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Zhou J, Liu ZY. [Interpretation of the pathological diagnostic criteria and characteristics of high-grade thyroid follicular-derived carcinoma in the 5 th edition WHO classification]. Zhonghua Yi Xue Za Zhi 2024; 104:1578-1583. [PMID: 38742344 DOI: 10.3760/cma.j.cn112137-20230902-00374] [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: 05/16/2024]
Abstract
The 5th edition WHO classification of thyroid tumors proposed high-grade non-anaplastic thyroid carcinoma, which includes traditional poorly differentiated thyroid carcinoma (PDTC) and differentiated high-grade thyroid carcinoma (DHGTC), with a prognosis between highly differentiated thyroid carcinoma and anaplastic thyroid carcinoma (ATC), in which about 50% of patients do not take radioactive iodine. Therefore, this classification is of great clinical significance. This article interprets the diagnostic criteria and genetic features of high-grade non-anaplastic thyroid carcinoma in 5th edition WHO classification, comparing with ATC.
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Affiliation(s)
- J Zhou
- Department of Pathology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Z Y Liu
- Department of Pathology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
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Cao B, Li Q, Xu P, Zhang Y, Cai S, Rao S, Zeng M, Dai Y, Jiang S, Zhou J. Vesical Imaging-Reporting and Data System (VI-RADS) as a grouping imaging biomarker combined with a decision-tree mode to preoperatively predict the pathological grade of bladder cancer. Clin Radiol 2024; 79:e725-e735. [PMID: 38360514 DOI: 10.1016/j.crad.2024.01.031] [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: 09/15/2023] [Revised: 01/12/2024] [Accepted: 01/22/2024] [Indexed: 02/17/2024]
Abstract
AIM To investigate whether the Vesical Imaging-Reporting and Data System (VI-RADS) could be used to develop a new non-invasive preoperative grade-prediction system to partially predict high-grade bladder cancer (HG-BC). MATERIALS AND METHODS The present study enrolled 89 primary BC patients prospectively from March 2022 to June 2023. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of VI-RADS for predicting HG-BC and muscle-invasive bladder cancer (MIBC) in the entire group. In the low VI-RADS (≤2) group, the decision tree-based method was used to obtain significant predictors and construct the decision-tree model (DT model). The performance of the DT model and low VI-RADS scores for predicting HG-BC was determined using ROC, calibration, and decision curve analyses. RESULTS At a cut-off of ≥3, the specificity and positive predictive value of VI-RADS for predicting HG-BC in the entire group was 100%, and the area under the ROC curve (AUC) was 0.697. Among 65 patients with low VI-RADS scores, the DT model showed an AUC of 0.884 in predicting HG-BC compared to 0.506 for low VI-RADS scores. Calibration and decision curve analyses showed that the DT model performed better than the low VI-RADS scores. CONCLUSION Most VI-RADS scores ≥3 correspond to HG-BCs. VI-RADS could be used as a grouping imaging biomarker for a pathological grade-prediction procedure, which in combination with the DT model for low VI-RADS (≤2) populations, would provide a potential preoperative non-invasive method of predicting HG-BC.
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Affiliation(s)
- B Cao
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Radiology, Shanghai Geriatric Medical Center, Shanghai, China
| | - Q Li
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, China
| | - P Xu
- Department of Urology, Xuhui Hospital, Fudan University, Shanghai, China
| | - Y Zhang
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - S Cai
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, China
| | - S Rao
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Radiology, Shanghai Geriatric Medical Center, Shanghai, China
| | - M Zeng
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Radiology, Shanghai Geriatric Medical Center, Shanghai, China
| | - Y Dai
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - S Jiang
- Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Urology, Zhongshan Hospital Wusong Branch, Fudan University, Shanghai, China.
| | - J Zhou
- Department of Radiology, Fudan University Zhongshan Hospital Xiamen Branch, Xiamen, China; Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China; Xiamen Key Clinical Specialty for Radiology, Xiamen, China.
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Yang R, Fu WG, Zhou J, Zhang YF, Yang L, Yang HB, Fu LZ. Enhanced detection of African swine fever virus in samples with low viral load using digital PCR technology. Heliyon 2024; 10:e28426. [PMID: 38689956 PMCID: PMC11059528 DOI: 10.1016/j.heliyon.2024.e28426] [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: 09/01/2023] [Revised: 03/16/2024] [Accepted: 03/19/2024] [Indexed: 05/02/2024] Open
Abstract
Detection of low viral load samples has long been a challenge for African swine fever (ASF) prevention and control. This study aimed to compare the detection efficacy of droplet digital PCR(ddPCR) and quantitative PCR(qPCR) for African swine fever virus (ASFV) at different viral loads, with a focus on assessing the accuracy of ddPCR in detecting low viral load samples. The results revealed that ddPCR had a detection limit of 1.97 (95% CI 1.48 - 4.12) copies/reaction and was 18.99 times more sensitive than qPCR (detection limit: 37.42, 95% CI 29.56 - 69.87 copies/reaction). In the quantification of high, medium, and low viral load samples, ddPCR showed superior stability with lower intra- (2.06% - 7.58%) and inter-assay (3.83% - 7.50%) coefficients of variation than those of qPCR (intra-assay: 8.08%-29.86%; inter-assay: 9.27%-34.58%). Bland-Altman analysis indicated acceptable consistency between ddPCR and qPCR for high and medium viral load samples; however, discrepancies were observed for low viral load samples, where two samples (2/24, 8.33%) exhibited deviations beyond the acceptable range (-46.18 copies/reaction). Moreover, ddPCR demonstrated better performance in detecting ASFV in clinical samples from asymptomatic pigs and environmental samples, with qPCR showing false negative rates of 7.69% (2/26) and 27.27% (12/44), respectively. McNemar analysis revealed significant differences between the two methods (P = 0.000) for samples with a viral load <100 copies/reaction. The results of this study demonstrate that ddPCR has better detection limits and adaptability than qPCR, allowing for a more accurate detection of ASFV in early-stage infections and low-concentration environmental samples. These findings highlight the potential of ddPCR in the prevention and control of ASF.
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Affiliation(s)
- R. Yang
- Chongqing Academy of Animal Science, Chongqing, China
- National Center of Technology Innovation for Pigs, Chongqing, China
- National Animal Disease-Chongqing Monitoring Station, Chongqing, China
- Chongqing Research Center of Veterinary Biological Products Engineering Technology, Chongqing, China
| | - W.-G. Fu
- Chongqing Academy of Animal Science, Chongqing, China
- National Center of Technology Innovation for Pigs, Chongqing, China
- National Animal Disease-Chongqing Monitoring Station, Chongqing, China
- Chongqing Research Center of Veterinary Biological Products Engineering Technology, Chongqing, China
| | - J. Zhou
- National Center of Technology Innovation for Pigs, Chongqing, China
| | - Y.-F. Zhang
- Chongqing Academy of Animal Science, Chongqing, China
- National Center of Technology Innovation for Pigs, Chongqing, China
- National Animal Disease-Chongqing Monitoring Station, Chongqing, China
- Chongqing Research Center of Veterinary Biological Products Engineering Technology, Chongqing, China
| | - L. Yang
- Chongqing Academy of Animal Science, Chongqing, China
- National Center of Technology Innovation for Pigs, Chongqing, China
- National Animal Disease-Chongqing Monitoring Station, Chongqing, China
- Chongqing Research Center of Veterinary Biological Products Engineering Technology, Chongqing, China
| | - H.-B. Yang
- Agricultural Science and Technology Promotion Center of Da'an District, Zigong City, Sichuan, China
| | - L.-Z. Fu
- Chongqing Academy of Animal Science, Chongqing, China
- National Center of Technology Innovation for Pigs, Chongqing, China
- National Animal Disease-Chongqing Monitoring Station, Chongqing, China
- Chongqing Research Center of Veterinary Biological Products Engineering Technology, Chongqing, China
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Jiang N, Tan P, Sun Y, Zhou J, Ren R, Li Z, Zhu S. Microstructural, Micromechanical Atlas of the Temporomandibular Joint Disc. J Dent Res 2024:220345241227822. [PMID: 38594786 DOI: 10.1177/00220345241227822] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024] Open
Abstract
The temporomandibular joint (TMJ) disc is mainly composed of collagen, with its arrangement responding to efficient stress distribution. However, microstructural and micromechanical transformations of the TMJ disc under resting, functional, and pathological conditions remain unclear. To address this, our study presents a high-resolution microstructural and mechanical atlas of the porcine TMJ disc. First, the naive microstructure and mechanical properties were investigated in porcine TMJ discs (resting and functional conditions). Subsequently, the perforation and tear models (pathological conditions) were compared. Following this, a rabbit model of anterior disc displacement (abnormal stress) was studied. Results show diverse microstructures and mechanical properties at the nanometer to micrometer scale. In the functional state, gradual unfolding of the crimping cycle in secondary and tertiary structures leads to D-cycle prolongation in the primary structure, causing tissue failure. Pathological conditions lead to stress concentration near the injury site due to collagen interfibrillar traffic patterns, resulting in earlier damage manifestation. Additionally, the abnormal stress model shows collagen damage initiating at the primary structure and extending to the superstructure over time. These findings highlight collagen's various roles in different pathophysiological states. Our study offers valuable insights into TMJ disc function and dysfunction, aiding the development of diagnostic and therapeutic strategies for TMJ disorders, as well as providing guidance for the design of structural biomimetic materials.
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Affiliation(s)
- N Jiang
- State Key Laboratory of Oral Diseases, & National Clinical Research Center for Oral Disease, & West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - P Tan
- State Key Laboratory of Oral Diseases, & National Clinical Research Center for Oral Disease, & West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y Sun
- State Key Laboratory of Oral Diseases, & National Clinical Research Center for Oral Disease, & West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - J Zhou
- State Key Laboratory of Oral Diseases, & National Clinical Research Center for Oral Disease, & West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - R Ren
- State Key Laboratory of Oral Diseases, & National Clinical Research Center for Oral Disease, & West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Z Li
- Ao Research Institute Davos, Davos, Graubünden, Switzerland
| | - S Zhu
- State Key Laboratory of Oral Diseases, & National Clinical Research Center for Oral Disease, & West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
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Xu J, Wang Q, Yang K, Wen L, Wang T, Lin D, Liu J, Zhou J, Liu Y, Dong Y, Cao C, Li S, Zhou X. [High-quality acceleration of the Chinese national schistosomiasis elimination programme to advance the building of Healthy China]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2024; 36:1-6. [PMID: 38604678 DOI: 10.16250/j.32.1374.2024051] [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: 04/13/2024]
Abstract
The goal of achieving elimination of schistosomiasis across all endemic counties in China by 2030 was proposed in the Outline of the Healthy China 2030 Plan. On June 16, 2023, the Action Plan to Accelerate the Elimination of Schistosomiasis in China (2023-2030) was jointly issued by National Disease Control and Prevention Administration and other 10 ministries, which deployed the targets and key tasks of the national schistosomiasis elimination programme in China. This article describes the progress of the national schistosomiasis control programme, analyzes the opportunities to eliminate schistosomiasis, and proposes targeted recommendations to tackle the challenges of schistosomiasis elimination, so as to accelerate the process towards schistosomiasis elimination and facilitate the building of a healthy China.
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Affiliation(s)
- J Xu
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai 200025, China
- School of Global Health, Chinese Center for Tropical Diseases Research and Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Q Wang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai 200025, China
| | - K Yang
- Jiangsu Institute of Parasitic Diseases, China
| | - L Wen
- Zhejiang Center for Schistosomiasis Control, China
| | - T Wang
- Anhui Institute for Schistosomiasis Control, China
| | - D Lin
- Jiangxi Institute of Parasitic Disease, China
| | - J Liu
- Hubei Center for Disease Control and Prevention, China
| | - J Zhou
- Hunan Provincial Bureau of Disease Control and Prevention, China
| | - Y Liu
- Sichuan Center for Disease Control and Prevention, China
| | - Y Dong
- Yunnan Institute for Endemic Disease Control, China
| | - C Cao
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai 200025, China
| | - S Li
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai 200025, China
- School of Global Health, Chinese Center for Tropical Diseases Research and Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - X Zhou
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai 200025, China
- School of Global Health, Chinese Center for Tropical Diseases Research and Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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Xu B, Kang B, Li S, Fan S, Zhou J. Sodium-glucose cotransporter 2 inhibitors and cancer: a systematic review and meta-analysis. J Endocrinol Invest 2024:10.1007/s40618-024-02351-0. [PMID: 38530620 DOI: 10.1007/s40618-024-02351-0] [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] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/24/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND The effect of sodium-glucose cotransporter 2 (SGLT2) inhibitors on cancer has yet to be fully elucidated. OBJECTIVE This systematic review and meta-analysis investigated the effects of SGLT2 inhibitors on cancer. METHODS We searched the PubMed and ClinicalTrials.gov databases up to July 15, 2023, to identify eligible randomized, double-blind, placebo-controlled trials that lasted at least ≥24 weeks. The primary outcome was the overall cancer incidence, and the secondary outcomes were the incidences of various types of cancer. We used the Mantel-Haenszel method, fixed effects model, risk ratio (RR) and 95% confidence interval (CI) to analyze dichotomous variables. Subgroup analysis was performed based on the SGLT2 inhibitor type, baseline conditions, and follow-up duration. All meta-analyses were performed using RevMan5.4.1 and Stata MP 16.0. RESULTS A total of 58 publications (59 trials) were included, comprising 113,909 participants with type 2 diabetes mellitus and/or chronic kidney disease and/or high cardiovascular risk and/or heart failure (SGLT2 inhibitor group, 63864; placebo group, 50045). Compared to the placebo SGLT2 inhibitors did not significantly increase the overall incidence of cancer (RR 1.01; 95% CI 0.94-1.08; p = 0.82). However, ertugliflozin did significantly increase the overall incidence of cancer (RR 1.29; 95% CI 1.01-1.64; p = 0.04). SGLT2 inhibitors did not increase the risks of bladder or breast cancer. However, dapagliflozin did significantly reduce the risk of bladder cancer by 47% (RR 0.53; 95% CI 0.35-0.81; p = 0.003). SGLT2 inhibitors had no significant effect on the risks of gastrointestinal, thyroid, skin, respiratory, prostate, uterine/endometrial, hepatic and pancreatic cancers. Dapagliflozin reduced the risk of respiratory cancer by 26% (RR 0.74; 95% CI 0.55-1.00; p = 0.05). SGLT2 inhibitors (particularly mediated by dapagliflozin and ertugliflozin but not statistically significant) were associated with a greater risk of renal cancer than the placebo (RR 1.39; 95% CI 1.04-1.87; p = 0.03). CONCLUSION SGLT2 inhibitors did not significantly increase the overall risk of cancer or the risks of bladder and breast cancers. However, the higher risk of renal cancer associated with SGLT2 inhibitors warrants concern.
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Affiliation(s)
- B Xu
- The First Affiliated Hospital, Hunan Provincial Clinical Medical Research Center for Drug Evaluation of Major Chronic Diseases, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
- The First Affiliated Hospital, Hengyang Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
- The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
- The First Affiliated Hospital, Pharmacy Department, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
- School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
| | - B Kang
- The First Affiliated Hospital, Hunan Provincial Clinical Medical Research Center for Drug Evaluation of Major Chronic Diseases, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
- The First Affiliated Hospital, Hengyang Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
- The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
- The First Affiliated Hospital, Pharmacy Department, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
| | - S Li
- The First Affiliated Hospital, Hunan Provincial Clinical Medical Research Center for Drug Evaluation of Major Chronic Diseases, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
- The First Affiliated Hospital, Hengyang Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
- The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
- The First Affiliated Hospital, Pharmacy Department, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
- The Affiliated Nanhua Hospital, Department of Docimasiology, Hengyang Medical School, University of South China, Hengyang, 421002, Hunan, China
| | - S Fan
- The First Affiliated Hospital, Hunan Provincial Clinical Medical Research Center for Drug Evaluation of Major Chronic Diseases, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
- The First Affiliated Hospital, Hengyang Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
- The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
- The First Affiliated Hospital, Pharmacy Department, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
| | - J Zhou
- The First Affiliated Hospital, Hunan Provincial Clinical Medical Research Center for Drug Evaluation of Major Chronic Diseases, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
- The First Affiliated Hospital, Hengyang Clinical Pharmacology Research Center, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
- The First Affiliated Hospital, Hengyang Key Laboratory of Clinical Pharmacology, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
- The First Affiliated Hospital, Pharmacy Department, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
- School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
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Yang J, Deng L, Jing M, Xu M, Liu X, Li S, Zhang L, Xi H, Yuan L, Zhou J. Added value of spectral computed tomography quantitative parameters for differentiating tuberculosis-associated fibrosing mediastinitis from endobronchial lung cancer: initial results. Clin Radiol 2024:S0009-9260(24)00132-6. [PMID: 38658213 DOI: 10.1016/j.crad.2024.02.010] [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] [Received: 12/05/2023] [Revised: 02/09/2024] [Accepted: 02/14/2024] [Indexed: 04/26/2024]
Abstract
OBJECTIVE The objective of this study was to explore the added value of spectral computed tomography (CT) parameters to conventional CT features for differentiating tuberculosis-associated fibrosing mediastinitis (TB-associated FM) from endobronchial lung cancer (EBLC). METHODS Chest spectral CT enhancement images from 109 patients with atelectasis were analyzed retrospectively. These patients were divided into two distinct categories: the TB-associated FM group (n = 77) and the EBLC group (n = 32), based on bronchoscopy and/or pathological findings. The selection of spectrum parameters was optimized with the least absolute shrinkage and selection operator regression analysis. The relationship between the spectrum parameters and conventional parameters was explored using Pearson's correlation. Multivariate logistic regression analysis was used to build spectrum model. The spectrum parameters in the spectrum model were replaced with their corresponding conventional parameters to build the conventional model. Diagnostic performances were evaluated using receiver operating characteristic curve analyses. RESULTS There was a moderate correlation between the parameters ㏒(L-AEFNIC) - ㏒(L-AEFC) (r= 0.419; p< 0.0001), ㏒(O-AEF40KeV) - ㏒(O-AEFC) (r= 0.475; p< 0.0001), [L-A-hydroxyapatite {HAP}(I)] - (L-U-CT) (r= 0.604; p< 0.0001), {arterial enhancement fraction (AEF) derived from normalized iodine concentration (NIC) of lymph node (L-AEFNIC), AEF derived from CT40KeV of bronchial obstruction (O-AEF40KeV), arterial-phase Hydroxyapatite (Iodine) concentration of lymph node [L-A-HAP(I)], AEF derived from conventional CT (AEFC), unenhanced CT value (U-CT)}. Spectrum model could improve diagnostic performances compared to conventional model (area under curve: 0.965 vs 0.916, p= 0.038). CONCLUSION There was a moderate correlation between spectrum parameters and conventional parameters. Integrating conventional CT features with spectrum parameters could further improve the ability in differentiating TB-associated FM from EBLC.
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Affiliation(s)
- J Yang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, China.
| | - L Deng
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, China.
| | - M Jing
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, China.
| | - M Xu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, China.
| | - X Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, China.
| | - S Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, China.
| | - L Zhang
- Zhang Ye People's Hospital Affiliated to Hexi University, Zhangye, 73400, China.
| | - H Xi
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, China.
| | - L Yuan
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, China.
| | - J Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, China.
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Wang H, Zhou ZK, Sui BD, Jin F, Zhou J, Zheng CX. [Analysis of the differences in the characteristics of mesenchymal stem cells derived from jaw and long bones based on single-cell RNA-sequencing]. Zhonghua Kou Qiang Yi Xue Za Zhi 2024; 59:247-254. [PMID: 38432656 DOI: 10.3760/cma.j.cn112144-20230824-00109] [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: 03/05/2024]
Abstract
Objective: To study the whole bone marrow cellular composition of jaw and long bones, and further analyze the heterogeneity of mesenchymal stem cells (MSCs) derived from these two tissue, aiming at exploring the differences in functional characteristics of bone MSCs from different lineage sources. Methods: The Seurat package of R language was used to analyze the mandibular and femur whole bone marrow single-cell RNA-sequencing (scRNA-seq) datasets in the literature, and the subpopulations were annotated by reference to the marker genes reported by previous studies. The differentially expressed genes between mandible-derived MSCs (M-MSCs) and femur-derived MSCs (F-MSCs) were calculated, and cell-cell communication analysis between M-MSCs or F-MSCs with other cell populations was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on up-regulated and down-regulated differentially expressed genes of M-MSCs, and Gene Set Enrichment Analysis (GSEA) was performed on M-MSCs or F-MSCs. Results: cRNA-seq analysis showed that the mandible and femur had the same bone marrow cell composition, but there were differences in the proportion of specific cell populations. Also, there were significantly differentially expressed genes between M-MSCs and F-MSCs. In addition, cell-cell communication analysis revealed differences in numbers of ligand-receptor pairs between M-MSCs or F-MSCs with other cell populations. Furthermore, GO, KEGG and GSEA analysis showed that M-MSCs had higher extracellular matrix production potential than F-MSCs, but had lower ability to regulate other cells in the bone marrow, especially immune cells. Conclusions: M-MSCs and F-MSCs showed distinct differences in the gene expression pattern and up-regulated signaling pathways, which may be closely related to the developmental sources and functional characteristics of jaw and long bones.
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Affiliation(s)
- H Wang
- Department of Oral Histopathology, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi International Joint Research Center for Oral Diseases, Xi'an 710032, China
| | - Z K Zhou
- School of Basic Medicine, The Fourth Military Medical University, Xi'an 710032, China
| | - B D Sui
- Department of Oral Histopathology, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi International Joint Research Center for Oral Diseases, Xi'an 710032, China
| | - F Jin
- Department of Orthodontics, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Clinical Research Center for Oral Diseases, Xi'an 710032, China
| | - J Zhou
- Department of Oral Histopathology, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi International Joint Research Center for Oral Diseases, Xi'an 710032, China
| | - C X Zheng
- Department of Oral Histopathology, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi International Joint Research Center for Oral Diseases, Xi'an 710032, China
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Wei XZ, Gao K, Zhang J, Zhao B, Liu ZG, Wu RQ, Ou MM, Zhang Q, Li W, Cheng Q, Xie YL, Zhang TY, Li YJ, Wang H, Wang ZM, Zhang W, Zhou J. [Effect of preemptive analgesia with ibuprofen on postoperative pain after mandibular third molar extraction: a randomized controlled trial]. Zhonghua Kou Qiang Yi Xue Za Zhi 2024; 59:230-236. [PMID: 38432654 DOI: 10.3760/cma.j.cn112144-20231203-00276] [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: 03/05/2024]
Abstract
Objective: To evaluate the impact of preemptive analgesia with ibuprofen on postoperative pain following the extraction of impacted mandibular third molars in a Chinese population, aiming to provide a clinical reference for its application. Methods: This multicenter, randomized, double-blind, placebo-controlled parallel-group trial was conducted from April 2022 to October 2023 at the Capital Medical University School of Stomatology (40 cases), Beijing TianTan Hospital, Capital Medical University (22 cases), and Beijing Chao-Yang Hospital, Capital Medical University (20 cases). It included 82 patients with impacted mandibular third molars, with 41 in the ibuprofen group and 41 in the control group. Participants in the ibuprofen group received 300 mg of sustained-release ibuprofen capsules orally 15 min before surgery, while the control group received a placebo. Both groups were instructed to take sustained-release ibuprofen capsules as planned for 3 days post-surgery. Pain intensity was measured using the numerical rating scale at 30 min, 4 h, 6 h, 8 h, 24 h, 48 h, and 72 h after surgery, and the use of additional analgesic medication was recorded during days 4 to 6 postoperatively. Results: All 82 patients completed the study according to the protocol. No adverse events such as nausea, vomiting, or allergies were reported in either group during the trial. The ibuprofen group exhibited significantly lower pain scores at 4 h [2.0 (1.0, 4.0) vs. 4.0 (3.0, 5.0)] (Z=-3.73, P<0.001), 6 h [2.0 (1.0, 4.0) vs. 5.0(2.5, 6.0)] (Z=-3.38, P<0.001), and 8 h [2.0 (1.0, 4.0) vs. 5.0 (2.0, 6.0)] (Z=-2.11, P=0.035) postoperatively compared to the control group. There were no statistically significant differences in pain scores between the groups at 30 min, 24 h, 48 h, and 72 h postoperatively (P>0.05). Additionally, 11 out of 41 patients (26.8%) in the ibuprofen group and 23 out of 41 patients (56.1%) in the control group required extra analgesic medication between days 4 and 6 post-surgery, with the ibuprofen group taking significantly fewer additional pills [0.0 (0.0, 1.0) vs. 1.0 (0.0, 3.0)] (Z=-2.81, P=0.005). Conclusions: A pain management regimen involving 300 mg of oral sustained-release ibuprofen capsules administered 15 minutes before surgery and continued for 3 d postoperatively effectively reduces pain levels and the total amount of analgesic medication used after the extraction of impacted mandibular third molars. Considering its efficacy, safety, and cost-effectiveness, ibuprofen is recommended as a first-line drug for perioperative pain management, enhancing patient comfort during diagnosis and treatment in a feasible manner.
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Affiliation(s)
- X Z Wei
- Department of Emergency and General Dentistry, Capital Medical University School of Stomatology, Beijing 100050, China
| | - K Gao
- Department of VIP Dental Service, Capital Medical University School of Stomatology, Beijing 100050, China
| | - J Zhang
- Department of Oral Maxillofacial Surgery, Capital Medical University School of Stomatology, Beijing 100050, China
| | - B Zhao
- Department of Pharmacy, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Z G Liu
- Statistics Department, Pharmacology Base, Beijing Anzhen Hospital, Capital Medical University, Beijing 100011, China
| | - R Q Wu
- Department of Stomatology, Beijing TianTan Hospital, Capital Medical University, Beijing 100070, China
| | - M M Ou
- Department of Stomatology, Beijing TianTan Hospital, Capital Medical University, Beijing 100070, China
| | - Q Zhang
- Department of Stomatology, Beijing TianTan Hospital, Capital Medical University, Beijing 100070, China
| | - W Li
- Department of Stomatology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China
| | - Q Cheng
- Department of Stomatology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China
| | - Y L Xie
- Department of Stomatology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China
| | - T Y Zhang
- Department of VIP Dental Service, Capital Medical University School of Stomatology, Beijing 100050, China
| | - Y J Li
- Department of VIP Dental Service, Capital Medical University School of Stomatology, Beijing 100050, China
| | - H Wang
- Department of Stomatology, Beijing TianTan Hospital, Capital Medical University, Beijing 100070, China
| | - Z M Wang
- Department of Stomatology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China
| | - W Zhang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
| | - J Zhou
- Department of VIP Dental Service, Capital Medical University School of Stomatology, Beijing 100050, China
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Zhou J, Zhuo XW, Jin M, Duan C, Zhang WH, Ren CH, Gong S, Tian XJ, Ding CH, Ren XT, Li JW. [Clinical and prognostic analysis of opsoclonus-myoclonus-ataxia syndrome in children]. Zhonghua Er Ke Za Zhi 2024; 62:256-261. [PMID: 38378288 DOI: 10.3760/cma.j.cn112140-20230911-00174] [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: 02/22/2024]
Abstract
Objective: To summarize the clinical and prognostic features of children with opsoclonus-myoclonus-ataxia syndrome (OMAS). Methods: A total of 46 patients who met the diagnostic criteria of OMAS in the Department of Neurology, Beijing Children's Hospital from June 2015 to June 2023 were retrospectively analyzed. Centralized online consultations or telephone visits were conducted between June and August 2023. The data of the children during hospitalization and follow-up were collected, including clinical manifestations, assistant examination, treatment and prognosis. According to the presence or absence of tumor, the patients were divided into two groups. The chi-square test or Mann-Whitney U test was used to compare the differences between the two groups. Univariate Logistic regression was used to analyze the factors related to OMAS recurrence and prognosis. Results: There were 46 patients, with 25 males and the onset age of 1.5 (1.2, 2.4) years. Twenty-six (57%) patients were diagnosed with neuroblastoma during the course of the disease, and no patients were categorized into the high-risk group. A total of 36 patients (78%) were followed up for≥6 months, and all of them were treated with first-line therapy with glucocorticoids, gammaglobulin and (or) adrenocorticotrophic hormone. Among the 36 patients, 9 patients (25%) were treated with second-line therapy for ≥3 months, including rituximab or cyclophosphamide, and 17 patients (47%) received chemotherapy related to neuroblastoma. At the follow-up time of 4.2 (2.2, 5.5) years, 10 patients (28%) had relapsed of OMAS. The Mitchell and Pike OMS rating scale score at the final follow-up was 0.5 (0, 2.0). Seven patients (19%) were mildly cognitively behind their peers and 6 patients (17%) were severely behind. Only 1 patient had tumor recurrence during follow-up. The history of vaccination or infection before onset was more common in the non-tumor group than in the tumor group (55%(11/20) vs. 23%(6/26), χ²=4.95, P=0.026). Myoclonus occurred more frequently in the non-tumor group (40%(8/20) vs. 4%(1/26), χ²=7.23, P=0.007) as the onset symptom. Univariate Logistic regression analysis showed that the tumor group had less recurrence (OR=0.19 (0.04-0.93), P=0.041). The use of second-line therapy or chemotherapy within 6 months of the disease course had a better prognosis (OR=11.64 (1.27-106.72), P=0.030). Conclusions: OMAS in children mostly starts in early childhood, and about half are combined with neuroblastoma. Neuroblastoma in combination with OMAS usually has a low risk classification and good prognosis. When comparing patients with OMAS with and without tumors, the latter have a more common infection or vaccination triggers, and myoclonus, as the onset symptom, is more common. Early addition of second-line therapy is associated with better prognosis in OMAS.
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Affiliation(s)
- J Zhou
- Department of Neurology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - X W Zhuo
- Department of Neurology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - M Jin
- Department of Medical Oncology, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - C Duan
- Department of Medical Oncology, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - W H Zhang
- Department of Neurology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - C H Ren
- Department of Neurology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - S Gong
- Department of Neurology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - X J Tian
- Department of Neurology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - C H Ding
- Department of Neurology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - X T Ren
- Department of Neurology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - J W Li
- Department of Neurology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
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Zhou J, Chen XF, Gao YH, Yan F, Xi HQ. [Prevalence and risk factors of sarcopenia after radical gastrectomy for gastric cancer]. Zhonghua Wei Chang Wai Ke Za Zhi 2024; 27:189-195. [PMID: 38413088 DOI: 10.3760/cma.j.cn441530-20230324-00093] [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: 02/29/2024]
Abstract
Objective: To investigate the prevalence and risk factors of sarcopenia in patients following radical gastrectomy with the aim of guiding clinical decisions. Methods: This was a retrospective observational study of data of patients who had undergone radical gastrectomy between June 2021 and June 2022 at the Department of General Surgery, First Medical Center of Chinese PLA General Hospital. Participants were reviewed 9-12 months after surgery. Inclusion criteria were as follows: (1) radical gastrectomy with a postoperative pathological diagnosis of primary gastric cancer; (2) no invasion of neighboring organs, peritoneal dissemination, or distant metastasis confirmed intra- or postoperatively; (3) availability of complete clinical data, including abdominal enhanced computed tomography and pertinent blood laboratory tests 9-12 after surgery. Exclusion criteria were as follows: (1) age <18 years; (2) presence of gastric stump cancer or previous gastrectomy; (3) history of or current other primary tumors within the past 5 years; (4) preoperative diagnosis of sarcopenia (skeletal muscle index [SMI) ≤52.4 cm²/m² for men, SMI ≤38.5 cm²/m² for women). The primary focus of the study was to investigate development of postoperative sarcopenia in the study cohort. Univariate and multivariate logistic regression were used to identify the factors associated with development of sarcopenia after radical gastrectomy. Results: The study cohort comprised 373 patients of average age of 57.1±12.3 years, comprising 292 (78.3%) men and 81 (21.7%) women. Postoperative sarcopenia was detected in 81 (21.7%) patients in the entire cohort. The SMI for the entire group was (41.79±7.70) cm2/m2: (46.40±5.03) cm2/m2 for men and (33.52±3.63) cm2/m2 for women. According to multivariate logistic regression analysis, age ≥60 years (OR=2.170, 95%CI: 1.175-4.007, P=0.013), high literacy (OR=2.512, 95%CI: 1.238-5.093, P=0.011), poor exercise habits (OR=3.263, 95%CI: 1.648-6.458, P=0.001), development of hypoproteinemia (OR=2.312, 95%CI: 1.088-4.913, P=0.029), development of hypertension (OR=2.169, 95%CI: 1.180-3.984, P=0.013), and total gastrectomy (OR=2.444, 95%CI:1.214-4.013,P=0.012) were independent risk factors for postoperative sarcopenia in post-gastrectomy patients who had had gastric cancer (P<0.05). Conclusion: Development of sarcopenia following radical gastrectomy demands attention. Older age, higher education, poor exercise habits, hypoproteinemia, hypertension, and total gastrectomy are risk factors for its development post-radical gastrectomy.
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Affiliation(s)
- J Zhou
- Division of Gastric Surgery, Senior Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - X F Chen
- Division of Gastric Surgery, Senior Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Y H Gao
- Division of Gastric Surgery, Senior Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - F Yan
- Department of Diagnostic Radiology,the First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - H Q Xi
- Division of Abdominal Trauma Surgery, Senior Department of General Surgery, the First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
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Zhou J, Kong FJ, Hu M, Wang SL. [Summary of the 6th Conference on Three-Dimensional Printing and Stomatology & the 70th Anniversary Academic Forum of the Chinese Journal of Stomatology]. Zhonghua Kou Qiang Yi Xue Za Zhi 2024; 59:204-206. [PMID: 38280742 DOI: 10.3760/cma.j.cn112144-20240115-00022] [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] [Subscribe] [Scholar Register] [Indexed: 01/29/2024]
Affiliation(s)
- J Zhou
- Department of VIP Dental Service, Capital Medical University School of Stomatology, Beijing 100050, China
| | - F J Kong
- Editorial Office of Chinese Journal of Stomatology, Publishing House of Chinese Medical Association, Beijing 100052, China
| | - M Hu
- Department of Oral and Maxillofacial Surgery, General Hospital of Chinese PLA, Beijing 100853, China
| | - S L Wang
- Beijing Laboratory of Oral Health, Capital Medical University, Beijing 100069, China Laboratory of Homeostatic Medicine, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China
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Wang Y, Hu D, Liu Y, Yang L, Huang J, Zhou J, Guo L, Fan X, Huang X, Peng M, Cheng C, Zhang W, Feng R, Tian X, Yu S, Xu KF. Sporadic lymphangioleiomyomatosis in a man with somatic mosaicism of TSC2 mutations, a case report. QJM 2024; 117:75-76. [PMID: 37843443 PMCID: PMC10849871 DOI: 10.1093/qjmed/hcad235] [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] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Indexed: 10/17/2023] Open
Affiliation(s)
- Y Wang
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - D Hu
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Y Liu
- McKusick-Zhang Center for Genetic Medicine, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China
| | - L Yang
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - J Huang
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - J Zhou
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - L Guo
- Department of Dermatology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Center, Beijing, China
| | - X Fan
- Clinical Genome Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangdong, China
| | - X Huang
- Clinical Genome Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangdong, China
| | - M Peng
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - C Cheng
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - W Zhang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - R Feng
- Department of Pathology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - X Tian
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - S Yu
- Clinical Genome Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangdong, China
| | - K -F Xu
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Li Z, Xue C, Li S, Jing M, Liu S, Sun J, Ren T, Zhou J. Preoperative CT histogram analysis to predict the expression of Ki-67 in solid pseudopapillary tumours of the pancreas. Clin Radiol 2024; 79:e197-e203. [PMID: 38007336 DOI: 10.1016/j.crad.2023.10.029] [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: 10/10/2022] [Revised: 10/11/2023] [Accepted: 10/22/2023] [Indexed: 11/27/2023]
Abstract
AIM To explore the value of preoperative computed tomography (CT) histogram features in predicting the expression status of Ki-67 in patients with solid pseudopapillary pancreatic tumours (SPTP). MATERIALS AND METHODS This retrospective study analysed venous phase CT images of 39 patients with SPTP confirmed at surgery and histopathology and measured using the Ki-67 proliferation index from November 2015 to February 2022. According to the Ki-67 proliferation index, they were divided into high expression (Ki-67 ≥ 4%) and low expression (Ki-67 < 4%) groups. The histogram features of quantitative parameters were extracted using MaZda software, and the quantitative parameters of CT histograms were compared between groups. The receiver operating characteristic (ROC) curves of the patients were plotted according to the parameters, with statistically significant differences. The area under the curve (AUC), sensitivity, and specificity were calculated, and the effectiveness of the histogram parameters in predicting Ki-67 expression was analysed and evaluated. RESULTS In total, 27 SPTP patients were enrolled, including 11 with high expression of Ki-67 and 16 with low expression. Comparative analysis of the Ki-67 high- and low-expression groups revealed a statistically significant in necrosis and variance (p<0.05). ROC curve analysis showed that the AUC of necrosis and variance predicting Ki-67 expression status were 0.753 and 0.841, the sensitivities were 81.8% and 81.3%, and the specificities were 68.7% and 81.8%, respectively. CONCLUSION Preoperative CT histogram features help predict Ki-67 expression status in patients with SPTP.
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Affiliation(s)
- Z Li
- Department of Imaging, Shaanxi Provincial People's Hospital, Xi'an, China
| | - C Xue
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - S Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - M Jing
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - S Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - J Sun
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - T Ren
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - J Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
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Zhou J, Xie JL, Zhou XG, Zhou XJ, Xia QX. [Follicular lymphoma with a predominantly diffuse growth pattern with 1p36 deletion: a clinicopathologic analysis of eight cases]. Zhonghua Bing Li Xue Za Zhi 2024; 53:34-39. [PMID: 38178744 DOI: 10.3760/cma.j.cn112151-20230905-00130] [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: 01/06/2024]
Abstract
Objective: To investigate the clinical and pathologic features and diagnosis of follicular lymphoma (FL) with a predominantly diffuse growth pattern (DFL) with 1p36 deletion. Methods: Eight cases of DFL with 1p36 deletion diagnosed at Department of Pathology, Beijing Friendship Hospital, Capital Medical University (n=5) and the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital (n=3) from January 2017 to January 2023 were included. Their clinicopathologic features and follow-up data were analyzed. Immunohistochemistry and fluorescence in situ hybridization (FISH) were performed. Results: There were five males and three females, with a median age of 67 years, and inguinal lymphadenopathy was found as the main symptom. Histologically, similar morphologic features were sheared among all cases, with effaced nodal structure and characterized by proliferation of centrocytes in a diffuse pattern, with or without follicular components. The germinal center-related markers such as CD10 and/or bcl-6 were expressed in the tumor cells, and 1p36 deletion but not bcl-2 translocation was appreciable in these cases. Conclusions: DFL with 1p36 deletion is a rare subtype of FL, with some overlaps with other types of FL or indolent B-cell lymphomas in their pathologic features. An accurate diagnosis requires comprehensive considerations based on their clinical, pathologic, immunohistochemical, and molecular features.
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Affiliation(s)
- J Zhou
- Department of Pathology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - J L Xie
- Department of Pathology, Beijing Friendship Hospital, Capital Medical University, Beijing 100020, China
| | - X G Zhou
- Department of Pathology, Beijing Friendship Hospital, Capital Medical University, Beijing 100020, China
| | - X J Zhou
- Department of Pathology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Q X Xia
- Department of Pathology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
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Lan W, Liu E, Sun D, Li W, Zhu J, Zhou J, Jin M, Jiang W. Red cell distribution in critically ill patients with chronic obstructive pulmonary disease. Pulmonology 2024; 30:34-42. [PMID: 35501276 DOI: 10.1016/j.pulmoe.2022.04.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [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: 01/10/2022] [Revised: 03/31/2022] [Accepted: 04/01/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Red blood cell distribution width (RDW) is associated with increased mortality risk in patients with chronic obstructive pulmonary disease (COPD). However, limited data are available for critically ill patients with COPD. METHODS Data from the Medical Information Mart for Intensive Care III V1.4 database were analyzed in this retrospective cohort research. The International Classification of Diseases codes were used to identify critically ill patients with COPD. The first value of RDW was extracted within the first 24 h after intensive care unit admission. The endpoint was 28-day all-cause mortality. Multivariable logistic regression analysis was performed to examine the relationship between RDW and 28-day mortality. Age, sex, ethnicity, anemia status, comorbidities, clinical therapy, and disease severity score were considered for subgroup analysis. RESULTS A total of 2,344 patients were included with mean (standard deviation) age of 72.3 (11.3) years, in which 1,739 (53.6%) patients were men. The increase in RDW was correlated with an increased risk of 28-day mortality in the multivariate logistic regression model (odds ratio [OR] 1.15; 95% confidence interval [CI] 1.09-1.21). In comparison with the low-RDW group, the middle and high-RDW groups tended to have higher risks of 28-day all-cause mortality (OR [95% CI] 1.03 [0.78-1.34]; OR [95% CI] 1.70 [1.29-2.22]; P trend < 0.0001). Subgroup analyses show no evidence of effect modifications on the correlation of RDW and 28-day all-cause mortality. CONCLUSION An increase in RDW was associated with an increased risk of 28-day all-cause mortality in critically ill patients with COPD. Further studies are required to investigate this association.
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Affiliation(s)
- W Lan
- Department of Respiratory and Critical Care Medicine, Lishui Municipal Central Hospital, Lishui, Zhejiang 323000, China
| | - E Liu
- Department of Infectious Diseases, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang Provincial Key Laboratory for Accurate Diagnosis and Treatment of Chronic Liver Disease, Wenzhou, Zhejiang 325000, China
| | - D Sun
- Department of Respiratory and Critical Care Medicine, Lishui Municipal Central Hospital, Lishui, Zhejiang 323000, China
| | - W Li
- Department of Respiratory and Critical Care Medicine, Lishui Municipal Central Hospital, Lishui, Zhejiang 323000, China
| | - J Zhu
- Department of Cardiology, Lishui Hospital, Zhejiang University School of Medicine, Lishui, Zhejiang 323000, China
| | - J Zhou
- Department of Pathology, Lishui Hospital, Zhejiang University School of Medicine, Lishui, Zhejiang 323000, China
| | - M Jin
- Department of Internal Medicine, Yunhe People's Hospital, Yunhe, Zhejiang 323600, China
| | - W Jiang
- Department of Gastroenterology, Lishui Municipal Central Hospital, Lishui, Zhejiang 323000, China.
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Liu F, Xiang Z, Li Q, Fang X, Zhou J, Yang X, Lin H, Yang Q. 18F-FDG PET/CT-based radiomics model for predicting the degree of pathological differentiation in non-small cell lung cancer: a multicentre study. Clin Radiol 2024; 79:e147-e155. [PMID: 37884401 DOI: 10.1016/j.crad.2023.09.017] [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: 04/28/2023] [Revised: 09/18/2023] [Accepted: 09/20/2023] [Indexed: 10/28/2023]
Abstract
AIM To explore the value of 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)/computed tomography (CT)-based radiomics model for predicting the degree of pathological differentiation in non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS Clinical characteristics of 182 NSCLC patients from four centres were collected, and radiomics features were extracted from 18F-FDG PET/CT images. Three logistic regression prediction models were established: clinical model; radiomics model; and nomogram combining radiomics signatures and clinical features. The predictive ability of the models was assessed using receiver operating characteristics curve analysis. RESULTS Patients from centre 1 were assigned randomly to the training and internal validation cohorts (7:3 ratio); patients from centres 2-4 served as the external validation cohort. The area under the curve (AUC) values for the clinical model in the training, internal validation, and external validation cohort were 0.74 (95% confidence interval [CI] = 0.64-0.84), 0.64 (95% CI = 0.46-0.81), and 0.74 (95% CI = 0.60-0.88), respectively. In the training (AUC: 0.84 [95% CI = 0.77-0.92]), internal validation (AUC: 0.81 [95% CI = 0.67-0.95]), and external validation cohorts (AUC: 0.74 [95% CI = 0.58-0.89]), the radiomics model showed good predictive ability for differentiation. Compared to the clinical and radiomics models, the nomogram has relatively better diagnostic performance, and the AUC values for nomogram in the training, internal validation, and external validation cohort were 0.86 (95% CI = 0.78-0.93), 0.83 (95% CI = 0.70-0.96), and 0.77 (95% CI = 0.62-0.92), respectively. CONCLUSIONS The 18F-FDG PET/CT-based radiomics model showed good ability for predicting the degree of differentiation of NSCLC. The nomogram combining the radiomics signature and clinical features has relatively better diagnostic performance.
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Affiliation(s)
- F Liu
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Z Xiang
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Q Li
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - X Fang
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
| | - J Zhou
- The Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212001, China
| | - X Yang
- Sichuan Science City Hospital, Mianyang, Sichuan 621000, China
| | - H Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha 410005, China
| | - Q Yang
- Center for Molecular Imaging Probe, Hunan Province Key Laboratory of Tumour Cellular and Molecular Pathology, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
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Deng J, Zhang W, Xu M, Liu X, Ren T, Li S, Sun Q, Xue C, Zhou J. Value of spectral CT parameters in predicting the efficacy of neoadjuvant chemotherapy for gastric cancer. Clin Radiol 2024; 79:51-59. [PMID: 37914603 DOI: 10.1016/j.crad.2023.08.023] [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: 05/06/2023] [Revised: 07/26/2023] [Accepted: 08/30/2023] [Indexed: 11/03/2023]
Abstract
AIM To investigate the value of pre-chemotherapy spectral computed tomography (CT) parameters in predicting neoadjuvant chemotherapy (NAC) response in gastric cancer (GC). MATERIALS AND METHODS Sixty patients with GC who received NAC and underwent spectral CT examination before chemotherapy were enrolled retrospectively and divided into a responsive group and a non-responsive group according to the postoperative pathological tumour regression grade. Clinical characteristics were collected. The iodine concentration (IC), water concentration (WC), and effective atomic number (Eff-Z) of the portal venous phases were measured before chemotherapy, and IC was normalised to that of the aorta to provide the normalised IC (NIC). An independent samples t-test, Mann-Whitney U-test, or chi-square test was used to analyse the differences between the two groups, and the receiver operating curve (ROC) was used to evaluate the predictive performance of different variables. RESULTS The neutrophil-to-lymphocyte ratio (NLR) was lower in the responsive group than in the non-responsive group (p<0.05). IC, NIC, and Eff-Z values were significantly higher in the responsive group than in the non-responsive group (p<0.01). The areas under the ROC curves for the NLR, IC, NIC, and Eff-Z were 0.694, 0.688, 0.799, and 0.690, respectively. The combination of NIC, Eff-Z, and NLR values showed good diagnostic performance in predicting response to NAC in GC, with an area under the ROC curve of 0.857, 76.92% sensitivity, 80% accuracy, and 85.71% specificity. CONCLUSION Spectral CT parameters may serve as non-invasive tools for predicting the response to NAC in patients with GC.
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Affiliation(s)
- J Deng
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - W Zhang
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - M Xu
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - X Liu
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - T Ren
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - S Li
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Q Sun
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - C Xue
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - J Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China.
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Li SY, Xie XY, Liu D, Cheng GR, Hu FF, Zeng DY, Chen XC, Jia LF, Wang YJ, Bu XL, Qiu C, Gao F, Gu JG, Liu MF, Li Y, Zhou YL, Chang HJ, Ou YM, Xu L, Wu ZX, Zhang JJ, Wang JY, Huang LY, Cui YY, Zhou J, Liu XC, Liu J, Nie QQ, Song D, Cai C, Han GB, Yang X, Tan W, Yu JT, Zeng Y. China Initiative for Multi-Domain Intervention (CHINA-IN-MUDI) to Prevent Cognitive Decline: Study Design and Progress. J Prev Alzheimers Dis 2024; 11:589-600. [PMID: 38706275 DOI: 10.14283/jpad.2024.63] [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: 05/07/2024]
Abstract
BACKGROUND Alzheimer's disease (AD), the most common type of irreversible dementia, is predicted to affect 152 million people by 2050. Evidence from large-scale preventive randomized controlled trials (RCTs) on modifiable risk variables in Europe has shown that multi-domain lifestyle treatments for older persons at high risk of dementia may be practical and effective. Given the substantial differences between the Chinese and European populations in terms of demographics and living conditions, direct adoption of the European program in China remains unfeasible. Although a RCT has been conducted in China previously, its participants were mainly from rural areas in northern China and, thus, are not representative of the entire nation.There is an urgent need to establish cohorts that represent different economic, cultural, and geographical situations in order to explore implementation strategies and evaluate the effects of early multi-domain interventions more comprehensively and accurately. MEDTODS We developed an integrated intervention procedure implemented in urban neighborhood settings, namely China Initiative for Multi-Domain Intervention (CHINA-IN-MUDI). CHINA-IN-MUDI is a 2-year multicenter open-label cluster-randomised controlled trial centered around a Chinese-style multi-domain intervention to prevent cognitive decline. Participants aged 60-80 years were recruited from a nationally representative study, i.e. China Healthy Aging and Dementia Study cohort. An external harmonization process was carried out to preserve the original FINGER design. Subsequently, we standardized a series of Chinese-style intervention programs to align with cultural and socioeconomic status. Additionally, we expanded the secondary outcome list to include genomic and proteomic analyses. To enhance adherence and facilitate implementation, we leveraged an e-health application. RESULTS Screening commenced in July 2022. Currently, 1,965 participants have been randomized into lifestyle intervention (n = 772) and control groups (n = 1,193). Both the intervention and control groups exhibited similar baseline characteristics. Several lifestyle and vascular risk factors were present, indicating a potential window of opportunity for intervention. The intervention will be completed by 2025. CONCLUSIONS This project will contribute to the evaluation of the effectiveness and safety of intervention strategies in controlling AD risk and reducing clinical events, providing a basis for public health decision-making in China.
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Affiliation(s)
- S-Y Li
- Yan Zeng, Brain Science and Advanced Technology Institute, School of Medicine, Wuhan University of Science and Technology, West Huangjiahu Road, Hongshan District, Wuhan 430065, China. ; Jin-Tai Yu, Department of Neurology and National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Huashan Hospital, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai 200040, China. ; Wei Tan, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, West Huangjiahu Road, Hongshan District, Wuhan 430065, China.
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Li D, Wang X, Zhou J, Duan Z, Yang R, Liu Y, Chen Y, Zhang L, Liu H, Li W, You J. Analysis of Efficacy and Safety of Small-Volume-Plasma Artificial Liver Model in the Treatment of Acute-On-Chronic Liver Failure. Physiol Res 2023; 72:767-782. [PMID: 38215063 PMCID: PMC10805255] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/11/2023] [Indexed: 01/14/2024] Open
Abstract
To explore the efficacy and safety of a small-volume-plasma artificial liver support system (ALSS) in the treatment of acute-on-chronic liver failure (ACLF). A retrospective analysis was performed. All ACLF patients received ALSS of plasma exchange & double plasma molecular absorb system (PE+DPMAS) treatment, and successfully completed this treatment. Patients were divided into small-volume and half-volume plasma groups. We compared the changes of the indicators on liver function, kidney function, blood coagulation function, and blood ammonia level before and after PE+DPMAS treatment; we compared the short-term and long-term curative effects between small-volume and half-volume plasma groups; and the factors influencing Week 4 and Week 12 mortality of ACLF patients were analyzed. The Week 4 improvement rates were 63.96 % and 66.86 % in the small-volume and half-volume plasma groups, respectively. The Week 12 survival rates in the small-volume-plasma and half-volume plasma groups were 66.72 % and 64.61 %, respectively. We found several risk factors affecting Week 4 and Week 12 mortality. Kaplan-Meier survival curves suggested no significant difference in Week 4 and Week 12 survival rates between the small-volume and half-volume plasma groups (P=0.34). The small-volume-plasma PE+DPMAS treatment could effectively reduce bilirubin and bile acids, and this was an approach with high safety and few complications, similar to the half-volume-plasma PE+DPMAS treatment. The small-volume-plasma PE+DPMAS has the advantage of greatly reducing the need for intraoperative plasma, which is especially of importance in times of shortage of plasma.
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Affiliation(s)
- D Li
- The First Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
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Lin J, Li B, Xu Q, Liu YS, Kang YL, Wang X, Wang Y, Lei Y, Bai YL, Li XM, Zhou J. DACH1 attenuated PA-induced renal tubular injury through TLR4/MyD88/NF-κB and TGF-β/Smad signalling pathway. J Endocrinol Invest 2023:10.1007/s40618-023-02253-7. [PMID: 38147289 DOI: 10.1007/s40618-023-02253-7] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/20/2023] [Indexed: 12/27/2023]
Abstract
BACKGROUND Palmitic acid (PA), the major saturated fatty acid in the blood, often induces the initiation and progression of diabetic kidney disease (DKD). However, the underlying mechanism remains unclear. DACH1 is an important regulator of kidney functions. Herein, we investigated the roles of DACH1 in PA-induced kidney injury. METHODS Clinical data from the NHANES database were subjected to analyse the association between serum PA (sPA), blood glucose and kidney function. Molecular docking of PA was performed with DACH1. Immunohistochemistry, cell viability, annexin V/7-AAD double staining, TUNEL assay, immunofluorescent staining, autophagic flux analysis, qRT-PCR and western blot were performed. RESULTS Clinical data confirmed that sPA was increased significantly in the pathoglycemia individuals compared with controls and correlated negatively with renal function. Our findings suggested that PA could dock with DACH1. DACH1 enhances cell viability by inhibiting apoptosis and attenuating autophagy blockage induced by PA. Furthermore, the results demonstrated that DACH1 ameliorated inflammation and fibrosis through TLR4/MyD88/NF-κB and TGF-β/Smad signalling pathway in PA-treated renal tubular epithelial cell line (HK-2). CONCLUSIONS This study proved that sPA presents a risk factor for kidney injuries and DACH1 might serve as a protective target against renal function deterioration in diabetic patients.
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Affiliation(s)
- J Lin
- Department of Endocrinology, Xijing Hospital, Air Force Medical University, No.127 Changle West Road, Xi'an, 710032, China
| | - B Li
- Department of Endocrinology, Xijing Hospital, Air Force Medical University, No.127 Changle West Road, Xi'an, 710032, China
| | - Q Xu
- Department of Endocrinology, Xijing Hospital, Air Force Medical University, No.127 Changle West Road, Xi'an, 710032, China
| | - Y S Liu
- Department of Pharmacology, Key Laboratory of Gastrointestinal Pharmacology of Chinese Materia Medical of the State Administration of Traditional Chinese Medicine, School of Pharmacy, Air Force Medical University, Xi'an, 710032, China
| | - Y L Kang
- Department of Microbiology and Pathogen Biology, School of Preclinical Medicine, Air Force Medical University, Xi'an, 710032, China
| | - X Wang
- Department of Endocrinology, Xijing Hospital, Air Force Medical University, No.127 Changle West Road, Xi'an, 710032, China
| | - Y Wang
- Department of Endocrinology, Xijing Hospital, Air Force Medical University, No.127 Changle West Road, Xi'an, 710032, China
| | - Y Lei
- The Second Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, 712099, China
| | - Y L Bai
- Department of Microbiology and Pathogen Biology, School of Preclinical Medicine, Air Force Medical University, Xi'an, 710032, China.
| | - X M Li
- Department of Endocrinology, Xijing Hospital, Air Force Medical University, No.127 Changle West Road, Xi'an, 710032, China.
| | - J Zhou
- Department of Endocrinology, Xijing Hospital, Air Force Medical University, No.127 Changle West Road, Xi'an, 710032, China.
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Zhou Y, Tang L, Tong Y, Huang J, Wang J, Zhang Y, Jiang H, Xu N, Gong Y, Yin J, Jiang Q, Zhou J, Zhou Y. [Spatial distribution characteristics of the prevalence of advanced schistosomiasis and seroprevalence of anti- Schistosoma antibody in Hunan Province in 2020]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2023; 35:444-450. [PMID: 38148532 DOI: 10.16250/j.32.1374.2023103] [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: 12/28/2023]
Abstract
OBJECTIVE To investigate the spatial distribution characteristics of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody, and to examine the correlation between the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020, so as to provide insights into advanced schistosomiais control in the province. METHODS The epidemiological data of schistosomiasis in Hunan Province in 2020 were collected, including number of permanent residents in survey villages, number of advanced schistosomiasis patients, number of residents receiving serological tests and number of residents seropositive for anti-Schistosoma antibody, and the prevalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody were descriptively analyzed. Village-based spatial distribution characteristics of prevalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody were identified in Hunan Province in 2020, and the correlation between the revalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody was examined using Spearman correlation analysis. RESULTS The prevalence of advanced schistosomiasis was 0 to 2.72% and the seroprevalence of anti-Schistosoma antibody was 0 to 20.25% in 1 153 schistosomiasis-endemic villages in Hunan Province in 2020. Spatial clusters were identified in both the prevalence of advanced schistosomiasis (global Moran's I = 0.416, P < 0.01) and the seroprevalence of anti-Schistosoma antibody (global Moran's I = 0.711, P < 0.01) in Hunan Province. Local spatial autocorrelation analysis identified 98 schistosomiasis-endemic villages with high-high clusters of the prevalence of advanced schistosomiasis, 134 endemic villages with high-high clusters of the seroprevalence of anti-Schistosoma antibody and 36 endemic villages with high-high clusters of both the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province. In addition, spearman correlation analysis showed a positive correlation between the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody (rs = 0.235, P < 0.05). CONCLUSIONS There were spatial clusters of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020, which were predominantly located in areas neighboring the Dongting Lake. These clusters should be given a high priority in the schistosomiasis control programs.
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Affiliation(s)
- Y Zhou
- Department of Epidemiology, School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Tropical Disease Research Center, Fudan University, Shanghai 200032, China
| | - L Tang
- Hunan Institute of Schistosomiasis Control, Yueyang, Hunan 414000, China
| | - Y Tong
- Department of Epidemiology, School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Tropical Disease Research Center, Fudan University, Shanghai 200032, China
| | - J Huang
- Department of Epidemiology, School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Tropical Disease Research Center, Fudan University, Shanghai 200032, China
| | - J Wang
- Department of Epidemiology, School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Tropical Disease Research Center, Fudan University, Shanghai 200032, China
| | - Y Zhang
- Department of Epidemiology, School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Tropical Disease Research Center, Fudan University, Shanghai 200032, China
| | - H Jiang
- Department of Epidemiology, School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Tropical Disease Research Center, Fudan University, Shanghai 200032, China
| | - N Xu
- Department of Epidemiology, School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Tropical Disease Research Center, Fudan University, Shanghai 200032, China
| | - Y Gong
- Department of Epidemiology, School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Tropical Disease Research Center, Fudan University, Shanghai 200032, China
| | - J Yin
- Department of Epidemiology, School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Tropical Disease Research Center, Fudan University, Shanghai 200032, China
| | - Q Jiang
- Department of Epidemiology, School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Tropical Disease Research Center, Fudan University, Shanghai 200032, China
| | - J Zhou
- Hunan Institute of Schistosomiasis Control, Yueyang, Hunan 414000, China
| | - Y Zhou
- Department of Epidemiology, School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Tropical Disease Research Center, Fudan University, Shanghai 200032, China
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Abdulhamid MI, Aboona BE, Adam J, Adams JR, Agakishiev G, Aggarwal I, Aggarwal MM, Ahammed Z, Aitbaev A, Alekseev I, Anderson DM, Aparin A, Aslam S, Atchison J, Averichev GS, Bairathi V, Baker W, Cap JGB, Barish K, Bhagat P, Bhasin A, Bhatta S, Bordyuzhin IG, Brandenburg JD, Brandin AV, Cai XZ, Caines H, Sánchez MCDLB, Cebra D, Ceska J, Chakaberia I, Chan BK, Chang Z, Chatterjee A, Chen D, Chen J, Chen JH, Chen Z, Cheng J, Cheng Y, Choudhury S, Christie W, Chu X, Crawford HJ, Dale-Gau G, Das A, Daugherity M, Dedovich TG, Deppner IM, Derevschikov AA, Dhamija A, Di Carlo L, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fazio S, Feng CJ, Feng Y, Finch E, Fisyak Y, Flor FA, Fu C, Gao T, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Hamed A, Han Y, Harasty MD, Harris JW, Harrison-Smith H, He W, He XH, He Y, Hu C, Hu Q, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Huang Y, Humanic TJ, Isenhower D, Isshiki M, Jacobs WW, Jalotra A, Jena C, Ji Y, Jia J, Jin C, Ju X, Judd EG, Kabana S, Kabir ML, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Keane D, Kechechyan A, Kelsey M, Kimelman B, Kiselev A, Knospe AG, Ko HS, Kochenda L, Korobitsin AA, Kravtsov P, Kumar L, Kumar S, Elayavalli RK, Lacey R, Landgraf JM, Lebedev A, Lednicky R, Lee JH, Leung YH, Lewis N, Li C, Li W, Li X, Li Y, Li Y, Li Z, Liang X, Liang Y, Lin T, Liu C, Liu F, Liu G, Liu H, Liu H, Liu L, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Lomicky O, Longacre RS, Loyd EM, Lu T, Lukow NS, Luo XF, Luong VB, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Matis HS, Mazer JA, McNamara G, Mi K, Minaev NG, Mohanty B, Mondal MM, Mooney I, Morozov DA, Mudrokh A, Nagy MI, Nain AS, Nam JD, Nasim M, Neff D, Nelson JM, Nemes DB, Nie M, Nigmatkulov G, Niida T, Nishitani R, Nogach LV, Nonaka T, Odyniec G, Ogawa A, Oh S, Okorokov VA, Okubo K, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Panebratsev Y, Pani T, Parfenov P, Paul A, Perkins C, Pokhrel BR, Posik M, Protzman T, Pruthi NK, Putschke J, Qin Z, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Ritter HG, Robertson CW, Rogachevsky OV, Aguilar MAR, Roy D, Ruan L, Sahoo AK, Sahoo NR, Sako H, Salur S, Samigullin E, Sato S, Schmidke WB, Schmitz N, Seger J, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao T, Sharma M, Sharma N, Sharma R, Sharma SR, Sheikh AI, Shen D, Shen DY, Shen K, Shi SS, Shi Y, Shou QY, Si F, Singh J, Singha S, Sinha P, Skoby MJ, Söhngen Y, Song Y, Srivastava B, Stanislaus TDS, Stewart DJ, Strikhanov M, Stringfellow B, Su Y, Sun C, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Sweger ZW, Tamis A, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Tlusty D, Todoroki T, Tokarev MV, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tsai OD, Tsang CY, Tu Z, Tyler J, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vasiliev AN, Verkest V, Videbæk F, Vokal S, Voloshin SA, Wang F, Wang G, Wang JS, Wang J, Wang X, Wang Y, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Westfall GD, Wieman H, Wilks G, Wissink SW, Wu J, Wu J, Wu X, Wu X, Wu Y, Xi B, Xiao ZG, Xie G, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Y, Xu Z, Xu Z, Yan G, Yan Z, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang W, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao F, Zhao J, Zhao M, Zhou C, Zhou J, Zhou S, Zhou Y, Zhu X, Zurek M, Zyzak M. Hyperon Polarization along the Beam Direction Relative to the Second and Third Harmonic Event Planes in Isobar Collisions at sqrt[s_{NN}]=200 GeV. Phys Rev Lett 2023; 131:202301. [PMID: 38039468 DOI: 10.1103/physrevlett.131.202301] [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: 03/16/2023] [Revised: 07/07/2023] [Accepted: 10/03/2023] [Indexed: 12/03/2023]
Abstract
The polarization of Λ and Λ[over ¯] hyperons along the beam direction has been measured relative to the second and third harmonic event planes in isobar Ru+Ru and Zr+Zr collisions at sqrt[s_{NN}]=200 GeV. This is the first experimental evidence of the hyperon polarization by the triangular flow originating from the initial density fluctuations. The amplitudes of the sine modulation for the second and third harmonic results are comparable in magnitude, increase from central to peripheral collisions, and show a mild p_{T} dependence. The azimuthal angle dependence of the polarization follows the vorticity pattern expected due to elliptic and triangular anisotropic flow, and qualitatively disagrees with most hydrodynamic model calculations based on thermal vorticity and shear induced contributions. The model results based on one of existing implementations of the shear contribution lead to a correct azimuthal angle dependence, but predict centrality and p_{T} dependence that still disagree with experimental measurements. Thus, our results provide stringent constraints on the thermal vorticity and shear-induced contributions to hyperon polarization. Comparison to previous measurements at RHIC and the LHC for the second-order harmonic results shows little dependence on the collision system size and collision energy.
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Affiliation(s)
| | - B E Aboona
- Texas A&M University, College Station, Texas 77843
| | - J Adam
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J R Adams
- The Ohio State University, Columbus, Ohio 43210
| | - 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
| | - A Aitbaev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - 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
| | - S Aslam
- Indian Institute Technology, Patna, Bihar 801106, India
| | - J Atchison
- Abilene Christian University, Abilene, Texas 79699
| | | | - 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
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - S Bhatta
- State University of New York, Stony Brook, New York 11794
| | - 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
| | - 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
| | - J Ceska
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - I Chakaberia
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - Z Chang
- Indiana University, Bloomington, Indiana 47408
| | - A Chatterjee
- National Institute of Technology Durgapur, Durgapur-713209, India
| | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai, 200433
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - Y Cheng
- University of California, Los Angeles, California 90095
| | | | - 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
| | - G Dale-Gau
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Das
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - 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
| | - 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
| | - 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
| | - S Fazio
- University of Calabria & INFN-Cosenza, Rende 87036, Italy
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - F A Flor
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - T Gao
- Shandong University, Qingdao, Shandong 266237
| | - 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
| | - A Hamed
- American University in Cairo, New Cairo 11835, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - M D Harasty
- University of California, Davis, California 95616
| | - J W Harris
- Yale University, New Haven, Connecticut 06520
| | | | - 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
| | - C Hu
- University of Chinese Academy of Sciences, Beijing 101408
| | - Q Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Hu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - 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
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Central China Normal University, Wuhan, Hubei 430079
| | - T J Humanic
- The Ohio State University, Columbus, Ohio 43210
| | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - M Isshiki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - A Jalotra
- University of Jammu, Jammu 180001, India
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - 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
| | - C Jin
- Rice University, Houston, Texas 77251
| | - 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
| | - D Kalinkin
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - K Kang
- Tsinghua University, Beijing 100084
| | - D Kapukchyan
- University of California, Riverside, California 92521
| | - K Kauder
- 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
| | - B Kimelman
- University of California, Davis, California 95616
| | - 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
| | | | - 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
| | - J M Landgraf
- 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
| | - J H Lee
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y H Leung
- University of Heidelberg, Heidelberg 69120, Germany
| | - N Lewis
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Li
- Shandong University, Qingdao, Shandong 266237
| | - W Li
- Rice University, Houston, Texas 77251
| | - X Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- Tsinghua University, Beijing 100084
| | - Z Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Liang
- University of California, Riverside, California 92521
| | - Y Liang
- Kent State University, Kent, Ohio 44242
| | - T Lin
- Shandong University, Qingdao, Shandong 266237
| | - C Liu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - F Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - G Liu
- South China Normal University, Guangzhou, Guangdong 510631
| | - H Liu
- Indiana University, Bloomington, Indiana 47408
| | - H Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - L Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - 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
- Central China Normal University, Wuhan, Hubei 430079
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - O Lomicky
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - R S Longacre
- Brookhaven National Laboratory, Upton, New York 11973
| | - E M Loyd
- University of California, Riverside, California 92521
| | - T Lu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
| | - X F Luo
- Central China Normal University, Wuhan, Hubei 430079
| | - V B Luong
- Joint Institute for Nuclear Research, Dubna 141 980
| | - L Ma
- Fudan University, Shanghai, 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y G Ma
- Fudan University, Shanghai, 200433
| | - N Magdy
- State University of New York, Stony Brook, New York 11794
| | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | | | - H S Matis
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J A Mazer
- Rutgers University, Piscataway, New Jersey 08854
| | - G McNamara
- Wayne State University, Detroit, Michigan 48201
| | - K Mi
- Central China Normal University, Wuhan, Hubei 430079
| | - 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
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - I Mooney
- Yale University, New Haven, Connecticut 06520
| | - D A Morozov
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - A Mudrokh
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M I Nagy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - A S Nain
- Panjab University, Chandigarh 160014, India
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - M Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - 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
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - 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
| | - G Odyniec
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - A Ogawa
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Oh
- Sejong University, Seoul 05006, South Korea
| | - V A Okorokov
- National Research Nuclear University MEPhI, Moscow 115409
| | - K Okubo
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - 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
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - T Pani
- Rutgers University, Piscataway, New Jersey 08854
| | - P Parfenov
- National Research Nuclear University MEPhI, Moscow 115409
| | - A Paul
- University of California, Riverside, California 92521
| | - C Perkins
- University of California, Berkeley, California 94720
| | - B R Pokhrel
- Temple University, Philadelphia, Pennsylvania 19122
| | - M Posik
- Temple University, Philadelphia, Pennsylvania 19122
| | - T Protzman
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - N K Pruthi
- Panjab University, Chandigarh 160014, India
| | - J Putschke
- Wayne State University, Detroit, Michigan 48201
| | - Z Qin
- Tsinghua University, Beijing 100084
| | - 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
| | - H G Ritter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | | | - D Roy
- Rutgers University, Piscataway, New Jersey 08854
| | - L Ruan
- Brookhaven National Laboratory, Upton, New York 11973
| | - A K Sahoo
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - N R Sahoo
- Texas A&M University, College Station, Texas 77843
| | - H Sako
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - S Salur
- Rutgers University, Piscataway, New Jersey 08854
| | - E Samigullin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | - 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
| | - J Seger
- Creighton University, Omaha, Nebraska 68178
| | - 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
| | | | - T Shao
- Fudan University, Shanghai, 200433
| | - M Sharma
- University of Jammu, Jammu 180001, India
| | - N Sharma
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - S R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | | | - D Shen
- Shandong University, Qingdao, Shandong 266237
| | - D Y Shen
- Fudan University, Shanghai, 200433
| | - K Shen
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S S Shi
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
| | - Q Y Shou
- Fudan University, Shanghai, 200433
| | - F Si
- University of Science and Technology of China, Hefei, Anhui 230026
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - P Sinha
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - M J Skoby
- Ball State University, Muncie, Indiana 47306
- Purdue University, West Lafayette, Indiana 47907
| | - Y Söhngen
- University of Heidelberg, Heidelberg 69120, Germany
| | - Y Song
- Yale University, New Haven, Connecticut 06520
| | - B Srivastava
- Purdue University, West Lafayette, Indiana 47907
| | | | - D J Stewart
- Wayne State University, Detroit, Michigan 48201
| | - M Strikhanov
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - Y Su
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Sun
- State University of New York, Stony Brook, New York 11794
| | - X Sun
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - 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
| | - A Tamis
- Yale University, New Haven, Connecticut 06520
| | - 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 V 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
| | - O D Tsai
- Brookhaven National Laboratory, Upton, New York 11973
- University of California, Los Angeles, California 90095
| | - C Y Tsang
- Brookhaven National Laboratory, Upton, New York 11973
- Kent State University, Kent, Ohio 44242
| | - Z Tu
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Tyler
- Texas A&M University, College Station, Texas 77843
| | - 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
- University of Science and Technology of China, Hefei, Anhui 230026
| | - G Van Buren
- Brookhaven National Laboratory, Upton, New York 11973
| | - A N Vasiliev
- National Research Nuclear University MEPhI, Moscow 115409
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - 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
| | - J Wang
- Shandong University, Qingdao, Shandong 266237
| | - X Wang
- Shandong University, Qingdao, Shandong 266237
| | - Y 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
| | | | - G D Westfall
- Michigan State University, East Lansing, Michigan 48824
| | - H Wieman
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - G Wilks
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - 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
| | - X Wu
- University of California, Los Angeles, California 90095
| | - X Wu
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Wu
- University of California, Riverside, California 92521
| | - B Xi
- Fudan University, Shanghai, 200433
| | - Z G Xiao
- Tsinghua University, Beijing 100084
| | - G Xie
- University of Chinese Academy of Sciences, Beijing 101408
| | - 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
| | - Y Xu
- Central China Normal University, Wuhan, Hubei 430079
| | - 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
| | - Z Yan
- State University of New York, Stony Brook, New York 11794
| | - C Yang
- Shandong University, Qingdao, Shandong 266237
| | - Q Yang
- Shandong University, Qingdao, Shandong 266237
| | - S Yang
- South China Normal University, Guangzhou, Guangdong 510631
| | - 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
| | - 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 Science and Technology of China, Hefei, Anhui 230026
| | - W Zhang
- South China Normal University, Guangzhou, Guangdong 510631
| | - X Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - 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
- Shandong University, Qingdao, Shandong 266237
| | - 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
| | - F Zhao
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - J Zhao
- Fudan University, Shanghai, 200433
| | - M Zhao
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Zhou
- Fudan University, Shanghai, 200433
| | - J Zhou
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - X Zhu
- Tsinghua University, Beijing 100084
| | - M Zurek
- Argonne National Laboratory, Argonne, Illinois 60439
- Brookhaven National Laboratory, Upton, New York 11973
| | - M Zyzak
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
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Wang T, Fu Y, Ma M, Zhou J, Sun Q, Feng AN, Meng FQ. [Pathological features and diagnostic significance of lung biopsy in occupational lung diseases]. Zhonghua Bing Li Xue Za Zhi 2023; 52:1114-1119. [PMID: 37899316 DOI: 10.3760/cma.j.cn112151-20230419-00272] [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: 10/31/2023]
Abstract
Objective: To investigate the clinicopathological characteristics of occupational lung diseases, to reduce the missed diagnoses and misdiagnoses of the diseases and to help standardize the diagnosis and treatment of these patients. Methods: A total of 4 813 lung biopsy specimens (including 1 935 consultation cases) collected at the Department of Pathology, Nanjing Drum Tower Hospital, Nanjing, China from January 1st, 2017 to December 31th, 2019 were retrospectively analyzed. Among them, 126 cases of occupational lung diseases were confirmed with clinical-radiological-pathological diagnosis. Special staining, PCR and scanning electron microscopy were also used to rule out the major differential diagnoses. Results: The 126 patients with occupational lung diseases included 102 males and 24 females. All of them had a history of exposure to occupational risk factor(s). Morphologically, 68.3% (86/126) of the cases mainly showed pulmonary fibrotic nodules, dust plaque formation or carbon end deposition in pulmonary parenchyma. 16.7% (21/126) of the cases mainly showed welding smoke particle deposition in the alveolar cavity and lung interstitium while 15.1% (19/126) of the cases showed granulomas with fibrous tissue hyperplasia, alveolar protein deposition or giant cell interstitial pneumonia. The qualitative and semi-quantitative analyses of residual dust components in the lung under scanning electron microscope were helpful for the diagnosis of welder's pneumoconiosis and hard metal lung disease. Conclusions: The morphological characteristics of lung biopsy tissue are important reference basis for the clinicopathological diagnosis and differential diagnosis of occupational lung diseases. Recognizing the characteristic morphology and proper use of auxiliary examination are the key to an accurate diagnosis of occupational lung diseases on biopsy specimens.
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Affiliation(s)
- T Wang
- Department of Pathology, Nanjing Drum Tower Hospital/the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Y Fu
- Department of Pathology, Nanjing Drum Tower Hospital/the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - M Ma
- Department of Respiratory Medicine, Nanjing Drum Tower Hospital/the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - J Zhou
- Department of Medical Imaging, Nanjing Drum Tower Hospital/the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Q Sun
- Department of Pathology, Nanjing Drum Tower Hospital/the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - A N Feng
- Department of Pathology, Nanjing Drum Tower Hospital/the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - F Q Meng
- Department of Pathology, Nanjing Drum Tower Hospital/the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
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Yang XR, Sun HC, Xie Q, Zhang WG, Jia WD, Zhao M, Zhao HT, Liu XF, Zhou LD, Yan S, Xu L, Wang NY, Ding Y, Zhu XD, Zhou J, Fan J. [Chinese expert guidance on overall application of lenvatinib in hepatocellular carcinoma]. Zhonghua Gan Zang Bing Za Zhi 2023; 31:1018-1029. [PMID: 38016765 DOI: 10.3760/cma.j.cn115610-20230201-00035-1] [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] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Lenvatinib mesylate is an oral receptor tyrosine kinase inhibitor against targets of vascular endothelial growth factor receptors 1-3, fibroblast growth factor receptors 1-4, platelet-derived growth factor receptor α, stem cell growth factor receptor, and rearranged during transfection, et al. Lenvatinib has been approved by the National Medical Products Administration of China on September 4, 2018, for the first-line treatment of patients with unresectable hepatocellular carcinoma who have not received systematic treatment before. Up to February 2023, Lenvatinib has been listed in China for more than 4 years, accumulating a series of post-marketing clinical research evidences. Based on the clinical practice before and after the launch of lenvatinib and referring to the clinical experience of other anti-angiogenesis inhibitors, domestic multidisciplinary experts and scholars adopt the Delphi method to formulate the Chinese Expert Guidance on Overall Application of Lenvatinib in Hepatocellular Carcinoma after repeated discussions and revisions, in order to provide reference for reasonable and effective clinical application of lenvatinib for clinicians.
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Affiliation(s)
- X R Yang
- Department of Liver Surgery, Research Institute of Liver Cancer, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - H C Sun
- Department of Liver Surgery, Research Institute of Liver Cancer, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Q Xie
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China
| | - W G Zhang
- Hepatic Surgery Center,Tongji Hospital,Tonji Medical College, Huazhong University of Science and Technolog, Wuhan 430030, China
| | - W D Jia
- Department of Liver Surgery, the First Affiliated Hospital of University of Science and Technology of China, Hefei 230001, China
| | - M Zhao
- Department of Minimally Invasive and Interventional, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Guangzhou 510060, China
| | - H T Zhao
- Department of Hepatic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - X F Liu
- Department of Oncology, Qinhuai Medical District, Eastern Theater General Hospital of PLA, Nanjing 210002, China
| | - L D Zhou
- Department of Liver Surgery, Xiangya Hospital of Central South University, Changsha 410008, China
| | - S Yan
- Department of Hepatobiliary and Pancreatic Surgery, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China
| | - L Xu
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - N Y Wang
- Department of Cancer Center, the First Hospital of Jilin University, Changchun 130021, China
| | - Y Ding
- Department of Hepatobiliary and Pancreatic Surgery, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China
| | - X D Zhu
- Department of Liver Surgery, Research Institute of Liver Cancer, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - J Zhou
- Department of Liver Surgery, Research Institute of Liver Cancer, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - J Fan
- Department of Liver Surgery, Research Institute of Liver Cancer, Zhongshan Hospital, Fudan University, Shanghai 200032, China
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Zhou J, Sheridan MA, Tian Y, Dahlgren KJ, Messler M, Peng T, Ezashi T, Schulz LC, Ulery BD, Roberts RM, Schust DJ. Development of properly-polarized trophoblast stem cell-derived organoids to model early human pregnancy. bioRxiv 2023:2023.09.30.560327. [PMID: 37873440 PMCID: PMC10592868 DOI: 10.1101/2023.09.30.560327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The development of human trophoblast stem cells (hTSC) and stem cell-derived trophoblast organoids has enabled investigation of placental physiology and disease and early maternal-fetal interactions during a stage of human pregnancy that previously had been severely restricted. A key shortcoming in existing trophoblast organoid methodologies is the non-physiologic position of the syncytiotrophoblast (STB) within the inner portion of the organoid, which neither recapitulates placental villous morphology in vivo nor allows for facile modeling of STB exposure to the endometrium or the contents of the intervillous space. Here we have successfully established properly-polarized human trophoblast stem cell (hTSC)-sourced organoids with STB forming on the surface of the organoid. These organoids can also be induced to give rise to the extravillous trophoblast (EVT) lineage with HLA-G + migratory cells that invade into an extracellular matrix-based hydrogel. Compared to previous hTSC organoid methods, organoids created by this method more closely mimic the architecture of the developing human placenta and provide a novel platform to study normal and abnormal human placental development and to model exposures to pharmaceuticals, pathogens and environmental insults. Motivation Human placental organoids have been generated to mimic physiological cell-cell interactions. However, those published models derived from human trophoblast stem cells (hTSCs) or placental villi display a non-physiologic "inside-out" morphology. In vivo , the placental villi have an outer layer of syncytialized cells that are in direct contact with maternal blood, acting as a conduit for gas and nutrient exchange, and an inner layer of progenitor, single cytotrophoblast cells that fuse to create the syncytiotrophoblast layer. Existing "inside-out" models put the cytotrophoblast cells in contact with culture media and substrate, making physiologic interactions between syncytiotrophoblast and other cells/tissues and normal and pathogenic exposures coming from maternal blood difficult to model. The goal of this study was to develop an hTSC-derived 3-D human trophoblast organoid model that positions the syncytiotrophoblast layer on the outside of the multicellular organoid. Graphical abstract
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Wen W, Qian L, Xie Y, Zhang X, Wang J, Zhou J, Liu R, Yu J, Chen D. Targeting XPO1 Combined with Radiotherapy to Enhance Systemic Anti-tumor Effects in Non-Small Cell Lung Cancer. Int J Radiat Oncol Biol Phys 2023; 117:e221-e222. [PMID: 37784904 DOI: 10.1016/j.ijrobp.2023.06.1124] [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 combination of radiation and radiosensitizing chemotherapeutic agents have shown promising anti-tumor effects in NSCLC. Acting as an oncogenic driver, XPO1 is frequently overexpressed and/or mutated in lung cancer. Thus, suppression of XPO1-mediated nuclear export presents a unique therapeutic strategy. We hypothesize that XPO1 inhibition combined with radiotherapy (XRT) may remodel the tumor immune microenvironment (TIME) and reduce radioresistance, thus enhance systemic anti-tumor effects. MATERIALS/METHODS Herein, we optimized a small molecule inhibitor, WJ01024, which can bind to XPO1 and antagonize its activity to inhibit nuclear export. In the C57BL/6 mouse subcutaneous tumor model, we evaluated the ability of different treatment regimens containing oral WJ01014 single or combined with XRT (one fractions of 15 Gy) in tumor control and tumor recurrence inhibition. The effects of each treatment regimen on the alterations of immunophenotypes, including the quantification, activation, proliferative capacity, exhaustion marker expression, and memory status, were evaluated by flow cytometry. RESULTS In our study, we found that the overexpression of XPO1 was associated with poor prognosis and survival in radioresistant patients with NSCLC. The combination therapy of WJ01024 and XRT resulted in an increase of apoptosis and a decrease of proliferation compared to monotherapy, which was closely correlated with tumor regression and improved survival in the C57BL/6 mouse subcutaneous tumor model. Notably, we found that WJ01024 were shown to enhance the therapeutic effect of XRT by remodeling TIME. Compared with XRT, the addition of WJ01024 increased the infiltration and proliferation of radiation-stimulated CD8+ T cells, which especially promoted the production of interferon-γ and granzyme B. Moreover, the combination therapy also reversed the immunosuppressive effect of radiation on the percentage of Tregs and exhausted T cells in mouse xenografts. Thus, the TIME was significantly improved in combination therapy. Strikingly, mechanistic studies suggested that the activation of cyclic GMP-AMP synthase/stimulator of interferon genes (cGAS/STING) signaling pathway is required to reshape TIME and produce synergistic anti-tumor effect with the combination of WJ01024 and XRT. CONCLUSION Our findings suggest that WJ01024 might be a potential synergistic treatment for radiotherapy to control the proliferation of NSCLC cells, promote tumor regression and prolong survival in mouse model of NSCLC by activating cGAS/STING signaling, and this in turn potentiate the immune microenvironment.
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Affiliation(s)
- W Wen
- Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - L Qian
- wigen biomedicine technology, Shanghai, China
| | - Y Xie
- wigen biomedicine technology, Shanghai, China
| | - X Zhang
- Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - J Wang
- Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - J Zhou
- Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - R Liu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - J Yu
- Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - D Chen
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
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Chang CW, Goette M, Kadom N, Wang Y, Wynne JF, Wang T, Liu T, Esiashvili N, Zhou J, Eaton BR, Yang X. Using Longitudinal MRI to Manage Proton Range Uncertainty for Pediatric Proton Craniospinal Irradiation. Int J Radiat Oncol Biol Phys 2023; 117:e505-e506. [PMID: 37785585 DOI: 10.1016/j.ijrobp.2023.06.1756] [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) Clinical evidence has shown that proton therapy can effectively reduce side effects for pediatric patients undergoing vertebral body-sparing craniospinal irradiation (VBS CSI), compared to conventional photon treatment modalities. However, radiation-induced growth impairment remains challenging for VBS CSI due to proton range uncertainty, compromising vertebral body sparing for growing children. Previous studies have shown that fatty marrow replacement can be observed in vertebral bodies 4-48 weeks after treatment is complete. This study aims to detect and quantify the fatty marrow replacement in vertebral bodies using longitudinal magnetic resonance (MR) to manage proton range uncertainty. MATERIALS/METHODS A prospective clinical trial of proton VBS CSI was designed, and ten pediatric patients were enrolled with prescribed doses of 15-36 Gy. The thecal sac and neural foramina were the clinical target volumes, and a Monte Carlo planning system was used to robustly optimize treatment plans with a 3.5% range margin. We analyzed patients' T1/T2 MR images acquired before, during, and after proton treatment to investigate the hematopoietic marrow transformation induced by irradiation. A metric was defined to calculate the ratio of fatty and hematopoietic marrow based on relative MR intensity histograms. We proposed a machine learning method via Gaussian fitting process (ML-GFP) to explore hidden correlations between marrow transition and radiation dose to 2 cm3 of the bone marrow (D2cc). We also leveraged this method to embed uncertainty to support potential proton range management for VBS enhancement. RESULTS The results indicated that fatty marrow replacement could be observed during inter-fractional treatment. For instance, an individual patient showed that the fatty marrow generation ratios were 0.54, 0.74, and 0.45, corresponding to 11, 18, and 65 days after the treatment started. Using ML-GFP, the fatty marrow transition was found to be quadratically correlated to treatment fractions, and the maximum transformation ranged from 40 to 50 days. Then marrow regeneration was observed due to the decrease in fatty marrow ratios. The fatty marrow ratios were also positively correlated to the D2cc doses ranging from 10 Gy to 36 Gy. Limited by insufficient low-dose data, the ML-GFP model extrapolated the data to predict the marrow transformation below 10 Gy. CONCLUSION We demonstrated the feasibility of using non-invasive longitudinal MR to quantify the fatty marrow transition from inter-fractional treatment. Based on this prospective study, the method can detect early fatty marrow generation in vertebrae caused by proton irradiation due to the conservative range margin used for robust optimization. The proposed method could be used to validate the actual proton range, allowing an accurate range margin to be defined to preserve bone marrow. Future investigation will likely focus on clinical implementation to improve life quality for pediatric CSI patients.
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Affiliation(s)
| | | | - N Kadom
- Emory University, Atlanta, GA
| | - Y Wang
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - J F Wynne
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - T Wang
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - T Liu
- Icahn School of Medicine at Mount Sinai, New York, NY
| | - N Esiashvili
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - J Zhou
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - B R Eaton
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - X Yang
- Department of Radiation Oncology, Emory University, Atlanta, GA
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Xue J, Shi R, Ma J, Liu Z, Feng G, Chen QQ, Li Y, He Y, Ji S, Shi J, Zhu X, Zhou J. Concurrent Chemoradiotherapy plus Programmed Death-1 (PD-1) Blockade for Locally Advanced Cervical Cancer: Preliminary Results of a Single-Arm, Open-Label, Phase II Trial. Int J Radiat Oncol Biol Phys 2023; 117:e542-e543. [PMID: 37785675 DOI: 10.1016/j.ijrobp.2023.06.1838] [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) This study aims to assess the anti-tumor activity and safety of concurrent chemoradiotherapy plus PD-1 blockade in patients with locally advanced cervical cancer. MATERIALS/METHODS This is a single-arm, open-label, prospective phase II study. The key inclusion criteria were treatment-naive patients aged 18-75 years with stage II A2-IVA (FIGO 2018) locally advanced cervical cancer. All patients were treated with concurrent chemoradiotherapy including 2 cycle cisplatin (75mg/m2, for three days, every 3 weeks[Q3W]), nedaplatin or carboplatin can be selected for patients who can't tolerate cisplatin. After CCRT, patients achieving complete response (CR), partial responses(PR), stable disease(SD) received adjuvant chemotherapy (docetaxel 75 mg/m2 day 1+ cisplatin DDP 25 mg/m2 day 1-3, Q3W) for 2 cycle. PD-1 blockade Sintilimab and Tislelizumab was administered intravenously at 200 mg every 3 weeks up to 1 year or until disease progression, unacceptable toxicity, or withdrawal of consent. The primary endpoint was objective response rate (ORR) assessed by investigators per Response Evaluation Criteria In Solid Tumours (RECIST) version 1.1. Secondary endpoints were the 12, 24-month overall survival (OS) rates, the 12, 24-month disease free survival (DFS) rates and safety. RESULTS From February 2020 to June 2022, a total of 15 patients was enrolled. Median age was 57 years (range, 36-74 years). Stage IIA1 was documented in 2 patients, stage IIA2 in two patients, stage IIIA in one patient, stage IIIC1 in eight patients, and stage IVA in two patients. And 66.7% (10/15) of patients had Metastatic lymph node. Four patients received adjuvant chemotherapy. The ORR was 100%, with 4 patients achieving CR and 11 PR. The 12 and 24-month OS rates are 93.3% and 84%, the 12 and 24-month DFS rates are 86% and 75.4%, respectively. Treatment-related adverse events (TRAEs) occurred in 86.7% (13/15) of patients. Grade 3 TRAEs are leukocyte (n = 1), thrombocytopenia (n = 1), hepatitis (n = 1), skin reaction (n = 1). No treatment-related deaths occurred. And IFN-γ was significantly elevated after radiotherapy (p = 0.0073). CONCLUSION Concurrent chemoradiotherapy plus PD-1 blockade showed promising antitumor activity and manageable toxicities in patients with locally advanced cervical cancer. Long-term outcomes are still pending to further evaluate their therapeutic effects. (ChiCTR2000032856).
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Affiliation(s)
- J Xue
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - R Shi
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - J Ma
- The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Z Liu
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - G Feng
- Department of Gynecology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - Q Q Chen
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Y Li
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - Y He
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - S Ji
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - J Shi
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - X Zhu
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - J Zhou
- Department of Radiotherapy Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
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Mo Y, Zhou J, Ma Y, Wen W, Wu M, Yu J, Chen D. Single-Cell RNA Sequencing Reveals a Subset of cMAS can Aggravate RIHD through CXCL1-CXCR2 Axis. Int J Radiat Oncol Biol Phys 2023; 117:S120. [PMID: 37784313 DOI: 10.1016/j.ijrobp.2023.06.457] [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) Radiation induced heart disease (RIHD) is any form of cardiac toxicity induced by radiation therapy (RT) for thoracic cancers. Our previous studies have shown that RT obviously contributed to cardiovascular diseases-specific death over 3 years while RT became protective in the short term within 2 years survival in non-small cell lung cancer patients. Here, single cell RNA sequencing (scRNA-seq) was performed to identify various cell subsets and investigate their functions and dynamics in RIHD which offered several targets for early clinical interventions to alleviate RIHD. MATERIALS/METHODS Based on evaluation of histopathological characteristics, ejection fraction and serum levels of cardiac injury biomarkers, we have established mouse models during different stages to simulate clinical RIHD progression. Hence, we performed single cell RNA-sequencing of RIHD models to characterize the diversity within specific cell types and obtain basic information of differently expressed genes (DEGs). We investigated the role of several cell clusters and DEGs in RIHD through bioinformatics analysis and experimental verification. In vivo, mouse models were given intraperitoneal injection of CXCR2 inhibitor. Bone marrow macrophages and primary cardiac fibroblasts were extracted for in vitro experiments. RESULTS RIHD processes were divided into acute injury, compensation and decompensation stage. Transcriptomes of 31769 single cells from cardiac suspension have been profiled. Analysis of scRNA-seq revealed that there were 30 cell clusters participating in RIHD. The fraction of cell populations varied greatly at three stages which indicated RIHD was a dynamic process and each cell cluster functioned differently at different stages. Notably, we observed cardiac resident macrophages (cMAS) subset accounted for the highest fraction during the compensatory period and decreased in decompensation period. Pseudotime analysis showed cMAS had a different developmental trajectory compared to myeloid derived cells. Moreover, CXCR2 was significantly expressed in cMAS cluster. Ligand-receptor interaction results suggested that CXCL1 secreted by cardiac fibroblasts bind primarily to CXCR2+ cMAS and participated in the formation of the extracellular matrix (ECM) related to cardiac fibrosis. Moreover, cardiac fibrosis of RIHD models were relieved after CXCR2 inhibitor treatment. CXCL1 expression in primary cardiac fibroblast elevated after RT. CONCLUSION The identification of main cell clusters provided a new insight to investigate RIHD through dynamics of cell phenotypes and cell-cell communications during RIHD processes. In compensation stage, CXCR2+ cMAS could be activated by CXCL1 secreted by cardiac fibroblasts. Both were associated with ECM and contribute to the decompensation stage.
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Affiliation(s)
- Y Mo
- The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China; Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - J Zhou
- Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Y Ma
- Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - W Wen
- Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - M Wu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - J Yu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - D Chen
- Shandong University Cancer Center, Department of Radiation Oncology and Shandong Provincial Key Laboratory of Radiation Oncology, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
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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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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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Chang CW, Bohannon D, Tian Z, Wang Y, McDonald MW, Yu DS, Liu T, Zhou J, Yang X. Estimating Potential Benefits of Online Adaptive Proton Therapy for Head-and-Neck Cancer: A Retrospective Study. Int J Radiat Oncol Biol Phys 2023; 117:e649. [PMID: 37785928 DOI: 10.1016/j.ijrobp.2023.06.2069] [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) Proton therapy is highly sensitive to anatomical changes and setup variations in head-and-neck (HN) treatments. To address this issue, proton centers often acquire patient CT images weekly to monitor patient anatomical changes during the treatment course and perform offline plan adaptation when needed. However, offline adaptation cannot fully account for daily setup variations or the anatomical changes occurring with high frequency. There are a few groups endeavoring to develop advanced technologies to enable online adaptive proton therapy (APT). However, the necessity of online APT remains controversial, as it is unknown that whether online APT will significantly improve treatment quality and outcomes compared to offline APT. The purpose of this study is to estimate the clinical potential of online APT in the management of HN cancers in relation to the current offline APT. MATERIALS/METHODS Our retrospective study was conducted with four HN patients (35 fractions per patient), who had been treated with intensity modulated proton therapy and had offline adaptation once or twice during their treatment courses. Synthetic CT (sCT) images were generated from 140 daily CBCT images for us to recalculate the dose of the treatment plan in patient's actual treatment anatomy for each treatment fraction and adapt the plan when warranted. These adaptations were assumed to be performed online before treatment delivery to mimic an online APT course. Accumulative doses were calculated for both courses using the CBCT-based sCT images of every fraction for us to compare the target coverage, organ at risk (OAR) sparing, tumor control probability (TCP) and normal tissue complication probability (NTCP). An in-house script was developed to semi-automate this process in a commercial treatment planning system to facilitate our study. RESULTS All patients would benefit from online APT to different extents. For the first patient, with OAR doses comparable to the actual offline course, the retrospective online APT course improved dose coverages of the three CTVs from 95.2%, 98.64% and 89.53% to 98.88%, 99.81%, 98.97%, which would lead to a 4.52% improvement in TCP. Similarly, online APT would yield a 2.66% improvement in TCP for the second patient. For the third patient, with comparable CTV dose coverages, the mean doses of right parotid and oral cavity were decreased from 29.52 Gy relative biological effectiveness (RBE) and 41.89 Gy RBE to 22.16 Gy RBE and 34.61 Gy RBE, leading to a reduce of 1.67% and 3.40% in NTCP. The mean dose of right parotid was decreased from 21.71 Gy RBE to 19.37 Gy RBE for the last patient, leading to a reduce of 0.73% in NTCP. CONCLUSION Our results showed that online APT could better maintain the treatment plan quality than offline APT for all the four patients, despite their significant anatomical changes. Future investigation will focus on collecting more patient data to obtain statistically significant results and help identify the patients to whom the online APT will be of most benefit.
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Affiliation(s)
| | - D Bohannon
- Department of Medical Physics, Georgia Institute of Technology, Atlanta, GA
| | | | - Y Wang
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - M W McDonald
- Winship Cancer Institute of Emory University, Department of Radiation Oncology, Atlanta, GA
| | - D S Yu
- Emory University, Atlanta, GA
| | - T Liu
- Icahn School of Medicine at Mount Sinai, New York, NY
| | - J Zhou
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - X Yang
- Department of Radiation Oncology, Emory University, Atlanta, GA
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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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Eaton BR, Zhou J, Wang Y, Langen KM, Esiashvili N. Prospective Feasibility Trial of Vertebral Body Sparing Pencil Beam Scanning Proton Craniospinal Irradiation in Growing Children. Int J Radiat Oncol Biol Phys 2023; 117:e510-e511. [PMID: 37785599 DOI: 10.1016/j.ijrobp.2023.06.1767] [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) Advanced proton therapy techniques now allow for delivery of craniospinal irradiation (CSI) to the entire brain and thecal sac while sparing many of the anterior vertebral bodies from doses expected to inhibit growth, though this technique has not been prospectively studied. The purpose of this trial is to test the feasibility and robustness of vertebral body sparing (VBS) CSI in children with standard image guidance, to assess the dosimetric and toxicity benefits of this approach, and to report early clinical outcomes. MATERIALS/METHODS Children 3-18 years old requiring CSI treatment were eligible for this IRB approved prospective clinical trial. The CSI clinical target volume (CTV) included the brain, entire thecal sac and neural foramina with no expansion. Select anterior vertebral bodies (AVB) could be included at the physician's discretion. The spinal portion of the CTV was treated with PA beam(s) with robust optimization (+/-3.5% range, 5mm positional uncertainty). Daily image guidance included kV/kV imaging. Cone beam CT (CBCT) was acquired weekly after final positioning and a virtual CT (vCT) was created for quality assurance (QA) analysis. Acute toxicity was prospectively assessed weekly during treatment and 1 month after per CTCAE v5.0. RESULTS Ten children with a median patient age and CSI dose of 9 years (range 3-16) and 36 Gy (RBE) (range 15-36 Gy (RBE)) were enrolled. Common diagnoses were medulloblastoma (n = 4) and non-germinomatous germ cell tumor (n = 3). Seven patients received prior chemotherapy; 2 patients were treated with palliative intent. Dose statistics for the anterior vertebral body varied according to age, CSI dose and portion of the spine, with the greatest sparing in the lower thoracic and lumbar vertebrae for all patients. Nine patients completed all QA CTs; one patient required a replan due to weight gain. For all remaining patients the treatment was highly robust: CTV V95 reduction at the C-spine, T-spine, and L-spine was 0.0±0.1%, 0.6±1.3%, and 0.8±1.1%, respectively. The highest grade non-hematologic acute toxicity was grade 2 alopecia (n = 9) and grade 2 nausea/vomiting (n = 5). One patient reported transient grade 1 esophagitis during treatment. Hematologic toxicity included >/ = grade 3 lymphopenia in 7 patients, >/ = grade 3 leukopenia in 1 patient, >/ = grade 2 anemia in 6 patients, and >/ = grade 1 thrombocytopenia in 3 patients. Median follow-up is 16.6 months (range 10-36 months). Three patients experience intracranial disease progression: 2 local and 1 distant intracranial failure. There were no failures within the spine. CONCLUSION Proton vertebral body sparing CSI targeting the thecal sac only is a highly robust treatment technique and is well tolerated. Weekly CBCT to assess changes in soft tissue posterior to the spine is recommended. Further follow-up is required to assess long-term growth outcomes.
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Affiliation(s)
- B R Eaton
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - J Zhou
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - Y Wang
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - K M Langen
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - N Esiashvili
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
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van Kootwijk A, Jonker BP, Wolvius EB, Saldivar MC, Leeflang MA, Zhou J, Tümer N, Mirzaali MJ, Zadpoor AA. Biomechanical evaluation of additively manufactured patient-specific mandibular cage implants designed with a semi-automated workflow: A cadaveric and retrospective case study. J Mech Behav Biomed Mater 2023; 146:106097. [PMID: 37678107 DOI: 10.1016/j.jmbbm.2023.106097] [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: 07/06/2023] [Revised: 08/24/2023] [Accepted: 08/28/2023] [Indexed: 09/09/2023]
Abstract
OBJECTIVE Mandibular reconstruction using patient-specific cage implants is a promising alternative to the vascularized free flap reconstruction for nonirradiated patients with adequate soft tissues, or for patients whose clinical condition is not conducive to microsurgical reconstruction. This study aimed to assess the biomechanical performance of 3D printed patient-specific cage implants designed with a semi-automated workflow in a combined cadaveric and retrospective case series study. METHODS We designed cage implants for two human cadaveric mandibles using our previously developed design workflow. The biomechanical performance of the implants was assessed with the finite element analysis (FEA) and quasi-static biomechanical testing. Digital image correlation (DIC) was used to measure the full-field strains and validate the FE models by comparing the distribution of maximum principal strains within the bone. The retrospective study of a case series involved three patients, each of whom was treated with a cage implant of similar design. The biomechanical performance of these implants was evaluated using the experimentally validated FEA under the scenarios of both mandibular union and nonunion. RESULTS No implant or screw failure was observed prior to contralateral bone fracture during the quasi-static testing of both cadaveric mandibles. The FEA and DIC strain contour plots indicated a strong linear correlation (r = 0.92) and a low standard error (SE=29.32με), with computational models yielding higher strain values by a factor of 2.7. The overall stresses acting on the case series' implants stayed well below the yield strength of additively manufactured (AM) commercially pure titanium, when simulated under highly strenuous chewing conditions. Simulating a full union between the graft and remnant mandible yielded a substantial reduction (72.7±1.5%) in local peak stresses within the implants as compared to a non-bonded graft. CONCLUSIONS This study shows the suitability of the developed semi-automated workflow in designing patient-specific cage implants with satisfactory mechanical functioning under demanding chewing conditions. The proposed workflow can aid clinical engineers in creating reconstruction systems and streamlining pre-surgical planning. Nevertheless, more research is still needed to evaluate the osteogenic potential of bone graft insertions.
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Affiliation(s)
- A van Kootwijk
- Department of Oral and Maxillofacial Surgery, Erasmus University Medical Center, Doctor Molewaterplein 40, 3015 GE, Rotterdam, the Netherlands
| | - B P Jonker
- Department of Oral and Maxillofacial Surgery, Erasmus University Medical Center, Doctor Molewaterplein 40, 3015 GE, Rotterdam, the Netherlands
| | - E B Wolvius
- Department of Oral and Maxillofacial Surgery, Erasmus University Medical Center, Doctor Molewaterplein 40, 3015 GE, Rotterdam, the Netherlands
| | - M Cruz Saldivar
- Department of Biomechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology (TU Delft), Mekelweg 2, 2628 CD, Delft, the Netherlands
| | - M A Leeflang
- Department of Biomechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology (TU Delft), Mekelweg 2, 2628 CD, Delft, the Netherlands
| | - J Zhou
- Department of Biomechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology (TU Delft), Mekelweg 2, 2628 CD, Delft, the Netherlands
| | - N Tümer
- Department of Biomechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology (TU Delft), Mekelweg 2, 2628 CD, Delft, the Netherlands.
| | - M J Mirzaali
- Department of Biomechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology (TU Delft), Mekelweg 2, 2628 CD, Delft, the Netherlands
| | - A A Zadpoor
- Department of Biomechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology (TU Delft), Mekelweg 2, 2628 CD, Delft, the Netherlands
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Lan W, Yao J, Cao M, Wang Z, Xiang B, Zhou J, Liao W, Liu X, Yang M, Zhang S, Zhao Y. Bifunctional Role of Monocyte Subsets in Modulating Radiotherapy Combined Intra-Tumor αCD40 Agonist Induced Abscopal Effect. Int J Radiat Oncol Biol Phys 2023; 117:S121. [PMID: 37784314 DOI: 10.1016/j.ijrobp.2023.06.459] [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) Abscopal effect induced by radiotherapy and immune checkpoint blockade is a promising yet far from satisfactory strategy in clinical. The underlying immune mechanism, especially driven by monocytes remains poorly undefined. Monocytes consist of two phenotypically and functionally distinct subsets distinguished by expression of chemokine receptors CCR2 and CX3CR1: classical inflammatory Ly6ChiCCR2hi monocytes and nonclassical patrolling Ly6CloCCR2loCX3CR1hi monocytes. Monocytes differentiate and transit to other myeloid cells such as dendritic cells and macrophages according to various environmental cues. Herein we investigated the roles of monocyte subsets in modulating tumor control consisting of combination RT and myeloid checkpoint agonist αCD40 to specifically ignite myeloid cell activation. MATERIALS/METHODS To establish abscopal model, contralateral tumors were implanted in each mouse, while only one side were treated with RT (8 Gy × 3) + αCD40 agonist (50 μg, intra-tumor). Tumor volume and mice survival were compared in each group (control, RT, αCD40 and RT + αCD40). Ccr2RFP/+ Cx3cr1GFP/+ (R2 × 3), Ccr2RFP/RFPCx3cr1+/+ (R2-KO) and Ccr2+/+Cx3cr1GFP/GFP (X3-KO) mice were used for cell tracking and to dissect chemokine receptor CCR2 and CX3CR1 on monocyte. Tumor infiltrating immune cells were analyzed by flowcytometry and RNA-seq. RESULTS RT combined with αCD40 significantly dampened tumor growth on both ipsilateral and contralateral sides in abscopal model (p< 0.01), accompanied by upregulation of chemokine receptors CCR2 and CX3CR1 on myeloid cells were both increased in tumor and peripheral blood. Chemokine ligands CCL2, CCL3, CCL5, CCL7, CCL12 and CX3CL1 were upregulated in tumor after RT and αCD40 treatment, recruiting CCR2 and CX3CR1 expressing monocytes in situ. To elucidate the roles of CCR2 and CX3CR1 in mediating local and systemic anti-tumor immunity, R2 × 3, R2-KO and X3-KO mice with combined treatment were used. Tumor size on ipsilateral leg were similar among groups. However, tumor growth was significantly delayed on contralateral side in X3-KO mice while accelerated in R2-KO mice compared with that in R2 × 3 mice. Mechanistically, remarkable decrease of antigen presenting dendritic cells (MHCII+Ly6ChiCD11c+) were observed in R2-KO mice. Moreover, phagocytosis was strengthened in macrophages (F4/80+CD11b+) of X3-KO mice. CONCLUSION CX3CR1 deletion ignite anti-tumor immunity elicited by RT and αCD40 through enhanced phagocytosis in macrophages, while CCR2 deletion renders inferior tumor control through reduction of dendritic cells. Preferential targeting nonclassical patrolling monocyte may lead to enhanced local and systemic tumor control.
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Affiliation(s)
- W Lan
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center; Cancer Hospital affiliate to University of Electronic Science and Technology of China, Chengdu, China
| | - J Yao
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center; Cancer Hospital affiliate to University of Electronic Science and Technology of China, Chengdu, China
| | - M Cao
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center; Cancer Hospital affiliate to University of Electronic Science and Technology of China, Chengdu, China; Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Z Wang
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center; Cancer Hospital affiliate to University of Electronic Science and Technology of China, Chengdu, China
| | - B Xiang
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center; Cancer Hospital affiliate to University of Electronic Science and Technology of China, Chengdu, China
| | - J Zhou
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center; Cancer Hospital affiliate to University of Electronic Science and Technology of China, Chengdu, China
| | - W Liao
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center; Cancer Hospital affiliate to University of Electronic Science and Technology of China, Chengdu, China
| | - X Liu
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center; Cancer Hospital affiliate to University of Electronic Science and Technology of China, Chengdu, China
| | - M Yang
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center; Cancer Hospital affiliate to University of Electronic Science and Technology of China, Chengdu, China
| | - S Zhang
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center; Cancer Hospital affiliate to University of Electronic Science and Technology of China, Chengdu, China
| | - Y Zhao
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center; Cancer Hospital affiliate to University of Electronic Science and Technology of China, Chengdu, China
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Li Y, Pavanram P, Bühring J, Rütten S, Schröder KU, Zhou J, Pufe T, Wang LN, Zadpoor AA, Jahr H. Physiomimetic biocompatibility evaluation of directly printed degradable porous iron implants using various cell types. Acta Biomater 2023; 169:589-604. [PMID: 37536493 DOI: 10.1016/j.actbio.2023.07.056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/04/2023] [Accepted: 07/27/2023] [Indexed: 08/05/2023]
Abstract
Additively manufactured (AM) degradable porous metallic biomaterials offer unique opportunities for satisfying the design requirements of an ideal bone substitute. Among the currently available biodegradable metals, iron has the highest elastic modulus, meaning that it would benefit the most from porous design. Given the successful preclinical applications of such biomaterials for the treatment of cardiovascular diseases, the moderate compatibility of AM porous iron with osteoblast-like cells, reported in earlier studies, has been surprising. This may be because, as opposed to static in vitro conditions, the biodegradation products of iron in vivo are transported away and excreted. To better mimic the in situ situations of biodegradable biomaterials after implantation, we compared the biodegradation behavior and cytocompatibility of AM porous iron under static conditions to the conditions with dynamic in situ-like fluid flow perfusion in a bioreactor. Furthermore, the compatibility of these scaffolds with four different cell types was evaluated to better understand the implications of these implants for the complex process of natural wound healing. These included endothelial cells, L929 fibroblasts, RAW264.7 macrophage-like cells, and osteoblastic MG-63 cells. The biodegradation rate of the scaffolds was significantly increased in the perfusion bioreactor as compared to static immersion. Under either condition, the compatibility with L929 cells was the best. Moreover, the compatibility with all the cell types was much enhanced under physiomimetic dynamic flow conditions as compared to static biodegradation. Our study highlights the importance of physiomimetic culture conditions and cell type selection when evaluating the cytocompatibility of degradable biomaterials in vitro. STATEMENT OF SIGNIFICANCE: Additively manufactured (AM) degradable porous metals offer unique opportunities for the treatment of large bony defects. Despite the successful preclinical applications of biodegradable iron in the cardiovascular field, the moderate compatibility of AM porous iron with osteoblast-like cells was reported. To better mimic the in vivo condition, we compared the biodegradation behavior and cytocompatibility of AM porous iron under static condition to dynamic perfusion. Furthermore, the compatibility of these scaffolds with various cell types was evaluated to better simulate the process of natural wound healing. Our study suggests that AM porous iron holds great promise for orthopedic applications, while also highlighting the importance of physio-mimetic culture conditions and cell type selection when evaluating the cytocompatibility of degradable biomaterials in vitro.
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Affiliation(s)
- Y Li
- Beijing Advanced Innovation Center for Materials Genome Engineering, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Department of Biomechanical Engineering, Delft University of Technology, Delft 2628CD, the Netherlands.
| | - P Pavanram
- Institute of Anatomy and Cell Biology, University Hospital RWTH Aachen, Aachen 52074, Germany
| | - J Bühring
- Institute of Structural Mechanics and Lightweight Design, RWTH Aachen University, 52062 Aachen, Germany
| | - S Rütten
- Institute of Pathology, Electron Microscopy Unit, University Hospital RWTH Aachen, Aachen 52074, Germany
| | - K-U Schröder
- Institute of Structural Mechanics and Lightweight Design, RWTH Aachen University, 52062 Aachen, Germany
| | - J Zhou
- Department of Biomechanical Engineering, Delft University of Technology, Delft 2628CD, the Netherlands
| | - T Pufe
- Institute of Anatomy and Cell Biology, University Hospital RWTH Aachen, Aachen 52074, Germany
| | - L-N Wang
- Beijing Advanced Innovation Center for Materials Genome Engineering, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
| | - A A Zadpoor
- Department of Biomechanical Engineering, Delft University of Technology, Delft 2628CD, the Netherlands
| | - H Jahr
- Institute of Anatomy and Cell Biology, University Hospital RWTH Aachen, Aachen 52074, Germany.; Institute of Structural Mechanics and Lightweight Design, RWTH Aachen University, 52062 Aachen, Germany.
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Cao Y, Yi H, Zhou J, Cheng Y, Mao Y. Regulations on e-cigarettes: China is taking action. Pulmonology 2023; 29:359-361. [PMID: 37012091 DOI: 10.1016/j.pulmoe.2023.02.007] [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] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/06/2023] [Accepted: 02/10/2023] [Indexed: 04/05/2023] Open
Affiliation(s)
- Y Cao
- Peking University Health Science Center, Peking University, Beijing 100191, China
| | - H Yi
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - J Zhou
- Department of Chemistry and Biochemistry, University of California San Diego, San Diego, CA 92093, USA
| | - Y Cheng
- Department of Thoracic Surgery, Xiangya Hospital, Central South University, Changsha 410008, China.
| | - Y Mao
- Department of Thoracic Surgery, 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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Chen S, Zhou J, Lu JY, Bao YQ, Xu JW, Zhu JK, Jia WP. [Efficacy and safety of ultra rapid lispro in the treatment of type 2 diabetes mellitus: a randomized controlled clinical trial]. Zhonghua Nei Ke Za Zhi 2023; 62:1093-1101. [PMID: 37650183 DOI: 10.3760/cma.j.cn112138-20230220-00098] [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: 09/01/2023]
Abstract
Objective: To evaluate and compare the efficacy and safety of ultra-rapid lispro insulin (URLi) and humalog lispro (HL) in the treatment of type 2 diabetes mellitus. Methods: This was an international multicenter, double-blind, randomized controlled study. From May 2019 to January 2021, a total of 481 patients with type 2 diabetes mellitus, who had been using insulin for at least 90 days and had poor glycemic control, were included. These patients were recruited from 34 research centers in China, including Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital. They were assigned to either the URLi group (319 patients) or the HL group (162 patients) using stratified blocked randomization. The primary endpoint was the change in hemoglobin A1c (HbA1c) relative to baseline after 26 weeks of treatment. Secondary endpoints included the proportion of patients who achieved HbA1c<7.0% and ≤6.5% after 26 weeks of treatment, 1-h postprandial glucose (1hPG) or 2-h postprandial glucose (2hPG) excursions during a mixed meal tolerance test at week 26, as well as safety parameters. Continuous variables were compared using mixed model repeated measures or analysis of covariance, and categorical variables were compared using logistic regression or Fisher's exact test. Results: Data based on the Chinese subgroup showed that there were no statistically significant differences between the URLi and HL groups in terms of male percentage [56.1% (179/319) vs. 56.2% (91/162); P=0.990], age [(59.5±8.4) vs. (59.6±9.3) years; P=0.839] and other baseline characteristics. Regarding the change in HbA1c relative to baseline, the URLi group was non-inferior to the HL group (-0.59%±0.05% vs. -0.66%±0.06%; P=0.312). There were no statistically significant differences between the URLi and HL groups in proportion of patients who achieved HbA1c<7.0% [47.3% (138/292) vs. 45.2% (70/155); P=0.907] and≤6.5% [27.7% (81/292) vs. 27.7% (43/155); P=0.816]. The excursions in 1hPG [(6.20±0.21) vs. (6.90±0.25) mmol/L; P=0.001] and 2hPG [(8.10±0.27) vs. (9.30±0.31) mmol/L; P<0.001] were lower in the URLi group than the HL group, with statistically significant differences. In terms of safety, there were no statistically significant differences in the percentage of subjects who reported treatment-emergent adverse events between the URLi and HL groups [49.8% (159/319) vs. 50.0% (81/162); P=1.000]. The event rate of nocturnal hypoglycemia was lower in the URLi group than the HL group, with statistically significant differences [(0.53±0.10) vs. (0.89±0.16) events per patient-year; P=0.040]. Conclusions: With good glycemic control, URLi showed non-inferiority for HbA1c improvement versus HL and was superior to HL for postprandial glucose excursion control. Meanwhile the rate and incidence of nocturnal hypoglycemia were lower in the URLi group than the HL group.
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Affiliation(s)
- S Chen
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Diabetes Institute, Shanghai 200233, China
| | - J Zhou
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Diabetes Institute, Shanghai 200233, China
| | - J Y Lu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Diabetes Institute, Shanghai 200233, China
| | - Y Q Bao
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Diabetes Institute, Shanghai 200233, China
| | - J W Xu
- Lilly Suzhou Pharmaceutical Co. Ltd., Shanghai 200041, China
| | - J K Zhu
- Lilly Suzhou Pharmaceutical Co. Ltd., Shanghai 200041, China
| | - W P Jia
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Diabetes Institute, Shanghai 200233, China
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Dou XJ, Wang HY, Chen W, Zhou J, Wei ZR. [Prospective study on the influence of dobutamine on blood perfusion in free flap repair of diabetic foot wounds]. Zhonghua Shao Shang Yu Chuang Mian Xiu Fu Za Zhi 2023; 39:746-752. [PMID: 37805785 DOI: 10.3760/cma.j.cn501225-20221220-00543] [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: 10/09/2023]
Abstract
Objective: To investigate the influence of clinical administration of dobutamine on blood perfusion in free flap repair of diabetic foot wounds. Methods: A prospective self-controlled study was conducted. From January to November 2022, 20 patients with diabetic foot who met the inclusion criteria were hospitalized in the Department of Burns and Plastic Surgery of Affiliated Hospital of Zunyi Medical University, including 9 males and 11 females, aged from 44 to 75 years, with the foot wounds area ranging from 5 cm×4 cm to 20 cm×10 cm, which were repaired by free anterolateral thigh flaps. Heart rate (HR) and mean arterial pressure (MAP) were recorded before anesthesia induction, 10 minutes after vascular recanalization, when the target blood pressure (i.e., MAP being 6-10 mmHg (1 mmHg=0.133 kPa) higher than that before anesthesia induction) was reached after infusion of dobutamine, and 10 minutes after tracheal catheter removal. Additionally, indocyanine green, a contrast agent, was injected intravenously at 10 minutes after vascular recanalization and when the target blood pressure was reached after infusion of dobutamine to assess flap blood perfusion using infrared imager, and the area ratio of flaps with hyperperfusion and hypoperfusion was calculated. Other recorded variables included flap harvesting area, surgical duration, total fluid infusion amount, infusion dose and total usage of dobutamine, intraoperative adverse events, postoperative flap complications, and follow-up outcomes. Data were statistically analyzed with paired sample t test, analysis of variance for repeated measurement, Bonferroni method, and generalized estimating equation. Results: Compared with those before anesthesia induction, HR and MAP of patients were significantly decreased at 10 minutes after vascular recanalization (P<0.05), while HR and MAP of patients were significantly increased when the target blood pressure was reached after infusion of dobutamine (P<0.05). Compared with those at 10 minutes after vascular recanalization, HR and MAP of patients were significantly increased when the target blood pressure was reached after infusion of dobutamine and at 10 minutes after tracheal catheter removal (P<0.05). Compared with those when the target blood pressure was reached after infusion of dobutamine, HR and MAP of patients were significantly decreased at 10 minutes after tracheal catheter removal (P<0.05). The area ratio of flaps with hyperperfusion of patients was 0.63±0.11 when the target blood pressure was reached after infusion of dobutamine, which was significantly higher than 0.31±0.09 at 10 minutes after vascular recanalization (t=-9.92, P<0.05). The area ratio of flaps with hypoperfusion of patients was 0.12±0.05 when the target blood pressure was reached after infusion of dobutamine, which was significantly lower than 0.45±0.10 at 10 minutes after vascular recanalization (t=17.05, P<0.05). The flap harvesting area of patients was (174±35) cm², the surgical duration was (372±52) min, the total fluid infusion amount was (2 485±361) mL, the infusion dose of dobutamine was 3-13 μg·kg⁻¹·min⁻¹, and the total usage of dobutamine was 5.7 (2.1, 9.7) mg. Two patients showed a significant increase in MAP during the infusion of dobutamine compared with that at 10 minutes after vascular recanalization, but before reaching 6 mmHg higher than that before anesthesia induction, their HR had reached the maximum (over 130 beats/min). The HR gradually returned to around 90 beats/min after the infusion of dobutamine was stopped. On post operation day 2, one patient had partial necrosis at the distal part of the flap, which was repaired by transplantation of thin split-thickness skin graft from the opposite thigh. During the follow-up of 3 to 6 months after operation, all the flaps survived well, with soft texture and well-formed shape, and no adverse cardiovascular events of patients were reported. Conclusions: The administration of dobutamine in free flap repair of diabetic foot wounds can significantly improve the MAP of patients, expand the area of hyperperfusion, reduce the area of hypoperfusion, and enhance the flap viability, with promising short-term follow-up results, which is suitable for promotion in clinical applications.
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Affiliation(s)
- X J Dou
- Department of Anesthesiology, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - H Y Wang
- Department of Anesthesiology, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - W Chen
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - J Zhou
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - Z R Wei
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
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Hu H, Lin X, Fan L, Fang L, Zhou J, Gao H. Acupuncture treatment for COVID-19-associated sensorineural hearing loss and tinnitus. QJM 2023; 116:605-607. [PMID: 36882180 DOI: 10.1093/qjmed/hcad028] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 02/16/2023] [Indexed: 03/09/2023] Open
Affiliation(s)
- H Hu
- From the Department of Acupuncture and Moxibustion, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou City, China
| | - X Lin
- The Third Clinical College, Zhejiang Chinese Medical University, Hangzhou City, China
| | - L Fan
- The Third Clinical College, Zhejiang Chinese Medical University, Hangzhou City, China
| | - L Fang
- From the Department of Acupuncture and Moxibustion, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou City, China
| | - J Zhou
- From the Department of Acupuncture and Moxibustion, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou City, China
| | - H Gao
- From the Department of Acupuncture and Moxibustion, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou City, China
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Sun H, Zhou J, Tang LJ, Cao WP, Li YM. New challenges in the long-COVID syndrome. QJM 2023; 116:608. [PMID: 36916751 DOI: 10.1093/qjmed/hcad041] [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: 03/10/2023] [Accepted: 03/12/2023] [Indexed: 03/16/2023] Open
Affiliation(s)
- H Sun
- Institute of Regenerative Medicine, Affiliated Hospital of Jiangsu University, Jiangsu University, Zhenjiang, Jiangsu 212001, China
- Department of Dermatology, Affiliated Hospital of Jiangsu University, Jiangsu University, Zhenjiang, Jiangsu, 212001, China
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu 212001, China
| | - J Zhou
- Zhenjiang Mental Health Center, The Fifth People's Hospital of Zhenjiang City, Zhenjiang, Jiangsu 212001, China
| | - L-J Tang
- Zhenjiang Mental Health Center, The Fifth People's Hospital of Zhenjiang City, Zhenjiang, Jiangsu 212001, China
| | - W-P Cao
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu 212001, China
- The Fourth Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212001, China
| | - Y-M Li
- Institute of Regenerative Medicine, Affiliated Hospital of Jiangsu University, Jiangsu University, Zhenjiang, Jiangsu 212001, China
- Department of Dermatology, Affiliated Hospital of Jiangsu University, Jiangsu University, Zhenjiang, Jiangsu, 212001, China
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu 212001, China
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Zhang ZY, Feng XY, Wang ZH, Huang YZ, Yang WB, Zhang WJ, Zhou J, Yuan ZY. [Similarities and differences of myocardial metabolic characteristics between HFpEF and HFrEF mice based on LC-MS/MS metabolomics]. Zhonghua Xin Xue Guan Bing Za Zhi 2023; 51:722-730. [PMID: 37460426 DOI: 10.3760/cma.j.cn112148-20230329-00182] [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: 07/20/2023]
Abstract
Objective: To reveal the similarities and differences in myocardial metabolic characteristics between heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF) mice using metabolomics. Methods: The experimental mice were divided into 4 groups, including control, HFpEF, sham and HFrEF groups (10 mice in each group). High fat diet and Nω-nitroarginine methyl ester hydrochloride (L-NAME) were applied to construct a"two-hit"HFpEF mouse model. Transverse aortic constriction (TAC) surgery was used to construct the HFrEF mouse model. The differential expression of metabolites in the myocardium of HFpEF and HFrEF mice was detected by untargeted metabolomics (UHPLC-QE-MS). Variable importance in projection>1 and P<0.05 were used as criteria to screen and classify the differentially expressed metabolites between the mice models. KEGG functional enrichment and pathway impact analysis demonstrated significantly altered metabolic pathways in both HFpEF and HFrEF mice. Results: One hundred and nine differentially expressed metabolites were detected in HFpEF mice, and 270 differentially expressed metabolites were detected in HFrEF mice. Compared with the control group, the most significantly changed metabolite in HFpEF mice was glycerophospholipids, while HFrEF mice presented with the largest proportion of carboxylic acids and their derivatives. KEGG enrichment and pathway impact analysis showed that the differentially expressed metabolites in HFpEF mice were mainly enriched in pathways such as biosynthesis of unsaturated fatty acids, ether lipid metabolism, amino sugar and nucleotide sugar metabolism, glycerophospholipid metabolism, arachidonic acid metabolism and arginine and proline metabolism. The differentially expressed metabolites in HFrEF mice were mainly enriched in arginine and proline metabolism, glycine, serine and threonine metabolism, pantothenate and CoA biosynthesis, glycerophospholipid metabolism, nicotinate and nicotinamide metabolism and arachidonic acid metabolism, etc. Conclusions: HFpEF mice have a significantly different myocardial metabolite expression profile compared with HFrEF mice. In addition, biosynthesis of unsaturated fatty acids, arachidonic acid metabolism, glycerophospholipid metabolism and arginine and proline metabolism are significantly altered in both HFpEF and HFrEF mice, suggesting that these metabolic pathways may play an important role in disease progression in both types of heart failure.
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Affiliation(s)
- Z Y Zhang
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - X Y Feng
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Z H Wang
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Y Z Huang
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - W B Yang
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - W J Zhang
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - J Zhou
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Z Y Yuan
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
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Liao XS, Chen W, Jiang HF, Zhou J, Wei ZR, Chang SS, Zhang F, Nie KY. [Clinical effects of superficial temporal artery lobulated perforator flaps in repairing skin and soft tissue defects after temporal tumor resection]. Zhonghua Shao Shang Yu Chuang Mian Xiu Fu Za Zhi 2023; 39:534-539. [PMID: 37805768 DOI: 10.3760/cma.j.cn501225-20220816-00347] [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: 10/09/2023]
Abstract
Objective: To explore the feasibility and clinical effects of using superficial temporal artery lobulated perforator flaps in repairing skin and soft tissue defects after tumor resection in the temporal region. Methods: A retrospective observational study method was used. From March 2017 to October 2022, ten patients with temporal skin tumors were admitted to the Affiliated Hospital of Zunyi Medical University, including six women and four men, with age ranging from 42 to 87 years. Among them, three patients had squamous cell carcinoma and seven patients had basal cell carcinoma, with disease duration ranging from 6 months to 5 years. All temporal tumors underwent expanded resection, leaving wound areas of 5.4 cm×4.2 cm to 7.0 cm×4.0 cm after tumor resection. Superficial temporal artery frontal branch flaps with areas of 5.5 cm×1.2 cm to 7.0 cm×1.5 cm, superficial temporal artery descending branch flaps with areas of 4.2 cm×3.5 cm to 5.0 cm×4.0 cm, and superficial temporal artery parietal branch flaps with areas of 4.2 cm×1.0 cm to 5.0 cm×1.0 cm were designed to repair the wounds and reconstruct the hairline. The donor areas of the flaps were closed and sutured directly. The survival of the flaps was observed on 3 to 5 days after surgery, and the healing of wounds on the donor and recipient sites was observed when the stitches were removed on 5 to 7 days after surgery. During follow-up after surgery, the appearance of the temporal area, scar hyperplasia, hairline reconstruction, and tumor recurrence were observed in the temporal region on the affected side. Results: All the flaps survived well on 3 to 5 days after surgery, and all the donor and recipient site wounds healed well on 5 to 7 days after surgery. During follow-up of 3 to 6 months after surgery, the surgical incisions were concealed; the flaps were not swollen, with a consistent color to the surrounding skin; there were no obvious hypertrophic scars; the reconstructed hairline on the affected side was not significantly different from that of the healthy side; there was no tumor recurrence in the local area. Conclusions: For large areas of skin and soft tissue defects in the temporal region, the use of superficial temporal artery lobulated perforator flaps can repair the wounds in different regions and suture the donor sites in the primary stage simultaneously. The surgical operation is simple, and the facial appearance conforms to the aesthetic requirement after surgery with no tumor recurrence in the local area but a good repair effect. This method is particularly suitable for repairing large areas of skin and soft tissue defects in the temporal region in elderly patients.
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Affiliation(s)
- X S Liao
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - W Chen
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - H F Jiang
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - J Zhou
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - Z R Wei
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - S S Chang
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - F Zhang
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - K Y Nie
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
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Bohannon D, Janopaul-Naylor J, Rudra S, Yang X, Chang CW, Wang Y, Ma C, Patel SA, McDonald MW, Zhou J. Prediction of plan adaptation in head and neck cancer proton therapy using clinical, radiographic, and dosimetric features. Acta Oncol 2023:1-8. [PMID: 37335043 DOI: 10.1080/0284186x.2023.2224050] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 06/01/2023] [Indexed: 06/21/2023]
Abstract
PURPOSE Because proton head and neck (HN) treatments are sensitive to anatomical changes, plan adaptation (re-plan) during the treatment course is needed for a significant portion of patients. We aim to predict re-plan at plan review stage for HN proton therapy with a neural network (NN) model trained with patients' dosimetric and clinical features. The model can serve as a valuable tool for planners to assess the probability of needing to revise the current plan. METHODS AND MATERIALS Mean beam dose heterogeneity index (BHI), defined as the ratio of the maximum beam dose to the prescription dose, plan robustness features (clinical target volume (CTV), V100 changes, and V100 > 95% passing rates in 21 robust evaluation scenarios), as well as clinical features (e.g., age, tumor site, and surgery/chemotherapy status) were gathered from 171 patients treated at our proton center in 2020, with a median age of 64 and stages from I-IVc across 13 HN sites. Statistical analyses of dosimetric parameters and clinical features were conducted between re-plan and no-replan groups. A NN was trained and tested using these features. Receiver operating characteristic (ROC) analysis was conducted to evaluate the performance of the prediction model. A sensitivity analysis was done to determine feature importance. RESULTS Mean BHI in the re-plan group was significantly higher than the no-replan group (p < .01). Tumor site (p < .01), chemotherapy status (p < .01), and surgery status (p < .01) were significantly correlated to re-plan. The model had sensitivities/specificities of 75.0%/77.4%, respectively, and an area under the ROC curve of .855. CONCLUSION There are several dosimetric and clinical features that correlate to re-plans, and NNs trained with these features can be used to predict HN re-plans, which can be used to reduce re-plan rate by improving plan quality.
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Affiliation(s)
- D Bohannon
- Department of Nuclear and Radiological Engineering, Georgia institute of Technology, Atlanta, GA, USA
| | - J Janopaul-Naylor
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - S Rudra
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - X Yang
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - C W Chang
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - Y Wang
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - C Ma
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - S A Patel
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - M W McDonald
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - J Zhou
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
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47
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Abdulhamid MI, Aboona BE, Adam J, Adams JR, Agakishiev G, Aggarwal I, Aggarwal MM, Ahammed Z, Aitbaev A, Alekseev I, Anderson DM, Aparin A, Aslam S, Atchison J, Averichev GS, Bairathi V, Baker W, Ball Cap JG, Barish K, Bhagat P, Bhasin A, Bhatta S, Bordyuzhin IG, Brandenburg JD, Brandin AV, Cai XZ, Caines H, Calderón de la Barca Sánchez M, Cebra D, Ceska J, Chakaberia I, Chan BK, Chang Z, Chatterjee A, Chen D, Chen J, Chen JH, Chen Z, Cheng J, Cheng Y, Choudhury S, Christie W, Chu X, Crawford HJ, Dale-Gau G, Das A, 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, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fazio S, Feng CJ, Feng Y, Finch E, Fisyak Y, Flor FA, Fu C, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Hamed A, Han Y, Harasty MD, Harris JW, Harrison-Smith H, He W, He XH, He Y, Hu C, Hu Q, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Huang Y, Humanic TJ, Isenhower D, Isshiki M, Jacobs WW, Jalotra A, Jena C, Ji Y, Jia J, Jin C, Ju X, Judd EG, Kabana S, Kabir ML, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Ke HW, Keane D, Kechechyan A, Kelsey M, Kimelman B, Kiselev A, Knospe AG, Ko HS, Kochenda L, Korobitsin AA, Kravtsov P, Kumar L, Kumar S, Kunnawalkam Elayavalli R, Lacey R, Landgraf JM, Lebedev A, Lednicky R, Lee JH, Leung YH, Lewis N, Li C, Li W, Li X, Li Y, Li Y, Li Z, Liang X, Liang Y, Lin T, Liu C, Liu F, Liu G, Liu H, Liu H, Liu L, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Lomicky O, Longacre RS, Loyd EM, Lu T, Lukow NS, Luo XF, Luong VB, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Matis HS, Mazer JA, McNamara G, Mi K, Minaev NG, Mohanty B, Mondal MM, Mooney I, Morozov DA, Mudrokh A, Nagy MI, Nain AS, Nam JD, Nasim M, Neff D, Nelson JM, Nemes DB, Nie M, Nigmatkulov G, Niida T, Nishitani R, Nogach LV, Nonaka T, Odyniec G, Ogawa A, Oh S, Okorokov VA, Okubo K, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Panebratsev Y, Pani T, Parfenov P, Paul A, Perkins C, Pokhrel BR, Posik M, Protzman T, Pruthi NK, Putschke J, Qin Z, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Ritter HG, Robertson CW, Rogachevsky OV, Rosales Aguilar MA, Roy D, Ruan L, Sahoo AK, Sahoo NR, Sako H, Salur S, Samigullin E, Sato S, Schmidke WB, Schmitz N, Seger J, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao T, Sharma M, Sharma N, Sharma R, Sharma SR, Sheikh AI, Shen DY, Shen K, Shi SS, Shi Y, Shou QY, Si F, Singh J, Singha S, Sinha P, Skoby MJ, Söhngen Y, Song Y, Srivastava B, Stanislaus TDS, Stewart DJ, Strikhanov M, Stringfellow B, Su Y, Sun C, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Sweger ZW, Tamis A, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Tlusty D, Todoroki T, Tokarev MV, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tsai OD, Tsang CY, Tu Z, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vasiliev AN, Verkest V, Videbæk F, Vokal S, Voloshin SA, Wang F, Wang G, Wang JS, Wang X, Wang Y, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Westfall GD, Wieman H, Wilks G, Wissink SW, Wu J, Wu J, Wu X, Wu Y, Xi B, Xiao ZG, Xie G, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Y, Xu Z, Xu Z, Yan G, Yan Z, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang W, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao F, Zhao J, Zhao M, Zhou C, Zhou J, Zhou S, Zhou Y, Zhu X, Zurek M, Zyzak M. Measurements of the Elliptic and Triangular Azimuthal Anisotropies in Central ^{3}He+Au, d+Au and p+Au Collisions at sqrt[s_{NN}]=200 GeV. Phys Rev Lett 2023; 130:242301. [PMID: 37390421 DOI: 10.1103/physrevlett.130.242301] [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: 10/20/2022] [Revised: 02/27/2023] [Accepted: 05/15/2023] [Indexed: 07/02/2023]
Abstract
The elliptic (v_{2}) and triangular (v_{3}) azimuthal anisotropy coefficients in central ^{3}He+Au, d+Au, and p+Au collisions at sqrt[s_{NN}]=200 GeV are measured as a function of transverse momentum (p_{T}) at midrapidity (|η|<0.9), via the azimuthal angular correlation between two particles both at |η|<0.9. While the v_{2}(p_{T}) values depend on the colliding systems, the v_{3}(p_{T}) values are system independent within the uncertainties, suggesting an influence on eccentricity from subnucleonic fluctuations in these small-sized systems. These results also provide stringent constraints for the hydrodynamic modeling of these systems.
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Affiliation(s)
- M I Abdulhamid
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - B E Aboona
- Texas A&M University, College Station, Texas 77843
| | - J Adam
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J R Adams
- The Ohio State University, Columbus, Ohio 43210
| | - 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
| | - A Aitbaev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - 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
| | - S Aslam
- Indian Institute Technology, Patna, Bihar 801106, India
| | - J Atchison
- Abilene Christian University, Abilene, Texas 79699
| | | | - 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
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - S Bhatta
- State University of New York, Stony Brook, New York 11794
| | - 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
| | - 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
| | - J Ceska
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - I Chakaberia
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - Z Chang
- Indiana University, Bloomington, Indiana 47408
| | - A Chatterjee
- National Institute of Technology Durgapur, Durgapur - 713209, India
| | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai, 200433
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - Y Cheng
- University of California, Los Angeles, California 90095
| | | | - 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
| | - G Dale-Gau
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Das
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - 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
| | - 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
| | - S Fazio
- University of Calabria & INFN-Cosenza, Rende 87036, Italy
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - F A Flor
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Central China Normal University, Wuhan, Hubei 430079
| | - 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
| | - A Hamed
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - M D Harasty
- University of California, Davis, California 95616
| | - J W Harris
- Yale University, New Haven, Connecticut 06520
| | | | - 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
| | - C Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Q Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Hu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - 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
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Central China Normal University, Wuhan, Hubei 430079
| | - T J Humanic
- The Ohio State University, Columbus, Ohio 43210
| | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - M Isshiki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - A Jalotra
- University of Jammu, Jammu 180001, India
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - 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
| | - C Jin
- Rice University, Houston, Texas 77251
| | - 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
| | - D Kalinkin
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - 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
| | - B Kimelman
- University of California, Davis, California 95616
| | - 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
| | | | - 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
| | - J M Landgraf
- 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
| | - J H Lee
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y H Leung
- University of Heidelberg, Heidelberg 69120, Germany
| | - N Lewis
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Li
- Shandong University, Qingdao, Shandong 266237
| | - W Li
- Rice University, Houston, Texas 77251
| | - X Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- Tsinghua University, Beijing 100084
| | - Z Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Liang
- University of California, Riverside, California 92521
| | - Y Liang
- Kent State University, Kent, Ohio 44242
| | - T Lin
- Shandong University, Qingdao, Shandong 266237
| | - C Liu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - F Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - G Liu
- South China Normal University, Guangzhou, Guangdong 510631
| | - H Liu
- Indiana University, Bloomington, Indiana 47408
| | - H Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - L Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - 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
- Central China Normal University, Wuhan, Hubei 430079
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - O Lomicky
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - R S Longacre
- Brookhaven National Laboratory, Upton, New York 11973
| | - E M Loyd
- University of California, Riverside, California 92521
| | - T Lu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
| | - X F Luo
- Central China Normal University, Wuhan, Hubei 430079
| | - V B Luong
- Joint Institute for Nuclear Research, Dubna 141 980
| | - L Ma
- Fudan University, Shanghai, 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y G Ma
- Fudan University, Shanghai, 200433
| | - N Magdy
- State University of New York, Stony Brook, New York 11794
| | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | | | - H S Matis
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J A Mazer
- Rutgers University, Piscataway, New Jersey 08854
| | - G McNamara
- Wayne State University, Detroit, Michigan 48201
| | - K Mi
- Central China Normal University, Wuhan, Hubei 430079
| | - 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
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - I Mooney
- Yale University, New Haven, Connecticut 06520
| | - D A Morozov
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - A Mudrokh
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M I Nagy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - A S Nain
- Panjab University, Chandigarh 160014, India
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - Md Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - 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
| | - G Odyniec
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - A Ogawa
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Oh
- Sejong University, Seoul, 05006, South Korea
| | - V A Okorokov
- National Research Nuclear University MEPhI, Moscow 115409
| | - K Okubo
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - 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
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - T Pani
- Rutgers University, Piscataway, New Jersey 08854
| | - P Parfenov
- National Research Nuclear University MEPhI, Moscow 115409
| | - A Paul
- University of California, Riverside, California 92521
| | - C Perkins
- University of California, Berkeley, California 94720
| | - B R Pokhrel
- Temple University, Philadelphia, Pennsylvania 19122
| | - M Posik
- Temple University, Philadelphia, Pennsylvania 19122
| | - T Protzman
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - N K Pruthi
- Panjab University, Chandigarh 160014, India
| | - J Putschke
- Wayne State University, Detroit, Michigan 48201
| | - Z Qin
- Tsinghua University, Beijing 100084
| | - 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
| | - H G Ritter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | | | - D Roy
- Rutgers University, Piscataway, New Jersey 08854
| | - L Ruan
- Brookhaven National Laboratory, Upton, New York 11973
| | - 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
| | - E Samigullin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute", Moscow 117218
| | - 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
| | - J Seger
- Creighton University, Omaha, Nebraska 68178
| | - 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
| | | | - T Shao
- Fudan University, Shanghai, 200433
| | - M Sharma
- University of Jammu, Jammu 180001, India
| | - N Sharma
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - S R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | | | - D Y Shen
- Fudan University, Shanghai, 200433
| | - K Shen
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S S Shi
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
| | - Q Y Shou
- Fudan University, Shanghai, 200433
| | - F Si
- University of Science and Technology of China, Hefei, Anhui 230026
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - P Sinha
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - M J Skoby
- Ball State University, Muncie, Indiana, 47306
- Purdue University, West Lafayette, Indiana 47907
| | - Y Söhngen
- University of Heidelberg, Heidelberg 69120, Germany
| | - Y Song
- Yale University, New Haven, Connecticut 06520
| | - B Srivastava
- Purdue University, West Lafayette, Indiana 47907
| | | | - D J Stewart
- Wayne State University, Detroit, Michigan 48201
| | - M Strikhanov
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - Y Su
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Sun
- State University of New York, Stony Brook, New York 11794
| | - X Sun
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - 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
| | - A Tamis
- Yale University, New Haven, Connecticut 06520
| | - 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 V 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
| | - O D Tsai
- Brookhaven National Laboratory, Upton, New York 11973
- University of California, Los Angeles, California 90095
| | - C Y Tsang
- Brookhaven National Laboratory, Upton, New York 11973
- Kent State University, Kent, Ohio 44242
| | - 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
| | - A N Vasiliev
- National Research Nuclear University MEPhI, Moscow 115409
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - 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
| | - X Wang
- Shandong University, Qingdao, Shandong 266237
| | - Y 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
| | | | - G D Westfall
- Michigan State University, East Lansing, Michigan 48824
| | - H Wieman
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - G Wilks
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - 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
| | - X Wu
- University of California, Los Angeles, California 90095
| | - 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
- University of Chinese Academy of Sciences, Beijing, 101408
| | - 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
| | - Y Xu
- Central China Normal University, Wuhan, Hubei 430079
| | - 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
| | - Z Yan
- State University of New York, Stony Brook, New York 11794
| | - C Yang
- Shandong University, Qingdao, Shandong 266237
| | - Q Yang
- Shandong University, Qingdao, Shandong 266237
| | - S Yang
- South China Normal University, Guangzhou, Guangdong 510631
| | - 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
| | - 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 Science and Technology of China, Hefei, Anhui 230026
| | - W Zhang
- South China Normal University, Guangzhou, Guangdong 510631
| | - X Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - 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
| | - F Zhao
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - J Zhao
- Fudan University, Shanghai, 200433
| | - M Zhao
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Zhou
- Fudan University, Shanghai, 200433
| | - J Zhou
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - X Zhu
- Tsinghua University, Beijing 100084
| | - M Zurek
- Argonne National Laboratory, Argonne, Illinois 60439
- Brookhaven National Laboratory, Upton, New York 11973
| | - M Zyzak
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
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48
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Zhou J, Wu S, Chen X, Hou L, Zhong Q, Luo W, Dai C, Dai X. Macrophage Gpx4 deficiency aggravates foam cell formation by regulating the expression of scavenger receptors, ABCA1, and ABCG1. Cell Biol Int 2023. [PMID: 37309064 DOI: 10.1002/cbin.12057] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/28/2023] [Accepted: 05/31/2023] [Indexed: 06/14/2023]
Abstract
Macrophage-derived foam cell formation is critical for the initiation and development of atherosclerosis, which contributes to atherosclerotic cardiovascular disease (ASCVD). Glutathione peroxidase 4 (GPX4), a crucial ferroptosis regulator, protects cells from excessive oxidative stress by neutralizing lipid peroxidation. However, the role of macrophage GPX4 in foam cell formation remains unknown. We reported that oxidized low-density lipoprotein (oxLDL) upregulated GPX4 expression in macrophages. Using the Cre-loxP system, we generated myeloid cell-specific Gpx4 knockout (Gpx4myel-KO ) mice. Bone marrow-derived macrophages (BMDMs) were isolated from WT and Gpx4myel-KO mice and incubated with modified low-density lipoprotein (LDL). We found that Gpx4 deficiency promoted foam cell formation and increased the internalization of modified LDL. Mechanistic studies unveiled that Gpx4 knockout upregulated scavenger receptor type A and LOX-1 expression and downregulated ABCA1 and ABCG1 expression. Collectively, our study lends a novel insight into the role of GPX4 in suppressing macrophage-derived foam cell formation and suggests GPX4 as a promising therapeutic target to interfere with atherosclerosis-related diseases.
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Affiliation(s)
- Jingquan Zhou
- The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, China
- Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Suhua Wu
- Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xiaoqin Chen
- Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, Guangdong, China
| | - Lianjie Hou
- The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, China
| | - Qiong Zhong
- The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, China
| | - Weixia Luo
- The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, China
| | - Chunni Dai
- Zhangjiajie Center for Disease Control and Prevention, Zhangjiajie, Hunan, China
| | - Xiaoyan Dai
- Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, Guangdong, China
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49
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Shan TT, Zhao NC, Zhou J. Application of the concept of fast-track surgery in pediatric ophthalmic surgery. J Fr Ophtalmol 2023:S0181-5512(23)00185-7. [PMID: 37268534 DOI: 10.1016/j.jfo.2022.12.027] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 12/01/2022] [Accepted: 12/27/2022] [Indexed: 06/04/2023]
Abstract
PURPOSE To explore the effects of the fast-track surgery (FTS) approach during the perioperative period of ophthalmic surgery in pediatric patients. METHODS A bidirectional cohort design was applied in this study. The traditional nursing mode was followed in relation to 40 pediatric patients admitted for ophthalmic surgery in March 2018 (control group), whereas the FTS mode was followed with regard to 40 pediatric patients admitted for ophthalmic surgery in April 2018 (observation group). The effects of the FTS mode were determined by comparing the postoperative pain score, restlessness score, and the incidence of postoperative nausea and vomiting between the two groups. RESULTS The pain and restlessness scores of the patients at 4hours after surgery in the observation group were significantly decreased compared with those in the control group (P<0.01). The incidence of postoperative nausea and vomiting in the observation group was also slightly lower than that in the control group (P>0.05). CONCLUSION A perioperative FTS-based nursing mode can effectively alleviate the postoperative pain and restlessness of pediatric patients without increasing their stress response.
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Affiliation(s)
- T T Shan
- Department of Ophthalmology, Children's Hospital of Nanjing Medical University, No.8 Jiangdon South Road, Jianye District, Nanjing, 210000, China
| | - N C Zhao
- Department of Cardiology, Children's Hospital of Nanjing Medical University, No.8 Jiangdon South Road, Jianye District, Nanjing, 210000, China.
| | - J Zhou
- Department of Ophthalmology, Children's Hospital of Nanjing Medical University, No.8 Jiangdon South Road, Jianye District, Nanjing, 210000, China.
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50
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Aboona BE, Adam J, Adams JR, Agakishiev G, Aggarwal I, Aggarwal MM, Ahammed Z, Aitbaev A, Alekseev I, Anderson DM, Aparin A, Atchison J, Averichev GS, Bairathi V, Baker W, Ball Cap JG, Barish K, Bhagat P, Bhasin A, Bhatta S, Bordyuzhin IG, Brandenburg JD, Brandin AV, Cai XZ, Caines H, Calderón de la Barca Sánchez M, Cebra D, Ceska J, Chakaberia I, Chan BK, Chang Z, Chen D, Chen J, Chen JH, Chen Z, Cheng J, Cheng Y, Choudhury S, Christie W, Chu X, Crawford HJ, Dale-Gau G, Das A, 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, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fazio S, Feng CJ, Feng Y, Finch E, Fisyak Y, Flor FA, Fu C, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Hamed A, Han Y, Harasty MD, Harris JW, Harrison H, He W, He XH, He Y, Hu C, Hu Q, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Huang Y, Humanic TJ, Isenhower D, Isshiki M, Jacobs WW, Jalotra A, Jena C, Ji Y, Jia J, Jin C, Ju X, Judd EG, Kabana S, Kabir ML, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Ke HW, Keane D, Kechechyan A, Kelsey M, Kimelman B, Kiselev A, Knospe AG, Ko HS, Kochenda L, Korobitsin AA, Kravtsov P, Kumar L, Kumar S, Kunnawalkam Elayavalli R, Lacey R, Landgraf JM, Lebedev A, Lednicky R, Lee JH, Leung YH, Lewis N, Li C, Li C, Li W, Li X, Li Y, Li Y, Li Z, Liang X, Liang Y, Lin T, Liu C, Liu F, Liu H, Liu H, Liu L, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Lomicky O, Longacre RS, Loyd E, Lu T, Lukow NS, Luo XF, Luong VB, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Matis HS, Mazer JA, McNamara G, Mi K, Minaev NG, Mohanty B, Mooney I, Morozov DA, Mudrokh A, Nagy MI, Nain AS, Nam JD, Nasim M, 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, Okubo K, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Panebratsev Y, Pani T, Parfenov P, Paul A, Perkins C, Pokhrel BR, Posik M, Protzman T, Pruthi NK, Putschke J, Qin Z, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Ritter HG, Robertson CW, Rogachevsky OV, Rosales Aguilar MA, Roy D, Ruan L, Sahoo AK, Sahoo NR, Sako H, Salur S, Samigullin E, Sato S, Schmidke WB, Schmitz N, Seger J, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao M, Shao T, Sharma M, Sharma N, Sharma R, Sharma SR, Sheikh AI, Shen DY, Shen K, Shi SS, Shi Y, Shou QY, Si F, Singh J, Singha S, Sinha P, Skoby MJ, Söhngen Y, Song Y, Srivastava B, Stanislaus TDS, Stewart DJ, Strikhanov M, Stringfellow B, Su Y, Sun C, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Sweger ZW, Tamis A, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Tlusty D, Todoroki T, Tokarev MV, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tsai OD, Tsang CY, Tu Z, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vasiliev AN, Verkest V, Videbæk F, Vokal S, Voloshin SA, Wang F, Wang G, Wang JS, Wang X, Wang Y, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Westfall GD, Wieman H, Wilks G, Wissink SW, Wu J, Wu J, Wu X, Wu Y, Xi B, Xiao ZG, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Y, Xu Z, Xu Z, Yan G, Yan Z, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao F, Zhao J, Zhao M, Zhou C, Zhou J, Zhou S, Zhou Y, Zhu X, Zurek M, Zyzak M. Observation of Directed Flow of Hypernuclei _{Λ}^{3}H and _{Λ}^{4}H in sqrt[s_{NN}]=3 GeV Au+Au Collisions at RHIC. Phys Rev Lett 2023; 130:212301. [PMID: 37295104 DOI: 10.1103/physrevlett.130.212301] [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: 11/30/2022] [Revised: 01/24/2023] [Accepted: 03/02/2023] [Indexed: 06/12/2023]
Abstract
We report here the first observation of directed flow (v_{1}) of the hypernuclei _{Λ}^{3}H and _{Λ}^{4}H in mid-central Au+Au collisions at sqrt[s_{NN}]=3 GeV at RHIC. These data are taken as part of the beam energy scan program carried out by the STAR experiment. From 165×10^{6} events in 5%-40% centrality, about 8400 _{Λ}^{3}H and 5200 _{Λ}^{4}H candidates are reconstructed through two- and three-body decay channels. We observe that these hypernuclei exhibit significant directed flow. Comparing to that of light nuclei, it is found that the midrapidity v_{1} slopes of _{Λ}^{3}H and _{Λ}^{4}H follow baryon number scaling, implying that the coalescence is the dominant mechanism for these hypernuclei production in the 3 GeV Au+Au collisions.
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Affiliation(s)
- B E Aboona
- Texas A&M University, College Station, Texas 77843
| | - J Adam
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J R Adams
- Ohio State University, Columbus, Ohio 43210
| | - 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
| | - A Aitbaev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - 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
| | - J Atchison
- Abilene Christian University, Abilene, Texas 79699
| | | | - 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
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - S Bhatta
- State University of New York, Stony Brook, New York 11794
| | - 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
| | - 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
| | - J Ceska
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - I Chakaberia
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - Z Chang
- Indiana University, Bloomington, Indiana 47408
| | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai, 200433
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - Y Cheng
- University of California, Los Angeles, California 90095
| | | | - 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
| | - G Dale-Gau
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Das
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - 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
| | - 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
| | - S Fazio
- University of Calabria & INFN-Cosenza, Italy
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - F A Flor
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Central China Normal University, Wuhan, Hubei 430079
| | - 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
| | - A Hamed
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - 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
| | - 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
| | - C Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Q Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Hu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - 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
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Central China Normal University, Wuhan, Hubei 430079
| | | | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - M Isshiki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - A Jalotra
- University of Jammu, Jammu 180001, India
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - 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
| | - C Jin
- Rice University, Houston, Texas 77251
| | - 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
| | - D Kalinkin
- Brookhaven National Laboratory, Upton, New York 11973
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - 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
| | - B Kimelman
- University of California, Davis, California 95616
| | - 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
| | | | - 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
| | - J M Landgraf
- 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
| | - J H Lee
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y H Leung
- University of Heidelberg, Heidelberg 69120, Germany
| | - 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
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- Tsinghua University, Beijing 100084
| | - Z Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Liang
- University of California, Riverside, California 92521
| | - Y Liang
- Kent State University, Kent, Ohio 44242
| | - T Lin
- Shandong University, Qingdao, Shandong 266237
| | - C Liu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - 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
| | - L Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - 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
- Central China Normal University, Wuhan, Hubei 430079
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - O Lomicky
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - R S Longacre
- Brookhaven National Laboratory, Upton, New York 11973
| | - E Loyd
- University of California, Riverside, California 92521
| | - T Lu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
| | - X F Luo
- Central China Normal University, Wuhan, Hubei 430079
| | - V B Luong
- Joint Institute for Nuclear Research, Dubna 141 980
| | - L Ma
- Fudan University, Shanghai, 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y G Ma
- Fudan University, Shanghai, 200433
| | - N Magdy
- State University of New York, Stony Brook, New York 11794
| | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | | | - H S Matis
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J A Mazer
- Rutgers University, Piscataway, New Jersey 08854
| | - G McNamara
- Wayne State University, Detroit, Michigan 48201
| | - K Mi
- Central China Normal University, Wuhan, Hubei 430079
| | - 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
| | - I Mooney
- Yale University, New Haven, Connecticut 06520
| | - D A Morozov
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - A Mudrokh
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M I Nagy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - A S Nain
- Panjab University, Chandigarh 160014, India
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - Md Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - 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
| | - K Okubo
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - 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
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - T Pani
- Rutgers University, Piscataway, New Jersey 08854
| | - P Parfenov
- National Research Nuclear University MEPhI, Moscow 115409
| | - A Paul
- University of California, Riverside, California 92521
| | - C Perkins
- University of California, Berkeley, California 94720
| | - B R Pokhrel
- Temple University, Philadelphia, Pennsylvania 19122
| | - M Posik
- Temple University, Philadelphia, Pennsylvania 19122
| | - T Protzman
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - N K Pruthi
- Panjab University, Chandigarh 160014, India
| | - J Putschke
- Wayne State University, Detroit, Michigan 48201
| | - Z Qin
- Tsinghua University, Beijing 100084
| | - 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
| | - H G Ritter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | | | - D Roy
- Rutgers University, Piscataway, New Jersey 08854
| | - L Ruan
- Brookhaven National Laboratory, Upton, New York 11973
| | - 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
| | - E Samigullin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | - 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
| | - J Seger
- Creighton University, Omaha, Nebraska 68178
| | - 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
| | - M Sharma
- University of Jammu, Jammu 180001, India
| | - N Sharma
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - S R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | | | - D Y Shen
- Fudan University, Shanghai, 200433
| | - K Shen
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S S Shi
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
| | - Q Y Shou
- Fudan University, Shanghai, 200433
| | - F Si
- University of Science and Technology of China, Hefei, Anhui 230026
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - P Sinha
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - M J Skoby
- Ball State University, Muncie, Indiana, 47306
- Purdue University, West Lafayette, Indiana 47907
| | - Y Söhngen
- University of Heidelberg, Heidelberg 69120, Germany
| | - Y Song
- Yale University, New Haven, Connecticut 06520
| | - B Srivastava
- Purdue University, West Lafayette, Indiana 47907
| | | | - D J Stewart
- Wayne State University, Detroit, Michigan 48201
| | - M Strikhanov
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - Y Su
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Sun
- State University of New York, Stony Brook, New York 11794
| | - X Sun
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - 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
| | - A Tamis
- Yale University, New Haven, Connecticut 06520
| | - 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 V 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
| | - O D Tsai
- Brookhaven National Laboratory, Upton, New York 11973
- University of California, Los Angeles, California 90095
| | - C Y Tsang
- Brookhaven National Laboratory, Upton, New York 11973
- Kent State University, Kent, Ohio 44242
| | - 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
| | - A N Vasiliev
- National Research Nuclear University MEPhI, Moscow 115409
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - 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
| | - X Wang
- Shandong University, Qingdao, Shandong 266237
| | - Y 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
| | | | - G D Westfall
- Michigan State University, East Lansing, Michigan 48824
| | - H Wieman
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - G Wilks
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - 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
| | - X Wu
- University of California, Los Angeles, California 90095
| | - 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
| | - 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
| | - Y Xu
- Central China Normal University, Wuhan, Hubei 430079
| | - 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
| | - Z Yan
- State University of New York, Stony Brook, New York 11794
| | - C Yang
- Shandong University, Qingdao, Shandong 266237
| | - Q Yang
- Shandong University, Qingdao, Shandong 266237
| | - S Yang
- South China Normal University, Guangzhou, Guangdong 510631
| | - 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
| | - 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 Science and Technology of China, Hefei, Anhui 230026
| | - X Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - 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
| | - F Zhao
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - J Zhao
- Fudan University, Shanghai, 200433
| | - M Zhao
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Zhou
- Fudan University, Shanghai, 200433
| | - J Zhou
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - 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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