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Li X, Liu W, Zhang J, Wang Z, Guo Z, Ali J, Wang L, Yu Z, Zhang X, Sun Y. Effective removal of microplastics by filamentous algae and its magnetic biochar: performance and mechanism. Chemosphere 2024; 358:142152. [PMID: 38679178 DOI: 10.1016/j.chemosphere.2024.142152] [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] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 04/06/2024] [Accepted: 04/24/2024] [Indexed: 05/01/2024]
Abstract
In recent years, filamentous algae blooms and microplastics (MPs) pollution have become two major ecological and environmental problems in urban water systems. In order to solve these two problems at the same time, this study explored the loading capacity of MPs on fresh filamentous algae, and successfully synthesized magnetic filamentous algae biochar loading with Fe3O4 by hydrothermal method, with the purpose of removing MPs from water. The magnetic filamentous algal biochar was characterized by scanning electron microscopy (SEM), Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and so on. Experiments on adsorption kinetics, adsorption isotherms and optimum pH were carried out to explore the adsorption mechanism of MPs on magnetic filamentous algal biochar. The adsorption kinetics and adsorption isotherm models were evaluated, and the selection criterion for the appropriate model was determined by using the residual sum of squares (RSS) and Bayesian information criterion (BIC). Microscope images revealed that fresh filamentous algae could interact with MPs in the form of entanglement, adhesion and encapsulation. The average load of MPs in filamentous algae samples was 14.1 ± 5 items/g dry weight. The theoretical maximum adsorption capacities of polystyrene MPs (PS-MPs) by raw biochar (A500) and magnetic biochar with Fe3O4 (M2A500) were 176.99 mg/g and 215.58 mg/g, respectively. The adsorbent materials gave better reusability because they could be reused up to five times. Overall, these findings have provided new insights into the use of filamentous algae for in situ remediation of fluvial MPs pollution, as well as feasible strategies for the recycling of algal waste.
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Affiliation(s)
- Xinyang Li
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
| | - Wenjia Liu
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingshen Zhang
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Zhibin Wang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Zhiwei Guo
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
| | - Jafar Ali
- Key Lab of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun 130021, China
| | - Lei Wang
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Zhisheng Yu
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiru Zhang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yangzhao Sun
- Norwegian Water Research Institute, Økernveien 94, 0579 Oslo, Norway
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Jia Y, Liu W, Wang J, Zhang R, Li M, Liu S. A pair of twins with multicystic dysplastic kidney and hydrocephalus caused by a novel homozygous mutation in SPATA33 and CDK10. QJM 2024; 117:302-303. [PMID: 38180891 DOI: 10.1093/qjmed/hcad289] [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: 12/21/2023] [Indexed: 01/07/2024] Open
Affiliation(s)
- Y Jia
- Medical Genetic Department, The Affiliated Hospital of Qingdao University, NO.16 Jiang Su Road, Qingdao 266071, China
- Prenatal Diagnosis Center, The Affiliated Hospital of Qingdao University, NO.16 Jiang Su Road, Qingdao 266071, China
| | - W Liu
- Medical Genetic Department, The Affiliated Hospital of Qingdao University, NO.16 Jiang Su Road, Qingdao 266071, China
- Prenatal Diagnosis Center, The Affiliated Hospital of Qingdao University, NO.16 Jiang Su Road, Qingdao 266071, China
| | - J Wang
- Medical Genetic Department, The Affiliated Hospital of Qingdao University, NO.16 Jiang Su Road, Qingdao 266071, China
- Prenatal Diagnosis Center, The Affiliated Hospital of Qingdao University, NO.16 Jiang Su Road, Qingdao 266071, China
| | - R Zhang
- Medical Genetic Department, The Affiliated Hospital of Qingdao University, NO.16 Jiang Su Road, Qingdao 266071, China
- Prenatal Diagnosis Center, The Affiliated Hospital of Qingdao University, NO.16 Jiang Su Road, Qingdao 266071, China
| | - M Li
- Department of Urology, Qingdao Municipal Hospital Group, NO.5 Middle Dong Hai Road, Qingdao 266071, China
| | - S Liu
- Medical Genetic Department, The Affiliated Hospital of Qingdao University, NO.16 Jiang Su Road, Qingdao 266071, China
- Prenatal Diagnosis Center, The Affiliated Hospital of Qingdao University, NO.16 Jiang Su Road, Qingdao 266071, China
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Rosenberg E, Andersen TI, Samajdar R, Petukhov A, Hoke JC, Abanin D, Bengtsson A, Drozdov IK, Erickson C, Klimov PV, Mi X, Morvan A, Neeley M, Neill C, Acharya R, Allen R, Anderson K, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bilmes A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Campero J, Chang HS, Chen Z, Chiaro B, Chik D, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Barba ADT, Demura S, Di Paolo A, Dunsworth A, Earle C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Garcia G, Genois É, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Dau AG, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hill G, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Mandrà S, Martin O, Martin S, McClean JR, McEwen M, Meeks S, Miao KC, Mieszala A, Montazeri S, Movassagh R, Mruczkiewicz W, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Omonije S, Opremcak A, Potter R, Pryadko LP, Quintana C, Rhodes DM, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Sivak V, Skruzny J, Smith WC, Somma RD, Sterling G, Strain D, Szalay M, Thor D, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Khemani V, Gopalakrishnan S, Prosen T, Roushan P. Dynamics of magnetization at infinite temperature in a Heisenberg spin chain. Science 2024; 384:48-53. [PMID: 38574139 DOI: 10.1126/science.adi7877] [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: 05/18/2023] [Accepted: 03/01/2024] [Indexed: 04/06/2024]
Abstract
Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain's center, [Formula: see text]. The first two moments of [Formula: see text] show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.
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Affiliation(s)
- E Rosenberg
- Google Research, Mountain View, CA, USA
- Department of Physics, Cornell University, Ithaca, NY, USA
| | | | - R Samajdar
- Department of Physics, Princeton University, Princeton, NJ, USA
- Princeton Center for Theoretical Science, Princeton University, Princeton, NJ, USA
| | | | - J C Hoke
- Department of Physics, Stanford University, Stanford, CA, USA
| | - D Abanin
- Google Research, Mountain View, CA, USA
| | | | - I K Drozdov
- Google Research, Mountain View, CA, USA
- Department of Physics, University of Connecticut, Storrs, CT, USA
| | | | | | - X Mi
- Google Research, Mountain View, CA, USA
| | - A Morvan
- Google Research, Mountain View, CA, USA
| | - M Neeley
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | - R Acharya
- Google Research, Mountain View, CA, USA
| | - R Allen
- Google Research, Mountain View, CA, USA
| | | | - M Ansmann
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - A Bilmes
- Google Research, Mountain View, CA, USA
| | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - J Campero
- Google Research, Mountain View, CA, USA
| | - H-S Chang
- Google Research, Mountain View, CA, USA
| | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - D Chik
- Google Research, Mountain View, CA, USA
| | - J Cogan
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | | | - C Earle
- Google Research, Mountain View, CA, USA
| | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - G Garcia
- Google Research, Mountain View, CA, USA
| | - É Genois
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | - R Gosula
- Google Research, Mountain View, CA, USA
| | | | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | - M C Hamilton
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - M Hansen
- Google Research, Mountain View, CA, USA
| | | | | | - P Heu
- Google Research, Mountain View, CA, USA
| | - G Hill
- Google Research, Mountain View, CA, USA
| | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Research, Mountain View, CA, USA
| | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- QSI, Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Kitaev
- Google Research, Mountain View, CA, USA
| | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | - S Mandrà
- Google Research, Mountain View, CA, USA
| | - O Martin
- Google Research, Mountain View, CA, USA
| | - S Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
| | - S Meeks
- Google Research, Mountain View, CA, USA
| | - K C Miao
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - J H Ng
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | - S Omonije
- Google Research, Mountain View, CA, USA
| | | | - R Potter
- Google Research, Mountain View, CA, USA
| | - L P Pryadko
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
| | | | | | - C Rocque
- Google Research, Mountain View, CA, USA
| | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - N Shutty
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - V Sivak
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | | | - R D Somma
- Google Research, Mountain View, CA, USA
| | | | - D Strain
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - D Thor
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - B W K Woo
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | | | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - G Young
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - N Zobrist
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | | | - V Khemani
- Department of Physics, Stanford University, Stanford, CA, USA
| | | | - T Prosen
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - P Roushan
- Google Research, Mountain View, CA, USA
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Yu X, Xiang J, Zhang Q, Chen S, Tang W, Li X, Sui Y, Liu W, Kong Q, Guo Y. Corrigendum to Triple-negative breast cancer: predictive model of early recurrence based on MRI features [78 (11) e798-e807]. Clin Radiol 2024; 79:e640. [PMID: 38316571 DOI: 10.1016/j.crad.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Affiliation(s)
- X Yu
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - J Xiang
- Guangdong Women and Children Hospital, No. 13 West Guangyuan Road, Guangzhou, Guangdong, 510010, China
| | - Q Zhang
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - S Chen
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - W Tang
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - X Li
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Y Sui
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - W Liu
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China.
| | - Q Kong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China.
| | - Y Guo
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China.
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Qi W, Cui L, Jiajue R, Pang Q, Chi Y, Liu W, Jiang Y, Wang O, Li M, Xing X, Tong A, Xia W. Deteriorated bone microarchitecture caused by sympathetic overstimulation in pheochromocytoma and paraganglioma. J Endocrinol Invest 2024; 47:843-856. [PMID: 37872466 DOI: 10.1007/s40618-023-02198-x] [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: 07/20/2023] [Accepted: 09/12/2023] [Indexed: 10/25/2023]
Abstract
PURPOSE Despite the potentially destructive effect of sympathetic activity on bone metabolism, its impact on bone microarchitecture, a key determinant of bone quality, has not been thoroughly investigated. This study aims to evaluate the impact of sympathetic activity on bone microarchitecture and bone strength in patients with pheochromocytoma and paraganglioma (PPGL). METHODS A cross-sectional study was conducted in 38 PPGL patients (15 males and 23 females). Bone turnover markers serum procollagen type 1 N-terminal propeptide (P1NP) and β-carboxy-terminal crosslinked telopeptide of type 1 collagen (β-CTX) were measured. 24-h urinary adrenaline (24hUE) and 24-h urinary norepinephrine levels (24hUNE) were measured to indicate sympathetic activity. High-resolution peripheral quantitative computed tomography (HR-pQCT) was conducted to evaluate bone microarchitecture in PPGL patients and 76 age-, sex-matched healthy controls (30 males and 46 females). Areal bone mineral density (aBMD) was measured by dual-energy X-ray absorptiometry (DXA) simultaneously. RESULTS PPGL patients had a higher level of β-CTX. HR-pQCT assessment revealed that PPGL patients had notably thinner and more sparse trabecular bone (decreased trabecular number and thickness with increased trabecular separation), significantly decreased volume BMD (vBMD), and bone strength at both the radius and tibia compared with healthy controls. The deterioration of Tt.vBMD, Tb.Sp, and Tb.1/N.SD was more pronounced in postmenopausal patients compared with the premenopausal subjects. Moreover, subjects in the highest 24hUNE quartile (Q4) showed markedly lower Tb.N and higher Tb.Sp and Tb.1/N.SD at the tibia than those in the lowest quartile (Q1). Age-related bone loss was also exacerbated in PPGL patients to a certain extent. CONCLUSIONS PPGL patients had significantly deteriorated bone microarchitecture and strength, especially in the trabecular bone, with an increased bone resorption rate. Our findings provide clinical evidence that sympathetic overstimulation may serve as a secondary cause of osteoporosis, especially in subjects with increased sympathetic activity.
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Affiliation(s)
- W Qi
- Department of Endocrinology, Key Laboratory of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Dongcheng District, National Commission of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Beijing, 100730, China
| | - L Cui
- Department of Endocrinology, Key Laboratory of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Dongcheng District, National Commission of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Beijing, 100730, China
| | - R Jiajue
- Department of Endocrinology, Key Laboratory of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Dongcheng District, National Commission of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Beijing, 100730, China
| | - Q Pang
- Department of Endocrinology, Key Laboratory of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Dongcheng District, National Commission of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Beijing, 100730, China
| | - Y Chi
- Department of Endocrinology, Key Laboratory of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Dongcheng District, National Commission of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Beijing, 100730, China
| | - W Liu
- Department of Endocrinology, Key Laboratory of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Dongcheng District, National Commission of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Beijing, 100730, China
| | - Y Jiang
- Department of Endocrinology, Key Laboratory of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Dongcheng District, National Commission of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Beijing, 100730, China
| | - O Wang
- Department of Endocrinology, Key Laboratory of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Dongcheng District, National Commission of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Beijing, 100730, China
| | - M Li
- Department of Endocrinology, Key Laboratory of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Dongcheng District, National Commission of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Beijing, 100730, China
| | - X Xing
- Department of Endocrinology, Key Laboratory of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Dongcheng District, National Commission of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Beijing, 100730, China
| | - A Tong
- Department of Endocrinology, Key Laboratory of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Dongcheng District, National Commission of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Beijing, 100730, China.
| | - W Xia
- Department of Endocrinology, Key Laboratory of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Dongcheng District, National Commission of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Beijing, 100730, China.
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6
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Wen X, Zhao C, Zhao B, Yuan M, Chang J, Liu W, Meng J, Shi L, Yang S, Zeng J, Yang Y. Application of deep learning in radiation therapy for cancer. Cancer Radiother 2024; 28:208-217. [PMID: 38519291 DOI: 10.1016/j.canrad.2023.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/17/2023] [Accepted: 07/18/2023] [Indexed: 03/24/2024]
Abstract
In recent years, with the development of artificial intelligence, deep learning has been gradually applied to clinical treatment and research. It has also found its way into the applications in radiotherapy, a crucial method for cancer treatment. This study summarizes the commonly used and latest deep learning algorithms (including transformer, and diffusion models), introduces the workflow of different radiotherapy, and illustrates the application of different algorithms in different radiotherapy modules, as well as the defects and challenges of deep learning in the field of radiotherapy, so as to provide some help for the development of automatic radiotherapy for cancer.
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Affiliation(s)
- X Wen
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - C Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Minhang District, Shanghai, China
| | - B Zhao
- Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - M Yuan
- Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - J Chang
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - W Liu
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - J Meng
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - L Shi
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - S Yang
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - J Zeng
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - Y Yang
- Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
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7
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Yang Q, Yi SH, Fu BS, Zhang T, Zeng KN, Feng X, Yao J, Tang H, Li H, Zhang J, Zhang YC, Yi HM, Lyu HJ, Liu JR, Luo GJ, Ge M, Yao WF, Ren FF, Zhuo JF, Luo H, Zhu LP, Ren J, Lyu Y, Wang KX, Liu W, Chen GH, Yang Y. [Clinical application of split liver transplantation: a single center report of 203 cases]. Zhonghua Wai Ke Za Zhi 2024; 62:324-330. [PMID: 38432674 DOI: 10.3760/cma.j.cn112139-20231225-00297] [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 investigate the safety and therapeutic effect of split liver transplantation (SLT) in clinical application. Methods: This is a retrospective case-series study. The clinical data of 203 consecutive SLT, 79 living donor liver transplantation (LDLT) and 1 298 whole liver transplantation (WLT) performed at the Third Affiliated Hospital of Sun Yat-sen University from July 2014 to July 2023 were retrospectively analyzed. Two hundred and three SLT liver grafts were obtained from 109 donors. One hundred and twenty-seven grafts were generated by in vitro splitting and 76 grafts were generated by in vivo splitting. There were 90 adult recipients and 113 pediatric recipients. According to time, SLT patients were divided into two groups: the early SLT group (40 cases, from July 2014 to December 2017) and the mature SLT technology group (163 cases, from January 2018 to July 2023). The survival of each group was analyzed and the main factors affecting the survival rate of SLT were analyzed. The Kaplan-Meier method and Log-rank test were used for survival analysis. Results: The cumulative survival rates at 1-, 3-, and 5-year were 74.58%, 71.47%, and 71.47% in the early SLT group, and 88.03%, 87.23%, and 87.23% in the mature SLT group, respectively. Survival rates in the mature SLT group were significantly higher than those in the early SLT group (χ2=5.560,P=0.018). The cumulative survival rates at 1-, 3- and 5-year were 93.41%, 93.41%, 89.95% in the LDLT group and 87.38%, 81.98%, 77.04% in the WLT group, respectively. There was no significant difference among the mature SLT group, the LDLT group and the WLT group (χ2=4.016, P=0.134). Abdominal hemorrhage, infection, primary liver graft nonfunction,and portal vein thrombosis were the main causes of early postoperative death. Conclusion: SLT can achieve results comparable to those of WLT and LDLT in mature technology liver transplant centers, but it needs to go through a certain time learning curve.
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Affiliation(s)
- Q Yang
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - S H Yi
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - B S Fu
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - T Zhang
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - K N Zeng
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - X Feng
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - J Yao
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - H Tang
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - H Li
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - J Zhang
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - Y C Zhang
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - H M Yi
- Organ transplant Intensive Care Unit, the Third Affiliated Hospital of Sun Yat-sen University,Guangzhou 510630
| | - H J Lyu
- Organ transplant Intensive Care Unit, the Third Affiliated Hospital of Sun Yat-sen University,Guangzhou 510630
| | - J R Liu
- Organ transplant Intensive Care Unit, the Third Affiliated Hospital of Sun Yat-sen University,Guangzhou 510630
| | - G J Luo
- Anesthesia & Surgery Center, the Third Affiliated Hospital of Sun Yat-sen University ,Guangzhou 510630
| | - M Ge
- Anesthesia & Surgery Center, the Third Affiliated Hospital of Sun Yat-sen University ,Guangzhou 510630
| | - W F Yao
- Anesthesia & Surgery Center, the Third Affiliated Hospital of Sun Yat-sen University ,Guangzhou 510630
| | - F F Ren
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - J F Zhuo
- Organ transplant Intensive Care Unit, the Third Affiliated Hospital of Sun Yat-sen University,Guangzhou 510630
| | - H Luo
- Anesthesia & Surgery Center, the Third Affiliated Hospital of Sun Yat-sen University ,Guangzhou 510630
| | - L P Zhu
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - J Ren
- Ultrasound Department of the Third Affiliated Hospital of Sun Yat-sen University,Guangzhou 510630
| | - Y Lyu
- Ultrasound Department of the Third Affiliated Hospital of Sun Yat-sen University,Guangzhou 510630
| | - K X Wang
- Organ Donation Department of the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - W Liu
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - G H Chen
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
| | - Y Yang
- Liver Surgery & Liver Transplantation Center, the Third Affiliated Hospital of Sun Yat-sen University, Institute of Organ Transplantation, Sun Yat-sen University, Guangdong Organ Transplantation Research Center, Guangdong Transplantation Medical Engineering Laboratory, Guangdong Provincial Key Laboratory of Liver Diseases, Guangzhou 510630
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8
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Cao Z, Aharonian F, Axikegu, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Bian W, Bukevich AV, Cao Q, Cao WY, Cao Z, Chang J, Chang JF, Chen AM, Chen ES, Chen HX, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen S, Chen SH, Chen SZ, Chen TL, Chen Y, Cheng N, Cheng YD, Cui MY, Cui SW, Cui XH, Cui YD, Dai BZ, Dai HL, Dai ZG, Danzengluobu, Dong XQ, Duan KK, Fan JH, Fan YZ, Fang J, Fang JH, Fang K, Feng CF, Feng H, Feng L, Feng SH, Feng XT, Feng Y, Feng YL, Gabici S, Gao B, Gao CD, Gao Q, Gao W, Gao WK, Ge MM, Geng LS, Giacinti G, Gong GH, Gou QB, Gu MH, Guo FL, Guo XL, Guo YQ, Guo YY, Han YA, Hasan M, He HH, He HN, He JY, He Y, Hor YK, Hou BW, Hou C, Hou X, Hu HB, Hu Q, Hu SC, Huang DH, Huang TQ, Huang WJ, Huang XT, Huang XY, Huang Y, Ji XL, Jia HY, Jia K, Jiang K, Jiang XW, Jiang ZJ, Jin M, Kang MM, Karpikov I, Kuleshov D, Kurinov K, Li BB, Li CM, Li C, Li C, Li D, Li F, Li HB, Li HC, Li J, Li J, Li K, Li SD, Li WL, Li WL, Li XR, Li X, Li YZ, Li Z, Li Z, Liang EW, Liang YF, Lin SJ, Liu B, Liu C, Liu D, Liu DB, Liu H, Liu HD, Liu J, Liu JL, Liu MY, Liu RY, Liu SM, Liu W, Liu Y, Liu YN, Luo Q, Luo Y, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Min Z, Mitthumsiri W, Mu HJ, Nan YC, Neronov A, Ou LJ, Pattarakijwanich P, Pei ZY, Qi JC, Qi MY, Qiao BQ, Qin JJ, Raza A, Ruffolo D, Sáiz A, Saeed M, Semikoz D, Shao L, Shchegolev O, Sheng XD, Shu FW, Song HC, Stenkin YV, Stepanov V, Su Y, Sun DX, Sun QN, Sun XN, Sun ZB, Takata J, Tam PHT, Tang QW, Tang R, Tang ZB, Tian WW, Wang C, Wang CB, Wang GW, Wang HG, Wang HH, Wang JC, Wang K, Wang K, Wang LP, Wang LY, Wang PH, Wang R, Wang W, Wang XG, Wang XY, Wang Y, Wang YD, Wang YJ, Wang ZH, Wang ZX, Wang Z, Wang Z, Wei DM, Wei JJ, Wei YJ, Wen T, Wu CY, Wu HR, Wu QW, Wu S, Wu XF, Wu YS, Xi SQ, Xia J, Xiang GM, Xiao DX, Xiao G, Xin YL, Xing Y, Xiong DR, Xiong Z, Xu DL, Xu RF, Xu RX, Xu WL, Xue L, Yan DH, Yan JZ, Yan T, Yang CW, Yang CY, Yang F, Yang FF, Yang LL, Yang MJ, Yang RZ, Yang WX, Yao YH, Yao ZG, Yin LQ, Yin N, You XH, You ZY, Yu YH, Yuan Q, Yue H, Zeng HD, Zeng TX, Zeng W, Zha M, Zhang BB, Zhang F, Zhang H, Zhang HM, Zhang HY, Zhang JL, Zhang L, Zhang PF, Zhang PP, Zhang R, Zhang SB, Zhang SR, Zhang SS, Zhang X, Zhang XP, Zhang YF, Zhang Y, Zhang Y, Zhao B, Zhao J, Zhao L, Zhao LZ, Zhao SP, Zhao XH, Zheng F, Zhong WJ, Zhou B, Zhou H, Zhou JN, Zhou M, Zhou P, Zhou R, Zhou XX, Zhou XX, Zhu BY, Zhu CG, Zhu FR, Zhu H, Zhu KJ, Zou YC, Zuo X. Measurements of All-Particle Energy Spectrum and Mean Logarithmic Mass of Cosmic Rays from 0.3 to 30 PeV with LHAASO-KM2A. Phys Rev Lett 2024; 132:131002. [PMID: 38613275 DOI: 10.1103/physrevlett.132.131002] [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: 11/13/2023] [Revised: 01/23/2024] [Accepted: 02/12/2024] [Indexed: 04/14/2024]
Abstract
We present the measurements of all-particle energy spectrum and mean logarithmic mass of cosmic rays in the energy range of 0.3-30 PeV using data collected from LHAASO-KM2A between September 2021 and December 2022, which is based on a nearly composition-independent energy reconstruction method, achieving unprecedented accuracy. Our analysis reveals the position of the knee at 3.67±0.05±0.15 PeV. Below the knee, the spectral index is found to be -2.7413±0.0004±0.0050, while above the knee, it is -3.128±0.005±0.027, with the sharpness of the transition measured with a statistical error of 2%. The mean logarithmic mass of cosmic rays is almost heavier than helium in the whole measured energy range. It decreases from 1.7 at 0.3 PeV to 1.3 at 3 PeV, representing a 24% decline following a power law with an index of -0.1200±0.0003±0.0341. This is equivalent to an increase in abundance of light components. Above the knee, the mean logarithmic mass exhibits a power law trend towards heavier components, which is reversal to the behavior observed in the all-particle energy spectrum. Additionally, the knee position and the change in power-law index are approximately the same. These findings suggest that the knee observed in the all-particle spectrum corresponds to the knee of the light component, rather than the medium-heavy components.
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Affiliation(s)
- Zhen Cao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - F Aharonian
- Dublin Institute for Advanced Studies, 31 Fitzwilliam Place, 2 Dublin, Ireland
- Max-Planck-Institut for Nuclear Physics, P.O. Box 103980, 69029 Heidelberg, Germany
| | - Axikegu
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y X Bai
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y W Bao
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - D Bastieri
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - X J Bi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y J Bi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W Bian
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - A V Bukevich
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - Q Cao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - W Y Cao
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Zhe Cao
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - J Chang
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J F Chang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - A M Chen
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - E S Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H X Chen
- Research Center for Astronomical Computing, Zhejiang Laboratory, 311121 Hangzhou, Zhejiang, China
| | - Liang Chen
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - Lin Chen
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Long Chen
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - M J Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M L Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Q H Chen
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - S Chen
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - S H Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - S Z Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - T L Chen
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - Y Chen
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - N Cheng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y D Cheng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M Y Cui
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S W Cui
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - X H Cui
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - Y D Cui
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - B Z Dai
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - H L Dai
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Z G Dai
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Danzengluobu
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - X Q Dong
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - K K Duan
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J H Fan
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - Y Z Fan
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J Fang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - J H Fang
- Research Center for Astronomical Computing, Zhejiang Laboratory, 311121 Hangzhou, Zhejiang, China
| | - K Fang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C F Feng
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - H Feng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
| | - L Feng
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S H Feng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X T Feng
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - Y Feng
- Research Center for Astronomical Computing, Zhejiang Laboratory, 311121 Hangzhou, Zhejiang, China
| | - Y L Feng
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - S Gabici
- APC, Université Paris Cité, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, 119 75205 Paris, France
| | - B Gao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C D Gao
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - Q Gao
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - W Gao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W K Gao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M M Ge
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - L S Geng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - G Giacinti
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - G H Gong
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Q B Gou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M H Gu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - F L Guo
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - X L Guo
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y Q Guo
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y Y Guo
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y A Han
- School of Physics and Microelectronics, Zhengzhou University, 450001 Zhengzhou, Henan, China
| | - M Hasan
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H H He
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H N He
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J Y He
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y He
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y K Hor
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - B W Hou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C Hou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X Hou
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - H B Hu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Q Hu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S C Hu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- China Center of Advanced Science and Technology, Beijing 100190, China
| | - D H Huang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - T Q Huang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W J Huang
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - X T Huang
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - X Y Huang
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y Huang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X L Ji
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - H Y Jia
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - K Jia
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - K Jiang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - X W Jiang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z J Jiang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - M Jin
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - M M Kang
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - I Karpikov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - D Kuleshov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - K Kurinov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - B B Li
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - C M Li
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - Cheng Li
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Cong Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - F Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - H B Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H C Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Jian Li
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Jie Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - K Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - S D Li
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - W L Li
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - W L Li
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - X R Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Xin Li
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Y Z Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Zhe Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Zhuo Li
- School of Physics, Peking University, 100871 Beijing, China
| | - E W Liang
- Guangxi Key Laboratory for Relativistic Astrophysics, School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - Y F Liang
- Guangxi Key Laboratory for Relativistic Astrophysics, School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - S J Lin
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - B Liu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - C Liu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Liu
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - D B Liu
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - H Liu
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - H D Liu
- School of Physics and Microelectronics, Zhengzhou University, 450001 Zhengzhou, Henan, China
| | - J Liu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J L Liu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M Y Liu
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - R Y Liu
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - S M Liu
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - W Liu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y Liu
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - Y N Liu
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Q Luo
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - Y Luo
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - H K Lv
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B Q Ma
- School of Physics, Peking University, 100871 Beijing, China
| | - L L Ma
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X H Ma
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J R Mao
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - Z Min
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W Mitthumsiri
- Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - H J Mu
- School of Physics and Microelectronics, Zhengzhou University, 450001 Zhengzhou, Henan, China
| | - Y C Nan
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - A Neronov
- APC, Université Paris Cité, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, 119 75205 Paris, France
| | - L J Ou
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - P Pattarakijwanich
- Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - Z Y Pei
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - J C Qi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M Y Qi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B Q Qiao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J J Qin
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - A Raza
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Ruffolo
- Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - A Sáiz
- Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - M Saeed
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Semikoz
- APC, Université Paris Cité, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, 119 75205 Paris, France
| | - L Shao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - O Shchegolev
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
- Moscow Institute of Physics and Technology, 141700 Moscow, Russia
| | - X D Sheng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - F W Shu
- Center for Relativistic Astrophysics and High Energy Physics, School of Physics and Materials Science and Institute of Space Science and Technology, Nanchang University, 330031 Nanchang, Jiangxi, China
| | - H C Song
- School of Physics, Peking University, 100871 Beijing, China
| | - Yu V Stenkin
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
- Moscow Institute of Physics and Technology, 141700 Moscow, Russia
| | - V Stepanov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - Y Su
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - D X Sun
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Q N Sun
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - X N Sun
- Guangxi Key Laboratory for Relativistic Astrophysics, School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - Z B Sun
- National Space Science Center, Chinese Academy of Sciences, 100190 Beijing, China
| | - J Takata
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - P H T Tam
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - Q W Tang
- Center for Relativistic Astrophysics and High Energy Physics, School of Physics and Materials Science and Institute of Space Science and Technology, Nanchang University, 330031 Nanchang, Jiangxi, China
| | - R Tang
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Z B Tang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - W W Tian
- University of Chinese Academy of Sciences, 100049 Beijing, China
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - C Wang
- National Space Science Center, Chinese Academy of Sciences, 100190 Beijing, China
| | - C B Wang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - G W Wang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - H G Wang
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - H H Wang
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - J C Wang
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - Kai Wang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - Kai Wang
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - L P Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - L Y Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - P H Wang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - R Wang
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - W Wang
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - X G Wang
- Guangxi Key Laboratory for Relativistic Astrophysics, School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - X Y Wang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - Y Wang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y D Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y J Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z H Wang
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - Z X Wang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - Zhen Wang
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Zheng Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - D M Wei
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J J Wei
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y J Wei
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - T Wen
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - C Y Wu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H R Wu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Q W Wu
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - S Wu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X F Wu
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y S Wu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - S Q Xi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J Xia
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - G M Xiang
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - D X Xiao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - G Xiao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y L Xin
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y Xing
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - D R Xiong
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - Z Xiong
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D L Xu
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - R F Xu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - R X Xu
- School of Physics, Peking University, 100871 Beijing, China
| | - W L Xu
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - L Xue
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - D H Yan
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - J Z Yan
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - T Yan
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C W Yang
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - C Y Yang
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - F Yang
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - F F Yang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - L L Yang
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - M J Yang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - R Z Yang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - W X Yang
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - Y H Yao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z G Yao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - L Q Yin
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - N Yin
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - X H You
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z Y You
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y H Yu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Q Yuan
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - H Yue
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H D Zeng
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - T X Zeng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - W Zeng
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - M Zha
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B B Zhang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - F Zhang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - H Zhang
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - H M Zhang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - H Y Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J L Zhang
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - Li Zhang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - P F Zhang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - P P Zhang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - R Zhang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S B Zhang
- University of Chinese Academy of Sciences, 100049 Beijing, China
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - S R Zhang
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - S S Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X Zhang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - X P Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y F Zhang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Yi Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Yong Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B Zhao
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - J Zhao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - L Zhao
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - L Z Zhao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - S P Zhao
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - X H Zhao
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - F Zheng
- National Space Science Center, Chinese Academy of Sciences, 100190 Beijing, China
| | - W J Zhong
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - B Zhou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H Zhou
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - J N Zhou
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - M Zhou
- Center for Relativistic Astrophysics and High Energy Physics, School of Physics and Materials Science and Institute of Space Science and Technology, Nanchang University, 330031 Nanchang, Jiangxi, China
| | - P Zhou
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - R Zhou
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - X X Zhou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X X Zhou
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - B Y Zhu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - C G Zhu
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - F R Zhu
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - H Zhu
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - K J Zhu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Y C Zou
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - X Zuo
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
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9
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Mi X, Michailidis AA, Shabani S, Miao KC, Klimov PV, Lloyd J, Rosenberg E, Acharya R, Aleiner I, Andersen TI, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Chou C, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Dau AG, Debroy DM, Del Toro Barba A, Demura S, Di Paolo A, Drozdov IK, Dunsworth A, Erickson C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Genois É, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Kechedzhi K, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Malone FD, Martin O, McClean JR, McEwen M, Mieszala A, Montazeri S, Morvan A, Movassagh R, Mruczkiewicz W, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Opremcak A, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Roushan P, Smelyanskiy V, Abanin DA. Stable quantum-correlated many-body states through engineered dissipation. Science 2024; 383:1332-1337. [PMID: 38513021 DOI: 10.1126/science.adh9932] [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: 03/27/2023] [Accepted: 02/13/2024] [Indexed: 03/23/2024]
Abstract
Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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Affiliation(s)
- X Mi
- Google Research, Mountain View, CA, USA
| | - A A Michailidis
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - S Shabani
- Google Research, Mountain View, CA, USA
| | - K C Miao
- Google Research, Mountain View, CA, USA
| | | | - J Lloyd
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | | | - R Acharya
- Google Research, Mountain View, CA, USA
| | - I Aleiner
- Google Research, Mountain View, CA, USA
| | | | - M Ansmann
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | | | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - D Chik
- Google Research, Mountain View, CA, USA
| | - C Chou
- Google Research, Mountain View, CA, USA
| | - J Cogan
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | - A G Dau
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | | | | | | | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - É Genois
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | - R Gosula
- Google Research, Mountain View, CA, USA
| | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | - M C Hamilton
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - M Hansen
- Google Research, Mountain View, CA, USA
| | | | | | - P Heu
- Google Research, Mountain View, CA, USA
| | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Research, Mountain View, CA, USA
| | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | | | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- Centre for Quantum Software and Information (QSI), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Kitaev
- Google Research, Mountain View, CA, USA
| | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | | | - O Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
| | | | | | - A Morvan
- Google Research, Mountain View, CA, USA
| | | | | | - M Neeley
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - J H Ng
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | | | | | - R Potter
- Google Research, Mountain View, CA, USA
| | - L P Pryadko
- Google Research, Mountain View, CA, USA
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
| | | | - C Rocque
- Google Research, Mountain View, CA, USA
| | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - N Shutty
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | - W C Smith
- Google Research, Mountain View, CA, USA
| | - R Somma
- Google Research, Mountain View, CA, USA
| | | | - D Strain
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - B W K Woo
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | - Z J Yao
- Google Research, Mountain View, CA, USA
| | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - G Young
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - N Zobrist
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | - P Roushan
- Google Research, Mountain View, CA, USA
| | | | - D A Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
- Department of Physics, Princeton University, Princeton, NJ, USA
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10
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Liu W, Zhou W, Zhang Y, Ge X, Qi W, Lin T, Cao Q, Cao L. Strictureplasty may lead to increased preference in the surgical management of Crohn's disease: a case-matched study. Tech Coloproctol 2024; 28:40. [PMID: 38507096 DOI: 10.1007/s10151-024-02915-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 03/05/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND Resection and strictureplasty are the two surgical modalities used in the management of Crohn's disease (CD). The objective of this study was to compare morbidity and clinical recurrence between patients who underwent strictureplasty and patients who underwent resection. METHODS Patients with CD who underwent strictureplasty between January 2012 and December 2022 were enrolled. The patients were well matched with patients who underwent resection without strictureplasty. Patient- and disease-specific characteristics, postoperative morbidity, and clinical recurrence were also analyzed. RESULTS A total of 118 patients who underwent a total of 192 strictureplasties were well matched to 118 patients who underwent resection. The strictureplasty group exhibited significantly less blood loss (30 ml versus 50 ml, p < 0.001) and stoma creation (2.5% versus 16.9%, p < 0.001). No significant difference was found regarding postoperative complications or length of postoperative stay. At the end of the follow-up, the overall rate of clinical recurrence was 39.4%, and no difference was observed between the two groups. Postoperative prophylactic use of biologics (odds ratio = 0.2, p < 0.001) was the only protective factor against recurrence. CONCLUSION Strictureplasty does not increase the risk of complications or recurrence compared with resection. It represents a viable alternative to resection in selected patients, and as such, it should have a broader scope of indications and greater acceptance among surgeons.
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Affiliation(s)
- W Liu
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, People's Republic of China
- Inflammatory Bowel Disease Center, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People's Republic of China
| | - W Zhou
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, People's Republic of China.
- Inflammatory Bowel Disease Center, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People's Republic of China.
| | - Y Zhang
- School of Medicine, Shantou University, Shantou, 515063, Guangdong Province, China
| | - X Ge
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, People's Republic of China
- Inflammatory Bowel Disease Center, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People's Republic of China
| | - W Qi
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, People's Republic of China
- Inflammatory Bowel Disease Center, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People's Republic of China
| | - T Lin
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, People's Republic of China
| | - Q Cao
- Inflammatory Bowel Disease Center, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People's Republic of China
| | - L Cao
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, People's Republic of China.
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11
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Liu W, Zhao TT, Feng S, Ma H, Sun JC, Wei MH. [Follicular dendritic cell sarcoma of the tonsil: a case report]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2024; 59:260-262. [PMID: 38561267 DOI: 10.3760/cma.j.cn115330-20230921-00109] [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] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Affiliation(s)
- W Liu
- Department of Head and Neck Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzheng Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - T T Zhao
- Department of Head and Neck Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzheng Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - S Feng
- Department of Pathology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzheng Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - H Ma
- Department of Head and Neck Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzheng Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - J C Sun
- Department of Head and Neck Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzheng Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - M H Wei
- Department of Head and Neck Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzheng Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
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12
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Liu W, Lewis FM, Oxford M, Kantrowitz-Gordon I. Common dyadic coping and its congruence in couples facing breast cancer: The impact on couples' psychological distress. Psychooncology 2024; 33:e6314. [PMID: 38459736 DOI: 10.1002/pon.6314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 01/28/2024] [Accepted: 02/16/2024] [Indexed: 03/10/2024]
Abstract
OBJECTIVE Psychological distress is prevalent in couples facing breast cancer. Couples often deal with breast cancer as a unit instead of as individuals. Couple's dyadic coping is important for their adjustment to breast cancer; however, little is known about how couple's coping congruence influences their distress. This study examined how common dyadic coping (CDC) and coping congruence impact psychological distress in couples facing breast cancer. METHODS Baseline data were analyzed from 343 women with recently diagnosed early-stage breast cancer and their partners who participated in a randomized clinical trial. Psychological distress was indicated by depressed mood and state anxiety. Common dyadic coping was measured by a self-report scale. Coping congruence was assessed by the absolute difference between a woman's and her partner's CDC scores. RESULTS Higher CDC scores were associated with lower psychological distress in both women and partners. In the CDC subscales, women who suffered less scored higher on open communication, sharing a positive outlook, and lower on avoidance coping. Partners who suffered less scored higher on open communication, sharing a positive outlook, spending time talking, and lower on avoidance coping. Greater congruence in CDC was associated with lower psychological distress in women and their partners. Congruence in sharing a positive outlook benefited both members of the dyad; congruence in avoidance coping significantly benefited patients; congruence in open communication significantly benefited partners. CONCLUSIONS CDC and its congruence in specific areas have potential benefit to couple's psychological distress when facing breast cancer. Health care providers could consider enhancing couple's CDC and coping congruence to improve their adjustment.
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Affiliation(s)
- Wenjia Liu
- Department of Child, Family, and Population Health Nursing, University of Washington School of Nursing, Seattle, Washington, USA
| | - Frances Marcus Lewis
- Department of Child, Family, and Population Health Nursing, University of Washington School of Nursing, Seattle, Washington, USA
- Affiliate, Public Health Sciences & Member, Clinical Sciences Divisions, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Monica Oxford
- Department of Child, Family, and Population Health Nursing, University of Washington School of Nursing, Seattle, Washington, USA
| | - Ira Kantrowitz-Gordon
- Department of Child, Family, and Population Health Nursing, University of Washington School of Nursing, Seattle, Washington, USA
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13
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Shi L, Huang S, Liu W. Infection prevention in induction chemotherapy for childhood acute leukaemia. J Hosp Infect 2024; 145:226-227. [PMID: 38103693 DOI: 10.1016/j.jhin.2023.11.019] [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: 09/11/2023] [Revised: 10/28/2023] [Accepted: 11/06/2023] [Indexed: 12/19/2023]
Affiliation(s)
- L Shi
- Key Laboratory of Paediatric Haematology, Children's Hospital Affiliated to Zhengzhou University/Henan Children's Hospital, Zhengzhou, People's Republic of China.
| | - S Huang
- Department of Hematology and Oncology, Children's Hospital Affiliated to Zhengzhou University/Henan Children's Hospital, Zhengzhou, People's Republic of China
| | - W Liu
- Department of Hematology and Oncology, Children's Hospital Affiliated to Zhengzhou University/Henan Children's Hospital, Zhengzhou, People's Republic of China
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14
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Lai FTT, Liu W, Hu Y, Wei C, Chu RYK, Lum DH, Leung JCN, Cheng FWT, Chui CSL, Li X, Wan EYF, Wong CKH, Cheung CL, Chan EWY, Hung IFN, Wong ICK. Elevated risk of multimorbidity post-COVID-19 infection: protective effect of vaccination. QJM 2024; 117:125-132. [PMID: 37824396 DOI: 10.1093/qjmed/hcad236] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 10/05/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND It is unclear how the coronavirus disease 2019 (Covid-19) pandemic has affected multimorbidity incidence among those with one pre-existing chronic condition, as well as how vaccination could modify this association. AIM To examine the association of Covid-19 infection with multimorbidity incidence among people with one pre-existing chronic condition, including those with prior vaccination. DESIGN Nested case-control study. METHODS We conducted a territory-wide nested case-control study with incidence density sampling using Hong Kong electronic health records from public healthcare facilities and mandatory Covid-19 reports. People with one listed chronic condition (based on a list of 30) who developed multimorbidity during 1 January 2020-15 November 2022 were selected as case participants and randomly matched with up to 10 people of the same age, sex and with the same first chronic condition without having developed multimorbidity at that point. Conditional logistic regression was used to estimate adjusted odds ratios (aORs) of multimorbidity. RESULTS In total, 127 744 case participants were matched with 1 230 636 control participants. Adjusted analysis showed that there were 28%-increased odds of multimorbidity following Covid-19 [confidence interval (CI) 22% to 36%] but only 3% (non-significant) with prior full vaccination with BNT162b2 or CoronaVac (95% CI -2% to 7%). Similar associations were observed in men, women, older people aged 65 or more, and people aged 64 or younger. CONCLUSIONS We found a significantly elevated risk of multimorbidity following a Covid-19 episode among people with one pre-existing chronic condition. Full vaccination significantly reduced this risk increase.
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Affiliation(s)
- F T T Lai
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, Centre for Safe Medication Practice and Research, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Sha Tin, Hong Kong SAR, China
| | - W Liu
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, Centre for Safe Medication Practice and Research, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Y Hu
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, Centre for Safe Medication Practice and Research, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - C Wei
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, Centre for Safe Medication Practice and Research, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - R Y K Chu
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, Centre for Safe Medication Practice and Research, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - D H Lum
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, Centre for Safe Medication Practice and Research, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - J C N Leung
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, Centre for Safe Medication Practice and Research, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - F W T Cheng
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, Centre for Safe Medication Practice and Research, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - C S L Chui
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Sha Tin, Hong Kong SAR, China
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - X Li
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, Centre for Safe Medication Practice and Research, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Sha Tin, Hong Kong SAR, China
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - E Y F Wan
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, Centre for Safe Medication Practice and Research, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Sha Tin, Hong Kong SAR, China
| | - C K H Wong
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, Centre for Safe Medication Practice and Research, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Sha Tin, Hong Kong SAR, China
| | - C L Cheung
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, Centre for Safe Medication Practice and Research, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Sha Tin, Hong Kong SAR, China
| | - E W Y Chan
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, Centre for Safe Medication Practice and Research, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Sha Tin, Hong Kong SAR, China
| | - I F N Hung
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - I C K Wong
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, Centre for Safe Medication Practice and Research, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Sha Tin, Hong Kong SAR, China
- Aston Pharmacy School, Aston University, Birmingham, England, UK
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15
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Yang G, He Q, Guo X, Li RY, Lin J, Lang Y, Tao W, Liu W, Lin H, Xing S, Qi Y, Xie Z, Han JDJ, Zhou B, Teng Y, Yang X. Identification of the metaphyseal skeletal stem cell building trabecular bone. Sci Adv 2024; 10:eadl2238. [PMID: 38394209 PMCID: PMC10889359 DOI: 10.1126/sciadv.adl2238] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 01/22/2024] [Indexed: 02/25/2024]
Abstract
Skeletal stem cells (SSCs) that are capable of self-renewal and multipotent differentiation contribute to bone development and homeostasis. Several populations of SSCs at different skeletal sites have been reported. Here, we identify a metaphyseal SSC (mpSSC) population whose transcriptional landscape is distinct from other bone mesenchymal stromal cells (BMSCs). These mpSSCs are marked by Sstr2 or Pdgfrb+Kitl-, located just underneath the growth plate, and exclusively derived from hypertrophic chondrocytes (HCs). These HC-derived mpSSCs have properties of self-renewal and multipotency in vitro and in vivo, producing most HC offspring postnatally. HC-specific deletion of Hgs, a component of the endosomal sorting complex required for transport, impairs the HC-to-mpSSC conversion and compromises trabecular bone formation. Thus, mpSSC is the major source of BMSCs and osteoblasts in bone marrow, supporting the postnatal trabecular bone formation.
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Affiliation(s)
- Guan Yang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
| | - Qi He
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
- Bioinformatics Center of AMMS, Beijing 100850, China
| | - Xiaoxiao Guo
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China
| | - Rong-Yu Li
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
| | - Jingting Lin
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yiming Lang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
| | - Wanyu Tao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China
| | - Wenjia Liu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
| | - Huisang Lin
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
| | - Shilai Xing
- School of Ophthalmology & Optometry and Eye Hospital, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou 325027, China
| | - Yini Qi
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
| | - Zhongliang Xie
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
| | - Jing-Dong J. Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China
| | - Bin Zhou
- New Cornerstone Science Laboratory, State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yan Teng
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
| | - Xiao Yang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
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16
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Liu W, Jiang D, Schulz M, Figueiredo C, Dondossola D, Meister FA, Tihanyi DK, Mehrabi A, Tolba RH, Czigany Z, Ernst L. Machine perfusion of the liver and in vivo animal models: A systematic review of the preclinical research landscape. PLoS One 2024; 19:e0297942. [PMID: 38329986 PMCID: PMC10852327 DOI: 10.1371/journal.pone.0297942] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 01/15/2024] [Indexed: 02/10/2024] Open
Abstract
Machine perfusion (MP) is often referred to as one of the most promising advancements in liver transplantation research of the last few decades, with various techniques and modalities being evaluated in preclinical studies using animal models. However, low scientific rigor and subpar reporting standards lead to limited reproducibility and translational potential, hindering progress. This pre-registered systematic review (PROSPERO: CRD42021234667) aimed to provide a thematic overview of the preclinical research landscape on MP in liver transplantation using in vivo transplantation models and to explore methodological and reporting standards, using the ARRIVE (Animal Research: Reporting of In Vivo Experiments) score. In total 56 articles were included. Studies were evenly distributed across Asia, Europe, and the Americas. Porcine models were used in 57.1% of the studies, followed by rats (39.3%) and dogs (3.6%). In terms of graft type, 55.4% of the studies used donation after cardiac death grafts, while donation after brain death grafts accounted for 37.5%. Regarding MP modalities, the distribution was as follows: 41.5% of articles utilized hypothermic MP, 21.5% normothermic MP, 13.8% subnormothermic MP, and 16.9% utilized hypothermic oxygenated MP. The stringent documentation of ARRIVE elements concerning precise experimental execution, group size and selection, the choice of statistical methods, as well as adherence to the principles of the 3Rs, was notably lacking in the majority of publications, with less than 30% providing comprehensive details. Postoperative analgesia and antibiotics treatment were not documented in 82.1% of all included studies. None of the analyzed studies fully adhered to the ARRIVE Guidelines. In conclusion, the present study emphasizes the importance of adhering to reporting standards to promote reproducibility and adequate animal welfare in preclinical studies in machine perfusion. At the same time, it highlights a clear deficiency in this field, underscoring the need for further investigations into animal welfare-related topics.
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Affiliation(s)
- Wenjia Liu
- Department of Surgery and Transplantation, Faculty of Medicine, University Hospital RWTH Aachen, Aachen, Germany
- Institute for Laboratory Animal Science and Experimental Surgery, Faculty of Medicine, RWTH, Aachen International University, Aachen, Germany
| | - Decan Jiang
- Department of Surgery and Transplantation, Faculty of Medicine, University Hospital RWTH Aachen, Aachen, Germany
| | - Mareike Schulz
- Institute for Laboratory Animal Science and Experimental Surgery, Faculty of Medicine, RWTH, Aachen International University, Aachen, Germany
| | - Constança Figueiredo
- Institute of Transfusion Medicine and Transplant Engineering, Hannover Medical School, Hannover, Germany
| | - Daniele Dondossola
- General and Liver Transplant Surgery Unit, Fondazione IRCCS Ca’Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Franziska Alexandra Meister
- Department of Surgery and Transplantation, Faculty of Medicine, University Hospital RWTH Aachen, Aachen, Germany
| | | | - Arianeb Mehrabi
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Rene Hany Tolba
- Institute for Laboratory Animal Science and Experimental Surgery, Faculty of Medicine, RWTH, Aachen International University, Aachen, Germany
| | - Zoltan Czigany
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Lisa Ernst
- Institute for Laboratory Animal Science and Experimental Surgery, Faculty of Medicine, RWTH, Aachen International University, Aachen, Germany
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17
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Chen Y, Chen J, Wang H, Yu Y, Wang W, Liu W, Yu S, Gong Y, Jia H, Li T, Sun X. Prevalence and optical coherence tomography analyses of outer retinal tubulations in Chinese population with inherited retinal diseases. Eye (Lond) 2024; 38:328-334. [PMID: 37553355 PMCID: PMC10810923 DOI: 10.1038/s41433-023-02686-7] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 07/14/2023] [Accepted: 07/26/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND To investigate the prevalence of outer retinal tubulation (ORT) and its correlations with optical coherence tomography (OCT) parameters in Chinese population with inherited retinal diseases (IRDs). METHODS This retrospective study enrolled consecutive patients identified with IRDs and referred for genetic testing between February 2016 and April 2021. Clinical characteristics from medical records and features of cross-sectional B-scans were reviewed and analysed. The associations of patient-specific and ocular features with the presence of ORT were evaluated using univariate and multivariate analyses. RESULTS Two hundred and three patients (401 eyes) with a mean age of 49.7 ± 16.7 years were enrolled. ORT was observed in 41 eyes (10.2%), including 26 of 28 eyes (92.9%) with Bietti crystalline corneoretinal dystrophy (BCD), 14 of 338 eyes (4.1%) with retinitis pigmentosa (RP), and 1 of 26 eyes (3.8%) in eyes with cone-rod dystrophy. Eyes with ORT showed significantly worse visual acuity than those without ORT (P = 0.002). Multivariate analysis indicated that the presence of ORT was positively correlated with choroidal atrophy and inner nuclear layer (INL) cysts (P < 0.01). ORTs were detected more frequently in eyes with BCD than RP (P = 0.024), most of which located exclusively within the extrafoveal area. Large choroidal vessels were detected underneath the corresponding ORTs in both patients with BCD and RP. CONCLUSIONS The prevalence of ORT varies among different IRDs phenotypes, with the highest prevalence in BCD. The presence of choroidal atrophy and INL cysts may be associated with an increased risk of ORT formation in patients with IRD.
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Affiliation(s)
- Yuhong Chen
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Ophthalmic Diseases, Shanghai, China
| | - Jieqiong Chen
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Ophthalmic Diseases, Shanghai, China
| | - Hong Wang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Ophthalmic Diseases, Shanghai, China
| | - Yang Yu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenqiu Wang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Ophthalmic Diseases, Shanghai, China
| | - Wenjia Liu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Ophthalmic Diseases, Shanghai, China
| | - Suqin Yu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Ophthalmic Diseases, Shanghai, China
| | - Yuanyuan Gong
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Ophthalmic Diseases, Shanghai, China
| | - Huixun Jia
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Ophthalmic Diseases, Shanghai, China
- Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| | - Tong Li
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- National Clinical Research Center for Ophthalmic Diseases, Shanghai, China.
| | - Xiaodong Sun
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- National Clinical Research Center for Ophthalmic Diseases, Shanghai, China.
- Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China.
- Shanghai Key Laboratory of Fundus Diseases, Shanghai, China.
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18
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Ding WH, Li YF, Liu W, Li W, Wu N, Hu SY, Shi JJ. Effect of occlusal stabilisation splint with or without arthroscopic disc repositioning on condylar bone remodelling in adolescent patients. Int J Oral Maxillofac Surg 2024; 53:156-164. [PMID: 37357072 DOI: 10.1016/j.ijom.2023.06.005] [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: 12/23/2022] [Revised: 06/03/2023] [Accepted: 06/06/2023] [Indexed: 06/27/2023]
Abstract
The aim of this study was to investigate the treatment effects of a stabilisation splint (SS) with and without arthroscopic disc repositioning (ADR) on condylar bone remodelling in adolescent patients with anterior disc displacement without reduction (ADDwoR). Cone beam computed tomography and magnetic resonance imaging were used to analyse condylar bone remodelling, condyle position, and disc position. Twenty-two temporomandibular joints of 14 patients who underwent ADR (age range 12-20 years; mean follow-up 12.5 ± 7.8 months) and 21 temporomandibular joints of 14 patients who did not undergo ADR (age range 13-20 years; mean follow-up 11.1 ± 5.1 months) were included. The change in bone volume (P < 0.001), rate of bone volume change (P < 0.001), and change in condyle height (P = 0.031) were significantly greater in patients with ADR than in those without ADR. The changes in posterior joint space (P = 0.013), superior joint space (P = 0.020), and ratio of condyle sagittal position (P = 0.013) were significantly greater in patients with ADR than in those without ADR. All discs in patients who underwent ADR and one disc in those who did not undergo ADR were backward repositioned. In conclusion, in adolescent patients with ADDwoR, ADR with SS therapy achieved better condyle and disc position than SS therapy alone, and also induced bone generation.
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Affiliation(s)
- W H Ding
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Y F Li
- The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China
| | - W Liu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou, Zhejiang, China
| | - W Li
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou, Zhejiang, China
| | - N Wu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou, Zhejiang, China
| | - S Y Hu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou, Zhejiang, China
| | - J J Shi
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou, Zhejiang, China.
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19
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Knorr IJ, Tix L, Liu W, Talbot SR, Schulz M, Bell L, Kögel B, Tolba R, Ernst L. Refinement in Post-Operative Care for Orthopaedic Models: Implementing a Sheep Walking Cast (SWC) for Effective Tibial Fracture Management. Biomedicines 2024; 12:343. [PMID: 38397945 PMCID: PMC10886840 DOI: 10.3390/biomedicines12020343] [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] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 01/24/2024] [Accepted: 01/28/2024] [Indexed: 02/25/2024] Open
Abstract
In the healthcare system, lower leg fractures remain relevant, incurring costs related to surgical treatment, hospitalization, and rehabilitation. The duration of treatment may vary depending on the individual case and its severity. Casting as a post-surgical fracture treatment is a common method in human and experimental veterinary medicine. Despite the high importance of sheep in preclinical testing materials for osteosynthesis, there is no standardised cast system ensuring proper stabilisation and functionality of hind limbs during the healing of tibia fractures or defects. Existing treatment approaches for tibial osteosynthesis in laboratory animal science include sling hanging, external fixators, or former Achilles tendon incision. These methods restrict animal movement for 4-6 weeks, limit species-typical behaviour, and impact social interactions. Our pilot study introduces a Standardised Walking Cast (SWC) for sheep, enabling immediate physiological movement post surgery. Seven Rhone sheep (female, 63.5 kg ± 6.45 kg) each with a single tibia defect (6 mm mechanical drilled defect) underwent SWC application for 4 weeks after plate osteosynthesis. The animals bore weight on their operated leg from day one, exhibiting slight lameness (grade 1-2 out of 5). Individual step lengths showed good uniformity (average deviation: 0.89 cm). Group housing successfully started on day three after surgery. Weekly X-rays and cast changes ensured proper placement, depicting the healing process. This study demonstrates the feasibility of using an SWC for up to 72 kg of body weight without sling hanging via ceiling mounting or external fixation techniques. Allowing species-typical movement and social behaviour can significantly improve the physiological behaviour of sheep in experiments, contributing to refinement.
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Affiliation(s)
- Ivonne Jeanette Knorr
- Institute for Laboratory Animal Science and Experimental Surgery, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany; (I.J.K.); (L.T.); (W.L.); (M.S.); (B.K.); (R.T.)
| | - Leonie Tix
- Institute for Laboratory Animal Science and Experimental Surgery, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany; (I.J.K.); (L.T.); (W.L.); (M.S.); (B.K.); (R.T.)
| | - Wenjia Liu
- Institute for Laboratory Animal Science and Experimental Surgery, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany; (I.J.K.); (L.T.); (W.L.); (M.S.); (B.K.); (R.T.)
| | - Steven R. Talbot
- Institute for Laboratory Animal Science and Central Animal Facility, Hannover Medical School, 30625 Hannover, Germany;
| | - Mareike Schulz
- Institute for Laboratory Animal Science and Experimental Surgery, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany; (I.J.K.); (L.T.); (W.L.); (M.S.); (B.K.); (R.T.)
| | - Laura Bell
- Audiovisual Media Center, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany;
| | - Babette Kögel
- Institute for Laboratory Animal Science and Experimental Surgery, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany; (I.J.K.); (L.T.); (W.L.); (M.S.); (B.K.); (R.T.)
| | - Rene Tolba
- Institute for Laboratory Animal Science and Experimental Surgery, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany; (I.J.K.); (L.T.); (W.L.); (M.S.); (B.K.); (R.T.)
| | - Lisa Ernst
- Institute for Laboratory Animal Science and Experimental Surgery, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany; (I.J.K.); (L.T.); (W.L.); (M.S.); (B.K.); (R.T.)
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20
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Liu W, Cai L, Li Y. Application of natural language processing to post-structuring of rectal cancer MRI reports. Clin Radiol 2024; 79:e204-e210. [PMID: 38042740 DOI: 10.1016/j.crad.2023.10.032] [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: 07/27/2023] [Revised: 10/20/2023] [Accepted: 10/26/2023] [Indexed: 12/04/2023]
Abstract
AIM To evaluate a natural language processing (NLP) system for extracting structured information from the free-form text of rectal cancer magnetic resonance imaging (MRI) reports written in Chinese. MATERIALS AND METHODS A rule-based NLP model that could extract 11 key image features of rectal cancer was constructed using 358 MRI reports of rectal cancer written between 2015 and 2021. Fifty reports written before 2015 and 50 written after 2021 were used as test datasets, and the reference standard was determined by manual extraction of information by two radiologists. The length and reporting rate of image features in pre-2015 and post-2021 datasets, as well as the accuracy, precision, recall, and F1 score of feature extraction by the NLP system, were compared. The time required for the NLP to extract data was compared with that required by the radiologists. RESULTS Reports written after 2021 had longer diagnostic impression sections than reports written before 2015. The reporting rate of key imaging features of rectal cancer was 36.55% before 2015 and 79.82% after 2021. The accuracy, precision, recall, and F1 score of NLP for correct extraction of values from reports were 93.82%, 95.63%, 87.06%, and 91.15%, respectively, for pre-2015 reports, and 92.55%, 98.53%, 94.15%, and 96.29%, respectively, for post-2021 reports. NLP generated all the structured information in <1 second. CONCLUSIONS The NLP system with rule-based pattern matching achieved rapid and accurate structured processing of rectal cancer MRI reports. MRI reports with structured templates are more suitable for NLP-based extraction of information.
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Affiliation(s)
- W Liu
- Department of Radiology, Aerospace Center Hospital, Beijing, 100049, China; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - L Cai
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Y Li
- Department of General Surgery, Aerospace Center Hospital, Beijing, 100049, China.
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21
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Sun J, Sun W, Yue K, Zhang Y, Wu X, Liu W, Zou L, Shi H. Abnormal Amygdala Subregion Functional Connectivity in Patients with Crohn's Disease with or without Anxiety and Depression. Behav Neurol 2024; 2024:1551807. [PMID: 38323301 PMCID: PMC10846920 DOI: 10.1155/2024/1551807] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/28/2023] [Accepted: 01/09/2024] [Indexed: 02/08/2024] Open
Abstract
Objective The aim of this study was to explore the resting-state functional connectivity (rsFC) of amygdala subregions in healthy controls (HCs) and in patients with Crohn's disease (CD) both with and without anxiety or depression. Materials and Methods A total of 33 patients with CD and with anxiety or depression (CDad group), 31 patients with CD but without anxiety or depression (CDnad group), and 29 age-, sex-, and education level-matched HCs underwent functional magnetic resonance imaging. rsFC analysis was used to analyze the FC between the amygdala subregions and other areas of the brain. Results Compared with the HC group, the CDad group demonstrated decreased rsFC between the right laterobasal subregion and the left hippocampus (P < .001) and right middle frontal gyrus (P < .001) and between the left superficial subregion and the left insula (P < .001). Compared with the HC group, the CDnad group demonstrated decreased rsFC between the left centromedial subregion and the left insula (P < .001). Compared with the CDnad group, the CDad group demonstrated decreased rsFC between the left centromedial subregion and the right precuneus (P < .001) and postcentral gyrus (P < .001), between the right laterobasal subregion and the left hippocampus (P < .001), and between the left superficial subregion and the right middle frontal gyrus (P < .001). Conclusions There are significant FC changes in the amygdala subregions in patients with CD. These changes may be related to the disease itself or to the symptoms of anxiety and depression.
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Affiliation(s)
- Jingwen Sun
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Wei Sun
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
- Graduate College, Dalian Medical University, Dalian, China
| | - Kecen Yue
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
- Graduate College, Dalian Medical University, Dalian, China
| | - Yin Zhang
- School of Computer and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Xintong Wu
- Graduate College, Dalian Medical University, Dalian, China
- Department of Gastroenterology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Wenjia Liu
- Department of Gastroenterology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Ling Zou
- School of Computer and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Haifeng Shi
- Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China
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22
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Liu X, Onda M, Schlomer J, Bassel L, Kozlov S, Tai CH, Zhou Q, Liu W, Tsao HE, Hassan R, Ho M, Pastan I. Tumor resistance to anti-mesothelin CAR-T cells caused by binding to shed mesothelin is overcome by targeting a juxtamembrane epitope. Proc Natl Acad Sci U S A 2024; 121:e2317283121. [PMID: 38227666 PMCID: PMC10823246 DOI: 10.1073/pnas.2317283121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/27/2023] [Indexed: 01/18/2024] Open
Abstract
Despite many clinical trials, CAR-T cells are not yet approved for human solid tumor therapy. One popular target is mesothelin (MSLN) which is highly expressed on the surface of about 30% of cancers including mesothelioma and cancers of the ovary, pancreas, and lung. MSLN is shed by proteases that cleave near the C terminus, leaving a short peptide attached to the cell. Most anti-MSLN antibodies bind to shed MSLN, which can prevent their binding to target cells. To overcome this limitation, we developed an antibody (15B6) that binds next to the membrane at the protease-sensitive region, does not bind to shed MSLN, and makes CAR-T cells that have much higher anti-tumor activity than a CAR-T that binds to shed MSLN. We have now humanized the Fv (h15B6), so the CAR-T can be used to treat patients and show that h15B6 CAR-T produces complete regressions in a hard-to-treat pancreatic cancer patient derived xenograft model, whereas CAR-T targeting a shed epitope (SS1) have no anti-tumor activity. In these pancreatic cancers, the h15B6 CAR-T replicates and replaces the cancer cells, whereas there are no CAR-T cells in the tumors receiving SS1 CAR-T. To determine the mechanism accounting for high activity, we used an OVCAR-8 intraperitoneal model to show that poorly active SS1-CAR-T cells are bound to shed MSLN, whereas highly active h15B6 CAR-T do not contain bound MSLN enabling them to bind to and kill cancer cells.
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Affiliation(s)
- X.F. Liu
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
| | - M. Onda
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
| | - J. Schlomer
- Center for Advanced Preclinical Research, Frederick National Lab for Cancer Research Center for Cancer Research, National Cancer Institute, NIH, Frederick, MD 21701
| | - L. Bassel
- Center for Advanced Preclinical Research, Frederick National Lab for Cancer Research Center for Cancer Research, National Cancer Institute, NIH, Frederick, MD 21701
| | - S. Kozlov
- Center for Advanced Preclinical Research, Frederick National Lab for Cancer Research Center for Cancer Research, National Cancer Institute, NIH, Frederick, MD 21701
| | - C.-H. Tai
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
| | - Q. Zhou
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
| | - W. Liu
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
| | - H.-E. Tsao
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
| | - R. Hassan
- Thoracic and Gastrointestinal Malignancies Branch, National Cancer Institute, NIH, Bethesda, MD20892
| | - M. Ho
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
| | - I. Pastan
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD20892
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Liu W, Lewis FM, Li M, Kantrowitz-Gordon I. Development of a common dyadic coping scale in couples facing breast cancer: the importance of open communication. J Psychosoc Oncol 2024:1-18. [PMID: 38240291 DOI: 10.1080/07347332.2024.2303523] [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: 02/28/2024]
Abstract
OBJECTIVE Couples' joint coping is important in managing the impact of breast cancer. However, measures assessing couples' communication as a way of coping are insufficient. This study aimed to generate a self-report valid and reliable measure of couples' coping with a particular focus on communication. METHOD We used baseline data of 343 couple dyads who participated in a randomized clinical trial targeting marital communication. Women were diagnosed with early-stage breast cancer in the past eight months; couples were married or in an intimate relationship for at least six months, could read and write English, and lived within 100 miles of the study center. An expert panel selected items with conceptual fit from the Mutuality and Interpersonal Sensitivity Scale (MIS) that was originally designed to evaluate marital communication about breast cancer. RESULTS Exploratory and confirmatory factor analyses supported a 12-item measurement model with four factors: Keeping the communication open with each other about breast cancer (4 items), Sharing a positive outlook on breast cancer (2 items), Avoiding discussion of negative thoughts and feelings about breast cancer (3 items), and Spending sufficient time together talking about breast cancer (3 items). Reliability ranged from 0.76 to 0.87 for women and 0.70 to 0.83 for spouses. CONCLUSION This new measure has potential application in clinical practice and future research to assess couple's joint coping efforts especially through communication.
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Affiliation(s)
- Wenjia Liu
- Department of Child, Family, and Population Health Nursing, University of Washington School of Nursing, Seattle, Washington, USA
| | - Frances Marcus Lewis
- Department of Child, Family, and Population Health Nursing, University of Washington School of Nursing, Seattle, Washington, USA
- Public Health Sciences and Clinical Sciences Divisions, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Min Li
- College of Education, University of Washington, Seattle, Washington, USA
| | - Ira Kantrowitz-Gordon
- Department of Child, Family, and Population Health Nursing, University of Washington School of Nursing, Seattle, Washington, USA
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Zhang Y, Wu X, Sun J, Yue K, Lu S, Wang B, Liu W, Shi H, Zou L. Exploring changes in brain function in IBD patients using SPCCA: a study of simultaneous EEG-fMRI. Math Biosci Eng 2024; 21:2646-2670. [PMID: 38454700 DOI: 10.3934/mbe.2024117] [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] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
Research on functional changes in the brain of inflammatory bowel disease (IBD) patients is emerging around the world, which brings new perspectives to medical research. In this paper, the methods of canonical correlation analysis (CCA), kernel canonical correlation analysis (KCCA), and sparsity preserving canonical correlation analysis (SPCCA) were applied to the fusion of simultaneous EEG-fMRI data from 25 IBD patients and 15 healthy individuals. The CCA, KCCA and SPCCA fusion methods were used for data processing to compare the results obtained by the three methods. The results clearly show that there is a significant difference in the activation intensity between IBD and healthy control (HC), not only in the frontal lobe (p < 0.01) and temporal lobe (p < 0.01) regions, but also in the posterior cingulate gyrus (p < 0.01), gyrus rectus (p < 0.01), and amygdala (p < 0.01) regions, which are usually neglected. The mean difference in the SPCCA activation intensity was 60.1. However, the mean difference in activation intensity was only 36.9 and 49.8 by using CCA and KCCA. In addition, the correlation of the relevant components selected during the SPCCA calculation was high, with correlation components of up to 0.955; alternatively, the correlations obtained from CCA and KCCA calculations were only 0.917 and 0.926, respectively. It can be seen that SPCCA is indeed superior to CCA and KCCA in processing high-dimensional multimodal data. This work reveals the process of analyzing the brain activation state in IBD disease, provides a further perspective for the study of brain function, and opens up a new avenue for studying the SPCCA method and the change in the intensity of brain activation in IBD disease.
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Affiliation(s)
- Yin Zhang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China
| | - Xintong Wu
- The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Department of Radiology, China
| | - Jingwen Sun
- The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Department of Radiology, China
| | - Kecen Yue
- The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Department of Radiology, China
| | - Shuangshuang Lu
- The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Department of Radiology, China
| | - Bingjian Wang
- The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Department of Radiology, China
| | - Wenjia Liu
- The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Department of Radiology, China
| | - Haifeng Shi
- The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Department of Radiology, China
| | - Ling Zou
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China
- School of Computer and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu 213164, China
- Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Province, Hangzhou 310018, China
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25
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Parshina EY, Liu W, Yusipovich AI, Gvozdev DA, He Y, Pirutin SK, Klimanova EA, Maksimov EG, Maksimov GV. Spectral and conformational characteristics of phycocyanin associated with changes of medium pH. Photosynth Res 2024:10.1007/s11120-023-01068-0. [PMID: 38224422 DOI: 10.1007/s11120-023-01068-0] [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] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 12/09/2023] [Indexed: 01/16/2024]
Abstract
C-phycocyanin (C-PC) is the main component of water-soluble light-harvesting complexes (phycobilisomes, PBS) of cyanobacteria. PBS are involved in the absorption of quantum energy and the transfer of electronic excitation energy to the photosystems. A specific environment of C-PC chromophoric groups is provided by the protein matrix structure including protein-protein contacts between different subunits. Registration of C-PC spectral characteristics and the fluorescence anisotropy decay have revealed a significant pH influence on the chromophore microenvironment: at pH 5.0, a chromophore is more significantly interacts with the solvent, whereas at pH 9.0 the chromophore microenvironment becomes more viscous. Conformations of chromophores and the C-PC protein matrix have been studied by Raman and infrared spectroscopy. A decrease in the medium pH results in changes in the secondary structure either the C-PC apoproteins and chromophores, the last one adopts a more folded conformation.
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Affiliation(s)
- E Yu Parshina
- Faculty of Biology, Shenzhen MSU-BIT University, Shenzhen, 518172, China.
- Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia, 119991.
| | - W Liu
- Faculty of Biology, Shenzhen MSU-BIT University, Shenzhen, 518172, China
| | - A I Yusipovich
- Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia, 119991
| | - D A Gvozdev
- Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia, 119991
| | - Y He
- Faculty of Biology, Shenzhen MSU-BIT University, Shenzhen, 518172, China
| | - S K Pirutin
- Faculty of Biology, Shenzhen MSU-BIT University, Shenzhen, 518172, China
- Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia, 119991
- Institute of Theoretical and Experimental Biophysics of Russian Academy of Sciences, Institutskaya St. 3, Pushchino, Russia, 142290
| | - E A Klimanova
- Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia, 119991
| | - E G Maksimov
- Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia, 119991
| | - G V Maksimov
- Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia, 119991
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Zhang S, Zheng M, Tian H, Liu W, Feng Z, Xing S, Han F. Are immune checkpoint inhibitors ineffective in treating patients with head and neck squamous cell carcinoma aged 75 years or Older? A Meta-Analysis. Oral Oncol 2024; 148:106632. [PMID: 38039875 DOI: 10.1016/j.oraloncology.2023.106632] [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: 07/17/2023] [Revised: 11/06/2023] [Accepted: 11/15/2023] [Indexed: 12/03/2023]
Abstract
OBJECTIVES The efficacy of immune checkpoint inhibitors (ICIs) is unclear in patients aged ≥ 75 years with head and neck squamous cell carcinoma (HNSCC). We conducted a systematic review and meta-analysis of randomized trials that compared ICIs with standard-of-care (SOC) therapy for recurrent/metastatic HNSCC. MATERIALS AND METHODS PubMed, EMBASE, Web of Science, and ClinicalTrials.gov were searched for eligible trials. We evaluated the overall survival (OS) benefit of ICIs versus SOC according to patient age (<75 versus ≥ 75 years). The OS benefit was evaluated and compared between the age subgroups using hazard ratios (HRs). Data were pooled using a random-effects model. RESULTS Five phase 3 trials involving 3437 patients were included. In patients aged ≥ 75 years (n = 207), ICIs did not improve OS compared to SOC (HR = 1.30, 95 % confidence interval [CI]: 0.93-1.81, P = 0.127). However, an improvement in OS was observed in patients aged < 75 years (n = 3230, HR = 0.90, 95 % CI: 0.83-0.99, P = 0.025). There is a significant difference in OS benefit between patients aged < 75 and ≥ 75 years (ratio of HR = 0.69, 95 % CI: 0.49-0.98, P = 0.036). Subgroup, meta-regression, and sensitivity analyses supported the reliability of the results. CONCLUSIONS Given the small sample size, our findings showing no improvement in OS suggest a lack of evidence to support the use of ICIs in patients with recurrent/metastatic HNSCC aged ≥ 75 years. Therefore, prospective studies are needed to clarify their efficacy among this age group.
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Affiliation(s)
- Siqi Zhang
- Cancer Center, The First Hospital of Jilin University, 71 Xinmin Ave, Changchun, China.
| | - Mengge Zheng
- Cancer Center, The First Hospital of Jilin University, 71 Xinmin Ave, Changchun, China.
| | - Huimin Tian
- Cancer Center, The First Hospital of Jilin University, 71 Xinmin Ave, Changchun, China.
| | - Wenjia Liu
- Cancer Center, The First Hospital of Jilin University, 71 Xinmin Ave, Changchun, China.
| | - Zhenbang Feng
- Cancer Center, The First Hospital of Jilin University, 71 Xinmin Ave, Changchun, China.
| | - Shasha Xing
- Cancer Center, The First Hospital of Jilin University, 71 Xinmin Ave, Changchun, China.
| | - Fujun Han
- Cancer Center, The First Hospital of Jilin University, 71 Xinmin Ave, Changchun, China.
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Wang Y, Chen J, Zhang M, Yu S, Gong Y, Lin F, Wu Y, Liu W, Sun J, Li T, Sun X. GENETIC FACTORS AND CHARACTERISTICS ON SPECTRAL-DOMAIN OPTICAL COHERENCE TOMOGRAPHY ARE ASSOCIATED WITH CHOROIDAL THICKNESS IN ABCA4 -RELATED RETINOPATHY. Retina 2024; 44:166-174. [PMID: 37695977 DOI: 10.1097/iae.0000000000003931] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
PURPOSE To investigate the possible correlation factors of choroidal thickness in ABCA4 -related retinopathy. METHODS A total of 66 patients were included in the cohort. It is a retrospective, cross-sectional laboratory investigation. The patients were tested using whole-exon sequencing and ophthalmic examinations, including slit-lamp examinations, best-corrected visual acuity, spectral-domain optical coherence tomography, fundus photograph, and fundus autofluorescence. RESULTS Besides demographic characteristics (age, onset age, duration), we selected genetic factors and ocular characteristics on spectral-domain optical coherence tomography as the candidates related to choroidal thickness. Mutation type (inframe mutation or premature termination codon), epiretinal membrane, retinal pigment epithelium- Bruch membrane integrity, and macular curvature changes were identified as related factors to choroidal thickness in ABCA4 -related retinopathy after the adjustment of Logistic LASSO regression. CONCLUSION Mutation type, epiretinal membrane, retinal pigment epithelium-Bruch membrane integrity, and macular curvature changes are related factors to choroidal thinning. These findings could provide us a further understanding for the pathological process and clinical features of ABCA4 mutation.
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Affiliation(s)
- Yimin Wang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Disease, Shanghai, China
- Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China
- Shanghai Engineering Center for Visual Science and Photomedicine
- Department of Ophthalmology, Zhongshan Hospital, Fudan University, Shanghai, China, Shanghai, China; and
| | - Jieqiong Chen
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Disease, Shanghai, China
- Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China
- Shanghai Engineering Center for Visual Science and Photomedicine
| | - Min Zhang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Disease, Shanghai, China
- Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China
- Shanghai Engineering Center for Visual Science and Photomedicine
| | - Suqin Yu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Disease, Shanghai, China
- Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China
- Shanghai Engineering Center for Visual Science and Photomedicine
| | - Yuanyuan Gong
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Disease, Shanghai, China
- Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China
- Shanghai Engineering Center for Visual Science and Photomedicine
| | - Feng Lin
- Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Yidong Wu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Disease, Shanghai, China
- Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China
- Shanghai Engineering Center for Visual Science and Photomedicine
| | - Wenjia Liu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Disease, Shanghai, China
- Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China
- Shanghai Engineering Center for Visual Science and Photomedicine
| | - Junran Sun
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Disease, Shanghai, China
- Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China
- Shanghai Engineering Center for Visual Science and Photomedicine
| | - Tong Li
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Disease, Shanghai, China
- Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China
- Shanghai Engineering Center for Visual Science and Photomedicine
| | - Xiaodong Sun
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Disease, Shanghai, China
- Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China
- Shanghai Engineering Center for Visual Science and Photomedicine
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Ren H, Wang Z, Shang X, Zhang X, Ma L, Bian Y, Wang D, Liu W. Involvement of GA3-oxidase in inhibitory effect of nitric oxide on primary root growth in Arabidopsis. Plant Biol (Stuttg) 2024; 26:117-125. [PMID: 38014496 DOI: 10.1111/plb.13600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/13/2023] [Indexed: 11/29/2023]
Abstract
Both NO and GAs are essential for regulating various physiological processes and stress responses in plants. However, the interaction between these two molecules remains unclear. We investigated the distinct response patterns of Arabidopsis thaliana Col-0 and GA synthesis functional deficiency mutants to NO by measuring root length. To investigate underlying mechanisms, we detected bioactive GA content using UHPLC-ESI-MS/MS, assessed the accumulation of ROS by chemical staining Arabidopsis roots. We also conducted RNA-seq analysis and compared results between Col-0 and ga3ox1, with and without SNP (as NO donor) treatment. Phenotypic results revealed that the inhibitory effect of NO on primary roots of Arabidopsis was primarily mediated by GA3-oxidase, rather than GA20-oxidase or GA2-oxidase. The content of GA3 decreased in Col-0 treated with SNP, whereas this decrease was not observed in ga3ox1. The deficiency of GA3-oxidase alleviated the buildup of H2 O2 in roots when treated with SNP. We identified 222 DEGs. GO annotation of these DEGs revealed that all top 20 GO terms were related to stress responses. Moreover, three DEGs were annotated to GA-related processes (DDF1, DDF2, EXPA1), and seven DEGs were associated with root development (RAV1, RGF2, ERF71, ZAT6, MYB77, XT1, and DTX50). In summary, NO inhibits primary root growth partially by repressing GA3-oxidase catalysed GA3 synthesis in Arabidopsis. ROS, Ca2+ , DDF1, DDF2, EXPA1 and seven root development-related genes may be involved in crosstalk between NO and GAs.
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Affiliation(s)
- H Ren
- Shanxi Normal University, Taiyuan, Shanxi, China
| | - Z Wang
- Shanxi Normal University, Taiyuan, Shanxi, China
| | - X Shang
- Shanxi Normal University, Taiyuan, Shanxi, China
| | - X Zhang
- Shanxi Normal University, Taiyuan, Shanxi, China
| | - L Ma
- Shanxi Normal University, Taiyuan, Shanxi, China
| | - Y Bian
- Shanxi Normal University, Taiyuan, Shanxi, China
| | - D Wang
- Shanxi Normal University, Taiyuan, Shanxi, China
| | - W Liu
- Shanxi Normal University, Taiyuan, Shanxi, China
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Zeng HQ, Li G, Zhou KX, Li AD, Liu W, Zhang Y. Causal link between gut microbiota and osteoporosis analyzed via Mendelian randomization. Eur Rev Med Pharmacol Sci 2024; 28:542-555. [PMID: 38305631 DOI: 10.26355/eurrev_202401_35052] [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: 02/03/2024]
Abstract
OBJECTIVE Osteoporosis (OP) is closely associated with gut microbiota (GM), yet the nature of their causal relationship remains elusive. Therefore, this study aims to reverse causality between GM and OP by using population cohorts and two-sample MR (TSMR) analysis. MATERIALS AND METHODS In this study, we conducted an extensive genome-wide association study (GWAS) using publicly accessible summary statistics data for GM and OP. Employing rigorous criteria (p < 1*e-5), we identified independent genetic loci that exhibited significant associations with GM relative abundances as instrumental variables (IVs). A causal evaluation was primarily carried out using the inverse variance-weighted (IVW) method, supplemented by additional analyses such as MR-Egger, weighted median, simple mode, and weighted mode. RESULTS We unveiled that increased abundances of the family Pasteurellaceae, order Pasteurellales, and genus Ruminococcaceae UCG004 were linked to an increased risk of OP. Conversely, the family Oxalobacteraceae, unknown family id.1000006161, genus Lachnospiraceae NK4A136 group, unknown genus id.1000006162, and order NB1n were associated with a reduced risk of OP. To ensure the reliability of our findings, we conducted quality assessments through Cochrane's Q test and a leave-one-out analysis. Furthermore, the stability and consistency of the results were confirmed by the MR-Egger intercept test, Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) global test, and sensitivity analysis (p > 0.05). Our study reveals the causal relationships between 211 GM taxa and OP, pinpointing specific GM taxa associated with the risk of OP. This research sheds light on the genetic mechanisms that underlie GM-mediated OP and opens up promising avenues for identifying valuable biomarkers and potential therapeutic targets in future OP research. CONCLUSIONS This study establishes a substantial GM-OP link with specific taxa being identified, offering biomarkers for early detection, tailored interventions, and improved patient education. These findings enhance OP diagnosis, prevention, and treatment, promising more effective, individualized care and inspiring future research.
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Affiliation(s)
- H-Q Zeng
- Traditional Chinese Medicine Department of Rheumatism, Women and Children Health Institute Futian Shenzhen, Shenzhen, China.
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Nian Z, Zhao Q, He Y, Xie R, Liu W, Chen T, Huang S, Dong L, Huang R, Yang L. Efficacy and Safety of First-line Therapies for Advanced Unresectable Oesophageal Squamous Cell Cancer: a Systematic Review and Network Meta-analysis. Clin Oncol (R Coll Radiol) 2024; 36:30-38. [PMID: 37827946 DOI: 10.1016/j.clon.2023.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/27/2023] [Accepted: 09/21/2023] [Indexed: 10/14/2023]
Abstract
AIM To compare the clinical efficacy and safety of first-line treatments for advanced unresectable oesophageal squamous cell cancer. MATERIALS AND METHODS A systematic review and network meta-analysis was carried out by retrieving and retaining relevant literature from databases. The studies were randomised controlled trials comparing first-line treatments for advanced unresectable oesophageal squamous cell cancer. A Bayesian network meta-analysis was used to assess clinical outcomes. RESULTS Nine studies including 4499 patients receiving first-line treatments were analysed. For all populations, toripalimab plus chemotherapy tended to provide the best overall survival (hazard ratio 0.58, 95% confidence intervals 0.43-0.78) and sintilimab plus chemotherapy provided the best progression-free survival (0.56, 0.46-0.68). Nivolumab plus chemotherapy presented the best objective response rate (odds ratio 2.45, 1.78-3.42) and camrelizumab plus chemotherapy (0.47, 0.29-0.74) appeared to be the safest. Sintilimab plus chemotherapy (0.55, 0.40-0.75) and nivolumab (0.54, 0.37-0.80) plus chemotherapy had the best overall survival in programmed death ligand 1 (PD-L1) tumour proportion score <1% and ≥1% subgroups. Toripalimab plus chemotherapy (0.61, 0.40-0.93) and pembrolizumab (0.57, 0.43-0.75) were the best in overall survival in combined positive score <10 and ≥10 subgroups, respectively. Toripalimab plus chemotherapy showed the best overall survival in the Asian group; pembrolizumab presented better overall survival in the Asian population than the non-Asian group. CONCLUSION Most immunotherapy combined with chemotherapy showed superior clinical benefits and sintilimab plus chemotherapy, toripalimab plus chemotherapy and tislelizumab plus chemotherapy had better comprehensive clinical efficacy. PD-L1 expression detection and ethnicity differences are still of great significance and most suitable regimens varied from each subgroup.
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Affiliation(s)
- Z Nian
- School of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Q Zhao
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Y He
- School of Pharmacy, Fujian Medical University, Fuzhou, China
| | - R Xie
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - W Liu
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - T Chen
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - S Huang
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - L Dong
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - R Huang
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - L Yang
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
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Ji Q, Lian W, Meng Y, Liu W, Zhuang M, Zheng N, Karlsson IK, Zhan Y. Cytomegalovirus Infection and Alzheimer's Disease: A Meta-Analysis. J Prev Alzheimers Dis 2024; 11:422-427. [PMID: 38374748 DOI: 10.14283/jpad.2023.126] [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: 02/21/2024]
Abstract
BACKGROUND Evidence on the association of cytomegalovirus (CMV) infection with Alzheimer's disease (AD) is scarce and the results are inconsistent. OBJECTIVE To investigate the association of CMV infection with the risk of AD. METHODS Observational studies on the relationship between CMV infection and AD were identified from PubMed, Embase, Web of Science, and the Cochrane Library until September 30, 2022. The quality of included studies was assessed using the Newcastle-Ottawa Scale. Random-effect meta-analysis was performed using a generic inverse-variance method, followed by sensitivity analyses and subgroup analyses based on study designs, regions, adjustments, and population types. RESULTS Our search yielded 870 articles, of which 200 were duplicates and 663 did not meet the inclusion criteria, and finally yielded seven studies with 6,772 participants. No strong evidence was observed in the summary analysis for the association of CMV infection and risk of AD (odds ratio [OR] = 1.33; 95% confidence interval [CI]: 0.88, 2.03, I2 =69.9%). However, subgroup analysis showed that an increased risk of AD was detected in East Asians (OR = 2.39; 95% CI: 1.63, 3.50, I2 = 0.00%), cohort studies (OR = 1.99; 95% CI: 1.35, 2.94, I2 = 28.20%), and studies with confounder adjustment (OR = 2.05; 95% CI: 1.52, 2.77, I2 = 0.00%). CONCLUSIONS This meta-analysis provides evidence to support the heterogeneity of the associations between CMV infection and AD. Future studies with larger sample sizes and multi-ethnic populations are necessary.
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Affiliation(s)
- Q Ji
- Yiqiang Zhan, Department of Epidemiology, School of Public Health (Shenzhen), Sun Yat-Sen University, Shenzhen, China; Tel: 0755-23260106; E-mail:
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Ji L, Ahmann AJ, Ahrén B, Capehorn MS, Hu P, Lingvay I, Liu W, Rodbard HW, Shen Z, Sorli C. Proportion of participants with type 2 diabetes achieving a metabolic composite endpoint with once-weekly semaglutide treatment versus comparators: Post hoc pooled analysis from SUSTAIN 1-5, 7-10 and SUSTAIN China. Diabetes Obes Metab 2024; 26:233-241. [PMID: 37822270 DOI: 10.1111/dom.15309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/05/2023] [Accepted: 09/15/2023] [Indexed: 10/13/2023]
Abstract
AIM To compare the proportion of participants with type 2 diabetes (T2D) treated with once-weekly (OW) subcutaneous (SC) semaglutide versus comparators who achieved a composite metabolic endpoint. MATERIALS AND METHODS SUSTAIN 1-5, 7-10 and SUSTAIN China trial data were pooled. Participants with T2D (aged ≥18 years) and glycated haemoglobin ≥7.0% (≥53 mmol/mol) who had been randomized to OW SC semaglutide (0.5 or 1.0 mg) or comparator in addition to background medication. Using patient-level data pooled by treatment, proportions of participants achieving the metabolic composite endpoint, defined as glycated haemoglobin <7% (<53 mmol/mol), blood pressure <140/90 mmHg and non-high-density lipoprotein cholesterol <130 mg/dl (<3.37 mmol/L), were evaluated following baseline adjustments. Endpoints were analysed per trial using a binomial logistic regression model with treatment, region/country and stratification factor as fixed effects and baseline value as covariate. Pooled analysis used logistic regression with treatment and trial as fixed effects and baseline value as covariate. RESULTS This post hoc analysis included data from 7633 participants across 10 trials. The proportion of participants who achieved the metabolic composite endpoint was significantly higher with OW SC semaglutide 0.5 and 1.0 mg versus comparators (23.7% and 32.0% vs. 11.5%, respectively; p < .0001). Likewise, when the OW SC semaglutide doses were pooled, significantly higher proportions of patients receiving semaglutide achieved the composite metabolic endpoint versus comparators (29.1% vs. 11.4%, respectively; p < .0001). CONCLUSIONS Treatment with OW SC semaglutide versus comparators was associated with increased proportions of participants with T2D meeting the composite metabolic endpoint.
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Affiliation(s)
- Linong Ji
- Peking University People's Hospital, Beijing, China
| | - A J Ahmann
- Oregon Health and Science University, Portland, Oregon, USA
| | - B Ahrén
- Lund University, Lund, Sweden
| | | | - P Hu
- Novo Nordisk (Shanghai) Pharma Trading Co., Ltd, Beijing, China
| | - I Lingvay
- University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - W Liu
- Novo Nordisk (Shanghai) Pharma Trading Co., Ltd, Beijing, China
| | - H W Rodbard
- Endocrine and Metabolic Consultants, Rockville, Maryland, USA
| | - Z Shen
- Novo Nordisk (Shanghai) Pharma Trading Co., Ltd, Beijing, China
| | - C Sorli
- Acerus Pharma, Toronto, Ontario, Canada
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Liu W, Li RY. [Enlightenment of World Health Organization fungal priority pathogens list]. Zhonghua Liu Xing Bing Xue Za Zhi 2023; 44:1984-1987. [PMID: 38129157 DOI: 10.3760/cma.j.cn112338-20230701-00410] [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: 12/23/2023]
Abstract
The WHO established the first fungal priority pathogens list (FPPL) in October 2022 to focus on and promote further research and policy interventions to strengthen the global response to fungal infections and antifungal resistance. The FPPL and its formulation process provide new significant insights for managing pathogenic fungi and invasive fungal disease (IFD) in China, necessitating the following key actions: Strengthen public health interventions for IFD. Further, it improves the ability of laboratory testing and clinical supervision for IFD and pathogenic fungi. Increase targeted investment and support for innovative research and development in IFD diagnosis, treatment, and prevention.
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Affiliation(s)
- W Liu
- Department of Dermatology, Peking University First Hospital/National Clinical Research Center for Skin and Immune Diseases/Research Center for Medical Mycology, Peking University/Beijing Key Laboratory of Molecular Diagnosis on Dermatoses/Peking University First Hospital-National Institute for Communicable Disease Control and Prevention Joint Laboratory of Pathogenic Fungi, Beijing 100034, China
| | - R Y Li
- Department of Dermatology, Peking University First Hospital/National Clinical Research Center for Skin and Immune Diseases/Research Center for Medical Mycology, Peking University/Beijing Key Laboratory of Molecular Diagnosis on Dermatoses/Peking University First Hospital-National Institute for Communicable Disease Control and Prevention Joint Laboratory of Pathogenic Fungi, Beijing 100034, China
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Gong J, Geng YY, Zhang S, Liu W, Wu WW, Li RY, Zhang JZ. [Analysis of public health risks associated with pathogenic fungi in China]. Zhonghua Liu Xing Bing Xue Za Zhi 2023; 44:1977-1983. [PMID: 38129156 DOI: 10.3760/cma.j.cn112338-20230615-00376] [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: 12/23/2023]
Abstract
At present, the public health risks caused by pathogenic fungi are greater in China and have attracted great attention from disease control departments. Due to the difficulty in diagnosing fungal infections, the public health risk of pathogenic fungi is currently hidden in the unexplained pneumonia/encephalitis/fever syndrome and is not effectively appreciated. From the public health perspective, the mainly focused fungal pathogens include highly pathogenic fungi (including dimorphic fungi and dematiaceous fungi), pathogenic fungi that cause regional aggregation infections, and drug-resistant pathogenic fungi. However, due to the lack of systematic monitoring data, the disease burden related to pathogenic fungi cannot be accurately quantified and evaluated. Therefore, to effectively reduce the serious harm of fungal infections to the public, systematic monitoring of pathogenic fungi should be carried out nationally.
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Affiliation(s)
- J Gong
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention/Peking University First Hospital-National Institute for Communicable Disease Control and Prevention Joint Laboratory of Pathogenic Fungi, Beijing 102206, China
| | - Y Y Geng
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention/Peking University First Hospital-National Institute for Communicable Disease Control and Prevention Joint Laboratory of Pathogenic Fungi, Beijing 102206, China
| | - S Zhang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention/Peking University First Hospital-National Institute for Communicable Disease Control and Prevention Joint Laboratory of Pathogenic Fungi, Beijing 102206, China
| | - W Liu
- Department of Dermatology, Peking University First Hospital/National Clinical Research Center for Skin and Immune Diseases/Research Center for Medical Mycology, Peking University/Beijing Key Laboratory of Molecular Diagnosis on Dermatoses/Peking University First Hospital-National Institute for Communicable Disease Control and Prevention Joint Laboratory of Pathogenic Fungi, Beijing 100034, China
| | - W W Wu
- Derpartment of Plastic and Dermatologic Surgery, the Fifth People's Hospital of Hainan Province, Haikou 570102, China
| | - R Y Li
- Department of Dermatology, Peking University First Hospital/National Clinical Research Center for Skin and Immune Diseases/Research Center for Medical Mycology, Peking University/Beijing Key Laboratory of Molecular Diagnosis on Dermatoses/Peking University First Hospital-National Institute for Communicable Disease Control and Prevention Joint Laboratory of Pathogenic Fungi, Beijing 100034, China
| | - J Z Zhang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention/Peking University First Hospital-National Institute for Communicable Disease Control and Prevention Joint Laboratory of Pathogenic Fungi, Beijing 102206, China
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Bai HH, Wang XF, Zhang BY, Liu W. A comparison of size exclusion chromatography-based tandem strategies for plasma exosome enrichment and proteomic analysis. Anal Methods 2023; 15:6245-6251. [PMID: 37955202 DOI: 10.1039/d3ay01704d] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2023]
Abstract
Proteomic analysis of exosomes from human plasma faces a tremendous challenge mainly due to the low abundance of the exosome itself and the complexity of the plasma matrix. Therefore, enrichment of exosomes from human plasma is an essential and indispensable step for large scale and in depth proteomic analysis. Size exclusion chromatography (SEC) is one of the most extensively used methods for exosome isolation from human plasma and many SEC-based tandem methods were established in order to increase the purity of the enriched exosomes and thus the accuracy of the proteomic analysis. To compare the advantages and disadvantages of the different isolation methods and subsequently to promote the establishment of a standardized method for plasma proteomic research, the capacities of the direct SEC method, the combination of SEC with ultracentrifugation (SEC-UC), ultrafiltration (SEC-UF), and titanium dioxide microspheres (SEC-TiO2) were systematically evaluated for exosome isolation from human plasma and thus proteomic analysis. The results demonstrated that the SEC-based tandem methods were superior to the direct SEC method in the purity of exosomes isolated from human plasma. Additionally, the SEC-UC method possessed the highest number of the total identified proteins and the overlapped proteins with the top 100 exosome markers in comparison with the other methods. The SEC-TiO2 method displayed the biggest capacity for plasma protein deleting. We expect that the research will have more beneficial values in the field of exosome research.
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Affiliation(s)
- H H Bai
- Department of Pharmacy, Beijing YouAn Hospital, Capital Medical University, Beijing 100069, PR China.
| | - X F Wang
- Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, PR China
| | - B Y Zhang
- Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, PR China
| | - W Liu
- Department of Pharmacy, Beijing YouAn Hospital, Capital Medical University, Beijing 100069, PR China.
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Yu DD, Liu W, Zhang L. [Pathophysiology, diagnosis, and therapy for the management of acquired clotting factor deficiency]. Zhonghua Xue Ye Xue Za Zhi 2023; 44:956-962. [PMID: 38185529 PMCID: PMC10753255 DOI: 10.3760/cma.j.issn.0253-2727.2023.11.014] [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] [Received: 03/17/2023] [Indexed: 01/09/2024]
Affiliation(s)
- D D Yu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin 300020, China Tianjin Institutes of Health Science, Tianjin 301600, China
| | - W Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin 300020, China Tianjin Institutes of Health Science, Tianjin 301600, China
| | - L Zhang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Tianjin 300020, China Tianjin Institutes of Health Science, Tianjin 301600, China
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Wang H, Liu W, Chen J, Wang Z. Applicability Domains Based on Molecular Graph Contrastive Learning Enable Graph Attention Network Models to Accurately Predict 15 Environmental End Points. Environ Sci Technol 2023; 57:16906-16917. [PMID: 37897806 DOI: 10.1021/acs.est.3c03860] [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] [Subscribe] [Scholar Register] [Indexed: 10/30/2023]
Abstract
In silico models for predicting physicochemical properties and environmental fate parameters are necessary for the sound management of chemicals. This study employed graph attention network (GAT) algorithms to construct such models on 15 end points. The results showed that the GAT models outperformed the previous state-of-the-art models, and their performance was not influenced by the presence or absence of compounds with certain structures. Molecular similarity density (ρs) was found to be a key metrics characterizing data set modelability, in addition to the proportion of compounds at activity cliffs. By introducing molecular graph (MG) contrastive learning, MG-based ρs and molecular inconsistency in activities (IA) were calculated and employed for characterizing the structure-activity landscape (SAL)-based applicability domain ADSAL{ρs, IA}. The GAT models coupled with ADSAL{ρs, IA} significantly improved the prediction coefficient of determination (R2) on all the end points by an average of 14.4% and enabled all the end points to have R2 > 0.9, which could hardly be achieved previously. The models were employed to screen persistent, mobile, and/or bioaccumulative chemicals from inventories consisting of about 106 chemicals. Given the current state-of-the-art model performance and coverage of the various environmental end points, the constructed models with ADSAL{ρs, IA} may serve as benchmarks for future efforts to improve modeling efficacy.
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Affiliation(s)
- Haobo Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Wenjia Liu
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Zhongyu Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
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Yu X, Xiang J, Zhang Q, Chen S, Tang W, Li X, Sui Y, Liu W, Kong Q, Guo Y. Triple-negative breast cancer: predictive model of early recurrence based on MRI features. Clin Radiol 2023; 78:e798-e807. [PMID: 37596179 DOI: 10.1016/j.crad.2023.07.008] [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: 05/03/2023] [Revised: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 08/20/2023]
Abstract
AIM To develop an integrated model based on preoperative magnetic resonance imaging (MRI) features for predicting early recurrence in patients with triple-negative breast cancer (TNBC). MATERIALS AND METHODS Women with TNBC who underwent breast MRI and surgery between 2009 and 2019 were evaluated retrospectively. Two breast radiologists reviewed MRI images independently based on the Breast Imaging Reporting and Data System Lexicon (BI-RADS), and classified the breast oedema scores on T2-weighted imaging (WI) as no oedema, peritumoural oedema, prepectoral oedema, or subcutaneous oedema. The relationship between disease-free survival (DFS) and MRI features was analysed by Cox regression, and a nomogram model was generated based on the results. RESULTS 150 patients with TNBC were included and divided into a training cohort (n=78) and validation cohort (n=72). MRI features including subcutaneous oedema and rim enhancement showed a tendency to worsen DFS in univariate analysis. Multivariate analysis showed that subcutaneous oedema (p=0.049, HR [95% confidence interval {CI} = 8.24 [1.01-67.52]) and rim enhancement (p=0.016, HR [95% CI] = 4.38 [1.32-14.54]) were independent predictors for DFS. In the nomogram, the areas under the curves (AUCs) of the training cohort was 0.808, and that of the validation cohort was 0.875. CONCLUSION The presence of subcutaneous oedema or rim enhancement on preoperative breast MRI was shown to be a good predictor of poor survival outcomes in patients with TNBC.
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Affiliation(s)
- X Yu
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - J Xiang
- Guangdong Women and Children Hospital, No. 13 West Guangyuan Road, Guangzhou, Guangdong, 510010, China
| | - Q Zhang
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - S Chen
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - W Tang
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - X Li
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - Y Sui
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
| | - W Liu
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China.
| | - Q Kong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China.
| | - Y Guo
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China.
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Zou Y, Liu Z, Liu W, Liu Z. Current knowledge and potential intervention of hexosamine biosynthesis pathway in lung cancer. World J Surg Oncol 2023; 21:334. [PMID: 37880766 PMCID: PMC10601224 DOI: 10.1186/s12957-023-03226-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 10/14/2023] [Indexed: 10/27/2023] Open
Abstract
Lung cancer is a highly prevalent malignancy characterized by significant metabolic alterations. Understanding the metabolic rewiring in lung cancer is crucial for the development of effective therapeutic strategies. The hexosamine biosynthesis pathway (HBP) is a metabolic pathway that plays a vital role in cellular metabolism and has been implicated in various cancers, including lung cancer. Abnormal activation of HBP is involved in the proliferation, progression, metastasis, and drug resistance of tumor cells. In this review, we will discuss the function and regulation of metabolic enzymes related to HBP in lung cancer. Furthermore, the implications of targeting the HBP for lung cancer treatment are also discussed, along with the challenges and future directions in this field. This review provides a comprehensive understanding of the role and intervention of HBP in lung cancer. Future research focusing on the HBP in lung cancer is essential to uncover novel treatment strategies and improve patient outcomes.
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Affiliation(s)
- Yi Zou
- College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250000, Shandong, China
| | - Zongkai Liu
- Department of Oncology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250000, Shandong, China
| | - Wenjia Liu
- Department of Oncology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250000, Shandong, China
| | - Zhaidong Liu
- Department of Oncology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250000, Shandong, China.
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Zhou CX, He LL, Zhu XY, Li ZX, Duan HL, Liu W, Gu LL, Li J. [Report content and prenatal diagnosis of non-invasive prenatal testing for sex chromosome aneuploidy]. Zhonghua Fu Chan Ke Za Zhi 2023; 58:766-773. [PMID: 37849257 DOI: 10.3760/cma.j.cn112141-20230412-00168] [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: 10/19/2023]
Abstract
Objective: To analyze the report content, the methods and results of prenatal diagnosis of high risk of sex chromosome aneuploidy (SCA) in non-invasive prenatal testing (NIPT). Methods: A total of 227 single pregnancy pregnant women who received genetic counseling and invasive prenatal diagnosis at Drum Tower Hospital Affiliated to the Medical School of Nanjing University from January 2015 to April 2022 due to the high risk of SCA suggested by NIPT were collected. The methods and results of prenatal diagnosis were retrospectively analyzed, and the results of chromosome karyotype analysis and chromosome microarray analysis (CMA) were compared. The relationship between NIPT screening and invasive prenatal diagnosis was analyzed. Results: (1) Prenatal diagnosis methods for 277 SCA high risk pregnant women included 73 cases of karyotyping, 41 cases of CMA and 163 cases of karyotyping combined with CMA, of which one case conducted amniocentesis secondly for further fluorescence in situ hybridization (FISH) testing. Results of invasive prenatal diagnosis were normal in 166 cases (59.9%, 166/277), and the abnormal results including one case of 45,X (0.4%, 1/277), 18 cases of 47,XXX (6.5%, 18/277), 36 cases of 47,XXY (13.0%, 36/277), 20 cases of 47,XYY (7.2%, 20/277), 1 case of 48,XXXX (0.4%, 1/277), 20 cases of mosaic SCA (7.2%, 20/277), 5 cases of sex chromosome structural abnormality or large segment abnormality (1.8%, 5/277), and 10 cases of other abnormalities [3.6%, 10/277; including 9 cases of copy number variation (CNV) and 1 case of balanced translocation]. Positive predictive value (PPV) for SCA screening by NIPT was 34.7% (96/277). (2) Among the 163 cases tested by karyotyping combined with CMA, 11 cases (6.7%, 11/163) showed inconsistent results by both methods, including 5 cases of mosaic SCA, 1 case of additional balanced translocation detected by karyotyping and 5 cases of additional CNV detected by CMA. (3) NIPT screening reports included 149 cases of "sex chromosome aneuploidy"(53.8%, 149/277), 54 cases of "number of sex chromosome increased" (19.5%, 54/277), and 74 cases of "number of sex chromosome or X chromosome decreased" (26.7%, 74/277). The PPV of "number of sex chromosome increased" and "number of sex chromosome or X chromosome decreased" were 72.2% (39/54) and 18.9% (14/74), respectively, and the difference was statistically significant (χ2=34.56, P<0.01). Conclusions: NIPT could be served as an important prenatal screening technique of SCA, especially for trisomy and mosaicism, but the PPV is comparatively low. More information of NIPT such as the specific SCA or maternal SCA might help improving the confidence of genetic counseling and thus guide clinic management. Multi technology platforms including karyotyping, CMA and FISH could be considered in the diagnosis of high risk of SCA by NIPT.
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Affiliation(s)
- C X Zhou
- Medical Center for Obstetrics and Gynecology, Drum Tower Hospital Affiliated to the Medical School of Nanjing University, Nanjing 210008, China
| | - L L He
- Medical Center for Obstetrics and Gynecology, Drum Tower Hospital Affiliated to the Medical School of Nanjing University, Nanjing 210008, China
| | - X Y Zhu
- Medical Center for Obstetrics and Gynecology, Drum Tower Hospital Affiliated to the Medical School of Nanjing University, Nanjing 210008, China
| | - Z X Li
- Medical Center for Obstetrics and Gynecology, Drum Tower Hospital Affiliated to the Medical School of Nanjing University, Nanjing 210008, China
| | - H L Duan
- Medical Center for Obstetrics and Gynecology, Drum Tower Hospital Affiliated to the Medical School of Nanjing University, Nanjing 210008, China
| | - W Liu
- Medical Center for Obstetrics and Gynecology, Drum Tower Hospital Affiliated to the Medical School of Nanjing University, Nanjing 210008, China
| | - L L Gu
- Medical Center for Obstetrics and Gynecology, Drum Tower Hospital Affiliated to the Medical School of Nanjing University, Nanjing 210008, China
| | - J Li
- Medical Center for Obstetrics and Gynecology, Drum Tower Hospital Affiliated to the Medical School of Nanjing University, Nanjing 210008, China
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Chang Z, Zhang L, Hang JT, Liu W, Xu GK. Viscoelastic Multiscale Mechanical Indexes for Assessing Liver Fibrosis and Treatment Outcomes. Nano Lett 2023; 23:9618-9625. [PMID: 37793647 PMCID: PMC10603793 DOI: 10.1021/acs.nanolett.3c03341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 09/28/2023] [Indexed: 10/06/2023]
Abstract
Understanding liver tissue mechanics, particularly in the context of liver pathologies like fibrosis, cirrhosis, and carcinoma, holds pivotal significance for assessing disease severity and prognosis. Although the static mechanical properties of livers have been gradually studied, the intricacies of their dynamic mechanics remain enigmatic. Here, we characterize the dynamic creep responses of healthy, fibrotic, and mesenchymal stem cells (MSCs)-treated fibrotic lives. Strikingly, we unearth a ubiquitous two-stage power-law rheology of livers across different time scales with the exponents and their distribution profiles highly correlated to liver status. Moreover, our self-similar hierarchical theory effectively captures the delicate changes in the dynamical mechanics of livers. Notably, the viscoelastic multiscale mechanical indexes (i.e., power-law exponents and elastic stiffnesses of different hierarchies) and their distribution characteristics prominently vary with liver fibrosis and MSCs therapy. This study unveils the viscoelastic characteristics of livers and underscores the potential of proposed mechanical criteria for assessing disease evolution and prognosis.
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Affiliation(s)
- Zhuo Chang
- Laboratory
for Multiscale Mechanics and Medical Science, Department of Engineering
Mechanics, State Key Laboratory for Strength and Vibration of Mechanical
Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Liqiang Zhang
- Institute
for Stem Cell & Regenerative Medicine, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, China
| | - Jiu-Tao Hang
- Laboratory
for Multiscale Mechanics and Medical Science, Department of Engineering
Mechanics, State Key Laboratory for Strength and Vibration of Mechanical
Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Wenjia Liu
- Institute
for Stem Cell & Regenerative Medicine, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, China
| | - Guang-Kui Xu
- Laboratory
for Multiscale Mechanics and Medical Science, Department of Engineering
Mechanics, State Key Laboratory for Strength and Vibration of Mechanical
Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
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42
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He T, Liu W, Shen ZA. [Research advances on application of pancreatic stone protein in the early diagnosis of sepsis]. Zhonghua Shao Shang Yu Chuang Mian Xiu Fu Za Zhi 2023; 39:985-988. [PMID: 37899565 DOI: 10.3760/cma.j.cn501225-20221120-00498] [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: 10/31/2023]
Abstract
Sepsis is a severe life-threatening syndrome characterized by an abnormal host response to infection that can rapidly evolve into septic shock and multiple organ failure. Treatment of sepsis depends on early identification and diagnosis as well as adequate and timely anti-infection and multi-organ functional support. In recent years, pancreatic stone protein has been widely studied as a new biomarker for sepsis. Existing evidence shows that compared with the commonly used inflammatory markers in clinical practice, pancreatic stone protein has higher sensitivity and specificity in the diagnosis of sepsis. It enables the early diagnosis of sepsis and assessment of the severity of septic patients to a certain extent. This article reviews the characteristics, biological functions, diagnostic features, and clinical application of pancreatic stone protein.
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Affiliation(s)
- T He
- Department of Burns and Plastic Surgery, the Fourth Medical Center of PLA General Hospital, Beijing 100048, China
| | - W Liu
- Department of Burns and Plastic Surgery, the Fourth Medical Center of PLA General Hospital, Beijing 100048, China
| | - Z A Shen
- Department of Burns and Plastic Surgery, the Fourth Medical Center of PLA General Hospital, Beijing 100048, China
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43
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Cao Z, Aharonian F, An Q, Axikegu, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Cai JT, Cao Q, Cao WY, Cao Z, Chang J, Chang JF, Chen AM, Chen ES, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen SH, Chen SZ, Chen TL, Chen Y, Cheng N, Cheng YD, Cui MY, Cui SW, Cui XH, Cui YD, Dai BZ, Dai HL, Dai ZG, Danzengluobu, Della Volpe D, Dong XQ, Duan KK, Fan JH, Fan YZ, Fang J, Fang K, Feng CF, Feng L, Feng SH, Feng XT, Feng YL, Gabici S, Gao B, Gao CD, Gao LQ, Gao Q, Gao W, Gao WK, Ge MM, Geng LS, Giacinti G, Gong GH, Gou QB, Gu MH, Guo FL, Guo XL, Guo YQ, Guo YY, Han YA, He HH, He HN, He JY, He XB, He Y, Heller M, Hor YK, Hou BW, Hou C, Hou X, Hu HB, Hu Q, Hu SC, Huang DH, Huang TQ, Huang WJ, Huang XT, Huang XY, Huang Y, Huang ZC, Ji XL, Jia HY, Jia K, Jiang K, Jiang XW, Jiang ZJ, Jin M, Kang MM, Ke T, Kuleshov D, Kurinov K, Li BB, Li C, Li C, Li D, Li F, Li HB, Li HC, Li HY, Li J, Li J, Li J, Li K, Li WL, Li WL, Li XR, Li X, Li YZ, Li Z, Li Z, Liang EW, Liang YF, Lin SJ, Liu B, Liu C, Liu D, Liu H, Liu HD, Liu J, Liu JL, Liu JY, Liu MY, Liu RY, Liu SM, Liu W, Liu Y, Liu YN, Lu R, Luo Q, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Min Z, Mitthumsiri W, Mu HJ, Nan YC, Neronov A, Ou ZW, Pang BY, Pattarakijwanich P, Pei ZY, Qi MY, Qi YQ, Qiao BQ, Qin JJ, Ruffolo D, Sáiz A, Semikoz D, Shao CY, Shao L, Shchegolev O, Sheng XD, Shu FW, Song HC, Stenkin YV, Stepanov V, Su Y, Sun QN, Sun XN, Sun ZB, Tam PHT, Tang QW, Tang ZB, Tian WW, Wang C, Wang CB, Wang GW, Wang HG, Wang HH, Wang JC, Wang K, Wang LP, Wang LY, Wang PH, Wang R, Wang W, Wang XG, Wang XY, Wang Y, Wang YD, Wang YJ, Wang ZH, Wang ZX, Wang Z, Wang Z, Wei DM, Wei JJ, Wei YJ, Wen T, Wu CY, Wu HR, Wu S, Wu XF, Wu YS, Xi SQ, Xia J, Xia JJ, Xiang GM, Xiao DX, Xiao G, Xin GG, Xin YL, Xing Y, Xiong Z, Xu DL, Xu RF, Xu RX, Xu WL, Xue L, Yan DH, Yan JZ, Yan T, Yang CW, Yang F, Yang FF, Yang HW, Yang JY, Yang LL, Yang MJ, Yang RZ, Yang SB, Yao YH, Yao ZG, Ye YM, Yin LQ, Yin N, You XH, You ZY, Yu YH, Yuan Q, Yue H, Zeng HD, Zeng TX, Zeng W, Zha M, Zhang BB, Zhang F, Zhang HM, Zhang HY, Zhang JL, Zhang LX, Zhang L, Zhang PF, Zhang PP, Zhang R, Zhang SB, Zhang SR, Zhang SS, Zhang X, Zhang XP, Zhang YF, Zhang Y, Zhang Y, Zhao B, Zhao J, Zhao L, Zhao LZ, Zhao SP, Zheng F, Zhou B, Zhou H, Zhou JN, Zhou M, Zhou P, Zhou R, Zhou XX, Zhu CG, Zhu FR, Zhu H, Zhu KJ, Zuo X. Measurement of Ultra-High-Energy Diffuse Gamma-Ray Emission of the Galactic Plane from 10 TeV to 1 PeV with LHAASO-KM2A. Phys Rev Lett 2023; 131:151001. [PMID: 37897763 DOI: 10.1103/physrevlett.131.151001] [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: 05/04/2023] [Revised: 07/08/2023] [Accepted: 08/18/2023] [Indexed: 10/30/2023]
Abstract
The diffuse Galactic γ-ray emission, mainly produced via interactions between cosmic rays and the interstellar medium and/or radiation field, is a very important probe of the distribution, propagation, and interaction of cosmic rays in the Milky Way. In this Letter, we report the measurements of diffuse γ rays from the Galactic plane between 10 TeV and 1 PeV energies, with the square kilometer array of the Large High Altitude Air Shower Observatory (LHAASO). Diffuse emissions from the inner (15°10 TeV). The energy spectrum in the inner Galaxy regions can be described by a power-law function with an index of -2.99±0.04, which is different from the curved spectrum as expected from hadronic interactions between locally measured cosmic rays and the line-of-sight integrated gas content. Furthermore, the measured flux is higher by a factor of ∼3 than the prediction. A similar spectrum with an index of -2.99±0.07 is found in the outer Galaxy region, and the absolute flux for 10≲E≲60 TeV is again higher than the prediction for hadronic cosmic ray interactions. The latitude distributions of the diffuse emission are consistent with the gas distribution, while the longitude distributions show clear deviation from the gas distribution. The LHAASO measurements imply that either additional emission sources exist or cosmic ray intensities have spatial variations.
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Affiliation(s)
- Zhen Cao
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - F Aharonian
- Dublin Institute for Advanced Studies, 31 Fitzwilliam Place, 2 Dublin, Ireland
- Max-Planck-Institut for Nuclear Physics, P.O. Box 103980, 69029 Heidelberg, Germany
| | - Q An
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Axikegu
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y X Bai
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y W Bao
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - D Bastieri
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - X J Bi
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y J Bi
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J T Cai
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - Q Cao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - W Y Cao
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Zhe Cao
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - J Chang
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J F Chang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
| | - A M Chen
- Tsung-Dao Lee Institute & School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - E S Chen
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Liang Chen
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - Lin Chen
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Long Chen
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - M J Chen
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M L Chen
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
| | - Q H Chen
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - S H Chen
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - S Z Chen
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - T L Chen
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - Y Chen
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - N Cheng
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y D Cheng
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M Y Cui
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S W Cui
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - X H Cui
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - Y D Cui
- School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai & 510275 Guangzhou, Guangdong, China
| | - B Z Dai
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - H L Dai
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
| | - Z G Dai
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Danzengluobu
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - D Della Volpe
- Département de Physique Nucléaire et Corpusculaire, Faculté de Sciences, Université de Genève, 24 Quai Ernest Ansermet, 1211 Geneva, Switzerland
| | - X Q Dong
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - K K Duan
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J H Fan
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - Y Z Fan
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J Fang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - K Fang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C F Feng
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - L Feng
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S H Feng
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X T Feng
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - Y L Feng
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - S Gabici
- APC, Université Paris Cité, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, 119 75205 Paris, France
| | - B Gao
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C D Gao
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - L Q Gao
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Q Gao
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - W Gao
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W K Gao
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M M Ge
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - L S Geng
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - G Giacinti
- Tsung-Dao Lee Institute & School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - G H Gong
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Q B Gou
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M H Gu
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
| | - F L Guo
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - X L Guo
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y Q Guo
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y Y Guo
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y A Han
- School of Physics and Microelectronics, Zhengzhou University, 450001 Zhengzhou, Henan, China
| | - H H He
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H N He
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J Y He
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - X B He
- School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai & 510275 Guangzhou, Guangdong, China
| | - Y He
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - M Heller
- Département de Physique Nucléaire et Corpusculaire, Faculté de Sciences, Université de Genève, 24 Quai Ernest Ansermet, 1211 Geneva, Switzerland
| | - Y K Hor
- School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai & 510275 Guangzhou, Guangdong, China
| | - B W Hou
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C Hou
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X Hou
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - H B Hu
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Q Hu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S C Hu
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D H Huang
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - T Q Huang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W J Huang
- School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai & 510275 Guangzhou, Guangdong, China
| | - X T Huang
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - X Y Huang
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y Huang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z C Huang
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - X L Ji
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
| | - H Y Jia
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - K Jia
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - K Jiang
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - X W Jiang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z J Jiang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - M Jin
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - M M Kang
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - T Ke
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Kuleshov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - K Kurinov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - B B Li
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - Cheng Li
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Cong Li
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Li
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - F Li
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
| | - H B Li
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H C Li
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H Y Li
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J Li
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Jian Li
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Jie Li
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
| | - K Li
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W L Li
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - W L Li
- Tsung-Dao Lee Institute & School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - X R Li
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Xin Li
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Y Z Li
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Zhe Li
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Zhuo Li
- School of Physics, Peking University, 100871 Beijing, China
| | - E W Liang
- School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - Y F Liang
- School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - S J Lin
- School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai & 510275 Guangzhou, Guangdong, China
| | - B Liu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - C Liu
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Liu
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - H Liu
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - H D Liu
- School of Physics and Microelectronics, Zhengzhou University, 450001 Zhengzhou, Henan, China
| | - J Liu
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J L Liu
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J Y Liu
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M Y Liu
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - R Y Liu
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - S M Liu
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - W Liu
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y Liu
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - Y N Liu
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - R Lu
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - Q Luo
- School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai & 510275 Guangzhou, Guangdong, China
| | - H K Lv
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B Q Ma
- School of Physics, Peking University, 100871 Beijing, China
| | - L L Ma
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X H Ma
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J R Mao
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - Z Min
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W Mitthumsiri
- Department of Physics, Faculty of Science, Mahidol University, 10400 Bangkok, Thailand
| | - H J Mu
- School of Physics and Microelectronics, Zhengzhou University, 450001 Zhengzhou, Henan, China
| | - Y C Nan
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - A Neronov
- APC, Université Paris Cité, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, 119 75205 Paris, France
| | - Z W Ou
- School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai & 510275 Guangzhou, Guangdong, China
| | - B Y Pang
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - P Pattarakijwanich
- Department of Physics, Faculty of Science, Mahidol University, 10400 Bangkok, Thailand
| | - Z Y Pei
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - M Y Qi
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y Q Qi
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - B Q Qiao
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J J Qin
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - D Ruffolo
- Department of Physics, Faculty of Science, Mahidol University, 10400 Bangkok, Thailand
| | - A Sáiz
- Department of Physics, Faculty of Science, Mahidol University, 10400 Bangkok, Thailand
| | - D Semikoz
- APC, Université Paris Cité, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, 119 75205 Paris, France
| | - C Y Shao
- School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai & 510275 Guangzhou, Guangdong, China
| | - L Shao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - O Shchegolev
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
- Moscow Institute of Physics and Technology, 141700 Moscow, Russia
| | - X D Sheng
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - F W Shu
- Center for Relativistic Astrophysics and High Energy Physics, School of Physics and Materials Science & Institute of Space Science and Technology, Nanchang University, 330031 Nanchang, Jiangxi, China
| | - H C Song
- School of Physics, Peking University, 100871 Beijing, China
| | - Yu V Stenkin
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
- Moscow Institute of Physics and Technology, 141700 Moscow, Russia
| | - V Stepanov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - Y Su
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Q N Sun
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - X N Sun
- School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - Z B Sun
- National Space Science Center, Chinese Academy of Sciences, 100190 Beijing, China
| | - P H T Tam
- School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai & 510275 Guangzhou, Guangdong, China
| | - Q W Tang
- Center for Relativistic Astrophysics and High Energy Physics, School of Physics and Materials Science & Institute of Space Science and Technology, Nanchang University, 330031 Nanchang, Jiangxi, China
| | - Z B Tang
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - W W Tian
- University of Chinese Academy of Sciences, 100049 Beijing, China
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - C Wang
- National Space Science Center, Chinese Academy of Sciences, 100190 Beijing, China
| | - C B Wang
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - G W Wang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - H G Wang
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - H H Wang
- School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai & 510275 Guangzhou, Guangdong, China
| | - J C Wang
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - K Wang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - L P Wang
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - L Y Wang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - P H Wang
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - R Wang
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - W Wang
- School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai & 510275 Guangzhou, Guangdong, China
| | - X G Wang
- School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - X Y Wang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - Y Wang
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y D Wang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y J Wang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z H Wang
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - Z X Wang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - Zhen Wang
- Tsung-Dao Lee Institute & School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Zheng Wang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
| | - D M Wei
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J J Wei
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y J Wei
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - T Wen
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - C Y Wu
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H R Wu
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - S Wu
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X F Wu
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y S Wu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - S Q Xi
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J Xia
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J J Xia
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - G M Xiang
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - D X Xiao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - G Xiao
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - G G Xin
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y L Xin
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y Xing
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - Z Xiong
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D L Xu
- Tsung-Dao Lee Institute & School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - R F Xu
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - R X Xu
- School of Physics, Peking University, 100871 Beijing, China
| | - W L Xu
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - L Xue
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - D H Yan
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - J Z Yan
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - T Yan
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C W Yang
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - F Yang
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - F F Yang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
| | - H W Yang
- School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai & 510275 Guangzhou, Guangdong, China
| | - J Y Yang
- School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai & 510275 Guangzhou, Guangdong, China
| | - L L Yang
- School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai & 510275 Guangzhou, Guangdong, China
| | - M J Yang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - R Z Yang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - S B Yang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - Y H Yao
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - Z G Yao
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y M Ye
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - L Q Yin
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - N Yin
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - X H You
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z Y You
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y H Yu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Q Yuan
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - H Yue
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H D Zeng
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - T X Zeng
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
| | - W Zeng
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - M Zha
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B B Zhang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - F Zhang
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - H M Zhang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - H Y Zhang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J L Zhang
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - L X Zhang
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - Li Zhang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - P F Zhang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - P P Zhang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - R Zhang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S B Zhang
- University of Chinese Academy of Sciences, 100049 Beijing, China
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - S R Zhang
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - S S Zhang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X Zhang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - X P Zhang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y F Zhang
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Yi Zhang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Yong Zhang
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B Zhao
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - J Zhao
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - L Zhao
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - L Z Zhao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - S P Zhao
- Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - F Zheng
- National Space Science Center, Chinese Academy of Sciences, 100190 Beijing, China
| | - B Zhou
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H Zhou
- Tsung-Dao Lee Institute & School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - J N Zhou
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - M Zhou
- Center for Relativistic Astrophysics and High Energy Physics, School of Physics and Materials Science & Institute of Space Science and Technology, Nanchang University, 330031 Nanchang, Jiangxi, China
| | - P Zhou
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - R Zhou
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - X X Zhou
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - C G Zhu
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - F R Zhu
- School of Physical Science and Technology & School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - H Zhu
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - K J Zhu
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, 230026 Hefei, China
| | - X Zuo
- Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
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Kilfoy A, Panesar P, Hashemi E, Masama T, Pereira M, Liu W, Alexander S, Korenblum C, Jibb LA. "It just made me feel better": qualitative examination of the implementation of a novel virtual psychosocial support program for adolescents with cancer. Support Care Cancer 2023; 31:610. [PMID: 37792141 DOI: 10.1007/s00520-023-08054-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/18/2023] [Indexed: 10/05/2023]
Abstract
PURPOSE Adolescents with cancer routinely report feelings of isolation and exclusion, including from medical decision-making. To address this problem and support adolescents, we designed and implemented the novel, virtual, weekly Teens4Teens peer support group and patient education program. We examined the views of participating adolescents, program guest speakers, and program moderators as they pertained to the need for the program, its feasibility, acceptability, and perceived impact. METHODS We recruited all available adolescents, moderators, and guest speakers who participated in Teens4Teens to take part in audio-recorded, semi-structured interviews. Interviews were transcribed, coded, and analyzed using thematic analysis. RESULTS We conducted 21 interviews across participant groups. We identified four broad themes: pathways into the Teen4Teens program, Teens4Teens implementation capacity, perspectives of the positive impact of Teens4Teens, and suggestions to improve Teens4Teens. These themes described a perceived need for adolescent-centered psychosocial programming in pediatric cancer care, provided lessons on how best to build and apply such a program, and highlighted the value of the program for both adolescents' and clinicians' acceptability, feasibility, and perceived utility. CONCLUSION Adolescents, guest speakers, and moderators valued Teens4Teens and made suggestions to improve capacity to routinely implement the program. Adolescent-tailored psychosocial programming, such as Teens4Teens, is positioned to be integrated into clinical care with relative ease and may serve to improve the cancer care experience of adolescents and their families. This study has potential to provide researchers and clinicians with valuable information about the content, design, and delivery of virtual peer support programming for adolescents with cancer.
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Affiliation(s)
- A Kilfoy
- Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, 155 College St, Toronto, ON, M5T 1P8, Canada
- Division of Hematology and Oncology, Hospital for Sick Children, 170 Elizabeth St, Toronto, ON, M5G 1E8, Canada
- Child Health Evaluative Sciences, Hospital for Sick Children, 686 Bay Street, Toronto, ON, M5G 0A4, Canada
| | - P Panesar
- Faculty of Health Sciences, McMaster University, 1280 Main Street West, ON, Hamilton, L8S 4L8, Canada
| | - E Hashemi
- Child Health Evaluative Sciences, Hospital for Sick Children, 686 Bay Street, Toronto, ON, M5G 0A4, Canada
| | - T Masama
- Division of Hematology and Oncology, Hospital for Sick Children, 170 Elizabeth St, Toronto, ON, M5G 1E8, Canada
| | - M Pereira
- Child Health Evaluative Sciences, Hospital for Sick Children, 686 Bay Street, Toronto, ON, M5G 0A4, Canada
| | - W Liu
- Faculty of Medicine, University of British Columbia, 317-2194 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada
| | - S Alexander
- Division of Hematology and Oncology, Hospital for Sick Children, 170 Elizabeth St, Toronto, ON, M5G 1E8, Canada
- Temerty Faculty of Medicine, University of Toronto, 1 King's College Cir, ON, Toronto, M5S 1A8, Canada
| | - C Korenblum
- Department of Supportive Care, University Health Network, Princess Margaret Cancer Centre, 610 University Ave, Toronto, ON, M6G 2C4, Canada
- Temerty Faculty of Medicine, University of Toronto, 1 King's College Cir, ON, Toronto, M5S 1A8, Canada
- Division of Adolescent Medicine, Hospital for Sick Children, 170 Elizabeth St, Toronto, ON, M5G 1E8, Canada
| | - L A Jibb
- Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, 155 College St, Toronto, ON, M5T 1P8, Canada.
- Division of Hematology and Oncology, Hospital for Sick Children, 170 Elizabeth St, Toronto, ON, M5G 1E8, Canada.
- Child Health Evaluative Sciences, Hospital for Sick Children, 686 Bay Street, Toronto, ON, M5G 0A4, Canada.
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Liu F, Wang H, Jiang C, He L, Xiao S, Ye X, Fan C, Wu X, Liu W, Li Y, Wu W, Zhao Q. Dose Painting Radiotherapy Guided by Diffusion-Weighted Magnetic Resonance vs. 18F-FDG-PET/CT in Locoregionally Advanced Nasopharyngeal Carcinoma: A Randomized, Controlled Clinical Trial. Int J Radiat Oncol Biol Phys 2023; 117:S100-S101. [PMID: 37784268 DOI: 10.1016/j.ijrobp.2023.06.054] [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 phase II randomized controlled trial aimed at comparing the efficacy and toxicity of diffusion-weighted magnetic resonance imaging (DWI)-guided dose painting radiotherapy (DP-RT), FDG-PET/CT-guided DP-RT, and conventional MRI-based radiotherapy (RT) in locoregionally advanced nasopharyngeal carcinoma (NPC). MATERIALS/METHODS A total of 330 patients with stage III-IVa NPC disease were randomly assigned in a 1:1:1 ratio to receive induction chemotherapy followed by concurrent chemoradiotherapy by DWI-guided DP-RT (group A, n = 110), FDG-PET/CT-guided DP-RT (group B, n = 110), or conventional MRI-based RT (group C, n = 110). All patients received volumetric modulated arc therapy (VMAT). In group A, subvolume GTVnx-DWI (gross tumor volume of nasopharynx in DWI) was defined as the areas within the GTVnx (gross tumor volume of nasopharynx) with an apparent diffusion coefficient (ADC) below the mean ADC (ADC < mean). In group B, subvolume GTVnx-PET (gross tumor volume of nasopharynx in PET images) was defined within GTVnx as the SUV50%max isocontour. The doses to GTVnx-DWI in group A and GTVnx-PET in group B were escalated to 75.2 Gy/32 fx in patients with T1-2 disease and to 77.55 Gy/33 fx in those with T3-4 disease in 2.35 Gy per fraction. In group C, planning gross tumor volume of nasopharynx (PGTVnx) was irradiated at 70.4 to 72.6 Gy/32 to 33 fx in 2.2 Gy per fraction. This trial is registered with chictr.org.cn (ChiCTR2200057476). RESULTS Group A and B showed significant higher complete response (CR) rates than group C (100%, 100%, and 96.4% for group A, B and C, respectively, p = 0.036). In groups A, B and C, the 1-year local recurrence-free survival (LRFS) rates were 100%, 100%, and 94.5%, respectively (p = 0.002). The 1-year disease-free survival (DFS) rates were 100%, 99.1%, and 92.7%, respectively (p = 0.001). The 1-year distant metastasis-free survival (DMFS) rates were 100%, 99.1%, and 93.6%, respectively (p = 0.004). The 1-year overall survival (OS) rates were 100%, 100%, and 95.4%, respectively (p = 0.006). Group A and B had significantly higher 1-year LRFS, DFS, DMFS, and OS than those in group C. No significant differences were observed in LRFS, DFS, DMFS and OS between group A and B. Group B (PET/CT group) had a higher incidence of grade 3-4 acute ototoxicity (3.6%) than group A (0%) and group C (0%, p = 0.036). No significant differences in other grade 3-4 acute adverse events and late toxic effects were observed among the three groups, and no patient had grade 5 toxicities. Multivariate analysis showed that dose painting (DWI-guided DP-RT and PET/CT-guided DP-RT vs conventional MRI-based RT) was associated with improved LRFS, DFS, DMFS and OS. CONCLUSION Both DWI-guided DP-RT and PET/CT-guided DP-RT plus chemotherapy are associated with improved LRFS, DFS, DMFS and OS compared with conventional MRI-based RT among patients with locoregionally advanced NPC. DWI-guided DP-RT does not increase toxicities, but PET/CT-guided DP-RT has higher incidence of acute ototoxicity.
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Affiliation(s)
- F Liu
- Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China, Changsha, China
| | - H Wang
- Department of Radiation Oncology, Hunan Cancer Hospital & the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - C Jiang
- Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China, Changsha, China
| | - L He
- Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - S Xiao
- Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - X Ye
- Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China, Changsha, China
| | - C Fan
- Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China, Changsha, China
| | - X Wu
- Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China, Changsha, China
| | - W Liu
- Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China, Changsha, China
| | - Y Li
- Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - W Wu
- Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China, Changsha, China
| | - Q Zhao
- Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China, Changsha, China
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Li J, Liu W, Sun W, Rao X, Chen X, Yu L. A Study on Autophagy Related Biomarkers in Alzheimer's Disease Based on Bioinformatics. Cell Mol Neurobiol 2023; 43:3693-3703. [PMID: 37418137 DOI: 10.1007/s10571-023-01379-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/20/2023] [Indexed: 07/08/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease with an annual incidence increase that poses significant health risks to people. However, the pathogenesis of AD is still unclear. Autophagy, as an intracellular mechanism can degrade damaged cellular components and abnormal proteins, which is closely related to AD pathology. The goal of this work is to uncover the intimate association between autophagy and AD, and to mine potential autophagy-related AD biomarkers by identifying key differentially expressed autophagy genes (DEAGs) and exploring the potential functions of these genes. GSE63061 and GSE140831 gene expression profiles of AD were downloaded from the Gene Expression Omnibus (GEO) database. R language was used to standardize and differentially expressed genes (DEGs) of AD expression profiles. A total of 259 autophagy-related genes were discovered through the autophagy gene databases ATD and HADb. The differential genes of AD and autophagy genes were integrated and analyzed to screen out DEAGs. Then the potential biological functions of DEAGs were predicted, and Cytoscape software was used to detect the key DEAGs. There were ten DEAGs associated with the AD development, including nine up-regulated genes (CAPNS1, GAPDH, IKBKB, LAMP1, LAMP2, MAPK1, PRKCD, RAB24, RAF1) and one down-regulated gene (CASP1). The correlation analysis reveals the potential correlation among 10 core DEAGs. Finally, the significance of the detected DEAGs expression was verified, and the value of DEAGs in AD pathology was detected by the receiver operating characteristic curve. The area under the curve values indicated that ten DEAGs are potentially valuable for the study of the pathological mechanism and may become biomarkers of AD. This pathway analysis and DEAG screening in this study found a strong association between autophagy-related genes and AD, providing new insights into the pathological progression of AD. Exploring the relationship between autophagy and AD: analysis of genes associated with autophagy in pathological mechanisms of AD using bioinformatics. 10 autophagy-related genes play an important role in the pathological mechanisms of AD.
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Affiliation(s)
- Jian Li
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Wenjia Liu
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Wen Sun
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Xin Rao
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Xiaodong Chen
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China.
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK.
| | - Liyang Yu
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China.
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Hu J, Schild SE, Liu W, Li J, Fatyga M. Improving Dose Volume Histogram (DVH) Based Analysis of Clinical Outcomes Using Modern Statistical Techniques: A Systematic Answer to Multiple Comparisons Concerns. Int J Radiat Oncol Biol Phys 2023; 117:S20. [PMID: 37784451 DOI: 10.1016/j.ijrobp.2023.06.242] [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) DVH constraints are essential in the clinical practice of radiation therapy. Historically, DVH constraints were found through sparse sampling of all possible DVH indices to find one that appeared to be most predictive for clinical toxicity. This approach can lead to inconsistent results among studies and to multiple comparison concerns. We aim to solve both problems by examining a full array of DVH indices using statistical methods that account for strong correlations among DVH indices and incorporate radiobiological knowledge constraints. MATERIALS/METHODS We extracted a dense array of V%_D indices from a treatment planning system using ESAPI interface, with V%_D corresponding to the volume fraction irradiated to dose D, or higher. We used Fused Lasso as the base model to compensate for correlations among DVH indices because it applies a penalty on the difference between DVH variables with adjacent dose. The base model was augmented with additional constraints based on radiobiological considerations: the positivity constraint (beta_i > 0) which assumes that any tissue irradiation cannot reduce the risk of toxicity, and monotonicity constraint (beta_i+1 > = beta_i) which assumes that higher dose to a fixed volume fraction cannot be associated with a lower risk of toxicity. We called the hybrid model KC-Lasso (Knowledge Constrained Lasso) and applied it to two clinical examples: grade 2 acute rectal toxicity in conventionally fractionated RT for 79 prostate cancer patients (77.4 Gy + MR based boost to 81-83 Gy) and cardiac toxicity in conventionally fractionated RT for 119 locally advanced Non-small Cell Lung Cancer (NSCLC) patients (Median prescribed dose 62 Gy). We further examined alternative data driven models to determine the importance of knowledge constraints. RESULTS KC-Lasso detected two distinct dose thresholds for grade 2 rectal toxicity, at 35 Gy and 78 Gy. A threshold of 51 Gy was detected for reduced overall survival due to cardiac irradiation in NSCLC patients. An examination of KC-Lasso models at varying step size suggested that a single mid-range index can be used as a treatment planning constraint while full model can be used for confirmatory, final plan evaluation. Alternative models which lack knowledge constraints show patterns of negative and isolated coefficients which are difficult to interpret and are not likely to be generalizable. CONCLUSION A more systematic approach to the analysis of correlations between DVH constraints and clinical toxicity can lead to greater consistency of results among different studies, better understanding of true dose thresholds and results which are more generalizable.
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Affiliation(s)
- J Hu
- Arizona State university, Tempe, AZ
| | - S E Schild
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ
| | - W Liu
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ
| | - J Li
- Georgia Institute of Technology, Atlanta, GA
| | - M Fatyga
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ
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Dai X, Yang Y, Liu W, Niedermayer TR, Kovalchuk N, Gensheimer MF, Beadle BM, Le QT, Xing L. Reinforcement Learning Powered Station Parameter Optimized Radiation Therapy (SPORT): A Novel Treatment Planning and Beam Delivery Technique. Int J Radiat Oncol Biol Phys 2023; 117:e658. [PMID: 37785951 DOI: 10.1016/j.ijrobp.2023.06.2091] [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) Conventional intensity modulated radiation therapy (IMRT) with a typical 5-20 fixed beams often does not provide sufficient angular sampling required for conformal dose shaping, whereas current volumetric modulated arc therapy (VMAT) discretizes the angular space into equally spaced control points without considering the differential need for intensity modulation of different angles, leading to undersampling at some angles while oversampling at some other angles. Our goal is to develop a node or station parameter optimized radiation therapy (SPORT) strategy with simultaneously optimized angular sampling and beam modulation by leveraging state-of-the-art reinforcement learning and the unique capability of modern digital LINACs in dose delivery through programmable nodal points. MATERIALS/METHODS We developed a SPORT optimization framework, in which, the process of programming control points (or station parameters) was formulated as a stochastic dynamic programming problem, which was solved by a reinforcement learning-based algorithm. On-policy reinforcement learning method, namely, state-action-reward-state-action (SARSA) was integrated with deep convolutional neural network to predict station parameters by utilizing the patient's anatomical structures meanwhile considering the delivery capability of a typical digital LINAC machine. Here, the deep convolutional neural network estimated the state-action value by using the quality of the plan with current station parameters when a next potential station parameter was selected. The state-action value was then updated by SARSA learning. The quality of the plan was quantified by dosimetry constraints. The model was assessed by a retrospective study on a cohort of patients underwent head-and-neck radiation therapy. Dosimetric analysis and delivery efficiency comparisons were used to evaluate the performance of the proposed framework. RESULTS Our model was used to generate 16 plans unseen in the original training set. All the plans predicted by our model achieved better dose distributions without violating clinical planning constraints. Moreover, instead of using 4 full standard arcs in the original clinically used plans obtained via manual optimization, the predicted plans only used one full standard arc (about 178 control points) plus boost from a few sub-arcs (less than 30 degrees of gantry angles), which significantly improved the efficiency of the beam delivery. We are in the process of integrating the sub-arcs into the full arc by considering the programmable capability of modern LINACs. CONCLUSION We demonstrated that a machine learning-based SPORT framework capable of optimizing the spatial sampling and beam modulation simultaneously for modern radiation therapy. The framework not only significantly improves the quality and efficiency of beam delivery, but also has the potential to be incorporated into current clinical workflow to improve the efficiency of dose planning and delivery.
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Affiliation(s)
- X Dai
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - Y Yang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - W Liu
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - T R Niedermayer
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - N Kovalchuk
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - M F Gensheimer
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - B M Beadle
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - Q T Le
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - L Xing
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
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Ding Y, Holmes J, Li B, Vargas CE, Vora SA, Wong WW, Fatyga M, Foote RL, Patel SH, Liu W. Patient-Specific 3D CT Images Reconstruction from 2D KV Images Via Vision Transformer-Based Deep-Learning. Int J Radiat Oncol Biol Phys 2023; 117:e660. [PMID: 37785958 DOI: 10.1016/j.ijrobp.2023.06.2095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed-imaging is available. The visibility of the tumor in kV images is limited since the patient's 3D anatomy is projected onto a 2D plane, especially when the tumor is behind a high-density structure such as bone. This can lead to a large patient setup error. A solution to this problem is to reconstruct the 3D CT image from the kV images obtained in the treatment position. MATERIALS/METHODS An asymmetric autoencoder-like network built with vision-transformer blocks was developed. The data was collected from a head and neck patient: 2 orthogonal kV images (1024X1024 voxels), 1 3D CT with padding (512X512X512) acquired from the in-room CT-on-rails before kVs were taken and 2 digitally-reconstructed-radiograph (DRR) images (512X512) based on the CT. We resampled kV images every 8 voxels and DRR and CT every 4 voxels, thus formed a dataset consisting of 262,144 samples, in which the images had a dimension of 128 for each direction. The value of each voxel in CT was normalized to range 0-1 with a uniform shift of 1000 and a denominator of 4000. For kV and DRR, we ranked all voxels value in an ascending order and normalized the values of the first 80% voxels to range 0-0.8 and the rest to range 0.8-1, thus yielding a quasi-Gaussian distribution, which was favorable by the deep neural networks. We further cropped kV and DRR images with a self-supervised bitmap based on the voxels' gradients. In training, both kV and DRR were utilized, and the encoder was encouraged to learn the same feature maps for kV images and its corresponding DRR images with mean-absolute-error (MAE) as the similarity loss. Then the decoder would reconstruct the 3D CT image from the feature maps of the kV images with the CT-on-rails as ground-truth (gCT) and MAE as the reconstruction loss. In testing, only independent kV images were used. The full-size synthetic CT (sCT) was achieved by concatenating the sCTs generated by the model according to their spatial information. The image quality of the sCT was evaluated using MAE and per-voxel-absolute-CT-number-difference volume histogram (CDVH). The proposed network was implemented with PyTorch deep learning library and both distributed data parallel (DDP) and automatic mixed precision (AMP) were applied to saving memory and accelerating the training speed. We used the AdamW optimizer with β1 = 0.9 and β2 = 0.999 and a cosine annealing learning rate scheduler with an initial learning of 1e-7 and 20 warm-up epochs. RESULTS The model achieved a MAE of <40HU and the CDVH showed that <5% of the voxels had a per-voxel-absolute-CT-number-difference larger than 185HU. The profile of a typical gCT slice and its corresponding sCT slice exhibited a high agreement, indicating the high similarity between the gCT and sCT. CONCLUSION A patient-specific vision-transformer-based network was developed and shown to be accurate and efficient to reconstruct 3D CT images from kV images.
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Affiliation(s)
- Y Ding
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ
| | - J Holmes
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ
| | - B Li
- Arizona State University, Tempe, AZ
| | - C E Vargas
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ
| | - S A Vora
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ
| | - W W Wong
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ
| | - M Fatyga
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ
| | - R L Foote
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN
| | - S H Patel
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ
| | - W Liu
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ
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Sun L, Zhao W, Lyu T, Chen Y, Xing L, Liu W. An Efficient Transformer Model for Synthesizing Dual Energy CT from Single Energy Scanner. Int J Radiat Oncol Biol Phys 2023; 117:e721-e722. [PMID: 37786104 DOI: 10.1016/j.ijrobp.2023.06.2231] [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) Dual-energy CT can be used to optimize radiation treatment. Recently, deep learning has been demonstrated to synthesize high-energy CT images from low-energy ones for dose reduction and lower CT system burden. As the state-of-the-art deep learning architecture, the computation burden of Transformer increases quadratically with the feature size, making the model training resource-demanding or even infeasible. Here, we introduce an efficient transformer for the balance between CT image synthesis quality and computational burden. MATERIALS/METHODS The model is a U-shape deep neural network with encoders and decoders built by Transformer blocks. The model input is low-energy 100kVp CT image and the output is high-energy 140kVp one. Each block has a Self Channel Correlation Unit (SCCU) and a Self Spatial Attention Unit (SSAU). Local shortcuts are applied for both units. Under-sampling operation achieved by pixel shuffling is used to obtain multi-scale feature maps, and the transformer block is applied on each feature scale. Symmetric skip connection sending features from shallow layers to deep layers, thus an additional 1 × 1 convolution is used for feature fusion in each decoder. In a SCCU, the feature is first mapped to one Query, one Key, and one Value. Then the Query and the Key tensors perform matrix multiplication to compute cross covariance of feature channels. The channel correlation score can be obtained by normalization of the covariance, and it is used to weight the Value tensor. As a result, the model complexity only increases linearly with the feature size. Besides the channel weighting, we enhance spatial information using SSAU, where the feature is mapped to two tensors. One tensor after activation is used to point-wisely calibrate another tensor. Additional Transformer blocks are cascaded to the last decoder for feature refinement. Because of the structure similarity of low- and high-energy CT images, a global shortcut is used to ease model training. Clinical iodine contrast-enhanced dual energy CT image datasets of 19 patients are used in this study. The dual-energy scanning is performed by a SOMATOM Definition Flash DECT scanner. We split the datasets into training dataset of 15 patients, validation dataset of 1 patient, and testing dataset of 3 patients. The image size is 512 × 512 with pixel size 0.5 × 0.5 mm2. RESULTS The U-Net model with 1.95M parameters and 44.87G FLOPS achieved the averaged PSNR value of 44.55 dB (s.t.d. 1.34) and averaged RMSE value of 0.0060 (s.t.d. 0.001). In comparison, our efficient Transformer with 1.408M parameters and 31.375G FLOPS achieved the averaged PSNR value of 44.78 dB (s.t.d. 1.37) and RMSE value of 0.0059 (s.t.d. 0.001), demonstrating our model has better performance with small model size and less computation. CONCLUSION The efficient Transformer model allows high-resolution CT image synthesis with small model scale and computation burden from low-energy CT image.
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Affiliation(s)
- L Sun
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - W Zhao
- School of physics, Beijing University, Beijing, China; Beihang Hangzhou Innovation Institute, Hangzhou, China
| | - T Lyu
- Zhejiang Lab, Hangzhou, China
| | - Y Chen
- School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - L Xing
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - W Liu
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
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