10.12122/j.issn.1673-4254.2023.04.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
OBJECTIVE To investigate the incidence trend and spatial clustering characteristics of scarlet fever in China from 2016 to 2020 to provide evidence for development of regional disease prevention and control strategies. METHODS The incidence data of scarlet fever in 31 provinces and municipalities in mainland China from 2016 to 2020 were obtained from the Chinese Health Statistics Yearbook and the Public Health Science Data Center led by the Chinese Center for Disease Control and Prevention.The three-dimensional spatial trend map of scarlet fever incidence in China was drawn using ArcGIS to determine the regional trend of scarlet fever incidence.GeoDa spatial autocorrelation analysis was used to explore the spatial aggregation of scarlet fever in China in recent years. RESULTS From 2016 to 2020, a total of 310 816 cases of scarlet fever were reported in 31 provinces, municipalities directly under the central government and autonomous regions, with an average annual incidence of 4.48/100 000.The reported incidence decreased from 4.32/100 000 in 2016 to 1.18/100 000 in 2020(Z=103.47, P < 0.001).The incidence of scarlet fever in China showed an obvious regional clustering from 2016 to 2019(Moran's I>0, P < 0.05), but was randomly distributed in 2020(Moran's I>0, P=0.16).The incidence of scarlet fever showed a U-shaped distribution in eastern and western regions of China, and increased gradually from the southern to northern regions.Inner Mongolia Autonomous Region and Hebei and Gansu provinces had the High-high (H-H) clusters of scarlet fever in China. CONCLUSION Scarlet fever still has a high incidence in China with an obvious spatial clustering.For the northern regions of China with H-H clusters of scarlet fever, the allocation of health resources and public health education dynamics should be strengthened, and local scarlet fever prevention and control policies should be made to contain the hotspots of scarlet fever.
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Affiliation(s)
- J Zhang
- Department of Epidemiology and Health Statistics/West China Fourth Hospital and West China School of Public Health, Sichuan University, Chengdu 610041, China
| | - R Yang
- Department of Epidemiology and Health Statistics/West China Fourth Hospital and West China School of Public Health, Sichuan University, Chengdu 610041, China
| | - S He
- Department of Epidemiology and Health Statistics/West China Fourth Hospital and West China School of Public Health, Sichuan University, Chengdu 610041, China
| | - P Yuan
- Department of Epidemiology and Health Statistics/West China Fourth Hospital and West China School of Public Health, Sichuan University, Chengdu 610041, China
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He S, Zhang J, Yang R, Yuan P. [Spatial distribution of cognitive dysfunction and its risk factors in Chinese population aged 45 years and above]. Nan Fang Yi Ke Da Xue Xue Bao 2023; 43:611-619. [PMID: 37202198 DOI: 10.12122/j.issn.1673-4254.2023.04.15] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
OBJECTIVE To analyze the spatial distribution of the prevalence of cognitive dysfunction and its risk factors in Chinese population aged 45 years and above to provide evidence for formulating regional prevention and control strategies. METHODS The study subjects with complete cognitive function data were selected from the follow-up data of the China Health and Retirement Longitudinal Study (CHARLS) Phase IV. ArcGis 10.4 software was used for spatial analysis of the prevalence of cognitive dysfunction in the population aged 45 years and above for each province based on the geographic information system (GIS) technology. RESULTS In 2018, the overall prevalence of cognitive dysfunction was 33.59% (5951/17716) in individuals aged 45 and above in China. Global spatial autocorrelation analysis indicated a spatial clustering and a positive autocorrelation (P < 0.001) of the prevalence of cognitive dysfunction in the study subjects, with a Moran's I value of 0.333085. The results of local spatial autocorrelation analysis showed that the southwestern region of China was the main aggregation area of patients with cognitive dysfunction. Geographically weighted regression analysis suggested that a male gender, an advanced age, and illiteracy were the major risk factors for cognitive dysfunction (P < 0.05). These 3 risk factors showed a spatial distribution heterogeneity with greater impact in the northern, western, and northwestern regions of China, respectively. CONCLUSION The prevalence of cognitive dysfunction is relatively high in individuals aged 45 years and above in China. A male gender, an advanced age, and illiteracy are the major risk factors for cognitive dysfunction and show different spatial distribution patterns, with the northern, western and northwestern regions of China as the key areas for prevention and control, where the prevention and control measures should be designed based on local conditions.
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Affiliation(s)
- S He
- Department of Epidemiology and Health Statistics, West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - J Zhang
- Department of Epidemiology and Health Statistics, West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - R Yang
- Department of Epidemiology and Health Statistics, West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - P Yuan
- Department of Epidemiology and Health Statistics, West China School of Public Health/West China Fourth Hospital, Sichuan University, Chengdu 610041, China
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Li W, Yang N, Li K, Fan H, Yu Q, Wu H, Wang Y, Meng X, Wu J, Wang Z, Liu Y, Wang X, Qin X, Lu K, Zhuang W, He S, Janne P, Seto T, Ou SH, Zhou C. 14MO Updated efficacy and safety of taletrectinib in patients (pts) with ROS1+ non-small cell lung cancer (NSCLC). J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00268-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Da JJ, Sun Y, Chen JC, Li Q, Yang YQ, He S, Yang NY, He PH, Hu Y, Long YJ, Yuan J, Zha Y. [Effect of hemoperfusion on protein energy wasting and long-term prognosis in patients on maintenance hemodialysis]. Zhonghua Yi Xue Za Zhi 2023; 103:559-565. [PMID: 36822866 DOI: 10.3760/cma.j.cn112137-20220925-02022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Objective: To explore the effect of hemoperfusion (HP) combined with hemodialysis (HD) (HD+HP) on protein energy wasting (PEW) and long-term prognosis in patients on maintenance HD (MHD). Methods: A prospective multicenter cohort study was conducted. Adult MHD patients who completed PEW assessment and underwent regular dialysis between July 2015 and July 2021 at 23 hemodialysis centers in Guizhou Province were selected. Demographic characteristics, physical indicators, laboratory indicators, 3-day diet diary and HP treatment data of the subjects were collected. The patients were divided into different groups according to the presence or absence of HP, the frequency of HP treatment and the type of cartridge, and then relevant indicators were compared. Multivariate logistic regression model and Cox proportional regression model were used to analyze the influence of HP treatment on PEW risk in MHD patients. Meanwhile, Kaplan-Meier method was used to plot the survival curve. Results: A total of 4 623 MHD patients (2 789 males and 1 834 females) aged (53.7±15.9) years were included in the study, with a median dialysis age of 64.3 (44.3, 92.3) months. There were 3 429 (74.2%) MHD patients treated with HD+HP, and 1 194 patients (25.8%) were not treated with HP. According to the 2008 diagnostic criteria of the International Society for Renal Nutrition and Metabolism (ISRNM), the incidence of PEW was 26.0% (1 204/4 623). Multivariate logistic regression analysis showed that female (OR=2.48, 95%CI: 1.55-3.95, P<0.001), diabetes (OR=1.75, 95%CI: 1.08-2.83, P=0.024) and high-sensitivity C-reactive protein (hs-CRP) (OR=1.02, 95%CI: 1.01-1.03, P=0.003) were risk factors for PEW, while treatment with HD+HP (OR=0.51, 95%CI: 0.31-0.87, P=0.012) and elevated triglyceride levels (OR=0.62, 95%CI: 0.48-0.80, P<0.001) were protective factors. Cox hazard ratio regression showed that among different HP treatment frequencies and cartridge types, 2 times/month (HR=0.40, 95%CI: 0.17-0.95, P=0.037), 3 times/month (HR=0.44, 95%CI: 0.23-0.85, P=0.014), 4 times/month (HR=0.54, 95%CI: 0.34-0.85, P=0.008), HA130 (HR=0.57, 95%CI: 0.36-0.89, P=0.014) and HA230 (HR=0.30, 95%CI: 0.15-0.63, P=0.001) had protective effects on the occurrence of PEW in MHD patients. The all-cause mortality rate was 11.3% (521/4 623) at 33 (24, 48) months of follow-up. Kaplan-Meier analysis showed that patients undergoing 4 times/month HP treatment (χ2=36.78, P<0.001) and using HA230 (χ2=9.46, P=0.002) had the highest survival rate. Conclusion: Treatment with HD+HP is a protective factor for PEW in patients with MHD, and 4 times/month HP treatment or HA230 significantly reduces the risk of PEW and all-cause mortality in patients with MHD.
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Affiliation(s)
- J J Da
- Department of Nephrology, Guizhou Provincial People's Hospital, NHC Key Laboratory of Pulmonary Immunological Diseases, Guiyang 550002, China
| | - Y Sun
- Department of Nephrology, General Hospital of Shougang Shuicheng Iron & Steel (Group) Co. Ltd, Liupanshui 553000, China
| | - J C Chen
- Department of Nephrology, General Hospital of Guizhou Water Mine Holding Group Co. Ltd, Liupanshui 553000, China
| | - Q Li
- Department of Nephrology, Guizhou Provincial People's Hospital, NHC Key Laboratory of Pulmonary Immunological Diseases, Guiyang 550002, China
| | - Y Q Yang
- Department of Nephrology, Guizhou Provincial People's Hospital, NHC Key Laboratory of Pulmonary Immunological Diseases, Guiyang 550002, China
| | - S He
- Department of Nephrology, Guizhou Provincial People's Hospital, NHC Key Laboratory of Pulmonary Immunological Diseases, Guiyang 550002, China
| | - N Y Yang
- Department of Nephrology, Guizhou Provincial People's Hospital, NHC Key Laboratory of Pulmonary Immunological Diseases, Guiyang 550002, China
| | - P H He
- Department of Nephrology, Guizhou Provincial People's Hospital, NHC Key Laboratory of Pulmonary Immunological Diseases, Guiyang 550002, China
| | - Y Hu
- Department of Nephrology, Guizhou Provincial People's Hospital, NHC Key Laboratory of Pulmonary Immunological Diseases, Guiyang 550002, China
| | - Y J Long
- Department of Nephrology, Guizhou Provincial People's Hospital, NHC Key Laboratory of Pulmonary Immunological Diseases, Guiyang 550002, China
| | - J Yuan
- Department of Nephrology, Guizhou Provincial People's Hospital, NHC Key Laboratory of Pulmonary Immunological Diseases, Guiyang 550002, China
| | - Y Zha
- Department of Nephrology, Guizhou Provincial People's Hospital, NHC Key Laboratory of Pulmonary Immunological Diseases, Guiyang 550002, China
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Wei M, He S, Meng D, Yang G, Wang Z. Hybrid Exercise Program Enhances Physical Fitness and Reverses Frailty in Older Adults: Insights and Predictions from Machine Learning. J Nutr Health Aging 2023; 27:894-902. [PMID: 37960913 DOI: 10.1007/s12603-023-1991-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 09/14/2023] [Indexed: 11/15/2023]
Abstract
PURPOSE The declining physical condition of the older adults is a pressing issue. Wu Qin Xi exercise, despite being low-intensity, is highly effective among older adults. Inspired by its characteristics, we designed a new exercise program for frail older adults, combining strength, endurance, and Wu Qin Xi. Furthermore, we employed machine learning to predict whether frailty can be reversed in older adults after the intervention. METHODS A total of 181 community-dwelling frail older adults aged 65 years or older participated in this single-center, randomized controlled study, with 54.7% (n=99) being female. The study assessed the effectiveness of several exercise modalities in reversing frailty. The Fried's frailty criterion was used to assess the degree of frailty of the subjects. Participants were assigned a three-digit code 001-163 and randomly assigned (1:1:1) by computer to three different groups based on the study participant number: the Wu Qin Xi group (WQX), the strength exercise mixed with endurance exercise training group (SE), and the WQXSE hybrid exercise group incorporated the above two. Body composition and frailty-related physical fitness factors were measured before and after a 24-week intervention. The measurements included Body height, Body mass, Timed Up and Go Test (TUGT), grip strength assessment (GS), 6min walk test (6 min WT), and 10 m maximum walk speed (10 m MWS). Data were analyzed using repeated measures ANOVA to determine group and time interaction effects and machine learning models were used to predict program effectiveness. RESULTS A total of 163 participants completed the study, with 53.9% (n=88) of them being female. The two items, 10 m maximum walking speed (10 m MWS) and grip strength, were significantly affected by the interaction of group and time. Compared to the other two groups, the WQXSE group showed the most improvement in the item 10 m MWS. In addition, following 24 weeks of training, 68 (41.7%) of the initially frail older adults had reversed their frailty status. Among them, 19 (36.5%) were in the WQX group, 24 (44.4%) were in the WQXSE group, and 25 (43.9%) were in the SE group. The stacking model exhibited superior performance when compared to other algorithms. CONCLUSION A hybrid exercise regimen comprising the Wu Qin Xi routine and exercises focused on both strength and endurance holds the potential to yield greater improvements in the physical fitness of older adults, as well as reducing frailty. Leveraging a stacking model, it is possible to forecast the likelihood of older adults successfully reversing their frailty status following participation in a prevention exercise program.
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Affiliation(s)
- M Wei
- Guang Yang, Ziheng Wang, Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, Changchun, 130024, Jilin, China, ;
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Xing B, Yu J, Liu Y, He S, Chen X, Li Z, He L, Yang N, Ping F, Xu L, Li W, Zhang H, Li Y. High Dietary Zinc Intake Is Associated with Shorter Leukocyte Telomere Length, Mediated by Tumor Necrosis Factor-α: A Study of China Adults. J Nutr Health Aging 2023; 27:904-910. [PMID: 37960914 DOI: 10.1007/s12603-023-1992-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 08/30/2023] [Indexed: 11/15/2023]
Abstract
OBJECTIVES Diet can influence peripheral leukocyte telomere length (LTL), and various micronutrients have been reported to correlate with it. Zinc is known for its antioxidant properties and immunomodulatory effects. However, there are few epidemiological investigations on the relationship between dietary zinc intake and LTL. This study analyzed the association between dietary zinc and LTL and the potential role of inflammation and oxidative stress among them. DESIGN Cross-sectional and community-based study. SETTING AND PARTICIPANTS 599 participants from rural communities in the Changping suburb of Beijing, China, were recruited. MEASUREMENTS Serum lipid profile, glycosylated hemoglobin (HbA1c), oxidative stress marker, and inflammatory cytokines levels were measured. Detailed dietary data were obtained using a 24 h food recall. LTL was assessed using a real-time PCR assay. Spearman analysis, restricted cubic splines (RCS), and general linear regression models were used to determine the association between dietary zinc intake and LTL. Simple regulatory models were also applied to analyze the role of inflammation and oxidative stress among them. RESULTS A total of 482 subjects were ultimately included in this analysis. Spearman analysis showed that dietary zinc intake and zinc intake under energy density were negatively correlated with LTL (r=-0.142 and -0.126, all P <0.05) and positively correlated with tumor necrosis factor-α (TNF-α) (r=0.138 and 0.202, all P <0.05) while only dietary zinc without energy adjustment had a positive correlation with superoxide dismutase (SOD). RCS (P for non-linearity=0.933) and multiple linear regression (B=-0.084, P=0.009) indicated a negative linear association between dietary zinc and LTL. The adjustment of TNF-α rather than SOD could abolish the relationship. The mediation model suggested that the unfavorable effect of dietary zinc on LTL was mediated by TNF-α. CONCLUSIONS High dietary zinc may correlate with telomere attrition, and TNF-α can act as a mediator in this relationship. In the future, more extensive cohort studies are needed to further explore the relationship between dietary zinc and cellular aging and the specific mechanisms.
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Affiliation(s)
- B Xing
- Wei Li, Huabing Zhang, Yuxiu Li, Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, Wei Li, ; Huabing Zhang, ; Yuxiu Li,
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Abdallah MS, Adam J, Adamczyk L, Adams JR, Adkins JK, Agakishiev G, Aggarwal I, Aggarwal MM, Ahammed Z, Alekseev I, Anderson DM, Aparin A, Aschenauer EC, Ashraf MU, Atetalla FG, Attri A, Averichev GS, Bairathi V, Baker W, Ball Cap JG, Barish K, Behera A, Bellwied R, Bhagat P, Bhasin A, Bielcik J, Bielcikova J, Bordyuzhin IG, Brandenburg JD, Brandin AV, Bunzarov I, Butterworth J, Cai XZ, Caines H, Calderón de la Barca Sánchez M, Cebra D, Chakaberia I, Chaloupka P, Chan BK, Chang FH, Chang Z, Chankova-Bunzarova N, Chatterjee A, Chattopadhyay S, Chen D, Chen J, Chen JH, Chen X, Chen Z, Cheng J, Chevalier M, Choudhury S, Christie W, Chu X, Crawford HJ, Csanád M, Daugherity M, Dedovich TG, Deppner IM, Derevschikov AA, Dhamija A, Di Carlo L, Didenko L, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Elsey N, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fawzi FM, Fazio S, Federic P, Fedorisin J, Feng CJ, Feng Y, Filip P, Finch E, Fisyak Y, Francisco A, Fu C, Fulek L, Gagliardi CA, Galatyuk T, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Guryn W, Hamad AI, Hamed A, Han Y, Harabasz S, Harasty MD, Harris JW, Harrison H, He S, He W, He XH, He Y, Heppelmann S, Heppelmann S, Herrmann N, Hoffman E, Holub L, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Humanic TJ, Igo G, Isenhower D, Jacobs WW, Jena C, Jentsch A, Ji Y, Jia J, Jiang K, Ju X, Judd EG, Kabana S, Kabir ML, Kagamaster S, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Ke HW, Keane D, Kechechyan A, Kelsey M, Khyzhniak YV, Kikoła DP, Kim C, Kimelman B, Kincses D, Kisel I, Kiselev A, Knospe AG, Kochenda L, Kosarzewski LK, Kramarik L, Kravtsov P, Kumar L, Kumar S, Kunnawalkam Elayavalli R, Kwasizur JH, Lacey R, Lan S, Landgraf JM, Lauret J, Lebedev A, Lednicky R, Lee JH, Leung YH, Li C, Li C, Li W, Li X, Li Y, Liang X, Liang Y, Licenik R, Lin T, Lin Y, Lisa MA, Liu F, Liu H, Liu H, Liu P, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Longacre RS, Loyd E, Lukow NS, Luo XF, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Markert C, Matis HS, Mazer JA, Minaev NG, Mioduszewski S, Mohanty B, Mondal MM, Mooney I, Morozov DA, Mukherjee A, Nagy M, Nam JD, Nasim M, Nayak K, Neff D, Nelson JM, Nemes DB, Nie M, Nigmatkulov G, Niida T, Nishitani R, Nogach LV, Nonaka T, Nunes AS, Odyniec G, Ogawa A, Oh S, Okorokov VA, Page BS, Pak R, Pandav A, Pandey AK, Panebratsev Y, Parfenov P, Pawlik B, Pawlowska D, Pei H, Perkins C, Pinsky L, Pintér RL, Pluta J, Pokhrel BR, Ponimatkin G, Porter J, Posik M, Prozorova V, Pruthi NK, Przybycien M, Putschke J, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Reed R, Ritter HG, Robotkova M, Rogachevskiy OV, Romero JL, Roy D, Ruan L, Rusnak J, Sahoo NR, Sako H, Salur S, Sandweiss J, Sato S, Schmidke WB, Schmitz N, Schweid BR, Seck F, Seger J, Sergeeva M, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao M, Shao T, Sheikh AI, Shen D, Shi SS, Shi Y, Shou QY, Sichtermann EP, Sikora R, Simko M, Singh J, Singha S, Skoby MJ, Smirnov N, Söhngen Y, Solyst W, Sorensen P, Spinka HM, Srivastava B, Stanislaus TDS, Stefaniak M, Stewart DJ, Strikhanov M, Stringfellow B, Suaide AAP, Sumbera M, Summa B, Sun XM, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Sweger ZW, Szymanski P, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Timmins AR, Tlusty D, Todoroki T, Tokarev M, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tripathy SK, Truhlar T, Trzeciak BA, Tsai OD, Tu Z, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vanek J, Vasiliev AN, Vassiliev I, Verkest V, Videbæk F, Vokal S, Voloshin SA, Wang G, Wang JS, Wang P, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Wen L, Westfall GD, Wieman H, Wissink SW, Wu J, Wu Y, Xi B, Xiao ZG, Xie G, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Z, Xu Z, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zbroszczyk H, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang S, Zhang XP, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao J, Zhou C, Zhu X, Zhu Z, Zurek M, Zyzak M. Collision-System and Beam-Energy Dependence of Anisotropic Flow Fluctuations. Phys Rev Lett 2022; 129:252301. [PMID: 36608250 DOI: 10.1103/physrevlett.129.252301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 08/17/2022] [Accepted: 11/16/2022] [Indexed: 06/17/2023]
Abstract
Elliptic flow measurements from two-, four-, and six-particle correlations are used to investigate flow fluctuations in collisions of U+U at sqrt[s_{NN}]=193 GeV, Cu+Au at sqrt[s_{NN}]=200 GeV and Au+Au spanning the range sqrt[s_{NN}]=11.5-200 GeV. The measurements show a strong dependence of the flow fluctuations on collision centrality, a modest dependence on system size, and very little if any, dependence on particle species and beam energy. The results, when compared to similar LHC measurements, viscous hydrodynamic calculations, and trento model eccentricities, indicate that initial-state-driven fluctuations predominate the flow fluctuations generated in the collisions studied.
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Affiliation(s)
- M S Abdallah
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - J Adam
- Brookhaven National Laboratory, Upton, New York 11973
| | - L Adamczyk
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - J R Adams
- The Ohio State University, Columbus, Ohio 43210
| | - J K Adkins
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - G Agakishiev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I Aggarwal
- Panjab University, Chandigarh 160014, India
| | | | - Z Ahammed
- Variable Energy Cyclotron Centre, Kolkata 700064, India
| | - I Alekseev
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
- National Research Nuclear University MEPhI, Moscow 115409
| | - D M Anderson
- Texas A&M University, College Station, Texas 77843
| | - A Aparin
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - M U Ashraf
- Central China Normal University, Wuhan, Hubei 430079
| | | | - A Attri
- Panjab University, Chandigarh 160014, India
| | | | - V Bairathi
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - W Baker
- University of California, Riverside, California 92521
| | | | - K Barish
- University of California, Riverside, California 92521
| | - A Behera
- State University of New York, Stony Brook, New York 11794
| | - R Bellwied
- University of Houston, Houston, Texas 77204
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - J Bielcik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J Bielcikova
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - I G Bordyuzhin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | | | - A V Brandin
- National Research Nuclear University MEPhI, Moscow 115409
| | - I Bunzarov
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - X Z Cai
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - H Caines
- Yale University, New Haven, Connecticut 06520
| | | | - D Cebra
- University of California, Davis, California 95616
| | - I Chakaberia
- Brookhaven National Laboratory, Upton, New York 11973
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - P Chaloupka
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - F-H Chang
- National Cheng Kung University, Tainan 70101
| | - Z Chang
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - A Chatterjee
- Central China Normal University, Wuhan, Hubei 430079
| | | | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai, 200433
| | - X Chen
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - M Chevalier
- University of California, Riverside, California 92521
| | | | - W Christie
- Brookhaven National Laboratory, Upton, New York 11973
| | - X Chu
- Brookhaven National Laboratory, Upton, New York 11973
| | - H J Crawford
- University of California, Berkeley, California 94720
| | - M Csanád
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - M Daugherity
- Abilene Christian University, Abilene, Texas 79699
| | - T G Dedovich
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I M Deppner
- University of Heidelberg, Heidelberg 69120, Germany
| | - A A Derevschikov
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - A Dhamija
- Panjab University, Chandigarh 160014, India
| | - L Di Carlo
- Wayne State University, Detroit, Michigan 48201
| | - L Didenko
- Brookhaven National Laboratory, Upton, New York 11973
| | - P Dixit
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - X Dong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | - J C Dunlop
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Elsey
- Wayne State University, Detroit, Michigan 48201
| | - J Engelage
- University of California, Berkeley, California 94720
| | - G Eppley
- Rice University, Houston, Texas 77251
| | - S Esumi
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - O Evdokimov
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Ewigleben
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - O Eyser
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Fatemi
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - F M Fawzi
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - S Fazio
- Brookhaven National Laboratory, Upton, New York 11973
| | - P Federic
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J Fedorisin
- Joint Institute for Nuclear Research, Dubna 141 980
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - P Filip
- Joint Institute for Nuclear Research, Dubna 141 980
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Francisco
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Central China Normal University, Wuhan, Hubei 430079
| | - L Fulek
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | | | - T Galatyuk
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - F Geurts
- Rice University, Houston, Texas 77251
| | - N Ghimire
- Temple University, Philadelphia, Pennsylvania 19122
| | - A Gibson
- Valparaiso University, Valparaiso, Indiana 46383
| | - K Gopal
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - X Gou
- Shandong University, Qingdao, Shandong 266237
| | - D Grosnick
- Valparaiso University, Valparaiso, Indiana 46383
| | - A Gupta
- University of Jammu, Jammu 180001, India
| | - W Guryn
- Brookhaven National Laboratory, Upton, New York 11973
| | - A I Hamad
- Kent State University, Kent, Ohio 44242
| | - A Hamed
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - S Harabasz
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - M D Harasty
- University of California, Davis, California 95616
| | - J W Harris
- Yale University, New Haven, Connecticut 06520
| | - H Harrison
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - S He
- Central China Normal University, Wuhan, Hubei 430079
| | - W He
- Fudan University, Shanghai, 200433
| | - X H He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y He
- Shandong University, Qingdao, Shandong 266237
| | - S Heppelmann
- University of California, Davis, California 95616
| | - S Heppelmann
- Pennsylvania State University, University Park, Pennsylvania 16802
| | - N Herrmann
- University of Heidelberg, Heidelberg 69120, Germany
| | - E Hoffman
- University of Houston, Houston, Texas 77204
| | - L Holub
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - Y Hu
- Fudan University, Shanghai, 200433
| | - H Huang
- National Cheng Kung University, Tainan 70101
| | - H Z Huang
- University of California, Los Angeles, California 90095
| | - S L Huang
- State University of New York, Stony Brook, New York 11794
| | - T Huang
- National Cheng Kung University, Tainan 70101
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | - T J Humanic
- The Ohio State University, Columbus, Ohio 43210
| | - G Igo
- University of California, Los Angeles, California 90095
| | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - A Jentsch
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Ji
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J Jia
- Brookhaven National Laboratory, Upton, New York 11973
- State University of New York, Stony Brook, New York 11794
| | - K Jiang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Ju
- University of Science and Technology of China, Hefei, Anhui 230026
| | - E G Judd
- University of California, Berkeley, California 94720
| | - S Kabana
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - M L Kabir
- University of California, Riverside, California 92521
| | - S Kagamaster
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - D Kalinkin
- Brookhaven National Laboratory, Upton, New York 11973
- Indiana University, Bloomington, Indiana 47408
| | - K Kang
- Tsinghua University, Beijing 100084
| | - D Kapukchyan
- University of California, Riverside, California 92521
| | - K Kauder
- Brookhaven National Laboratory, Upton, New York 11973
| | - H W Ke
- Brookhaven National Laboratory, Upton, New York 11973
| | - D Keane
- Kent State University, Kent, Ohio 44242
| | - A Kechechyan
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M Kelsey
- Wayne State University, Detroit, Michigan 48201
| | - Y V Khyzhniak
- National Research Nuclear University MEPhI, Moscow 115409
| | - D P Kikoła
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - C Kim
- University of California, Riverside, California 92521
| | - B Kimelman
- University of California, Davis, California 95616
| | - D Kincses
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - I Kisel
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
| | - A Kiselev
- Brookhaven National Laboratory, Upton, New York 11973
| | - A G Knospe
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - L Kochenda
- National Research Nuclear University MEPhI, Moscow 115409
| | - L K Kosarzewski
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - L Kramarik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - P Kravtsov
- National Research Nuclear University MEPhI, Moscow 115409
| | - L Kumar
- Panjab University, Chandigarh 160014, India
| | - S Kumar
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | | | - R Lacey
- State University of New York, Stony Brook, New York 11794
| | - S Lan
- Central China Normal University, Wuhan, Hubei 430079
| | - J M Landgraf
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Lauret
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Lebedev
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Lednicky
- Joint Institute for Nuclear Research, Dubna 141 980
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J H Lee
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y H Leung
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - C Li
- Shandong University, Qingdao, Shandong 266237
| | - C Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - W Li
- Rice University, Houston, Texas 77251
| | - X Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- Tsinghua University, Beijing 100084
| | - X Liang
- University of California, Riverside, California 92521
| | - Y Liang
- Kent State University, Kent, Ohio 44242
| | - R Licenik
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - T Lin
- Shandong University, Qingdao, Shandong 266237
| | - Y Lin
- Central China Normal University, Wuhan, Hubei 430079
| | - M A Lisa
- The Ohio State University, Columbus, Ohio 43210
| | - F Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - H Liu
- Indiana University, Bloomington, Indiana 47408
| | - H Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - P Liu
- State University of New York, Stony Brook, New York 11794
| | - T Liu
- Yale University, New Haven, Connecticut 06520
| | - X Liu
- The Ohio State University, Columbus, Ohio 43210
| | - Y Liu
- Texas A&M University, College Station, Texas 77843
| | - Z Liu
- University of Science and Technology of China, Hefei, Anhui 230026
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - R S Longacre
- Brookhaven National Laboratory, Upton, New York 11973
| | - E Loyd
- University of California, Riverside, California 92521
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
| | - X F Luo
- Central China Normal University, Wuhan, Hubei 430079
| | - L Ma
- Fudan University, Shanghai, 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y G Ma
- Fudan University, Shanghai, 200433
| | - N Magdy
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | | | - C Markert
- University of Texas, Austin, Texas 78712
| | - H S Matis
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J A Mazer
- Rutgers University, Piscataway, New Jersey 08854
| | - N G Minaev
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
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- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - M M Mondal
- State University of New York, Stony Brook, New York 11794
| | - I Mooney
- Wayne State University, Detroit, Michigan 48201
| | - D A Morozov
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - A Mukherjee
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - M Nagy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - Md Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - K Nayak
- Central China Normal University, Wuhan, Hubei 430079
| | - D Neff
- University of California, Los Angeles, California 90095
| | - J M Nelson
- University of California, Berkeley, California 94720
| | - D B Nemes
- Yale University, New Haven, Connecticut 06520
| | - M Nie
- Shandong University, Qingdao, Shandong 266237
| | - G Nigmatkulov
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Niida
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - R Nishitani
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - L V Nogach
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - T Nonaka
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - A S Nunes
- Brookhaven National Laboratory, Upton, New York 11973
| | - G Odyniec
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - A Ogawa
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Oh
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - V A Okorokov
- National Research Nuclear University MEPhI, Moscow 115409
| | - B S Page
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Pak
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Pandav
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - A K Pandey
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | | | - P Parfenov
- National Research Nuclear University MEPhI, Moscow 115409
| | - B Pawlik
- Institute of Nuclear Physics PAN, Cracow 31-342, Poland
| | - D Pawlowska
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - H Pei
- Central China Normal University, Wuhan, Hubei 430079
| | - C Perkins
- University of California, Berkeley, California 94720
| | - L Pinsky
- University of Houston, Houston, Texas 77204
| | - R L Pintér
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - J Pluta
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - B R Pokhrel
- Temple University, Philadelphia, Pennsylvania 19122
| | - G Ponimatkin
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J Porter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - M Posik
- Temple University, Philadelphia, Pennsylvania 19122
| | - V Prozorova
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - N K Pruthi
- Panjab University, Chandigarh 160014, India
| | - M Przybycien
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - J Putschke
- Wayne State University, Detroit, Michigan 48201
| | - H Qiu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - A Quintero
- Temple University, Philadelphia, Pennsylvania 19122
| | - C Racz
- University of California, Riverside, California 92521
| | | | - N Raha
- Wayne State University, Detroit, Michigan 48201
| | - R L Ray
- University of Texas, Austin, Texas 78712
| | - R Reed
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H G Ritter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - M Robotkova
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | | | - J L Romero
- University of California, Davis, California 95616
| | - D Roy
- Rutgers University, Piscataway, New Jersey 08854
| | - L Ruan
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Rusnak
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - N R Sahoo
- Shandong University, Qingdao, Shandong 266237
| | - H Sako
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - S Salur
- Rutgers University, Piscataway, New Jersey 08854
| | - J Sandweiss
- Yale University, New Haven, Connecticut 06520
| | - S Sato
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W B Schmidke
- Brookhaven National Laboratory, Upton, New York 11973
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- Max-Planck-Institut für Physik, Munich 80805, Germany
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- State University of New York, Stony Brook, New York 11794
| | - F Seck
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - J Seger
- Creighton University, Omaha, Nebraska 68178
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- University of California, Los Angeles, California 90095
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- University of California, Riverside, California 92521
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- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - N Shah
- Indian Institute Technology, Patna, Bihar 801106, India
| | - E Shahaliev
- Joint Institute for Nuclear Research, Dubna 141 980
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- University of Science and Technology of China, Hefei, Anhui 230026
| | - T Shao
- Fudan University, Shanghai, 200433
| | | | - D Shen
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - S S Shi
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
| | - Q Y Shou
- Fudan University, Shanghai, 200433
| | - E P Sichtermann
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - R Sikora
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - M Simko
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - M J Skoby
- Purdue University, West Lafayette, Indiana 47907
| | - N Smirnov
- Yale University, New Haven, Connecticut 06520
| | - Y Söhngen
- University of Heidelberg, Heidelberg 69120, Germany
| | - W Solyst
- Indiana University, Bloomington, Indiana 47408
| | - P Sorensen
- Brookhaven National Laboratory, Upton, New York 11973
| | - H M Spinka
- Argonne National Laboratory, Argonne, Illinois 60439
| | - B Srivastava
- Purdue University, West Lafayette, Indiana 47907
| | | | - M Stefaniak
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - D J Stewart
- Yale University, New Haven, Connecticut 06520
| | - M Strikhanov
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - A A P Suaide
- Universidade de São Paulo, São Paulo, Brazil 05314-970
| | - M Sumbera
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - B Summa
- Pennsylvania State University, University Park, Pennsylvania 16802
| | - X M Sun
- Central China Normal University, Wuhan, Hubei 430079
| | - X Sun
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - Y Sun
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Sun
- Huzhou University, Huzhou, Zhejiang 313000
| | - B Surrow
- Temple University, Philadelphia, Pennsylvania 19122
| | - D N Svirida
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | - Z W Sweger
- University of California, Davis, California 95616
| | - P Szymanski
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - A H Tang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Tang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - A Taranenko
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Tarnowsky
- Michigan State University, East Lansing, Michigan 48824
| | - J H Thomas
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | - D Tlusty
- Creighton University, Omaha, Nebraska 68178
| | - T Todoroki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - M Tokarev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - C A Tomkiel
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - S Trentalange
- University of California, Los Angeles, California 90095
| | - R E Tribble
- Texas A&M University, College Station, Texas 77843
| | - P Tribedy
- Brookhaven National Laboratory, Upton, New York 11973
| | - S K Tripathy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - T Truhlar
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - B A Trzeciak
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - O D Tsai
- University of California, Los Angeles, California 90095
| | - Z Tu
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Ullrich
- Brookhaven National Laboratory, Upton, New York 11973
| | - D G Underwood
- Argonne National Laboratory, Argonne, Illinois 60439
- Valparaiso University, Valparaiso, Indiana 46383
| | - I Upsal
- Brookhaven National Laboratory, Upton, New York 11973
- Shandong University, Qingdao, Shandong 266237
| | - G Van Buren
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Vanek
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - A N Vasiliev
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - I Vassiliev
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
| | - V Verkest
- Wayne State University, Detroit, Michigan 48201
| | - F Videbæk
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Vokal
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - G Wang
- University of California, Los Angeles, California 90095
| | - J S Wang
- Huzhou University, Huzhou, Zhejiang 313000
| | - P Wang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Wang
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Wang
- Tsinghua University, Beijing 100084
| | - Z Wang
- Shandong University, Qingdao, Shandong 266237
| | - J C Webb
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - L Wen
- University of California, Los Angeles, California 90095
| | - G D Westfall
- Michigan State University, East Lansing, Michigan 48824
| | - H Wieman
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - S W Wissink
- Indiana University, Bloomington, Indiana 47408
| | - J Wu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Wu
- University of California, Riverside, California 92521
| | - B Xi
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - Z G Xiao
- Tsinghua University, Beijing 100084
| | - G Xie
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - W Xie
- Purdue University, West Lafayette, Indiana 47907
| | - H Xu
- Huzhou University, Huzhou, Zhejiang 313000
| | - N Xu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Q H Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Shandong University, Qingdao, Shandong 266237
| | - Z Xu
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Xu
- University of California, Los Angeles, California 90095
| | - C Yang
- Shandong University, Qingdao, Shandong 266237
| | - Q Yang
- Shandong University, Qingdao, Shandong 266237
| | - S Yang
- Rice University, Houston, Texas 77251
| | - Y Yang
- National Cheng Kung University, Tainan 70101
| | - Z Ye
- Rice University, Houston, Texas 77251
| | - Z Ye
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - L Yi
- Shandong University, Qingdao, Shandong 266237
| | - K Yip
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Yu
- Shandong University, Qingdao, Shandong 266237
| | - H Zbroszczyk
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - W Zha
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Zhang
- State University of New York, Stony Brook, New York 11794
| | - D Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - J Zhang
- Shandong University, Qingdao, Shandong 266237
| | - S Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - S Zhang
- Fudan University, Shanghai, 200433
| | | | - Y Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - Z J Zhang
- National Cheng Kung University, Tainan 70101
| | - Z Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - J Zhao
- Purdue University, West Lafayette, Indiana 47907
| | - C Zhou
- Fudan University, Shanghai, 200433
| | - X Zhu
- Tsinghua University, Beijing 100084
| | - Z Zhu
- Shandong University, Qingdao, Shandong 266237
| | - M Zurek
- Argonne National Laboratory, Argonne, Illinois 60439
| | - M Zyzak
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
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Zhang ZY, Yang LT, Yue Q, Kang KJ, Li YJ, Agartioglu M, An HP, Chang JP, Chen YH, Cheng JP, Dai WH, Deng Z, Fang CH, Geng XP, Gong H, Guo QJ, Guo XY, He L, He SM, Hu JW, Huang HX, Huang TC, Jia HT, Jiang X, Li HB, Li JM, Li J, Li QY, Li RMJ, Li XQ, Li YL, Liang YF, Liao B, Lin FK, Lin ST, Liu SK, Liu YD, Liu Y, Liu YY, Liu ZZ, Ma H, Mao YC, Nie QY, Ning JH, Pan H, Qi NC, Ren J, Ruan XC, Saraswat K, Sharma V, She Z, Singh MK, Sun TX, Tang CJ, Tang WY, Tian Y, Wang GF, Wang L, Wang Q, Wang Y, Wang YX, Wong HT, Wu SY, Wu YC, Xing HY, Xu R, Xu Y, Xue T, Yan YL, Yeh CH, Yi N, Yu CX, Yu HJ, Yue JF, Zeng M, Zeng Z, Zhang BT, Zhang FS, Zhang L, Zhang ZH, Zhao KK, Zhao MG, Zhou JF, Zhou ZY, Zhu JJ. Constraints on Sub-GeV Dark Matter-Electron Scattering from the CDEX-10 Experiment. Phys Rev Lett 2022; 129:221301. [PMID: 36493436 DOI: 10.1103/physrevlett.129.221301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/25/2022] [Accepted: 10/20/2022] [Indexed: 06/17/2023]
Abstract
We present improved germanium-based constraints on sub-GeV dark matter via dark matter-electron (χ-e) scattering using the 205.4 kg·day dataset from the CDEX-10 experiment. Using a novel calculation technique, we attain predicted χ-e scattering spectra observable in high-purity germanium detectors. In the heavy mediator scenario, our results achieve 3 orders of magnitude of improvement for m_{χ} larger than 80 MeV/c^{2} compared to previous germanium-based χ-e results. We also present the most stringent χ-e cross-section limit to date among experiments using solid-state detectors for m_{χ} larger than 90 MeV/c^{2} with heavy mediators and m_{χ} larger than 100 MeV/c^{2} with electric dipole coupling. The result proves the feasibility and demonstrates the vast potential of a new χ-e detection method with high-purity germanium detectors in ultralow radioactive background.
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Affiliation(s)
- Z Y Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - L T Yang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q Yue
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - K J Kang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y J Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - M Agartioglu
- Institute of Physics, Academia Sinica, Taipei 11529
| | - H P An
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- Department of Physics, Tsinghua University, Beijing 100084
| | | | - Y H Chen
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J P Cheng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - W H Dai
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Z Deng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - C H Fang
- College of Physics, Sichuan University, Chengdu 610065
| | - X P Geng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H Gong
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q J Guo
- School of Physics, Peking University, Beijing 100871
| | - X Y Guo
- YaLong River Hydropower Development Company, Chengdu 610051
| | - L He
- NUCTECH Company, Beijing 100084
| | - S M He
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J W Hu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H X Huang
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - T C Huang
- Sino-French Institute of Nuclear and Technology, Sun Yat-sen University, Zhuhai 519082
| | - H T Jia
- College of Physics, Sichuan University, Chengdu 610065
| | - X Jiang
- College of Physics, Sichuan University, Chengdu 610065
| | - H B Li
- Institute of Physics, Academia Sinica, Taipei 11529
| | - J M Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - J Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q Y Li
- College of Physics, Sichuan University, Chengdu 610065
| | - R M J Li
- College of Physics, Sichuan University, Chengdu 610065
| | - X Q Li
- School of Physics, Nankai University, Tianjin 300071
| | - Y L Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y F Liang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - B Liao
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - F K Lin
- Institute of Physics, Academia Sinica, Taipei 11529
| | - S T Lin
- College of Physics, Sichuan University, Chengdu 610065
| | - S K Liu
- College of Physics, Sichuan University, Chengdu 610065
| | - Y D Liu
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - Y Liu
- College of Physics, Sichuan University, Chengdu 610065
| | - Y Y Liu
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - Z Z Liu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H Ma
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y C Mao
- School of Physics, Peking University, Beijing 100871
| | - Q Y Nie
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - J H Ning
- YaLong River Hydropower Development Company, Chengdu 610051
| | - H Pan
- NUCTECH Company, Beijing 100084
| | - N C Qi
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J Ren
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - X C Ruan
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - K Saraswat
- Institute of Physics, Academia Sinica, Taipei 11529
| | - V Sharma
- Institute of Physics, Academia Sinica, Taipei 11529
- Department of Physics, Banaras Hindu University, Varanasi 221005, India
| | - Z She
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - M K Singh
- Institute of Physics, Academia Sinica, Taipei 11529
- Department of Physics, Banaras Hindu University, Varanasi 221005, India
| | - T X Sun
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - C J Tang
- College of Physics, Sichuan University, Chengdu 610065
| | - W Y Tang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y Tian
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - G F Wang
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - L Wang
- Department of Physics, Beijing Normal University, Beijing 100875
| | - Q Wang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- Department of Physics, Tsinghua University, Beijing 100084
| | - Y Wang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- Department of Physics, Tsinghua University, Beijing 100084
| | - Y X Wang
- School of Physics, Peking University, Beijing 100871
| | - H T Wong
- Institute of Physics, Academia Sinica, Taipei 11529
| | - S Y Wu
- YaLong River Hydropower Development Company, Chengdu 610051
| | - Y C Wu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H Y Xing
- College of Physics, Sichuan University, Chengdu 610065
| | - R Xu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y Xu
- School of Physics, Nankai University, Tianjin 300071
| | - T Xue
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y L Yan
- College of Physics, Sichuan University, Chengdu 610065
| | - C H Yeh
- Institute of Physics, Academia Sinica, Taipei 11529
| | - N Yi
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - C X Yu
- School of Physics, Nankai University, Tianjin 300071
| | - H J Yu
- NUCTECH Company, Beijing 100084
| | - J F Yue
- YaLong River Hydropower Development Company, Chengdu 610051
| | - M Zeng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Z Zeng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - B T Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - F S Zhang
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - L Zhang
- College of Physics, Sichuan University, Chengdu 610065
| | - Z H Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - K K Zhao
- College of Physics, Sichuan University, Chengdu 610065
| | - M G Zhao
- School of Physics, Nankai University, Tianjin 300071
| | - J F Zhou
- YaLong River Hydropower Development Company, Chengdu 610051
| | - Z Y Zhou
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - J J Zhu
- College of Physics, Sichuan University, Chengdu 610065
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Dai WH, Jia LP, Ma H, Yue Q, Kang KJ, Li YJ, An HP, C G, Chang JP, Chen YH, Cheng JP, Deng Z, Fang CH, Geng XP, Gong H, Guo QJ, Guo XY, He L, He SM, Hu JW, Huang HX, Huang TC, Jia HT, Jiang X, Karmakar S, Li HB, Li JM, Li J, Li QY, Li RMJ, Li XQ, Li YL, Liang YF, Liao B, Lin FK, Lin ST, Liu SK, Liu YD, Liu Y, Liu YY, Liu ZZ, Mao YC, Nie QY, Ning JH, Pan H, Qi NC, Ren J, Ruan XC, She Z, Singh MK, Sun TX, Tang CJ, Tang WY, Tian Y, Wang GF, Wang L, Wang Q, Wang Y, Wang YX, Wong HT, Wu SY, Wu YC, Xing HY, Xu R, Xu Y, Xue T, Yan YL, Yang LT, Yi N, Yu CX, Yu HJ, Yue JF, Zeng M, Zeng Z, Zhang BT, Zhang FS, Zhang L, Zhang ZH, Zhang ZY, Zhao KK, Zhao MG, Zhou JF, Zhou ZY, Zhu JJ. Exotic Dark Matter Search with the CDEX-10 Experiment at China's Jinping Underground Laboratory. Phys Rev Lett 2022; 129:221802. [PMID: 36493447 DOI: 10.1103/physrevlett.129.221802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 11/07/2022] [Indexed: 06/17/2023]
Abstract
A search for exotic dark matter (DM) in the sub-GeV mass range has been conducted using 205 kg day data taken from a p-type point contact germanium detector of the CDEX-10 experiment at China's Jinping underground laboratory. New low-mass dark matter searching channels, neutral current fermionic DM absorption (χ+A→ν+A) and DM-nucleus 3→2 scattering (χ+χ+A→ϕ+A), have been analyzed with an energy threshold of 160 eVee. No significant signal was found; thus new limits on the DM-nucleon interaction cross section are set for both models at the sub-GeV DM mass region. A cross section limit for the fermionic DM absorption is set to be 2.5×10^{-46} cm^{2} (90% C.L.) at DM mass of 10 MeV/c^{2}. For the DM-nucleus 3→2 scattering scenario, limits are extended to DM mass of 5 and 14 MeV/c^{2} for the massless dark photon and bound DM final state, respectively.
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Affiliation(s)
- W H Dai
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - L P Jia
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H Ma
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q Yue
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - K J Kang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y J Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H P An
- Department of Physics, Tsinghua University, Beijing 100084
| | - Greeshma C
- Institute of Physics, Academia Sinica, Taipei 11529
| | | | - Y H Chen
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J P Cheng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - Z Deng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - C H Fang
- College of Physics, Sichuan University, Chengdu 610065
| | - X P Geng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H Gong
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q J Guo
- School of Physics, Peking University, Beijing 100871
| | - X Y Guo
- YaLong River Hydropower Development Company, Chengdu 610051
| | - L He
- NUCTECH Company, Beijing 100084
| | - S M He
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J W Hu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H X Huang
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - T C Huang
- Sino-French Institute of Nuclear and Technology, Sun Yat-sen University, Zhuhai 519082
| | - H T Jia
- College of Physics, Sichuan University, Chengdu 610065
| | - X Jiang
- College of Physics, Sichuan University, Chengdu 610065
| | - S Karmakar
- Institute of Physics, Academia Sinica, Taipei 11529
| | - H B Li
- Institute of Physics, Academia Sinica, Taipei 11529
| | - J M Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - J Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Q Y Li
- College of Physics, Sichuan University, Chengdu 610065
| | - R M J Li
- College of Physics, Sichuan University, Chengdu 610065
| | - X Q Li
- School of Physics, Nankai University, Tianjin 300071
| | - Y L Li
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y F Liang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - B Liao
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - F K Lin
- Institute of Physics, Academia Sinica, Taipei 11529
| | - S T Lin
- College of Physics, Sichuan University, Chengdu 610065
| | - S K Liu
- College of Physics, Sichuan University, Chengdu 610065
| | - Y D Liu
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - Y Liu
- College of Physics, Sichuan University, Chengdu 610065
| | - Y Y Liu
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - Z Z Liu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y C Mao
- School of Physics, Peking University, Beijing 100871
| | - Q Y Nie
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - J H Ning
- YaLong River Hydropower Development Company, Chengdu 610051
| | - H Pan
- NUCTECH Company, Beijing 100084
| | - N C Qi
- YaLong River Hydropower Development Company, Chengdu 610051
| | - J Ren
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - X C Ruan
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - Z She
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - M K Singh
- Institute of Physics, Academia Sinica, Taipei 11529
- Department of Physics, Banaras Hindu University, Varanasi 221005
| | - T X Sun
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - C J Tang
- College of Physics, Sichuan University, Chengdu 610065
| | - W Y Tang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y Tian
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - G F Wang
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - L Wang
- Department of Physics, Beijing Normal University, Beijing 100875
| | - Q Wang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- Department of Physics, Tsinghua University, Beijing 100084
| | - Y Wang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
- Department of Physics, Tsinghua University, Beijing 100084
| | - Y X Wang
- School of Physics, Peking University, Beijing 100871
| | - H T Wong
- Institute of Physics, Academia Sinica, Taipei 11529
| | - S Y Wu
- YaLong River Hydropower Development Company, Chengdu 610051
| | - Y C Wu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - H Y Xing
- College of Physics, Sichuan University, Chengdu 610065
| | - R Xu
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y Xu
- School of Physics, Nankai University, Tianjin 300071
| | - T Xue
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Y L Yan
- College of Physics, Sichuan University, Chengdu 610065
| | - L T Yang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - N Yi
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - C X Yu
- School of Physics, Nankai University, Tianjin 300071
| | - H J Yu
- NUCTECH Company, Beijing 100084
| | - J F Yue
- YaLong River Hydropower Development Company, Chengdu 610051
| | - M Zeng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Z Zeng
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - B T Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - F S Zhang
- College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875
| | - L Zhang
- College of Physics, Sichuan University, Chengdu 610065
| | - Z H Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - Z Y Zhang
- Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics, Tsinghua University, Beijing 100084
| | - K K Zhao
- College of Physics, Sichuan University, Chengdu 610065
| | - M G Zhao
- School of Physics, Nankai University, Tianjin 300071
| | - J F Zhou
- YaLong River Hydropower Development Company, Chengdu 610051
| | - Z Y Zhou
- Department of Nuclear Physics, China Institute of Atomic Energy, Beijing 102413
| | - J J Zhu
- College of Physics, Sichuan University, Chengdu 610065
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He S, Wang Y, Wang C, Peng Z, Chen Y. 221O Induction chemotherapy regimen of docetaxel plus cisplatin vs docetaxel, cisplatin plus fluorouracil followed by concurrent chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma: Preliminary results of a phase III multicenter randomized controlled trial. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.10.256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
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Li J, Mei B, Mei H, He S, Zhu Y, Huang J, Wang D, Zhang G. 186P Degradation of BRCA2 expression by hyperthermia sensitizes HRD-negative (BRCA2 wild-type) ovarian cancer cells to niraparib. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.10.222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
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He Y, Pang Y, Su Z, Zhou Y, Wang Y, Lu Y, Jiang Y, Han X, Song L, Wang L, Li Z, Lv X, Wang Y, Yao J, Liu X, Zhou X, He S, Zhang Y, Song L, Li J, Wang B, Tang L. Symptom burden, psychological distress, and symptom management status in hospitalized patients with advanced cancer: a multicenter study in China. ESMO Open 2022; 7:100595. [PMID: 36252435 PMCID: PMC9808454 DOI: 10.1016/j.esmoop.2022.100595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/31/2022] [Accepted: 09/02/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The management of physical symptoms and psychological distress of cancer patients is an important component of cancer care. The purpose of this study was to evaluate the symptom burden, psychological distress, and management status of hospitalized patients with advanced cancer in China and explore the potential influencing factors of undertreatment and non-treatment of symptoms. PATIENTS AND METHODS A total of 2930 hospitalized patients with advanced cancer (top six types of cancer in China) were recruited from 10 centers all over China. Patient-reported MD Anderson Symptom Inventory, Hospital Anxiety and Depression Scale (HADS), and Patient Health Questionnaire-9 (PHQ-9) scales and symptom management-related information were collected and linked with the patient's clinical data. The proportion of patients reporting moderate-to-severe (MS) symptoms and whether they were currently well managed were examined. Multivariable logistic regression models were applied to explore the factors correlated to undertreatment and non-treatment of symptoms. RESULTS About 27% of patients reported over three MS symptoms, 16% reported over five, and 9% reported over seven. Regarding psychological distress, the prevalence of HADS-anxiety was 29% and that of PHQ-9 depression was 11%. Sixty-one percent of patients have at least one MS symptom without any treatment. Sex [odds ratio (OR) = 2.238, 95% confidence interval (95% CI) 1.502-3.336], Eastern Cooperative Oncology Group (ECOG; OR = 0.404, 95% CI 0.241-0.676), and whether currently undergoing anticancer treatment (OR = 0.667, 95% CI 0.503-0.886) are the main factors correlated with the undertreatment of symptoms. Age (OR = 1.972, 95% CI 1.263-3.336), sex (OR = 0.626, 95% CI 0.414-0.948), ECOG (OR = 0.266, 95% CI 0.175-0.403), whether currently undergoing anticancer treatment (OR = 0.356, 95% CI 0.249-0.509), and comorbidity (OR = 0.713, 95% CI 0.526-0.966) are the main factors correlated with the non-treatment of symptoms. CONCLUSIONS This study shows that hospitalized patients with advanced cancer had a variety of physical and psychological symptoms but lacked adequate management and suggests that a complete symptom screening and management system is needed to deal with this complex problem.
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Affiliation(s)
- Y. He
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Y. Pang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Z. Su
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Y. Zhou
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Y. Wang
- Department of Breast Cancer Radiotherapy, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Y. Lu
- The Fifth Department of Chemotherapy, The Affiliated Cancer Hospital of Guangxi Medical University, Guangxi Zhuang Autonomous Region, Nanning, China
| | - Y. Jiang
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - X. Han
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - L. Song
- Department of Breast Medical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - L. Wang
- Department of Oncology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Z. Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - X. Lv
- Department of Oncology, Xiamen Humanity Hospital, Xiamen, China
| | - Y. Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - J. Yao
- Department of Integrated Chinese and Western Medicine, Shaanxi Provincial Cancer Hospital Affiliated to Medical College of Xi'an Jiaotong University, Xi'an, China
| | - X. Liu
- Department of Clinical Spiritual Care, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - X. Zhou
- Radiotherapy Center, Hubei Cancer Hospital, Wuhan, China
| | - S. He
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Y. Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - L. Song
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - J. Li
- Department of Psycho-oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - B. Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - L. Tang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, China,Correspondence to: Dr Lili Tang, Fu-Cheng Road 52, Hai-Dian District, Beijing 100142, China. Tel: +86-1088196648
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Li RR, Wang Y, Guo X, Li Y, Zhang LL, Meng Y, Ren HQ, He S, Lu RX, Zhu XL, Zhao R, Sun X. [Clinical significance of autoantibodies against ubiquitin carboxyl hydrolase L1 epitopes in the screening and diagnosis of Sjögren syndrome]. Zhonghua Yi Xue Za Zhi 2022; 102:2590-2595. [PMID: 36058683 DOI: 10.3760/cma.j.cn112137-20220311-00508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Objective: To study the clinical significance of autoantibodies against different ubiquitin carboxyl hydrolase L1 (UCH-L1) epitopes in Sjogren syndrome (SS). Methods: The serum levels of different UCH-L1 epitope autoantibodies in 98 SS patients [SS group, 17 males and 81 females, aged (49.1±12.3) years] in the Fifth Affiliated Hospital of Zhengzhou University and Peking University People's Hospital from January 2017 to January 2020 and 37 healthy controls [control group, 6 males and 31 females, aged (46.3±5.8) years] were determined by enzyme-linked immunosorbent assay (ELISA). Three potential epitopes of UCH-L1 protein were analyzed and synthesized and anti-UCH-L1203-214 and anti-UCH-L158-69 antibodies were studied between the two groups. The levels of the two anti-UCH-L1 antibodies in the two groups were compared. The correlation between the levels of UCH-L1 antibodies and clinical data of SS patients were analyzed by Pearson correlation analysis. Results: The serum levels of anti-UCH-L1203-214 and anti-UCH-L158-69 antibody in SS patients were significantly higher than those in healthy controls (HCs) (anti-UCH-L1203-214: 108.2±54.3 vs 78.9±25.8, P<0.001, anti-UCH-L158-69: 86.8±33.3 vs 60.4±21.5, P<0.001). The positive rates of anti-UCH-L1203-214 and anti-UCH-L158-69 antibodies in serums of SS patients were 27.6 % (27/98) and 25.5% (25/98), and those in HCs were 2.7%(1/37) and 5.4 %(2/37), respectively. In SS patients with positive serum anti-UCH-L158-69 antibody, the levels of IgG, γ globulin and rheumatoid factor (RF) and anti-SS-related antigen B (anti-SSB) antibody positive rate were all significantly higher than those in patients with negative antibody (all P<0.05). In SS patients with negative antinuclear antibody (ANA), anti-RNA binding protein (anti-RNP) antibody, anti-SS-related antigen A (anti-SSA) antibody and anti-SSB antibody, the positive rates of anti-UCH-L1203-214 antibody was 32.1%(9/28), 27.2%(25/92), 36.4%(12/33), 28.6%(18/63), respectively; and the positive rates of anti-UCH-L158-69 antibody was 21.4%(6/28), 30.4%(28/92), 30.3%(10/33), 20.6%(13/63), respectively. The level of serum anti-UCH-L1203-214 antibody in SS patients was positively correlated with the IgA level (r=0.21, P=0.024). The level of anti-UCH-L158-69 antibody in SS patients was positively correlated with the levels of γ-globulin, IgG and RF (r=0.35, 0.33, 0.32, all P<0.01). Conclusion: Autoantibodies against UCH-L1 epitopes are correlated with some clinical parameters of SS patients, which is of promising significance in the screening and diagnosis of SS.
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Affiliation(s)
- R R Li
- Department of Rheumatology and Immunology, the Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - Y Wang
- Department of Rheumatology and Immunology, the Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - X Guo
- Department of Rheumatology and Immunology, Peking University People's Hospital, Beijing 100044, China
| | - Y Li
- Department of Rheumatology and Immunology, Peking University People's Hospital, Beijing 100044, China
| | - L L Zhang
- Department of Rheumatology and Immunology, Peking University People's Hospital, Beijing 100044, China
| | - Y Meng
- Department of Rheumatology and Immunology, the Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - H Q Ren
- Department of Rheumatology and Immunology, the Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - S He
- Department of Rheumatology and Immunology, the Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - R X Lu
- Department of Rheumatology and Immunology, the Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - X L Zhu
- Department of Rheumatology and Immunology, the Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - Ruixiao Zhao
- Department of Rheumatology and Immunology, the Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - Xiaolin Sun
- Department of Rheumatology and Immunology, Peking University People's Hospital, Beijing 100044, China
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Abdallah MS, Aboona BE, Adam J, Adamczyk L, Adams JR, Adkins JK, Agakishiev G, Aggarwal I, Aggarwal MM, Ahammed Z, Alekseev I, Anderson DM, Aparin A, Aschenauer EC, Ashraf MU, Atetalla FG, Attri A, Averichev GS, Bairathi V, Baker W, Ball Cap JG, Barish K, Behera A, Bellwied R, Bhagat P, Bhasin A, Bielcik J, Bielcikova J, Bordyuzhin IG, Brandenburg JD, Brandin AV, Bunzarov I, Cai XZ, Caines H, Calderón de la Barca Sánchez M, Cebra D, Chakaberia I, Chaloupka P, Chan BK, Chang FH, Chang Z, Chankova-Bunzarova N, Chatterjee A, Chattopadhyay S, Chen D, Chen J, Chen JH, Chen X, Chen Z, Cheng J, Chevalier M, Choudhury S, Christie W, Chu X, Crawford HJ, Csanád M, Daugherity M, Dedovich TG, Deppner IM, Derevschikov AA, Dhamija A, Di Carlo L, Didenko L, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Elsey N, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fawzi FM, Fazio S, Federic P, Fedorisin J, Feng CJ, Feng Y, Filip P, Finch E, Fisyak Y, Francisco A, Fu C, Fulek L, Gagliardi CA, Galatyuk T, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Guryn W, Hamad AI, Hamed A, Han Y, Harabasz S, Harasty MD, Harris JW, Harrison H, He S, He W, He XH, He Y, Heppelmann S, Heppelmann S, Herrmann N, Hoffman E, Holub L, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Humanic TJ, Igo G, Isenhower D, Jacobs WW, Jena C, Jentsch A, Ji Y, Jia J, Jiang K, Ju X, Judd EG, Kabana S, Kabir ML, Kagamaster S, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Ke HW, Keane D, Kechechyan A, Kelsey M, Khyzhniak YV, Kikoła DP, Kim C, Kimelman B, Kincses D, Kisel I, Kiselev A, Knospe AG, Ko HS, Kochenda L, Kosarzewski LK, Kramarik L, Kravtsov P, Kumar L, Kumar S, Kunnawalkam Elayavalli R, Kwasizur JH, Lacey R, Lan S, Landgraf JM, Lauret J, Lebedev A, Lednicky R, Lee JH, Leung YH, Lewis N, Li C, Li C, Li W, Li X, Li Y, Liang X, Liang Y, Licenik R, Lin T, Lin Y, Lisa MA, Liu F, Liu H, Liu H, Liu P, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Longacre RS, Loyd E, Lukow NS, Luo XF, Ma L, Ma R, Ma YG, Magdy Abdelwahab Abdelrahman N, Mallick D, Margetis S, Markert C, Matis HS, Mazer JA, Minaev NG, Mioduszewski S, Mohanty B, Mondal MM, Mooney I, Morozov DA, Mukherjee A, Nagy M, Nam JD, Nasim M, Nayak K, Neff D, Nelson JM, Nemes DB, Nie M, Nigmatkulov G, Niida T, Nishitani R, Nogach LV, Nonaka T, Nunes AS, Odyniec G, Ogawa A, Oh S, Okorokov VA, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Panebratsev Y, Parfenov P, Pawlik B, Pawlowska D, Perkins C, Pinsky L, Pintér RL, Pluta J, Pokhrel BR, Ponimatkin G, Porter J, Posik M, Prozorova V, Pruthi NK, Przybycien M, Putschke J, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Reed R, Ritter HG, Robotkova M, Rogachevskiy OV, Romero JL, Roy D, Ruan L, Rusnak J, Sahoo AK, Sahoo NR, Sako H, Salur S, Sandweiss J, Sato S, Schmidke WB, Schmitz N, Schweid BR, Seck F, Seger J, Sergeeva M, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao M, Shao T, Sheikh AI, Shen DY, Shi SS, Shi Y, Shou QY, Sichtermann EP, Sikora R, Simko M, Singh J, Singha S, Skoby MJ, Smirnov N, Söhngen Y, Solyst W, Sorensen P, Spinka HM, Srivastava B, Stanislaus TDS, Stefaniak M, Stewart DJ, Strikhanov M, Stringfellow B, Suaide AAP, Sumbera M, Summa B, Sun XM, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Sweger ZW, Szymanski P, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Timmins AR, Tlusty D, Todoroki T, Tokarev M, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tripathy SK, Truhlar T, Trzeciak BA, Tsai OD, Tu Z, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vanek J, Vasiliev AN, Vassiliev I, Verkest V, Videbæk F, Vokal S, Voloshin SA, Wang F, Wang G, Wang JS, Wang P, Wang X, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Wen L, Westfall GD, Wieman H, Wissink SW, Wu J, Wu J, Wu Y, Xi B, Xiao ZG, Xie G, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Z, Xu Z, Yan G, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zbroszczyk H, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang S, Zhang XP, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao J, Zhou C, Zhou Y, Zhu X, Zurek M, Zyzak M. Evidence for Nonlinear Gluon Effects in QCD and Their Mass Number Dependence at STAR. Phys Rev Lett 2022; 129:092501. [PMID: 36083674 DOI: 10.1103/physrevlett.129.092501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 07/12/2022] [Accepted: 07/29/2022] [Indexed: 06/15/2023]
Abstract
The STAR Collaboration reports measurements of back-to-back azimuthal correlations of di-π^{0}s produced at forward pseudorapidities (2.6<η<4.0) in p+p, p+Al, and p+Au collisions at a center-of-mass energy of 200 GeV. We observe a clear suppression of the correlated yields of back-to-back π^{0} pairs in p+Al and p+Au collisions compared to the p+p data. The observed suppression of back-to-back pairs as a function of transverse momentum suggests nonlinear gluon dynamics arising at high parton densities. The larger suppression found in p+Au relative to p+Al collisions exhibits a dependence of the saturation scale Q_{s}^{2} on the mass number A. A linear scaling of the suppression with A^{1/3} is observed with a slope of -0.09±0.01.
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Affiliation(s)
- M S Abdallah
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - B E Aboona
- Texas A&M University, College Station, Texas 77843
| | - J Adam
- Brookhaven National Laboratory, Upton, New York 11973
| | - L Adamczyk
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - J R Adams
- The Ohio State University, Columbus, Ohio 43210
| | - J K Adkins
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - G Agakishiev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I Aggarwal
- Panjab University, Chandigarh 160014, India
| | | | - Z Ahammed
- Variable Energy Cyclotron Centre, Kolkata 700064, India
| | - I Alekseev
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
- National Research Nuclear University MEPhI, Moscow 115409
| | - D M Anderson
- Texas A&M University, College Station, Texas 77843
| | - A Aparin
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - M U Ashraf
- Central China Normal University, Wuhan, Hubei 430079
| | | | - A Attri
- Panjab University, Chandigarh 160014, India
| | | | - V Bairathi
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - W Baker
- University of California, Riverside, California 92521
| | | | - K Barish
- University of California, Riverside, California 92521
| | - A Behera
- State University of New York, Stony Brook, New York 11794
| | - R Bellwied
- University of Houston, Houston, Texas 77204
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - J Bielcik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J Bielcikova
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - I G Bordyuzhin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | | | - A V Brandin
- National Research Nuclear University MEPhI, Moscow 115409
| | - I Bunzarov
- Joint Institute for Nuclear Research, Dubna 141 980
| | - X Z Cai
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - H Caines
- Yale University, New Haven, Connecticut 06520
| | | | - D Cebra
- University of California, Davis, California 95616
| | - I Chakaberia
- Brookhaven National Laboratory, Upton, New York 11973
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - P Chaloupka
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - F-H Chang
- National Cheng Kung University, Tainan 70101
| | - Z Chang
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - A Chatterjee
- Central China Normal University, Wuhan, Hubei 430079
| | | | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai, 200433
| | - X Chen
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - M Chevalier
- University of California, Riverside, California 92521
| | | | - W Christie
- Brookhaven National Laboratory, Upton, New York 11973
| | - X Chu
- Brookhaven National Laboratory, Upton, New York 11973
| | - H J Crawford
- University of California, Berkeley, California 94720
| | - M Csanád
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - M Daugherity
- Abilene Christian University, Abilene, Texas 79699
| | - T G Dedovich
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I M Deppner
- University of Heidelberg, Heidelberg 69120, Germany
| | - A A Derevschikov
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - A Dhamija
- Panjab University, Chandigarh 160014, India
| | - L Di Carlo
- Wayne State University, Detroit, Michigan 48201
| | - L Didenko
- Brookhaven National Laboratory, Upton, New York 11973
| | - P Dixit
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - X Dong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | - J C Dunlop
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Elsey
- Wayne State University, Detroit, Michigan 48201
| | - J Engelage
- University of California, Berkeley, California 94720
| | - G Eppley
- Rice University, Houston, Texas 77251
| | - S Esumi
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - O Evdokimov
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Ewigleben
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - O Eyser
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Fatemi
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - F M Fawzi
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - S Fazio
- Brookhaven National Laboratory, Upton, New York 11973
| | - P Federic
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J Fedorisin
- Joint Institute for Nuclear Research, Dubna 141 980
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - P Filip
- Joint Institute for Nuclear Research, Dubna 141 980
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Francisco
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Central China Normal University, Wuhan, Hubei 430079
| | - L Fulek
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | | | - T Galatyuk
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - F Geurts
- Rice University, Houston, Texas 77251
| | - N Ghimire
- Temple University, Philadelphia, Pennsylvania 19122
| | - A Gibson
- Valparaiso University, Valparaiso, Indiana 46383
| | - K Gopal
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - X Gou
- Shandong University, Qingdao, Shandong 266237
| | - D Grosnick
- Valparaiso University, Valparaiso, Indiana 46383
| | - A Gupta
- University of Jammu, Jammu 180001, India
| | - W Guryn
- Brookhaven National Laboratory, Upton, New York 11973
| | - A I Hamad
- Kent State University, Kent, Ohio 44242
| | - A Hamed
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - S Harabasz
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - M D Harasty
- University of California, Davis, California 95616
| | - J W Harris
- Yale University, New Haven, Connecticut 06520
| | - H Harrison
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - S He
- Central China Normal University, Wuhan, Hubei 430079
| | - W He
- Fudan University, Shanghai, 200433
| | - X H He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y He
- Shandong University, Qingdao, Shandong 266237
| | - S Heppelmann
- University of California, Davis, California 95616
| | - S Heppelmann
- Pennsylvania State University, University Park, Pennsylvania 16802
| | - N Herrmann
- University of Heidelberg, Heidelberg 69120, Germany
| | - E Hoffman
- University of Houston, Houston, Texas 77204
| | - L Holub
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - Y Hu
- Fudan University, Shanghai, 200433
| | - H Huang
- National Cheng Kung University, Tainan 70101
| | - H Z Huang
- University of California, Los Angeles, California 90095
| | - S L Huang
- State University of New York, Stony Brook, New York 11794
| | - T Huang
- National Cheng Kung University, Tainan 70101
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | - T J Humanic
- The Ohio State University, Columbus, Ohio 43210
| | - G Igo
- University of California, Los Angeles, California 90095
| | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - A Jentsch
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Ji
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J Jia
- Brookhaven National Laboratory, Upton, New York 11973
- State University of New York, Stony Brook, New York 11794
| | - K Jiang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Ju
- University of Science and Technology of China, Hefei, Anhui 230026
| | - E G Judd
- University of California, Berkeley, California 94720
| | - S Kabana
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - M L Kabir
- University of California, Riverside, California 92521
| | - S Kagamaster
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - D Kalinkin
- Brookhaven National Laboratory, Upton, New York 11973
- Indiana University, Bloomington, Indiana 47408
| | - K Kang
- Tsinghua University, Beijing 100084
| | - D Kapukchyan
- University of California, Riverside, California 92521
| | - K Kauder
- Brookhaven National Laboratory, Upton, New York 11973
| | - H W Ke
- Brookhaven National Laboratory, Upton, New York 11973
| | - D Keane
- Kent State University, Kent, Ohio 44242
| | - A Kechechyan
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M Kelsey
- Wayne State University, Detroit, Michigan 48201
| | - Y V Khyzhniak
- National Research Nuclear University MEPhI, Moscow 115409
| | - D P Kikoła
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - C Kim
- University of California, Riverside, California 92521
| | - B Kimelman
- University of California, Davis, California 95616
| | - D Kincses
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - I Kisel
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
| | - A Kiselev
- Brookhaven National Laboratory, Upton, New York 11973
| | - A G Knospe
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H S Ko
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - L Kochenda
- National Research Nuclear University MEPhI, Moscow 115409
| | - L K Kosarzewski
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - L Kramarik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - P Kravtsov
- National Research Nuclear University MEPhI, Moscow 115409
| | - L Kumar
- Panjab University, Chandigarh 160014, India
| | - S Kumar
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | | | - R Lacey
- State University of New York, Stony Brook, New York 11794
| | - S Lan
- Central China Normal University, Wuhan, Hubei 430079
| | - J M Landgraf
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Lauret
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Lebedev
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Lednicky
- Joint Institute for Nuclear Research, Dubna 141 980
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J H Lee
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y H Leung
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - N Lewis
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Li
- Shandong University, Qingdao, Shandong 266237
| | - C Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - W Li
- Rice University, Houston, Texas 77251
| | - X Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- Tsinghua University, Beijing 100084
| | - X Liang
- University of California, Riverside, California 92521
| | - Y Liang
- Kent State University, Kent, Ohio 44242
| | - R Licenik
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - T Lin
- Shandong University, Qingdao, Shandong 266237
| | - Y Lin
- Central China Normal University, Wuhan, Hubei 430079
| | - M A Lisa
- The Ohio State University, Columbus, Ohio 43210
| | - F Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - H Liu
- Indiana University, Bloomington, Indiana 47408
| | - H Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - P Liu
- State University of New York, Stony Brook, New York 11794
| | - T Liu
- Yale University, New Haven, Connecticut 06520
| | - X Liu
- The Ohio State University, Columbus, Ohio 43210
| | - Y Liu
- Texas A&M University, College Station, Texas 77843
| | - Z Liu
- University of Science and Technology of China, Hefei, Anhui 230026
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - R S Longacre
- Brookhaven National Laboratory, Upton, New York 11973
| | - E Loyd
- University of California, Riverside, California 92521
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
| | - X F Luo
- Central China Normal University, Wuhan, Hubei 430079
| | - L Ma
- Fudan University, Shanghai, 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y G Ma
- Fudan University, Shanghai, 200433
| | | | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | | | - C Markert
- University of Texas, Austin, Texas 78712
| | - H S Matis
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J A Mazer
- Rutgers University, Piscataway, New Jersey 08854
| | - N G Minaev
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | | | - B Mohanty
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - M M Mondal
- State University of New York, Stony Brook, New York 11794
| | - I Mooney
- Wayne State University, Detroit, Michigan 48201
| | - D A Morozov
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - A Mukherjee
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - M Nagy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - Md Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - K Nayak
- Central China Normal University, Wuhan, Hubei 430079
| | - D Neff
- University of California, Los Angeles, California 90095
| | - J M Nelson
- University of California, Berkeley, California 94720
| | - D B Nemes
- Yale University, New Haven, Connecticut 06520
| | - M Nie
- Shandong University, Qingdao, Shandong 266237
| | - G Nigmatkulov
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Niida
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - R Nishitani
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - L V Nogach
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - T Nonaka
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - A S Nunes
- Brookhaven National Laboratory, Upton, New York 11973
| | - G Odyniec
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - A Ogawa
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Oh
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - V A Okorokov
- National Research Nuclear University MEPhI, Moscow 115409
| | - B S Page
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Pak
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Pan
- Texas A&M University, College Station, Texas 77843
| | - A Pandav
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - A K Pandey
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | | | - P Parfenov
- National Research Nuclear University MEPhI, Moscow 115409
| | - B Pawlik
- Institute of Nuclear Physics PAN, Cracow 31-342, Poland
| | - D Pawlowska
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - C Perkins
- University of California, Berkeley, California 94720
| | - L Pinsky
- University of Houston, Houston, Texas 77204
| | - R L Pintér
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - J Pluta
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - B R Pokhrel
- Temple University, Philadelphia, Pennsylvania 19122
| | - G Ponimatkin
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J Porter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - M Posik
- Temple University, Philadelphia, Pennsylvania 19122
| | - V Prozorova
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - N K Pruthi
- Panjab University, Chandigarh 160014, India
| | - M Przybycien
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - J Putschke
- Wayne State University, Detroit, Michigan 48201
| | - H Qiu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - A Quintero
- Temple University, Philadelphia, Pennsylvania 19122
| | - C Racz
- University of California, Riverside, California 92521
| | | | - N Raha
- Wayne State University, Detroit, Michigan 48201
| | - R L Ray
- University of Texas, Austin, Texas 78712
| | - R Reed
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H G Ritter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - M Robotkova
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | | | - J L Romero
- University of California, Davis, California 95616
| | - D Roy
- Rutgers University, Piscataway, New Jersey 08854
| | - L Ruan
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Rusnak
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - A K Sahoo
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - N R Sahoo
- Shandong University, Qingdao, Shandong 266237
| | - H Sako
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - S Salur
- Rutgers University, Piscataway, New Jersey 08854
| | - J Sandweiss
- Yale University, New Haven, Connecticut 06520
| | - S Sato
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W B Schmidke
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Schmitz
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - B R Schweid
- State University of New York, Stony Brook, New York 11794
| | - F Seck
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - J Seger
- Creighton University, Omaha, Nebraska 68178
| | - M Sergeeva
- University of California, Los Angeles, California 90095
| | - R Seto
- University of California, Riverside, California 92521
| | - P Seyboth
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - N Shah
- Indian Institute Technology, Patna, Bihar 801106, India
| | - E Shahaliev
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - M Shao
- University of Science and Technology of China, Hefei, Anhui 230026
| | - T Shao
- Fudan University, Shanghai, 200433
| | | | - D Y Shen
- Fudan University, Shanghai, 200433
| | - S S Shi
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
| | - Q Y Shou
- Fudan University, Shanghai, 200433
| | - E P Sichtermann
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - R Sikora
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - M Simko
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - M J Skoby
- Purdue University, West Lafayette, Indiana 47907
| | - N Smirnov
- Yale University, New Haven, Connecticut 06520
| | - Y Söhngen
- University of Heidelberg, Heidelberg 69120, Germany
| | - W Solyst
- Indiana University, Bloomington, Indiana 47408
| | - P Sorensen
- Brookhaven National Laboratory, Upton, New York 11973
| | - H M Spinka
- Argonne National Laboratory, Argonne, Illinois 60439
| | - B Srivastava
- Purdue University, West Lafayette, Indiana 47907
| | | | - M Stefaniak
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - D J Stewart
- Yale University, New Haven, Connecticut 06520
| | - M Strikhanov
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - A A P Suaide
- Universidade de São Paulo, São Paulo, Brazil 05314-970
| | - M Sumbera
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - B Summa
- Pennsylvania State University, University Park, Pennsylvania 16802
| | - X M Sun
- Central China Normal University, Wuhan, Hubei 430079
| | - X Sun
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - Y Sun
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Sun
- Huzhou University, Huzhou, Zhejiang 313000
| | - B Surrow
- Temple University, Philadelphia, Pennsylvania 19122
| | - D N Svirida
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | - Z W Sweger
- University of California, Davis, California 95616
| | - P Szymanski
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - A H Tang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Tang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - A Taranenko
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Tarnowsky
- Michigan State University, East Lansing, Michigan 48824
| | - J H Thomas
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | - D Tlusty
- Creighton University, Omaha, Nebraska 68178
| | - T Todoroki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - M Tokarev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - C A Tomkiel
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - S Trentalange
- University of California, Los Angeles, California 90095
| | - R E Tribble
- Texas A&M University, College Station, Texas 77843
| | - P Tribedy
- Brookhaven National Laboratory, Upton, New York 11973
| | - S K Tripathy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - T Truhlar
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - B A Trzeciak
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - O D Tsai
- University of California, Los Angeles, California 90095
| | - Z Tu
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Ullrich
- Brookhaven National Laboratory, Upton, New York 11973
| | - D G Underwood
- Argonne National Laboratory, Argonne, Illinois 60439
- Valparaiso University, Valparaiso, Indiana 46383
| | - I Upsal
- Rice University, Houston, Texas 77251
| | - G Van Buren
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Vanek
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - A N Vasiliev
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - I Vassiliev
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
| | - V Verkest
- Wayne State University, Detroit, Michigan 48201
| | - F Videbæk
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Vokal
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - F Wang
- Purdue University, West Lafayette, Indiana 47907
| | - G Wang
- University of California, Los Angeles, California 90095
| | - J S Wang
- Huzhou University, Huzhou, Zhejiang 313000
| | - P Wang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Wang
- Shandong University, Qingdao, Shandong 266237
| | - Y Wang
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Wang
- Tsinghua University, Beijing 100084
| | - Z Wang
- Shandong University, Qingdao, Shandong 266237
| | - J C Webb
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - L Wen
- University of California, Los Angeles, California 90095
| | - G D Westfall
- Michigan State University, East Lansing, Michigan 48824
| | - H Wieman
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - S W Wissink
- Indiana University, Bloomington, Indiana 47408
| | - J Wu
- Central China Normal University, Wuhan, Hubei 430079
| | - J Wu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Wu
- University of California, Riverside, California 92521
| | - B Xi
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - Z G Xiao
- Tsinghua University, Beijing 100084
| | - G Xie
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - W Xie
- Purdue University, West Lafayette, Indiana 47907
| | - H Xu
- Huzhou University, Huzhou, Zhejiang 313000
| | - N Xu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Q H Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Shandong University, Qingdao, Shandong 266237
| | - Z Xu
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Xu
- University of California, Los Angeles, California 90095
| | - G Yan
- Shandong University, Qingdao, Shandong 266237
| | - C Yang
- Shandong University, Qingdao, Shandong 266237
| | - Q Yang
- Shandong University, Qingdao, Shandong 266237
| | - S Yang
- Rice University, Houston, Texas 77251
| | - Y Yang
- National Cheng Kung University, Tainan 70101
| | - Z Ye
- Rice University, Houston, Texas 77251
| | - Z Ye
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - L Yi
- Shandong University, Qingdao, Shandong 266237
| | - K Yip
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Yu
- Shandong University, Qingdao, Shandong 266237
| | - H Zbroszczyk
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - W Zha
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Zhang
- State University of New York, Stony Brook, New York 11794
| | - D Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - J Zhang
- Shandong University, Qingdao, Shandong 266237
| | - S Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - S Zhang
- Fudan University, Shanghai, 200433
| | | | - Y Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - Z J Zhang
- National Cheng Kung University, Tainan 70101
| | - Z Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - J Zhao
- Purdue University, West Lafayette, Indiana 47907
| | - C Zhou
- Fudan University, Shanghai, 200433
| | - Y Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - X Zhu
- Tsinghua University, Beijing 100084
| | - M Zurek
- Argonne National Laboratory, Argonne, Illinois 60439
| | - M Zyzak
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
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Fan H, Liu K, Hong B, He S, Han P, Li M, Wang S, Tong Y. [Progress in the study of antiviral activity of cepharanthine against SARS-CoV-2]. Nan Fang Yi Ke Da Xue Xue Bao 2022; 42:955-956. [PMID: 35790449 DOI: 10.12122/j.issn.1673-4254.2022.06.22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
As a member of the dibenzyl isoquinoline alkaloid family, cepharathine is an alkaloid from the traditional Chinese medicine cepharathine, which is mainly used for treatment of leukopenia and other diseases. Recent studies of the inhibitory effect of cepharathine against SARS-CoV-2 have attracted widespread attention and aroused heated discussion. As the original discoverer of the anti-SARS-CoV-2 activity of cepharanthine, here we briefly summarize the discovery of cepharanthine and review important progress in relevant studies concerning the discovery and validation of anti-SARS-CoV-2 activity of cepharathine, its antiviral mechanisms and clinical trials of its applications in COVID-19 therapy.
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Affiliation(s)
- H Fan
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - K Liu
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - B Hong
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - S He
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - P Han
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - M Li
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - S Wang
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Y Tong
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.,Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
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47
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Wang SX, Ke Y, Liu YM, Liu SY, Song SB, He S, Zhang YM, Dou LZ, Liu Y, Liu XD, Wu HR, Su FX, Zhang FY, Zhang W, Wang GQ. [Establishment and clinical validation of an artificial intelligence YOLOv51 model for the detection of precancerous lesions and superficial esophageal cancer in endoscopic procedure]. Zhonghua Zhong Liu Za Zhi 2022; 44:395-401. [PMID: 35615795 DOI: 10.3760/cma.j.cn112152-20211126-00877] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Objective: To construct the diagnostic model of superficial esophageal squamous cell carcinoma (ESCC) and precancerous lesions in endoscopic images based on the YOLOv5l model by using deep learning method of artificial intelligence to improve the diagnosis of early ESCC and precancerous lesions under endoscopy. Methods: 13, 009 endoscopic esophageal images of white light imaging (WLI), narrow band imaging (NBI) and lugol chromoendoscopy (LCE) were collected from June 2019 to July 2021 from 1, 126 patients at the Cancer Hospital, Chinese Academy of Medical Sciences, including low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia, ESCC limited to the mucosal layer, benign esophageal lesions and normal esophagus. By computerized random function method, the images were divided into a training set (11, 547 images from 1, 025 patients) and a validation set (1, 462 images from 101 patients). The YOLOv5l model was trained and constructed with the training set, and the model was validated with the validation set, while the validation set was diagnosed by two senior and two junior endoscopists, respectively, to compare the diagnostic results of YOLOv5l model and those of the endoscopists. Results: In the validation set, the accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the YOLOv5l model in diagnosing early ESCC and precancerous lesions in the WLI, NBI and LCE modes were 96.9%, 87.9%, 98.3%, 88.8%, 98.1%, and 98.6%, 89.3%, 99.5%, 94.4%, 98.2%, and 93.0%, 77.5%, 98.0%, 92.6%, 93.1%, respectively. The accuracy in the NBI model was higher than that in the WLI model (P<0.05) and lower than that in the LCE model (P<0.05). The diagnostic accuracies of YOLOv5l model in the WLI, NBI and LCE modes for the early ESCC and precancerous lesions were similar to those of the 2 senior endoscopists (96.9%, 98.8%, 94.3%, and 97.5%, 99.6%, 91.9%, respectively; P>0.05), but significantly higher than those of the 2 junior endoscopists (84.7%, 92.9%, 81.6% and 88.3%, 91.9%, 81.2%, respectively; P<0.05). Conclusion: The constructed YOLOv5l model has high accuracy in diagnosing early ESCC and precancerous lesions in endoscopic WLI, NBI and LCE modes, which can assist junior endoscopists to improve diagnosis and reduce missed diagnoses.
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Affiliation(s)
- S X Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Ke
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y M Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S Y Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S B Song
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S He
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y M Zhang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Z Dou
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - X D Liu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - H R Wu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - F X Su
- Department of Endoscopy, National Cancer Center/Cancer Hospital& Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - F Y Zhang
- Department of Endoscopy, National Cancer Center/Cancer Hospital& Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - W Zhang
- Department of Endoscopy, National Cancer Center/Cancer Hospital& Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - G Q Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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48
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Abdallah MS, Aboona BE, Adam J, Adamczyk L, Adams JR, Adkins JK, Agakishiev G, Aggarwal I, Aggarwal MM, Ahammed Z, Alekseev I, Anderson DM, Aparin A, Aschenauer EC, Ashraf MU, Atetalla FG, Attri A, Averichev GS, Bairathi V, Baker W, Ball Cap JG, Barish K, Behera A, Bellwied R, Bhagat P, Bhasin A, Bielcik J, Bielcikova J, Bordyuzhin IG, Brandenburg JD, Brandin AV, Bunzarov I, Cai XZ, Caines H, Calderón de la Barca Sánchez M, Cebra D, Chakaberia I, Chaloupka P, Chan BK, Chang FH, Chang Z, Chankova-Bunzarova N, Chatterjee A, Chattopadhyay S, Chen D, Chen J, Chen JH, Chen X, Chen Z, Cheng J, Chevalier M, Choudhury S, Christie W, Chu X, Crawford HJ, Csanád M, Daugherity M, Dedovich TG, Deppner IM, Derevschikov AA, Dhamija A, Di Carlo L, Didenko L, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Elsey N, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fawzi FM, Fazio S, Federic P, Fedorisin J, Feng CJ, Feng Y, Filip P, Finch E, Fisyak Y, Francisco A, Fu C, Fulek L, Gagliardi CA, Galatyuk T, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Guryn W, Hamad AI, Hamed A, Han Y, Harabasz S, Harasty MD, Harris JW, Harrison H, He S, He W, He XH, He Y, Heppelmann S, Heppelmann S, Herrmann N, Hoffman E, Holub L, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Humanic TJ, Igo G, Isenhower D, Jacobs WW, Jena C, Jentsch A, Ji Y, Jia J, Jiang K, Ju X, Judd EG, Kabana S, Kabir ML, Kagamaster S, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Ke HW, Keane D, Kechechyan A, Kelsey M, Khyzhniak YV, Kikoła DP, Kim C, Kimelman B, Kincses D, Kisel I, Kiselev A, Knospe AG, Ko HS, Kochenda L, Kosarzewski LK, Kramarik L, Kravtsov P, Kumar L, Kumar S, Kunnawalkam Elayavalli R, Kwasizur JH, Lacey R, Lan S, Landgraf JM, Lauret J, Lebedev A, Lednicky R, Lee JH, Leung YH, Lewis N, Li C, Li C, Li W, Li X, Li Y, Liang X, Liang Y, Licenik R, Lin T, Lin Y, Lisa MA, Liu F, Liu H, Liu H, Liu P, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Longacre RS, Loyd E, Lukow NS, Luo XF, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Markert C, Matis HS, Mazer JA, Minaev NG, Mioduszewski S, Mohanty B, Mondal MM, Mooney I, Morozov DA, Mukherjee A, Nagy M, Nam JD, Nasim M, Nayak K, Neff D, Nelson JM, Nemes DB, Nie M, Nigmatkulov G, Niida T, Nishitani R, Nogach LV, Nonaka T, Nunes AS, Odyniec G, Ogawa A, Oh S, Okorokov VA, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Panebratsev Y, Parfenov P, Pawlik B, Pawlowska D, Perkins C, Pinsky L, Pluta J, Pokhrel BR, Ponimatkin G, Porter J, Posik M, Prozorova V, Pruthi NK, Przybycien M, Putschke J, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Reed R, Ritter HG, Robotkova M, Rogachevskiy OV, Romero JL, Roy D, Ruan L, Rusnak J, Sahoo AK, Sahoo NR, Sako H, Salur S, Sandweiss J, Sato S, Schmidke WB, Schmitz N, Schweid BR, Seck F, Seger J, Sergeeva M, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao M, Shao T, Sheikh AI, Shen DY, Shi SS, Shi Y, Shou QY, Sichtermann EP, Sikora R, Simko M, Singh J, Singha S, Skoby MJ, Smirnov N, Söhngen Y, Solyst W, Song Y, Sorensen P, Spinka HM, Srivastava B, Stanislaus TDS, Stefaniak M, Stewart DJ, Strikhanov M, Stringfellow B, Suaide AAP, Sumbera M, Summa B, Sun XM, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Sweger ZW, Szymanski P, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Timmins AR, Tlusty D, Todoroki T, Tokarev M, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tripathy SK, Truhlar T, Trzeciak BA, Tsai OD, Tu Z, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vanek J, Vasiliev AN, Vassiliev I, Verkest V, Videbæk F, Vokal S, Voloshin SA, Wang F, Wang G, Wang JS, Wang P, Wang X, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Wen L, Westfall GD, Wieman H, Wissink SW, Witt R, Wu J, Wu J, Wu Y, Xi B, Xiao ZG, Xie G, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Z, Xu Z, Yan G, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zbroszczyk H, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang S, Zhang XP, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao J, Zhou C, Zhou Y, Zhu X, Zurek M, Zyzak M. Measurements of Proton High-Order Cumulants in sqrt[s_{NN}]=3 GeV Au+Au Collisions and Implications for the QCD Critical Point. Phys Rev Lett 2022; 128:202303. [PMID: 35657878 DOI: 10.1103/physrevlett.128.202303] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 04/11/2022] [Indexed: 06/15/2023]
Abstract
We report cumulants of the proton multiplicity distribution from dedicated fixed-target Au+Au collisions at sqrt[s_{NN}]=3.0 GeV, measured by the STAR experiment in the kinematic acceptance of rapidity (y) and transverse momentum (p_{T}) within -0.5<y<0 and 0.4<p_{T}<2.0 GeV/c. In the most central 0%-5% collisions, a proton cumulant ratio is measured to be C_{4}/C_{2}=-0.85±0.09 (stat)±0.82 (syst), which is 2σ below the Poisson baseline with respect to both the statistical and systematic uncertainties. The hadronic transport UrQMD model reproduces our C_{4}/C_{2} in the measured acceptance. Compared to higher energy results and the transport model calculations, the suppression in C_{4}/C_{2} is consistent with fluctuations driven by baryon number conservation and indicates an energy regime dominated by hadronic interactions. These data imply that the QCD critical region, if created in heavy-ion collisions, could only exist at energies higher than 3 GeV.
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Affiliation(s)
- M S Abdallah
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - B E Aboona
- Texas A&M University, College Station, Texas 77843
| | - J Adam
- Brookhaven National Laboratory, Upton, New York 11973
| | - L Adamczyk
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - J R Adams
- Ohio State University, Columbus, Ohio 43210
| | - J K Adkins
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - G Agakishiev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I Aggarwal
- Panjab University, Chandigarh 160014, India
| | | | - Z Ahammed
- Variable Energy Cyclotron Centre, Kolkata 700064, India
| | - I Alekseev
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute", Moscow 117218
- National Research Nuclear University MEPhI, Moscow 115409
| | - D M Anderson
- Texas A&M University, College Station, Texas 77843
| | - A Aparin
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - M U Ashraf
- Central China Normal University, Wuhan, Hubei 430079
| | | | - A Attri
- Panjab University, Chandigarh 160014, India
| | | | - V Bairathi
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - W Baker
- University of California, Riverside, California 92521
| | | | - K Barish
- University of California, Riverside, California 92521
| | - A Behera
- State University of New York, Stony Brook, New York 11794
| | - R Bellwied
- University of Houston, Houston, Texas 77204
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - J Bielcik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J Bielcikova
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - I G Bordyuzhin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute", Moscow 117218
| | | | - A V Brandin
- National Research Nuclear University MEPhI, Moscow 115409
| | - I Bunzarov
- Joint Institute for Nuclear Research, Dubna 141 980
| | - X Z Cai
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - H Caines
- Yale University, New Haven, Connecticut 06520
| | | | - D Cebra
- University of California, Davis, California 95616
| | - I Chakaberia
- Brookhaven National Laboratory, Upton, New York 11973
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - P Chaloupka
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - F-H Chang
- National Cheng Kung University, Tainan 70101
| | - Z Chang
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - A Chatterjee
- Central China Normal University, Wuhan, Hubei 430079
| | | | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai, 200433
| | - X Chen
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - M Chevalier
- University of California, Riverside, California 92521
| | | | - W Christie
- Brookhaven National Laboratory, Upton, New York 11973
| | - X Chu
- Brookhaven National Laboratory, Upton, New York 11973
| | - H J Crawford
- University of California, Berkeley, California 94720
| | - M Csanád
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - M Daugherity
- Abilene Christian University, Abilene, Texas 79699
| | - T G Dedovich
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I M Deppner
- University of Heidelberg, Heidelberg 69120, Germany
| | - A A Derevschikov
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - A Dhamija
- Panjab University, Chandigarh 160014, India
| | - L Di Carlo
- Wayne State University, Detroit, Michigan 48201
| | - L Didenko
- Brookhaven National Laboratory, Upton, New York 11973
| | - P Dixit
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - X Dong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | - J C Dunlop
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Elsey
- Wayne State University, Detroit, Michigan 48201
| | - J Engelage
- University of California, Berkeley, California 94720
| | - G Eppley
- Rice University, Houston, Texas 77251
| | - S Esumi
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - O Evdokimov
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Ewigleben
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - O Eyser
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Fatemi
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - F M Fawzi
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - S Fazio
- Brookhaven National Laboratory, Upton, New York 11973
| | - P Federic
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J Fedorisin
- Joint Institute for Nuclear Research, Dubna 141 980
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - P Filip
- Joint Institute for Nuclear Research, Dubna 141 980
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Francisco
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Central China Normal University, Wuhan, Hubei 430079
| | - L Fulek
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | | | - T Galatyuk
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - F Geurts
- Rice University, Houston, Texas 77251
| | - N Ghimire
- Temple University, Philadelphia, Pennsylvania 19122
| | - A Gibson
- Valparaiso University, Valparaiso, Indiana 46383
| | - K Gopal
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - X Gou
- Shandong University, Qingdao, Shandong 266237
| | - D Grosnick
- Valparaiso University, Valparaiso, Indiana 46383
| | - A Gupta
- University of Jammu, Jammu 180001, India
| | - W Guryn
- Brookhaven National Laboratory, Upton, New York 11973
| | - A I Hamad
- Kent State University, Kent, Ohio 44242
| | - A Hamed
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - S Harabasz
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - M D Harasty
- University of California, Davis, California 95616
| | - J W Harris
- Yale University, New Haven, Connecticut 06520
| | - H Harrison
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - S He
- Central China Normal University, Wuhan, Hubei 430079
| | - W He
- Fudan University, Shanghai, 200433
| | - X H He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y He
- Shandong University, Qingdao, Shandong 266237
| | - S Heppelmann
- University of California, Davis, California 95616
| | - S Heppelmann
- Pennsylvania State University, University Park, Pennsylvania 16802
| | - N Herrmann
- University of Heidelberg, Heidelberg 69120, Germany
| | - E Hoffman
- University of Houston, Houston, Texas 77204
| | - L Holub
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - Y Hu
- Fudan University, Shanghai, 200433
| | - H Huang
- National Cheng Kung University, Tainan 70101
| | - H Z Huang
- University of California, Los Angeles, California 90095
| | - S L Huang
- State University of New York, Stony Brook, New York 11794
| | - T Huang
- National Cheng Kung University, Tainan 70101
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | | | - G Igo
- University of California, Los Angeles, California 90095
| | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - A Jentsch
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Ji
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J Jia
- Brookhaven National Laboratory, Upton, New York 11973
- State University of New York, Stony Brook, New York 11794
| | - K Jiang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Ju
- University of Science and Technology of China, Hefei, Anhui 230026
| | - E G Judd
- University of California, Berkeley, California 94720
| | - S Kabana
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - M L Kabir
- University of California, Riverside, California 92521
| | - S Kagamaster
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - D Kalinkin
- Brookhaven National Laboratory, Upton, New York 11973
- Indiana University, Bloomington, Indiana 47408
| | - K Kang
- Tsinghua University, Beijing 100084
| | - D Kapukchyan
- University of California, Riverside, California 92521
| | - K Kauder
- Brookhaven National Laboratory, Upton, New York 11973
| | - H W Ke
- Brookhaven National Laboratory, Upton, New York 11973
| | - D Keane
- Kent State University, Kent, Ohio 44242
| | - A Kechechyan
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M Kelsey
- Wayne State University, Detroit, Michigan 48201
| | - Y V Khyzhniak
- National Research Nuclear University MEPhI, Moscow 115409
| | - D P Kikoła
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - C Kim
- University of California, Riverside, California 92521
| | - B Kimelman
- University of California, Davis, California 95616
| | - D Kincses
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - I Kisel
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
| | - A Kiselev
- Brookhaven National Laboratory, Upton, New York 11973
| | - A G Knospe
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H S Ko
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - L Kochenda
- National Research Nuclear University MEPhI, Moscow 115409
| | - L K Kosarzewski
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - L Kramarik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - P Kravtsov
- National Research Nuclear University MEPhI, Moscow 115409
| | - L Kumar
- Panjab University, Chandigarh 160014, India
| | - S Kumar
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | | | - R Lacey
- State University of New York, Stony Brook, New York 11794
| | - S Lan
- Central China Normal University, Wuhan, Hubei 430079
| | - J M Landgraf
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Lauret
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Lebedev
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Lednicky
- Joint Institute for Nuclear Research, Dubna 141 980
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J H Lee
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y H Leung
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - N Lewis
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Li
- Shandong University, Qingdao, Shandong 266237
| | - C Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - W Li
- Rice University, Houston, Texas 77251
| | - X Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- Tsinghua University, Beijing 100084
| | - X Liang
- University of California, Riverside, California 92521
| | - Y Liang
- Kent State University, Kent, Ohio 44242
| | - R Licenik
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - T Lin
- Shandong University, Qingdao, Shandong 266237
| | - Y Lin
- Central China Normal University, Wuhan, Hubei 430079
| | - M A Lisa
- Ohio State University, Columbus, Ohio 43210
| | - F Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - H Liu
- Indiana University, Bloomington, Indiana 47408
| | - H Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - P Liu
- State University of New York, Stony Brook, New York 11794
| | - T Liu
- Yale University, New Haven, Connecticut 06520
| | - X Liu
- Ohio State University, Columbus, Ohio 43210
| | - Y Liu
- Texas A&M University, College Station, Texas 77843
| | - Z Liu
- University of Science and Technology of China, Hefei, Anhui 230026
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - R S Longacre
- Brookhaven National Laboratory, Upton, New York 11973
| | - E Loyd
- University of California, Riverside, California 92521
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
| | - X F Luo
- Central China Normal University, Wuhan, Hubei 430079
| | - L Ma
- Fudan University, Shanghai, 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y G Ma
- Fudan University, Shanghai, 200433
| | - N Magdy
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | | | - C Markert
- University of Texas, Austin, Texas 78712
| | - H S Matis
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J A Mazer
- Rutgers University, Piscataway, New Jersey 08854
| | - N G Minaev
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | | | - B Mohanty
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - M M Mondal
- State University of New York, Stony Brook, New York 11794
| | - I Mooney
- Wayne State University, Detroit, Michigan 48201
| | - D A Morozov
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - A Mukherjee
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - M Nagy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - Md Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - K Nayak
- Central China Normal University, Wuhan, Hubei 430079
| | - D Neff
- University of California, Los Angeles, California 90095
| | - J M Nelson
- University of California, Berkeley, California 94720
| | - D B Nemes
- Yale University, New Haven, Connecticut 06520
| | - M Nie
- Shandong University, Qingdao, Shandong 266237
| | - G Nigmatkulov
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Niida
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - R Nishitani
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - L V Nogach
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - T Nonaka
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - A S Nunes
- Brookhaven National Laboratory, Upton, New York 11973
| | - G Odyniec
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - A Ogawa
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Oh
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - V A Okorokov
- National Research Nuclear University MEPhI, Moscow 115409
| | - B S Page
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Pak
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Pan
- Texas A&M University, College Station, Texas 77843
| | - A Pandav
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - A K Pandey
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | | | - P Parfenov
- National Research Nuclear University MEPhI, Moscow 115409
| | - B Pawlik
- Institute of Nuclear Physics PAN, Cracow 31-342, Poland
| | - D Pawlowska
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - C Perkins
- University of California, Berkeley, California 94720
| | - L Pinsky
- University of Houston, Houston, Texas 77204
| | - J Pluta
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - B R Pokhrel
- Temple University, Philadelphia, Pennsylvania 19122
| | - G Ponimatkin
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J Porter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - M Posik
- Temple University, Philadelphia, Pennsylvania 19122
| | - V Prozorova
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - N K Pruthi
- Panjab University, Chandigarh 160014, India
| | - M Przybycien
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - J Putschke
- Wayne State University, Detroit, Michigan 48201
| | - H Qiu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - A Quintero
- Temple University, Philadelphia, Pennsylvania 19122
| | - C Racz
- University of California, Riverside, California 92521
| | | | - N Raha
- Wayne State University, Detroit, Michigan 48201
| | - R L Ray
- University of Texas, Austin, Texas 78712
| | - R Reed
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H G Ritter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - M Robotkova
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | | | - J L Romero
- University of California, Davis, California 95616
| | - D Roy
- Rutgers University, Piscataway, New Jersey 08854
| | - L Ruan
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Rusnak
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - A K Sahoo
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - N R Sahoo
- Shandong University, Qingdao, Shandong 266237
| | - H Sako
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - S Salur
- Rutgers University, Piscataway, New Jersey 08854
| | - J Sandweiss
- Yale University, New Haven, Connecticut 06520
| | - S Sato
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W B Schmidke
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Schmitz
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - B R Schweid
- State University of New York, Stony Brook, New York 11794
| | - F Seck
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - J Seger
- Creighton University, Omaha, Nebraska 68178
| | - M Sergeeva
- University of California, Los Angeles, California 90095
| | - R Seto
- University of California, Riverside, California 92521
| | - P Seyboth
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - N Shah
- Indian Institute Technology, Patna, Bihar 801106, India
| | - E Shahaliev
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - M Shao
- University of Science and Technology of China, Hefei, Anhui 230026
| | - T Shao
- Fudan University, Shanghai, 200433
| | | | - D Y Shen
- Fudan University, Shanghai, 200433
| | - S S Shi
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
| | - Q Y Shou
- Fudan University, Shanghai, 200433
| | - E P Sichtermann
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - R Sikora
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - M Simko
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - M J Skoby
- Purdue University, West Lafayette, Indiana 47907
| | - N Smirnov
- Yale University, New Haven, Connecticut 06520
| | - Y Söhngen
- University of Heidelberg, Heidelberg 69120, Germany
| | - W Solyst
- Indiana University, Bloomington, Indiana 47408
| | - Y Song
- Yale University, New Haven, Connecticut 06520
| | - P Sorensen
- Brookhaven National Laboratory, Upton, New York 11973
| | - H M Spinka
- Argonne National Laboratory, Argonne, Illinois 60439
| | - B Srivastava
- Purdue University, West Lafayette, Indiana 47907
| | | | - M Stefaniak
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - D J Stewart
- Yale University, New Haven, Connecticut 06520
| | - M Strikhanov
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - A A P Suaide
- Universidade de S ao Paulo, S ao Paulo, Brazil 05314-970
| | - M Sumbera
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - B Summa
- Pennsylvania State University, University Park, Pennsylvania 16802
| | - X M Sun
- Central China Normal University, Wuhan, Hubei 430079
| | - X Sun
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - Y Sun
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Sun
- Huzhou University, Huzhou, Zhejiang 313000
| | - B Surrow
- Temple University, Philadelphia, Pennsylvania 19122
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- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute", Moscow 117218
| | - Z W Sweger
- University of California, Davis, California 95616
| | - P Szymanski
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - A H Tang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Tang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - A Taranenko
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Tarnowsky
- Michigan State University, East Lansing, Michigan 48824
| | - J H Thomas
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | - D Tlusty
- Creighton University, Omaha, Nebraska 68178
| | - T Todoroki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - M Tokarev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - C A Tomkiel
- Lehigh University, Bethlehem, Pennsylvania 18015
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- University of California, Los Angeles, California 90095
| | - R E Tribble
- Texas A&M University, College Station, Texas 77843
| | - P Tribedy
- Brookhaven National Laboratory, Upton, New York 11973
| | - S K Tripathy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - T Truhlar
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - B A Trzeciak
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - O D Tsai
- University of California, Los Angeles, California 90095
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- Brookhaven National Laboratory, Upton, New York 11973
| | - T Ullrich
- Brookhaven National Laboratory, Upton, New York 11973
| | - D G Underwood
- Argonne National Laboratory, Argonne, Illinois 60439
- Valparaiso University, Valparaiso, Indiana 46383
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- Rice University, Houston, Texas 77251
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- Brookhaven National Laboratory, Upton, New York 11973
| | - J Vanek
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - A N Vasiliev
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - I Vassiliev
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
| | - V Verkest
- Wayne State University, Detroit, Michigan 48201
| | - F Videbæk
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Vokal
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - F Wang
- Purdue University, West Lafayette, Indiana 47907
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- University of California, Los Angeles, California 90095
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- Huzhou University, Huzhou, Zhejiang 313000
| | - P Wang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Wang
- Shandong University, Qingdao, Shandong 266237
| | - Y Wang
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Wang
- Tsinghua University, Beijing 100084
| | - Z Wang
- Shandong University, Qingdao, Shandong 266237
| | - J C Webb
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - L Wen
- University of California, Los Angeles, California 90095
| | - G D Westfall
- Michigan State University, East Lansing, Michigan 48824
| | - H Wieman
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
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- Indiana University, Bloomington, Indiana 47408
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- United States Naval Academy, Annapolis, Maryland 21402
| | - J Wu
- Central China Normal University, Wuhan, Hubei 430079
| | - J Wu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Wu
- University of California, Riverside, California 92521
| | - B Xi
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - Z G Xiao
- Tsinghua University, Beijing 100084
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- Lawrence Berkeley National Laboratory, Berkeley, California 94720
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- Purdue University, West Lafayette, Indiana 47907
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- Huzhou University, Huzhou, Zhejiang 313000
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- Lawrence Berkeley National Laboratory, Berkeley, California 94720
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- Shandong University, Qingdao, Shandong 266237
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- Shandong University, Qingdao, Shandong 266237
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- Brookhaven National Laboratory, Upton, New York 11973
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- University of California, Los Angeles, California 90095
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- Shandong University, Qingdao, Shandong 266237
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- Shandong University, Qingdao, Shandong 266237
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- Shandong University, Qingdao, Shandong 266237
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- Rice University, Houston, Texas 77251
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- National Cheng Kung University, Tainan 70101
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- Rice University, Houston, Texas 77251
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- University of Illinois at Chicago, Chicago, Illinois 60607
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- Shandong University, Qingdao, Shandong 266237
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- Brookhaven National Laboratory, Upton, New York 11973
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- Shandong University, Qingdao, Shandong 266237
| | - H Zbroszczyk
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - W Zha
- University of Science and Technology of China, Hefei, Anhui 230026
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- State University of New York, Stony Brook, New York 11794
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- Central China Normal University, Wuhan, Hubei 430079
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- Shandong University, Qingdao, Shandong 266237
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- University of Illinois at Chicago, Chicago, Illinois 60607
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- Fudan University, Shanghai, 200433
| | | | - Y Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - Z J Zhang
- National Cheng Kung University, Tainan 70101
| | - Z Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - J Zhao
- Purdue University, West Lafayette, Indiana 47907
| | - C Zhou
- Fudan University, Shanghai, 200433
| | - Y Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - X Zhu
- Tsinghua University, Beijing 100084
| | - M Zurek
- Argonne National Laboratory, Argonne, Illinois 60439
| | - M Zyzak
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
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Abdallah MS, Aboona BE, Adam J, Adamczyk L, Adams JR, Adkins JK, Agakishiev G, Aggarwal I, Aggarwal MM, Ahammed Z, Alekseev I, Anderson DM, Aparin A, Aschenauer EC, Ashraf MU, Atetalla FG, Attri A, Averichev GS, Bairathi V, Baker W, Ball Cap JG, Barish K, Behera A, Bellwied R, Bhagat P, Bhasin A, Bielcik J, Bielcikova J, Bordyuzhin IG, Brandenburg JD, Brandin AV, Bunzarov I, Cai XZ, Caines H, Calderón de la Barca Sánchez M, Cebra D, Chakaberia I, Chaloupka P, Chan BK, Chang FH, Chang Z, Chankova-Bunzarova N, Chatterjee A, Chattopadhyay S, Chen D, Chen J, Chen JH, Chen X, Chen Z, Cheng J, Chevalier M, Choudhury S, Christie W, Chu X, Crawford HJ, Csanád M, Daugherity M, Dedovich TG, Deppner IM, Derevschikov AA, Dhamija A, Di Carlo L, Didenko L, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Elsey N, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fawzi FM, Fazio S, Federic P, Fedorisin J, Feng CJ, Feng Y, Filip P, Finch E, Fisyak Y, Francisco A, Fu C, Fulek L, Gagliardi CA, Galatyuk T, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Guryn W, Hamad AI, Hamed A, Han Y, Harabasz S, Harasty MD, Harris JW, Harrison H, He S, He W, He XH, He Y, Heppelmann S, Heppelmann S, Herrmann N, Hoffman E, Holub L, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Humanic TJ, Igo G, Isenhower D, Jacobs WW, Jena C, Jentsch A, Ji Y, Jia J, Jiang K, Ju X, Judd EG, Kabana S, Kabir ML, Kagamaster S, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Ke HW, Keane D, Kechechyan A, Kelsey M, Khyzhniak YV, Kikoła DP, Kim C, Kimelman B, Kincses D, Kisel I, Kiselev A, Knospe AG, Ko HS, Kochenda L, Kosarzewski LK, Kramarik L, Kravtsov P, Kumar L, Kumar S, Kunnawalkam Elayavalli R, Kwasizur JH, Lacey R, Lan S, Landgraf JM, Lauret J, Lebedev A, Lednicky R, Lee JH, Leung YH, Lewis N, Li C, Li C, Li W, Li X, Li Y, Liang X, Liang Y, Licenik R, Lin T, Lin Y, Lisa MA, Liu F, Liu H, Liu H, Liu P, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Longacre RS, Loyd E, Lukow NS, Luo XF, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Markert C, Matis HS, Mazer JA, Minaev NG, Mioduszewski S, Mohanty B, Mondal MM, Mooney I, Morozov DA, Mukherjee A, Nagy M, Nam JD, Nasim M, Nayak K, Neff D, Nelson JM, Nemes DB, Nie M, Nigmatkulov G, Niida T, Nishitani R, Nogach LV, Nonaka T, Nunes AS, Odyniec G, Ogawa A, Oh S, Okorokov VA, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Panebratsev Y, Parfenov P, Pawlik B, Pawlowska D, Perkins C, Pinsky L, Pintér RL, Pluta J, Pokhrel BR, Ponimatkin G, Porter J, Posik M, Prozorova V, Pruthi NK, Przybycien M, Putschke J, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Reed R, Ritter HG, Robotkova M, Rogachevskiy OV, Romero JL, Roy D, Ruan L, Rusnak J, Sahoo AK, Sahoo NR, Sako H, Salur S, Sandweiss J, Sato S, Schmidke WB, Schmitz N, Schweid BR, Seck F, Seger J, Sergeeva M, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao M, Shao T, Sheikh AI, Shen DY, Shi SS, Shi Y, Shou QY, Sichtermann EP, Sikora R, Simko M, Singh J, Singha S, Skoby MJ, Smirnov N, Söhngen Y, Solyst W, Sorensen P, Spinka HM, Srivastava B, Stanislaus TDS, Stefaniak M, Stewart DJ, Strikhanov M, Stringfellow B, Suaide AAP, Sumbera M, Summa B, Sun XM, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Sweger ZW, Szymanski P, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Timmins AR, Tlusty D, Todoroki T, Tokarev M, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tripathy SK, Truhlar T, Trzeciak BA, Tsai OD, Tu Z, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vanek J, Vasiliev AN, Vassiliev I, Verkest V, Videbaek F, Vokal S, Voloshin SA, Wang F, Wang G, Wang JS, Wang P, Wang X, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Wen L, Westfall GD, Wieman H, Wissink SW, Witt R, Wu J, Wu J, Wu Y, Xi B, Xiao ZG, Xie G, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Z, Xu Z, Yan G, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zbroszczyk H, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang S, Zhang XP, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao J, Zhou C, Zhou Y, Zhu X, Zurek M, Zyzak M. Measurements of _{Λ}^{3}H and _{Λ}^{4}H Lifetimes and Yields in Au+Au Collisions in the High Baryon Density Region. Phys Rev Lett 2022; 128:202301. [PMID: 35657899 DOI: 10.1103/physrevlett.128.202301] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 01/26/2022] [Accepted: 04/05/2022] [Indexed: 06/15/2023]
Abstract
We report precision measurements of hypernuclei _{Λ}^{3}H and _{Λ}^{4}H lifetimes obtained from Au+Au collisions at sqrt[s_{NN}]=3.0 GeV and 7.2 GeV collected by the STAR experiment at the Relativistic Heavy Ion Collider, and the first measurement of _{Λ}^{3}H and _{Λ}^{4}H midrapidity yields in Au+Au collisions at sqrt[s_{NN}]=3.0 GeV. _{Λ}^{3}H and _{Λ}^{4}H, being the two simplest bound states composed of hyperons and nucleons, are cornerstones in the field of hypernuclear physics. Their lifetimes are measured to be 221±15(stat)±19(syst) ps for _{Λ}^{3}H and 218±6(stat)±13(syst) ps for _{Λ}^{4}H. The p_{T}-integrated yields of _{Λ}^{3}H and _{Λ}^{4}H are presented in different centrality and rapidity intervals. It is observed that the shape of the rapidity distribution of _{Λ}^{4}H is different for 0%-10% and 10%-50% centrality collisions. Thermal model calculations, using the canonical ensemble for strangeness, describes the _{Λ}^{3}H yield well, while underestimating the _{Λ}^{4}H yield. Transport models, combining baryonic mean-field and coalescence (jam) or utilizing dynamical cluster formation via baryonic interactions (phqmd) for light nuclei and hypernuclei production, approximately describe the measured _{Λ}^{3}H and _{Λ}^{4}H yields. Our measurements provide means to precisely assess our understanding of the fundamental baryonic interactions with strange quarks, which can impact our understanding of more complicated systems involving hyperons, such as the interior of neutron stars or exotic hypernuclei.
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Affiliation(s)
- M S Abdallah
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - B E Aboona
- Texas A&M University, College Station, Texas 77843
| | - J Adam
- Brookhaven National Laboratory, Upton, New York 11973
| | - L Adamczyk
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - J R Adams
- The Ohio State University, Columbus, Ohio 43210
| | - J K Adkins
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - G Agakishiev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I Aggarwal
- Panjab University, Chandigarh 160014, India
| | | | - Z Ahammed
- Variable Energy Cyclotron Centre, Kolkata 700064, India
| | - I Alekseev
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute", Moscow 117218
- National Research Nuclear University MEPhI, Moscow 115409
| | - D M Anderson
- Texas A&M University, College Station, Texas 77843
| | - A Aparin
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - M U Ashraf
- Central China Normal University, Wuhan, Hubei 430079
| | | | - A Attri
- Panjab University, Chandigarh 160014, India
| | | | - V Bairathi
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - W Baker
- University of California, Riverside, California 92521
| | | | - K Barish
- University of California, Riverside, California 92521
| | - A Behera
- State University of New York, Stony Brook, New York 11794
| | - R Bellwied
- University of Houston, Houston, Texas 77204
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - J Bielcik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J Bielcikova
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - I G Bordyuzhin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute", Moscow 117218
| | | | - A V Brandin
- National Research Nuclear University MEPhI, Moscow 115409
| | - I Bunzarov
- Joint Institute for Nuclear Research, Dubna 141 980
| | - X Z Cai
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - H Caines
- Yale University, New Haven, Connecticut 06520
| | | | - D Cebra
- University of California, Davis, California 95616
| | - I Chakaberia
- Brookhaven National Laboratory, Upton, New York 11973
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - P Chaloupka
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - F-H Chang
- National Cheng Kung University, Tainan 70101
| | - Z Chang
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - A Chatterjee
- Central China Normal University, Wuhan, Hubei 430079
| | | | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai, 200433
| | - X Chen
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - M Chevalier
- University of California, Riverside, California 92521
| | | | - W Christie
- Brookhaven National Laboratory, Upton, New York 11973
| | - X Chu
- Brookhaven National Laboratory, Upton, New York 11973
| | - H J Crawford
- University of California, Berkeley, California 94720
| | - M Csanád
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - M Daugherity
- Abilene Christian University, Abilene, Texas 79699
| | - T G Dedovich
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I M Deppner
- University of Heidelberg, Heidelberg 69120, Germany
| | - A A Derevschikov
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - A Dhamija
- Panjab University, Chandigarh 160014, India
| | - L Di Carlo
- Wayne State University, Detroit, Michigan 48201
| | - L Didenko
- Brookhaven National Laboratory, Upton, New York 11973
| | - P Dixit
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - X Dong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | - J C Dunlop
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Elsey
- Wayne State University, Detroit, Michigan 48201
| | - J Engelage
- University of California, Berkeley, California 94720
| | - G Eppley
- Rice University, Houston, Texas 77251
| | - S Esumi
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - O Evdokimov
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Ewigleben
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - O Eyser
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Fatemi
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - F M Fawzi
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - S Fazio
- Brookhaven National Laboratory, Upton, New York 11973
| | - P Federic
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J Fedorisin
- Joint Institute for Nuclear Research, Dubna 141 980
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - P Filip
- Joint Institute for Nuclear Research, Dubna 141 980
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Francisco
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Central China Normal University, Wuhan, Hubei 430079
| | - L Fulek
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | | | - T Galatyuk
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - F Geurts
- Rice University, Houston, Texas 77251
| | - N Ghimire
- Temple University, Philadelphia, Pennsylvania 19122
| | - A Gibson
- Valparaiso University, Valparaiso, Indiana 46383
| | - K Gopal
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - X Gou
- Shandong University, Qingdao, Shandong 266237
| | - D Grosnick
- Valparaiso University, Valparaiso, Indiana 46383
| | - A Gupta
- University of Jammu, Jammu 180001, India
| | - W Guryn
- Brookhaven National Laboratory, Upton, New York 11973
| | - A I Hamad
- Kent State University, Kent, Ohio 44242
| | - A Hamed
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - S Harabasz
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - M D Harasty
- University of California, Davis, California 95616
| | - J W Harris
- Yale University, New Haven, Connecticut 06520
| | - H Harrison
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - S He
- Central China Normal University, Wuhan, Hubei 430079
| | - W He
- Fudan University, Shanghai, 200433
| | - X H He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y He
- Shandong University, Qingdao, Shandong 266237
| | - S Heppelmann
- University of California, Davis, California 95616
| | - S Heppelmann
- Pennsylvania State University, University Park, Pennsylvania 16802
| | - N Herrmann
- University of Heidelberg, Heidelberg 69120, Germany
| | - E Hoffman
- University of Houston, Houston, Texas 77204
| | - L Holub
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - Y Hu
- Fudan University, Shanghai, 200433
| | - H Huang
- National Cheng Kung University, Tainan 70101
| | - H Z Huang
- University of California, Los Angeles, California 90095
| | - S L Huang
- State University of New York, Stony Brook, New York 11794
| | - T Huang
- National Cheng Kung University, Tainan 70101
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | - T J Humanic
- The Ohio State University, Columbus, Ohio 43210
| | - G Igo
- University of California, Los Angeles, California 90095
| | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - A Jentsch
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Ji
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J Jia
- Brookhaven National Laboratory, Upton, New York 11973
- State University of New York, Stony Brook, New York 11794
| | - K Jiang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Ju
- University of Science and Technology of China, Hefei, Anhui 230026
| | - E G Judd
- University of California, Berkeley, California 94720
| | - S Kabana
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - M L Kabir
- University of California, Riverside, California 92521
| | - S Kagamaster
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - D Kalinkin
- Brookhaven National Laboratory, Upton, New York 11973
- Indiana University, Bloomington, Indiana 47408
| | - K Kang
- Tsinghua University, Beijing 100084
| | - D Kapukchyan
- University of California, Riverside, California 92521
| | - K Kauder
- Brookhaven National Laboratory, Upton, New York 11973
| | - H W Ke
- Brookhaven National Laboratory, Upton, New York 11973
| | - D Keane
- Kent State University, Kent, Ohio 44242
| | - A Kechechyan
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M Kelsey
- Wayne State University, Detroit, Michigan 48201
| | - Y V Khyzhniak
- National Research Nuclear University MEPhI, Moscow 115409
| | - D P Kikoła
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - C Kim
- University of California, Riverside, California 92521
| | - B Kimelman
- University of California, Davis, California 95616
| | - D Kincses
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - I Kisel
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
| | - A Kiselev
- Brookhaven National Laboratory, Upton, New York 11973
| | - A G Knospe
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H S Ko
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - L Kochenda
- National Research Nuclear University MEPhI, Moscow 115409
| | - L K Kosarzewski
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - L Kramarik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - P Kravtsov
- National Research Nuclear University MEPhI, Moscow 115409
| | - L Kumar
- Panjab University, Chandigarh 160014, India
| | - S Kumar
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | | | - R Lacey
- State University of New York, Stony Brook, New York 11794
| | - S Lan
- Central China Normal University, Wuhan, Hubei 430079
| | - J M Landgraf
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Lauret
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Lebedev
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Lednicky
- Joint Institute for Nuclear Research, Dubna 141 980
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J H Lee
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y H Leung
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - N Lewis
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Li
- Shandong University, Qingdao, Shandong 266237
| | - C Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - W Li
- Rice University, Houston, Texas 77251
| | - X Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- Tsinghua University, Beijing 100084
| | - X Liang
- University of California, Riverside, California 92521
| | - Y Liang
- Kent State University, Kent, Ohio 44242
| | - R Licenik
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - T Lin
- Shandong University, Qingdao, Shandong 266237
| | - Y Lin
- Central China Normal University, Wuhan, Hubei 430079
| | - M A Lisa
- The Ohio State University, Columbus, Ohio 43210
| | - F Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - H Liu
- Indiana University, Bloomington, Indiana 47408
| | - H Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - P Liu
- State University of New York, Stony Brook, New York 11794
| | - T Liu
- Yale University, New Haven, Connecticut 06520
| | - X Liu
- The Ohio State University, Columbus, Ohio 43210
| | - Y Liu
- Texas A&M University, College Station, Texas 77843
| | - Z Liu
- University of Science and Technology of China, Hefei, Anhui 230026
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - R S Longacre
- Brookhaven National Laboratory, Upton, New York 11973
| | - E Loyd
- University of California, Riverside, California 92521
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
| | - X F Luo
- Central China Normal University, Wuhan, Hubei 430079
| | - L Ma
- Fudan University, Shanghai, 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y G Ma
- Fudan University, Shanghai, 200433
| | - N Magdy
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | | | - C Markert
- University of Texas, Austin, Texas 78712
| | - H S Matis
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J A Mazer
- Rutgers University, Piscataway, New Jersey 08854
| | - N G Minaev
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | | | - B Mohanty
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - M M Mondal
- State University of New York, Stony Brook, New York 11794
| | - I Mooney
- Wayne State University, Detroit, Michigan 48201
| | - D A Morozov
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - A Mukherjee
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - M Nagy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - Md Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - K Nayak
- Central China Normal University, Wuhan, Hubei 430079
| | - D Neff
- University of California, Los Angeles, California 90095
| | - J M Nelson
- University of California, Berkeley, California 94720
| | - D B Nemes
- Yale University, New Haven, Connecticut 06520
| | - M Nie
- Shandong University, Qingdao, Shandong 266237
| | - G Nigmatkulov
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Niida
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - R Nishitani
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - L V Nogach
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - T Nonaka
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - A S Nunes
- Brookhaven National Laboratory, Upton, New York 11973
| | - G Odyniec
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - A Ogawa
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Oh
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - V A Okorokov
- National Research Nuclear University MEPhI, Moscow 115409
| | - B S Page
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Pak
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Pan
- Texas A&M University, College Station, Texas 77843
| | - A Pandav
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - A K Pandey
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | | | - P Parfenov
- National Research Nuclear University MEPhI, Moscow 115409
| | - B Pawlik
- Institute of Nuclear Physics PAN, Cracow 31-342, Poland
| | - D Pawlowska
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - C Perkins
- University of California, Berkeley, California 94720
| | - L Pinsky
- University of Houston, Houston, Texas 77204
| | - R L Pintér
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - J Pluta
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - B R Pokhrel
- Temple University, Philadelphia, Pennsylvania 19122
| | - G Ponimatkin
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J Porter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - M Posik
- Temple University, Philadelphia, Pennsylvania 19122
| | - V Prozorova
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - N K Pruthi
- Panjab University, Chandigarh 160014, India
| | - M Przybycien
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - J Putschke
- Wayne State University, Detroit, Michigan 48201
| | - H Qiu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - A Quintero
- Temple University, Philadelphia, Pennsylvania 19122
| | - C Racz
- University of California, Riverside, California 92521
| | | | - N Raha
- Wayne State University, Detroit, Michigan 48201
| | - R L Ray
- University of Texas, Austin, Texas 78712
| | - R Reed
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H G Ritter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - M Robotkova
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | | | - J L Romero
- University of California, Davis, California 95616
| | - D Roy
- Rutgers University, Piscataway, New Jersey 08854
| | - L Ruan
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Rusnak
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - A K Sahoo
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - N R Sahoo
- Shandong University, Qingdao, Shandong 266237
| | - H Sako
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - S Salur
- Rutgers University, Piscataway, New Jersey 08854
| | - J Sandweiss
- Yale University, New Haven, Connecticut 06520
| | - S Sato
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W B Schmidke
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Schmitz
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - B R Schweid
- State University of New York, Stony Brook, New York 11794
| | - F Seck
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - J Seger
- Creighton University, Omaha, Nebraska 68178
| | - M Sergeeva
- University of California, Los Angeles, California 90095
| | - R Seto
- University of California, Riverside, California 92521
| | - P Seyboth
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - N Shah
- Indian Institute Technology, Patna, Bihar 801106, India
| | - E Shahaliev
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - M Shao
- University of Science and Technology of China, Hefei, Anhui 230026
| | - T Shao
- Fudan University, Shanghai, 200433
| | | | - D Y Shen
- Fudan University, Shanghai, 200433
| | - S S Shi
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
| | - Q Y Shou
- Fudan University, Shanghai, 200433
| | - E P Sichtermann
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - R Sikora
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - M Simko
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - M J Skoby
- Purdue University, West Lafayette, Indiana 47907
| | - N Smirnov
- Yale University, New Haven, Connecticut 06520
| | - Y Söhngen
- University of Heidelberg, Heidelberg 69120, Germany
| | - W Solyst
- Indiana University, Bloomington, Indiana 47408
| | - P Sorensen
- Brookhaven National Laboratory, Upton, New York 11973
| | - H M Spinka
- Argonne National Laboratory, Argonne, Illinois 60439
| | - B Srivastava
- Purdue University, West Lafayette, Indiana 47907
| | | | - M Stefaniak
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - D J Stewart
- Yale University, New Haven, Connecticut 06520
| | - M Strikhanov
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - A A P Suaide
- Universidade de São Paulo, São Paulo, Brazil 05314-970
| | - M Sumbera
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - B Summa
- Pennsylvania State University, University Park, Pennsylvania 16802
| | - X M Sun
- Central China Normal University, Wuhan, Hubei 430079
| | - X Sun
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - Y Sun
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Sun
- Huzhou University, Huzhou, Zhejiang 313000
| | - B Surrow
- Temple University, Philadelphia, Pennsylvania 19122
| | - D N Svirida
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute", Moscow 117218
| | - Z W Sweger
- University of California, Davis, California 95616
| | - P Szymanski
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - A H Tang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Tang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - A Taranenko
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Tarnowsky
- Michigan State University, East Lansing, Michigan 48824
| | - J H Thomas
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | - D Tlusty
- Creighton University, Omaha, Nebraska 68178
| | - T Todoroki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - M Tokarev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - C A Tomkiel
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - S Trentalange
- University of California, Los Angeles, California 90095
| | - R E Tribble
- Texas A&M University, College Station, Texas 77843
| | - P Tribedy
- Brookhaven National Laboratory, Upton, New York 11973
| | - S K Tripathy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - T Truhlar
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - B A Trzeciak
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - O D Tsai
- University of California, Los Angeles, California 90095
| | - Z Tu
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Ullrich
- Brookhaven National Laboratory, Upton, New York 11973
| | - D G Underwood
- Argonne National Laboratory, Argonne, Illinois 60439
- Valparaiso University, Valparaiso, Indiana 46383
| | - I Upsal
- Rice University, Houston, Texas 77251
| | - G Van Buren
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Vanek
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - A N Vasiliev
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - I Vassiliev
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
| | - V Verkest
- Wayne State University, Detroit, Michigan 48201
| | - F Videbaek
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Vokal
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - F Wang
- Purdue University, West Lafayette, Indiana 47907
| | - G Wang
- University of California, Los Angeles, California 90095
| | - J S Wang
- Huzhou University, Huzhou, Zhejiang 313000
| | - P Wang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Wang
- Shandong University, Qingdao, Shandong 266237
| | - Y Wang
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Wang
- Tsinghua University, Beijing 100084
| | - Z Wang
- Shandong University, Qingdao, Shandong 266237
| | - J C Webb
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - L Wen
- University of California, Los Angeles, California 90095
| | - G D Westfall
- Michigan State University, East Lansing, Michigan 48824
| | - H Wieman
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - S W Wissink
- Indiana University, Bloomington, Indiana 47408
| | - R Witt
- United States Naval Academy, Annapolis, Maryland 21402
| | - J Wu
- Central China Normal University, Wuhan, Hubei 430079
| | - J Wu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Wu
- University of California, Riverside, California 92521
| | - B Xi
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - Z G Xiao
- Tsinghua University, Beijing 100084
| | - G Xie
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - W Xie
- Purdue University, West Lafayette, Indiana 47907
| | - H Xu
- Huzhou University, Huzhou, Zhejiang 313000
| | - N Xu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Q H Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Shandong University, Qingdao, Shandong 266237
| | - Z Xu
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Xu
- University of California, Los Angeles, California 90095
| | - G Yan
- Shandong University, Qingdao, Shandong 266237
| | - C Yang
- Shandong University, Qingdao, Shandong 266237
| | - Q Yang
- Shandong University, Qingdao, Shandong 266237
| | - S Yang
- Rice University, Houston, Texas 77251
| | - Y Yang
- National Cheng Kung University, Tainan 70101
| | - Z Ye
- Rice University, Houston, Texas 77251
| | - Z Ye
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - L Yi
- Shandong University, Qingdao, Shandong 266237
| | - K Yip
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Yu
- Shandong University, Qingdao, Shandong 266237
| | - H Zbroszczyk
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - W Zha
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Zhang
- State University of New York, Stony Brook, New York 11794
| | - D Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - J Zhang
- Shandong University, Qingdao, Shandong 266237
| | - S Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - S Zhang
- Fudan University, Shanghai, 200433
| | | | - Y Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - Z J Zhang
- National Cheng Kung University, Tainan 70101
| | - Z Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - J Zhao
- Purdue University, West Lafayette, Indiana 47907
| | - C Zhou
- Fudan University, Shanghai, 200433
| | - Y Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - X Zhu
- Tsinghua University, Beijing 100084
| | - M Zurek
- Argonne National Laboratory, Argonne, Illinois 60439
| | - M Zyzak
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
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Zhou JG, Yang J, Wang H, Wong AH, Tan F, Chen X, He S, Shen G, Wang YJ, Frey B, Fietkau R, Hecht M, Ma H, Gaipl U. 60P Machine learning based on blood biomarkers predicts fast progression in advanced NSCLC patients treated with immunotherapy. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.02.069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
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