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Abratenko P, Alterkait O, Andrade Aldana D, Anthony J, Arellano L, Asaadi J, Ashkenazi A, Balasubramanian S, Baller B, Barr G, Barrow J, Basque V, Benevides Rodrigues O, Berkman S, Bhanderi A, Bhat A, Bhattacharya M, Bishai M, Blake A, Bogart B, Bolton T, Book JY, Camilleri L, Cao Y, Caratelli D, Caro Terrazas I, Cavanna F, Cerati G, Chen Y, Conrad JM, Convery M, Cooper-Troendle L, Crespo-Anadón JI, Del Tutto M, Dennis SR, Detje P, Devitt A, Diurba R, Djurcic Z, Dorrill R, Duffy K, Dytman S, Eberly B, Englezos P, Ereditato A, Evans JJ, Fine R, Finnerud OG, Foreman W, Fleming BT, Foppiani N, Franco D, Furmanski AP, Garcia-Gamez D, Gardiner S, Ge G, Gollapinni S, Goodwin O, Gramellini E, Green P, Greenlee H, Gu W, Guenette R, Guzowski P, Hagaman L, Hen O, Hicks R, Hilgenberg C, Horton-Smith GA, Imani Z, Irwin B, Itay R, James C, Ji X, Jiang L, Jo JH, Johnson RA, Jwa YJ, Kalra D, Kamp N, Karagiorgi G, Ketchum W, Kirby M, Kobilarcik T, Kreslo I, Leibovitch MB, Lepetic I, Li JY, Li K, Li Y, Lin K, Littlejohn BR, Louis WC, Luo X, Mariani C, Marsden D, Marshall J, Martinez N, Martinez Caicedo DA, Mason K, Mastbaum A, McConkey N, Meddage V, Miller K, Mills J, Mogan A, Mohayai T, Mooney M, Moor AF, Moore CD, Mora Lepin L, Mulleriababu S, Naples D, Navrer-Agasson A, Nayak N, Nebot-Guinot M, Nowak J, Oza N, Palamara O, Pallat N, Paolone V, Papadopoulou A, Papavassiliou V, Parkinson HB, Pate SF, Patel N, Pavlovic Z, Piasetzky E, Ponce-Pinto ID, Pophale I, Prince S, Qian X, Raaf JL, Radeka V, Rafique A, Reggiani-Guzzo M, Ren L, Rochester L, Rodriguez Rondon J, Rosenberg M, Ross-Lonergan M, Rudolf von Rohr C, Scanavini G, Schmitz DW, Schukraft A, Seligman W, Shaevitz MH, Sharankova R, Shi J, Snider EL, Soderberg M, Söldner-Rembold S, Spitz J, Stancari M, John JS, Strauss T, Sword-Fehlberg S, Szelc AM, Tang W, Taniuchi N, Terao K, Thorpe C, Torbunov D, Totani D, Toups M, Tsai YT, Tyler J, Uchida MA, Usher T, Viren B, Weber M, Wei H, White AJ, Williams Z, Wolbers S, Wongjirad T, Wospakrik M, Wresilo K, Wright N, Wu W, Yandel E, Yang T, Yates LE, Yu HW, Zeller GP, Zennamo J, Zhang C. First Measurement of η Meson Production in Neutrino Interactions on Argon with MicroBooNE. Phys Rev Lett 2024; 132:151801. [PMID: 38683006 DOI: 10.1103/physrevlett.132.151801] [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: 05/30/2023] [Revised: 01/04/2024] [Accepted: 03/13/2024] [Indexed: 05/01/2024]
Abstract
We present a measurement of η production from neutrino interactions on argon with the MicroBooNE detector. The modeling of resonant neutrino interactions on argon is a critical aspect of the neutrino oscillation physics program being carried out by the DUNE and Short Baseline Neutrino programs. η production in neutrino interactions provides a powerful new probe of resonant interactions, complementary to pion channels, and is particularly suited to the study of higher-order resonances beyond the Δ(1232). We measure a flux-integrated cross section for neutrino-induced η production on argon of 3.22±0.84(stat)±0.86(syst) 10^{-41} cm^{2}/nucleon. By demonstrating the successful reconstruction of the two photons resulting from η production, this analysis enables a novel calibration technique for electromagnetic showers in GeV accelerator neutrino experiments.
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Affiliation(s)
- P Abratenko
- Tufts University, Medford, Massachusetts 02155, USA
| | - O Alterkait
- Tufts University, Medford, Massachusetts 02155, USA
| | - D Andrade Aldana
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - J Anthony
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - L Arellano
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - J Asaadi
- University of Texas, Arlington, Texas 76019, USA
| | - A Ashkenazi
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - S Balasubramanian
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - B Baller
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Barr
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - J Barrow
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - V Basque
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | | | - S Berkman
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - A Bhanderi
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - A Bhat
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - M Bhattacharya
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Bishai
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - A Blake
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - B Bogart
- University of Michigan, Ann Arbor, Michigan 48109, USA
| | - T Bolton
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - J Y Book
- Harvard University, Cambridge, Massachusetts 02138, USA
| | - L Camilleri
- Columbia University, New York, New York 10027, USA
| | - Y Cao
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - D Caratelli
- University of California, Santa Barbara, California 93106, USA
| | - I Caro Terrazas
- Colorado State University, Fort Collins, Colorado 80523, USA
| | - F Cavanna
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Cerati
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - Y Chen
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - J M Conrad
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - M Convery
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - L Cooper-Troendle
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - J I Crespo-Anadón
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid E-28040, Spain
| | - M Del Tutto
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - S R Dennis
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - P Detje
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - A Devitt
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - R Diurba
- Universität Bern, Bern CH-3012, Switzerland
| | - Z Djurcic
- Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
| | - R Dorrill
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - K Duffy
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - S Dytman
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - B Eberly
- University of Southern Maine, Portland, Maine 04104, USA
| | - P Englezos
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - A Ereditato
- University of Chicago, Chicago, Illinois, 60637, USA
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J J Evans
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - R Fine
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - O G Finnerud
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - W Foreman
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - B T Fleming
- University of Chicago, Chicago, Illinois, 60637, USA
| | - N Foppiani
- Harvard University, Cambridge, Massachusetts 02138, USA
| | - D Franco
- University of Chicago, Chicago, Illinois, 60637, USA
| | - A P Furmanski
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | | | - S Gardiner
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Ge
- Columbia University, New York, New York 10027, USA
| | - S Gollapinni
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
- University of Tennessee, Knoxville, Tennessee 37996, USA
| | - O Goodwin
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - E Gramellini
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - P Green
- The University of Manchester, Manchester M13 9PL, United Kingdom
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - H Greenlee
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - W Gu
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - R Guenette
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - P Guzowski
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - L Hagaman
- University of Chicago, Chicago, Illinois, 60637, USA
| | - O Hen
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - R Hicks
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - C Hilgenberg
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | | | - Z Imani
- Tufts University, Medford, Massachusetts 02155, USA
| | - B Irwin
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - R Itay
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - C James
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - X Ji
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - L Jiang
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - J H Jo
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - R A Johnson
- University of Cincinnati, Cincinnati, Ohio 45221, USA
| | - Y-J Jwa
- Columbia University, New York, New York 10027, USA
| | - D Kalra
- Columbia University, New York, New York 10027, USA
| | - N Kamp
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - G Karagiorgi
- Columbia University, New York, New York 10027, USA
| | - W Ketchum
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Kirby
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Kobilarcik
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - I Kreslo
- Universität Bern, Bern CH-3012, Switzerland
| | - M B Leibovitch
- University of California, Santa Barbara, California 93106, USA
| | - I Lepetic
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - J-Y Li
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - K Li
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - Y Li
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - K Lin
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - B R Littlejohn
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - W C Louis
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - X Luo
- University of California, Santa Barbara, California 93106, USA
| | - C Mariani
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - D Marsden
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - J Marshall
- University of Warwick, Coventry CV4 7AL, United Kingdom
| | - N Martinez
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - D A Martinez Caicedo
- South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA
| | - K Mason
- Tufts University, Medford, Massachusetts 02155, USA
| | - A Mastbaum
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - N McConkey
- The University of Manchester, Manchester M13 9PL, United Kingdom
- University College London, London WC1E 6BT, United Kingdom
| | - V Meddage
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - K Miller
- University of Chicago, Chicago, Illinois, 60637, USA
| | - J Mills
- Tufts University, Medford, Massachusetts 02155, USA
| | - A Mogan
- Colorado State University, Fort Collins, Colorado 80523, USA
| | - T Mohayai
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Mooney
- Colorado State University, Fort Collins, Colorado 80523, USA
| | - A F Moor
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - C D Moore
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - L Mora Lepin
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | | | - D Naples
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - A Navrer-Agasson
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - N Nayak
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - M Nebot-Guinot
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - J Nowak
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - N Oza
- Columbia University, New York, New York 10027, USA
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - O Palamara
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - N Pallat
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - V Paolone
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - A Papadopoulou
- Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - V Papavassiliou
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - H B Parkinson
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - S F Pate
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - N Patel
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - Z Pavlovic
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - E Piasetzky
- Tel Aviv University, Tel Aviv, Israel, 69978
| | | | - I Pophale
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - S Prince
- Harvard University, Cambridge, Massachusetts 02138, USA
| | - X Qian
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - J L Raaf
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - V Radeka
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - A Rafique
- Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
| | - M Reggiani-Guzzo
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - L Ren
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - L Rochester
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - J Rodriguez Rondon
- South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA
| | - M Rosenberg
- Tufts University, Medford, Massachusetts 02155, USA
| | - M Ross-Lonergan
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | | | - G Scanavini
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - D W Schmitz
- University of Chicago, Chicago, Illinois, 60637, USA
| | - A Schukraft
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - W Seligman
- Columbia University, New York, New York 10027, USA
| | - M H Shaevitz
- Columbia University, New York, New York 10027, USA
| | - R Sharankova
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J Shi
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - E L Snider
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Soderberg
- Syracuse University, Syracuse, New York 13244, USA
| | | | - J Spitz
- University of Michigan, Ann Arbor, Michigan 48109, USA
| | - M Stancari
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J St John
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Strauss
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - S Sword-Fehlberg
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - A M Szelc
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - W Tang
- University of Tennessee, Knoxville, Tennessee 37996, USA
| | - N Taniuchi
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - K Terao
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - C Thorpe
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - D Torbunov
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - D Totani
- University of California, Santa Barbara, California 93106, USA
| | - M Toups
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - Y-T Tsai
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - J Tyler
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - M A Uchida
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - T Usher
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - B Viren
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - M Weber
- Universität Bern, Bern CH-3012, Switzerland
| | - H Wei
- Louisiana State University, Baton Rouge, Louisiana 70803, USA
| | - A J White
- University of Chicago, Chicago, Illinois, 60637, USA
| | - Z Williams
- University of Texas, Arlington, Texas 76019, USA
| | - S Wolbers
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Wongjirad
- Tufts University, Medford, Massachusetts 02155, USA
| | - M Wospakrik
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - K Wresilo
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - N Wright
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - W Wu
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - E Yandel
- University of California, Santa Barbara, California 93106, USA
| | - T Yang
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - L E Yates
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - H W Yu
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - G P Zeller
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J Zennamo
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - C Zhang
- Brookhaven National Laboratory (BNL), Upton, New York 11973, 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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Tang W, Chen J, Wang S, Jiang X, Lu Y, Wu S, Yang L, Tian M, Zhang H, Zhang Y, Xu J, Sun Z. Debt, sleep deprivation and psychological distress among online ride-hailing drivers: evidence from China. Gen Psychiatr 2024; 37:e101332. [PMID: 38495074 PMCID: PMC10941107 DOI: 10.1136/gpsych-2023-101332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 02/09/2024] [Indexed: 03/19/2024] Open
Affiliation(s)
- Wanjie Tang
- Mental Health Centre, Sichuan University, Chengdu, Sichuan, China
- Department of Child & Adolescent Psychiatry, King's College London Institute of Psychiatry Psychology & Neuroscience, London, UK
| | - Jingyue Chen
- Sichuan University Business School, Chengdu, Sichuan, China
| | - Simiao Wang
- Department of Psychology, King's College London Institute of Psychiatry Psychology & Neuroscience, London, UK
| | - Xianglan Jiang
- Sichuan University Business School, Chengdu, Sichuan, China
| | - Yi Lu
- Sichuan University Business School, Chengdu, Sichuan, China
| | - Siqi Wu
- Sichuan University West China School of Medicine, Chengdu, Sichuan, China
| | - Luyu Yang
- The University of Edinburgh School of Health in Social Science, Edinburgh, Edinburgh, UK
| | - Meng Tian
- Department of Psychology, University of Warwick, Coventry, UK
| | - Han Zhang
- Division of Psychiatry, University College London Faculty of Brain Sciences, London, UK
| | - Yinan Zhang
- Wuxi Mental Health Center, Nanjing Medical University, Wuxi, Jiangsu, China
| | - Jiuping Xu
- Sichuan University Business School, Chengdu, Sichuan, China
| | - Zeyuan Sun
- Department of Child & Adolescent Psychiatry, King's College London Institute of Psychiatry Psychology & Neuroscience, London, UK
- King's College London Centre for the Developing Brain, London, UK
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Tang W, Zong SM, Du PY, Xiao HJ. [Auditory brainstem implant: current states and future prospects]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2024; 59:266-270. [PMID: 38561269 DOI: 10.3760/cma.j.cn115330-20230725-00017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Affiliation(s)
- W Tang
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - S M Zong
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - P Y Du
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - H J Xiao
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
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Tang W, Li L, Li XB, Qiu XT, Ger DL. [The accuracy and feasibility study of freehand pedicle screw insertion for subaxial cervical spine assisted with safe core-referred technique]. Zhonghua Wai Ke Za Zhi 2024; 62:202-209. [PMID: 38291665 DOI: 10.3760/cma.j.cn112139-20230820-00052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Objectives: To construct the "safe core" of the pedicle screw trajectory using CT imaging data of the subaxial cervical spine in adults, and to assess the accuracy and feasibility of the pedicle screw insertion assisted with the "safe core-referred technique" for subaxial cervical spine with a cadaver specimen study. Methods: This is an experimental study. From January 2015 to March 2020,60 adults' CT images data of the cervical spine were collected from the database of the First Affiliated Hospital of Gannan Medical University,and were imported into Mimics 20.0 software. Virtual cervical pedicle trajectory and safe core were constructed according to the self-designed "virtual construction method of pedicle in the subaxial cervical spine". The success rate of the construction and the spatial position data of the virtual safe core of was recorded,including the distance between the safe core and the tangent line of the upper and lower outer edge of Luschka's joint on coronal plane,and the distance between the safe core and the posterior edge of the vertebral body on sagittal plane.The 3.5 mm column was used to simulate the pedicle screw placement,using the safe core as the only hub in pedicle screw trajectory.The length of the anterior pedicle screw trajectory,the interval of the abductive angle of the pedicle screw in axial plane, and the projection area of the entry area on periapical radiograph was calculated.In addition,8 adult cervical cadaver specimens were collected for the pedicle screw insertion experiment.The left side group used the "safe core-referred technique" for pedicle screw insertion,while the right side group used the Abumi method for pedicle screw insertion.The accuracy of pedicle screw placement was verified by CT scan.The difference between the accuracy of subjective judgment based on X-ray monitoring of operator and the actual accuracy of pedicle screw insertion verified by CT scan was compared between the two groups.The chi-square test was used to compare the intergroup data. Results: The total success rate of the virtual construction method for the safe core of the subaxial cervical spine was 97.0% (291/300); The distance between the safe core and the tangent line of the upper and lower outer edge of Luschka's joint on coronal plane was (M(IQR)) 0.91 (0.98) mm (range: 0 to 1.85 mm);The distance between the safe core and the posterior wall on the sagittal plane of the vertebral body was (2.01±0.86) mm (range: 0.67 to 3.53 mm). The distance (anterior pedicle screw trajectory) from the posterior cortex to the central point of the safe core was (11.58±1.00)mm (range: 8.27 to 14.93 mm).The projection area of the entry point on the coronal plane was (36.18±11.67) mm2 (range: 13.38 to 83.11 mm2). Pedicle screw insertion experiment in cervical cadaver specimen showed the rate of intraoperative correction of the pedicle screw trajectory was 7.5% (3/40) in the experimental group and 12.5% (5/40) in the control group (χ2=0.139,P=0.709). The operator 's correct rate of subjective judgment on CT in the stage of pedicle screw trajectory preparation was 100% (40/40) in the experimental group and 82.5% (33/40) in the control group, the difference was statistically significant (χ2=5.638,P=0.018). The actual correct rate of CT verification in the stage of pedicle screw insertion was 100% (40/40) in the experimental group and 90.0% (36/40) in the control group, the difference was statistically significant (χ2=2.368,P=0.124); The operator 's correct rate of subjective judgment in the stage of pedicle screw insertion completion was 100% (83/83) in the experimental group and 92.9% (79/85) in the control group (χ2=4.199,P=0.040). Conclusions: The virtual safe-core of subaxial cervical spine can be use as a reliable anatomical fluoroscopy landmark for freehand pedicle screw insertion."Safe core-referred technique" can improve the accuracy rate of the operator's subjective judgment on the intraoperative fluoroscopy monitoring,and hence improve the accuracy of freehand pedicle screw insertion technology for subaxial cervical spine. And it still needs to be further verified in clinical practice.
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Affiliation(s)
- W Tang
- Department of Orthopaedics,Trauma Center, the First Affiliated Hospital of Gannan Medical University,Ganzhou 341000,China
| | - L Li
- Department of Spine Surgery, 903 Hospital,Jiangyou 621700,China
| | - X B Li
- Center for Information Technology and Network Management,Gannan Medical University,Ganzhou 341000,China
| | - X T Qiu
- Department of Medical Imaging,the First Affiliated Hospital of Gannan Medical University,Ganzhou 341000,China
| | - D L Ger
- Gannan Medical University, Ganzhou 341000, China
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Zhou J, Hu T, Xue S, Dong Z, Tang W. The association of childhood trauma with suicidality in adult psychiatric patients: The mediating role of NSSI and the moderating role of self-esteem. J Clin Psychol 2024; 80:664-677. [PMID: 38265412 DOI: 10.1002/jclp.23646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 01/03/2024] [Accepted: 01/10/2024] [Indexed: 01/25/2024]
Abstract
BACKGROUND The contribution of specific childhood trauma subtypes to suicidal thoughts and the associated mechanisms remains unclear, particularly in psychiatric patients. METHODS Face-to-face interviews were conducted with 449 psychiatric patients aged 18-73. Childhood trauma, self-esteem, nonsuicidal self-injury (NSSI), and suicidality were assessed retrospectively. Regression and moderated mediation model were employed to examine these relationships. RESULTS Emotional and sexual abuse were independently associated with suicidality. Female patients reported higher levels of emotional and sexual abuse, lower self-esteem, and a heightened risk of suicide. Self-esteem moderated the links between childhood trauma and NSSI, as well as between NSSI and suicidality. NSSI served as a mediator between childhood trauma and suicidality. CONCLUSIONS Suicide prevention in mentally ill patients should involve targeted programs addressing specific childhood trauma. Additionally, psychological interventions to enhance self-esteem and assist individuals engaging in NSSI behavior are crucial.
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Affiliation(s)
- Jing Zhou
- Department of Psychosomatic Medicine, Leshan People's Hospital, Leshan, Sichuan, China
- Department of Psychiatry, Mental Health Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Tao Hu
- School of Education and Psychology, Chengdu Normal University, Chengdu, China
- Business School, Sichuan University, Chengdu, China
| | - Shuang Xue
- Department of Sociology and Psychology, School of Public Administration, Sichuan University, Chengdu, China
| | - Zaiquan Dong
- Department of Psychiatry, Mental Health Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Wanjie Tang
- Department of Psychiatry, Mental Health Centre, West China Hospital, Sichuan University, Chengdu, China
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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7
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Zhang H, Chen C, Zhang L, Xue S, Tang W. The association between the deviation from balanced time perspective on adolescent pandemic mobile phone addiction: the moderating role of self-control and the mediating role of psychological distress. Front Psychol 2024; 14:1298256. [PMID: 38390401 PMCID: PMC10883043 DOI: 10.3389/fpsyg.2023.1298256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 12/26/2023] [Indexed: 02/24/2024] Open
Abstract
Background Few studies have examined the impact that the deviation from balanced time perspective (DBTP) had on mobile phone addiction during the COVID-19 normalization prevention and control phase. Therefore, this study sought to determine the associations between DBTP, depression and anxiety, self-control, and adolescent mobile phone addiction. Methods The moderated mediating model was tested using the SPSS PROCESS model. The sample was 1,164 adolescents from different regional areas of Sichuan, China. From February to March 2020, participants completed the Zimbardo time perspective inventory (ZTPI), the brief symptom inventory for physical and mental health (BSI-18), the self-control scale (SCS), and the Chinese version of the mobile phone addiction index (MPAI). Results The DBTP was significantly and positively correlated with mobile phone addiction, depressive and anxiety symptoms mediated the relationship between DBTP and mobile phone addiction, self-control moderated the indirect effect of DBTP on mobile phone addiction, and as the level of self-control increased, the effect of DBTP on anxiety and depression and the effect of depression and anxiety on mobile phone addiction weakened. Conclusion During the COVID-19 pandemic, DBTP and lower self-control were risk factors for higher mobile phone addiction in adolescents. Therefore, guiding adolescents to balance their time perspective and enhance their self-control could strengthen their psychological well-being and reduce addictive mobile phone behaviors. This research was supported by "Youth Fund of the Ministry of Education" (18YJCZH233): "Research on the plastic mechanism of decision-making impulsiveness of anxious groups in the context of risk society."
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Affiliation(s)
- Hao Zhang
- School of Education and Psychology, Chengdu Normal University, Chengdu, China
| | - Canjie Chen
- Guangzhou Panyu Polytechnic, Guangzhou, China
| | - Luoyi Zhang
- Faculty of Humanities and Social Sciences, City University of Macau, Macao, Macao SAR, China
- Institute of Analytical Psychology, City University of Macau, Macao, Macao SAR, China
| | - Shuang Xue
- Department of Sociology and Psychology, School of Public Administration, Sichuan University, Chengdu, China
| | - Wanjie Tang
- Department of Sociology and Psychology, School of Public Administration, Sichuan University, Chengdu, China
- Mental Health Centre, Sichuan University, Chengdu, China
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8
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Yau YK, Su Q, Xu Z, Tang W, Ching JYL, Cheung CP, Fung M, Ip M, Chan PKS, Chan FKL, Ng SC. Faecal microbiota transplantation for patients with irritable bowel syndrome: abridged secondary publication. Hong Kong Med J 2024; 30 Suppl 1:34-38. [PMID: 38413211] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024] Open
Affiliation(s)
- Y K Yau
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Microbiota I-Center (MagIC), Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Q Su
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Microbiota I-Center (MagIC), Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Z Xu
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Microbiota I-Center (MagIC), Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - W Tang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Microbiota I-Center (MagIC), Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - J Y L Ching
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Microbiota I-Center (MagIC), Hong Kong SAR, China
| | - C P Cheung
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Microbiota I-Center (MagIC), Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - M Fung
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Microbiota I-Center (MagIC), Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - M Ip
- Department of Microbiology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - P K S Chan
- Department of Microbiology, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - F K L Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Microbiota I-Center (MagIC), Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - S C Ng
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Microbiota I-Center (MagIC), Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China
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9
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Abratenko P, Alterkait O, Andrade Aldana D, Arellano L, Asaadi J, Ashkenazi A, Balasubramanian S, Baller B, Barr G, Barrow D, Barrow J, Basque V, Benevides Rodrigues O, Berkman S, Bhanderi A, Bhat A, Bhattacharya M, Bishai M, Blake A, Bogart B, Bolton T, Book JY, Brunetti MB, Camilleri L, Cao Y, Caratelli D, Cavanna F, Cerati G, Chappell A, Chen Y, Conrad JM, Convery M, Cooper-Troendle L, Crespo-Anadón JI, Cross R, Del Tutto M, Dennis SR, Detje P, Devitt A, Diurba R, Djurcic Z, Dorrill R, Duffy K, Dytman S, Eberly B, Englezos P, Ereditato A, Evans JJ, Fine R, Finnerud OG, Foreman W, Fleming BT, Franco D, Furmanski AP, Gao F, Garcia-Gamez D, Gardiner S, Ge G, Gollapinni S, Gramellini E, Green P, Greenlee H, Gu L, Gu W, Guenette R, Guzowski P, Hagaman L, Hen O, Hilgenberg C, Horton-Smith GA, Imani Z, Irwin B, Ismail M, James C, Ji X, Jo JH, Johnson RA, Jwa YJ, Kalra D, Kamp N, Karagiorgi G, Ketchum W, Kirby M, Kobilarcik T, Kreslo I, Leibovitch MB, Lepetic I, Li JY, Li K, Li Y, Lin K, Littlejohn BR, Liu H, Louis WC, Luo X, Mariani C, Marsden D, Marshall J, Martinez N, Martinez Caicedo DA, Martynenko S, Mastbaum A, Mawby I, McConkey N, Meddage V, Micallef J, Miller K, Mogan A, Mohayai T, Mooney M, Moor AF, Moore CD, Mora Lepin L, Moudgalya MM, Mulleriababu S, Naples D, Navrer-Agasson A, Nayak N, Nebot-Guinot M, Nowak J, Oza N, Palamara O, Pallat N, Paolone V, Papadopoulou A, Papavassiliou V, Parkinson HB, Pate SF, Patel N, Pavlovic Z, Piasetzky E, Pophale I, Qian X, Raaf JL, Radeka V, Rafique A, Reggiani-Guzzo M, Ren L, Rochester L, Rodriguez Rondon J, Rosenberg M, Ross-Lonergan M, Rudolf von Rohr C, Safa I, Scanavini G, Schmitz DW, Schukraft A, Seligman W, Shaevitz MH, Sharankova R, Shi J, Snider EL, Soderberg M, Söldner-Rembold S, Spitz J, Stancari M, St John J, Strauss T, Szelc AM, Tang W, Taniuchi N, Terao K, Thorpe C, Torbunov D, Totani D, Toups M, Tsai YT, Tyler J, Uchida MA, Usher T, Viren B, Weber M, Wei H, White AJ, Wolbers S, Wongjirad T, Wospakrik M, Wresilo K, Wu W, Yandel E, Yang T, Yates LE, Yu HW, Zeller GP, Zennamo J, Zhang C. Search for Heavy Neutral Leptons in Electron-Positron and Neutral-Pion Final States with the MicroBooNE Detector. Phys Rev Lett 2024; 132:041801. [PMID: 38335355 DOI: 10.1103/physrevlett.132.041801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 11/30/2023] [Indexed: 02/12/2024]
Abstract
We present the first search for heavy neutral leptons (HNLs) decaying into νe^{+}e^{-} or νπ^{0} final states in a liquid-argon time projection chamber using data collected with the MicroBooNE detector. The data were recorded synchronously with the NuMI neutrino beam from Fermilab's main injector corresponding to a total exposure of 7.01×10^{20} protons on target. We set upper limits at the 90% confidence level on the mixing parameter |U_{μ4}|^{2} in the mass ranges 10≤m_{HNL}≤150 MeV for the νe^{+}e^{-} channel and 150≤m_{HNL}≤245 MeV for the νπ^{0} channel, assuming |U_{e4}|^{2}=|U_{τ4}|^{2}=0. These limits represent the most stringent constraints in the mass range 35
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Affiliation(s)
- P Abratenko
- Tufts University, Medford, Massachusetts 02155, USA
| | - O Alterkait
- Tufts University, Medford, Massachusetts 02155, USA
| | - D Andrade Aldana
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - L Arellano
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - J Asaadi
- University of Texas, Arlington, Texas 76019, USA
| | - A Ashkenazi
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - S Balasubramanian
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - B Baller
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Barr
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - D Barrow
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - J Barrow
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - V Basque
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | | | - S Berkman
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
- Michigan State University, East Lansing, Michigan 48824, USA
| | - A Bhanderi
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - A Bhat
- University of Chicago, Chicago, Illinois 60637, USA
| | - M Bhattacharya
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Bishai
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - A Blake
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - B Bogart
- University of Michigan, Ann Arbor, Michigan 48109, USA
| | - T Bolton
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - J Y Book
- Harvard University, Cambridge, Massachusetts 02138, USA
| | - M B Brunetti
- University of Warwick, Coventry CV4 7AL, United Kingdom
| | - L Camilleri
- Columbia University, New York, New York 10027, USA
| | - Y Cao
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - D Caratelli
- University of California, Santa Barbara, California 93106, USA
| | - F Cavanna
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Cerati
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - A Chappell
- University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Y Chen
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - J M Conrad
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - M Convery
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | | | - J I Crespo-Anadón
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid E-28040, Spain
| | - R Cross
- University of Warwick, Coventry CV4 7AL, United Kingdom
| | - M Del Tutto
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - S R Dennis
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - P Detje
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - A Devitt
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - R Diurba
- Universität Bern, Bern CH-3012, Switzerland
| | - Z Djurcic
- Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
| | - R Dorrill
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - K Duffy
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - S Dytman
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - B Eberly
- University of Southern Maine, Portland, Maine 04104, USA
| | - P Englezos
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - A Ereditato
- University of Chicago, Chicago, Illinois 60637, USA
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J J Evans
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - R Fine
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - O G Finnerud
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - W Foreman
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - B T Fleming
- University of Chicago, Chicago, Illinois 60637, USA
| | - D Franco
- University of Chicago, Chicago, Illinois 60637, USA
| | - A P Furmanski
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - F Gao
- University of California, Santa Barbara, California 93106, USA
| | | | - S Gardiner
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Ge
- Columbia University, New York, New York 10027, USA
| | - S Gollapinni
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - E Gramellini
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - P Green
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - H Greenlee
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - L Gu
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - W Gu
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - R Guenette
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - P Guzowski
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - L Hagaman
- University of Chicago, Chicago, Illinois 60637, USA
| | - O Hen
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - C Hilgenberg
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | | | - Z Imani
- Tufts University, Medford, Massachusetts 02155, USA
| | - B Irwin
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - M Ismail
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - C James
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - X Ji
- Nankai University, Nankai District, Tianjin 300071, China
| | - J H Jo
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - R A Johnson
- University of Cincinnati, Cincinnati, Ohio 45221, USA
| | - Y-J Jwa
- Columbia University, New York, New York 10027, USA
| | - D Kalra
- Columbia University, New York, New York 10027, USA
| | - N Kamp
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - G Karagiorgi
- Columbia University, New York, New York 10027, USA
| | - W Ketchum
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Kirby
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Kobilarcik
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - I Kreslo
- Universität Bern, Bern CH-3012, Switzerland
| | - M B Leibovitch
- University of California, Santa Barbara, California 93106, USA
| | - I Lepetic
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - J-Y Li
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - K Li
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - Y Li
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - K Lin
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - B R Littlejohn
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - H Liu
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - W C Louis
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - X Luo
- University of California, Santa Barbara, California 93106, USA
| | - C Mariani
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Viriginia 24061, USA
| | - D Marsden
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - J Marshall
- University of Warwick, Coventry CV4 7AL, United Kingdom
| | - N Martinez
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - D A Martinez Caicedo
- South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA
| | - S Martynenko
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - A Mastbaum
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - I Mawby
- University of Warwick, Coventry CV4 7AL, United Kingdom
| | - N McConkey
- University College London, London WC1E 6BT, United Kingdom
| | - V Meddage
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - J Micallef
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
- Tufts University, Medford, Massachusetts 02155, USA
| | - K Miller
- University of Chicago, Chicago, Illinois 60637, USA
| | - A Mogan
- Colorado State University, Fort Collins, Colorado 80523, USA
| | - T Mohayai
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
- Indiana University, Bloomington, Indiana 47405, USA
| | - M Mooney
- Colorado State University, Fort Collins, Colorado 80523, USA
| | - A F Moor
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - C D Moore
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - L Mora Lepin
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - M M Moudgalya
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | | | - D Naples
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - A Navrer-Agasson
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - N Nayak
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - M Nebot-Guinot
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - J Nowak
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - N Oza
- Columbia University, New York, New York 10027, USA
| | - O Palamara
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - N Pallat
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - V Paolone
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - A Papadopoulou
- Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
| | - V Papavassiliou
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - H B Parkinson
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - S F Pate
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - N Patel
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - Z Pavlovic
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - E Piasetzky
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - I Pophale
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - X Qian
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - J L Raaf
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - V Radeka
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - A Rafique
- Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
| | - M Reggiani-Guzzo
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - L Ren
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - L Rochester
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - J Rodriguez Rondon
- South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA
| | - M Rosenberg
- Tufts University, Medford, Massachusetts 02155, USA
| | - M Ross-Lonergan
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | | | - I Safa
- Columbia University, New York, New York 10027, USA
| | - G Scanavini
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - D W Schmitz
- University of Chicago, Chicago, Illinois 60637, USA
| | - A Schukraft
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - W Seligman
- Columbia University, New York, New York 10027, USA
| | - M H Shaevitz
- Columbia University, New York, New York 10027, USA
| | - R Sharankova
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J Shi
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - E L Snider
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Soderberg
- Syracuse University, Syracuse, New York 13244, USA
| | | | - J Spitz
- University of Michigan, Ann Arbor, Michigan 48109, USA
| | - M Stancari
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J St John
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Strauss
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - A M Szelc
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - W Tang
- University of Tennessee, Knoxville, Tennessee 37996, USA
| | - N Taniuchi
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - K Terao
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - C Thorpe
- Lancaster University, Lancaster LA1 4YW, United Kingdom
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - D Torbunov
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - D Totani
- University of California, Santa Barbara, California 93106, USA
| | - M Toups
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - Y-T Tsai
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - J Tyler
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - M A Uchida
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - T Usher
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - B Viren
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - M Weber
- Universität Bern, Bern CH-3012, Switzerland
| | - H Wei
- Louisiana State University, Baton Rouge, Louisiana 70803, USA
| | - A J White
- University of Chicago, Chicago, Illinois 60637, USA
| | - S Wolbers
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Wongjirad
- Tufts University, Medford, Massachusetts 02155, USA
| | - M Wospakrik
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - K Wresilo
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - W Wu
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - E Yandel
- University of California, Santa Barbara, California 93106, USA
| | - T Yang
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - L E Yates
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - H W Yu
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - G P Zeller
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J Zennamo
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - C Zhang
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
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Tang W, Zhou LJ, Zhang WQ, Jia YJ, Ge MW, Hu FH, Chen HL. Association of radiotherapy for prostate cancer and second primary colorectal cancer: a US population-based analysis. Tech Coloproctol 2023; 28:14. [PMID: 38095784 DOI: 10.1007/s10151-023-02883-2] [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: 08/18/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND Radiotherapy (RT) is a common treatment for prostate cancer, yet the risk of second primary colorectal cancer (SPCRC) in patients with prostate cancer undergoing RT has not been adequately studied. METHODS This study employed a population-based cohort design using the US Surveillance, Epidemiology, and End Results (SEER) database to identify individuals diagnosed between January 1975 and December 2015. The cumulative incidence of SPCRC was estimated using Fine-Gray competing risk regression. Poisson regression analysis was used to estimate the risk associated with RT. Survival outcomes of patients with SPCRC were evaluated using the Kaplan-Meier method. RESULTS A total of 287,607 patients diagnosed with prostate cancer were identified. The cumulative incidences were higher in patients who did not receive RT (2.00%) compared to those who underwent RT (2.47%) after 25 years. After adjustment for multiple variables, RT was associated with an increased risk of developing combined SPCRC (adjusted HR 1.590). Additionally, the overall survival was significantly lower in patients who developed colorectal cancer after receiving RT as compared to those who did not receive RT. CONCLUSION These findings underscore the need for diligent long-term monitoring and effective management strategies to detect SPCRC in patients treated with RT for prostate cancer.
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Affiliation(s)
- W Tang
- Medical School, Nantong University, Nantong, China
| | - L-J Zhou
- Nursing Department, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, China
| | - W-Q Zhang
- Medical School, Nantong University, Nantong, China
| | - Y-J Jia
- Medical School, Nantong University, Nantong, China
| | - M-W Ge
- Medical School, Nantong University, Nantong, China
| | - F-H Hu
- Medical School, Nantong University, Nantong, China
| | - H-L Chen
- School of Public Health, Nantong University, 9#Seyuan Road, Nantong, 226000, Jiangsu, China.
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Chen S, Sui Y, Ding S, Chen C, Liu C, Zhong Z, Liang Y, Kong Q, Tang W, Guo Y. A simple and convenient model combining multiparametric MRI and clinical features to predict tumour-infiltrating lymphocytes in breast cancer. Clin Radiol 2023; 78:e1065-e1074. [PMID: 37813758 DOI: 10.1016/j.crad.2023.08.029] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/30/2023] [Accepted: 08/31/2023] [Indexed: 10/11/2023]
Abstract
AIM To develop a simple and convenient method based on multiparametric magnetic resonance imaging (MRI) and clinical features to non-invasively predict tumour-infiltrating lymphocytes (TILs) in breast cancer (BC) and to explore the relationship between TIL levels and disease-free survival (DFS). MATERIALS AND METHODS A total of 172 BC patients were enrolled between November 2017 and June 2021 in this retrospective study. The patients were divided into high (≥10%) and low (<10%) TIL groups. Clinicopathological data were collected. MRI features were reviewed by two radiologists. Predictors associated with TILs were determined by using multivariable logistic regression analyses. Kaplan-Meier survival curves based on TIL levels were used to estimate DFS. RESULTS A total of 102 patients with low TILs and 70 patients with high TILs were included in the study. Tumour size (odds ratio [OR], 1.040; 95% confidence interval [CI]: 1.006, 1.075; p=0.020), apparent diffusion coefficient (ADC; OR, 1.003; 95% CI: 1.001, 1.005; p=0.015), clinical axillary lymph node status (CALNS; OR, 3.222; 95% CI: 1.372,7.568; p=0.007), and enhancement pattern (OR, 0.284; 95% CI: 0.143, 0.563; p<0.001) were independently associated with TIL levels. These features were used in the ALSE model (where A is ADC, L is CALNS, S is size, and E is enhancement pattern). High TILs were associated with better DFS (p=0.016). CONCLUSION The ALSE model derived from multiparametric MRI and clinical features could non-invasively predict TIL levels in BC, and high TILs were associated with longer DFS, especially in human epidermal growth factor receptor 2 (HER2)-positive BC and triple-negative BC (TNBC).
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Affiliation(s)
- S Chen
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China
| | - Y Sui
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China; Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou, 510005, China
| | - S Ding
- Department of Radiology, Liuzhou People's Hospital, Guangxi Medical University, Liuzhou, 545006, China
| | - C Chen
- Department of Pathology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China
| | - C Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Z Zhong
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China
| | - Y Liang
- Department of Pathology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China
| | - Q Kong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China.
| | - W Tang
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China.
| | - Y Guo
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China.
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Peng Q, Wu N, Huang Y, Zhao SJ, Tang W, Liang M, Ran YL, Xiao T, Yang L, Liang X. [Diagnostic values of conventional tumor markers and their combination with chest CT for patients with stageⅠA lung cancer]. Zhonghua Zhong Liu Za Zhi 2023; 45:934-941. [PMID: 37968078 DOI: 10.3760/cma.j.cn112152-20220208-00082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/17/2023]
Abstract
Objective: To investigate the diagnostic efficiency of conventional serum tumor markers and their combination with chest CT for stage ⅠA lung cancer. Methods: A total of 1 155 patients with stage ⅠA lung cancer and 200 patients with benign lung lesions (confirmed by surgery) treated at the Cancer Hospital, Chinese Academy of Medical Sciences from January 2016 to October 2020 were retrospectively enrolled in this study. Six conventional serum tumor markers [carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), squamous cell carcinoma associated antigen (SCCA), cytokeratin 19 fragment (CYFRA21-1), neuron-specific enolase (NSE), and gastrin-releasing peptide precursor (ProGRP)] and chest thin-slice CT were performed on all patients one month before surgery. Pathology was taken as the gold standard to analyze the difference of positivity rates of tumor markers between the lung cancer group and the benign group, the moderate/poor differentiation group and the well differentiation group, the adenocarcinoma group and the squamous cell carcinoma group, the lepidic and non-lepidic predominant adenocarcinoma groups, the solid nodule group and the subsolid nodule group based on thin-slice CT, and subgroups of ⅠA1 to ⅠA3 lung cancers. The diagnostic performance of tumor markers and tumor markers combined with chest CT was analyzed using the receiver operating characteristic curve. Results: The positivity rates of six serum tumor markers in the lung cancer group and the benign group were 2.32%-20.08% and 0-13.64%, respectively; only the SCCA positivity rate in the lung cancer group was higher than that in the benign group (10.81% and 0, P=0.022). There were no significant differences in the positivity rates of other serum tumor markers between the two groups (all P>0.05). The combined detection of six tumor markers showed that the positivity rate of the lung cancer group was higher than that of the benign group (40.93% and 18.18%, P=0.004), and the positivity rate of the adenocarcinoma group was lower than that of the squamous cell carcinoma group (35.66% and 47.41%, P=0.045). The positivity rates in the poorly differentiated group and moderately differentiated group were higher than that in the well differentiated group (46.48%, 43.75% and 22.73%, P=0.025). The positivity rate in the non-lepidic adenocarcinoma group was higher than that in lepidic adenocarcinoma group (39.51% and 21.74%, P=0.001). The positivity rate of subsolid nodules was lower than that of solid nodules (30.01% vs 58.71%, P=0.038), and the positivity rates of stageⅠA1, ⅠA2 and ⅠA3 lung cancers were 33.33%, 48.96% and 69.23%, respectively, showing an increasing trend (P=0.005). The sensitivity and specificity of the combined detection of six tumor markers in the diagnosis of stage ⅠA lung cancer were 74.00% and 56.30%, respectively, and the area under the curve (AUC) was 0.541. The sensitivity and specificity of the combined detection of six serum tumor markers with CT in the diagnosis of stage ⅠA lung cancer were 83.0% and 78.3%, respectively, and the AUC was 0.721. Conclusions: For stage ⅠA lung cancer, the positivity rates of commonly used clinical tumor markers are generally low. The combined detection of six markers can increase the positivity rate. The positivity rate of markers tends to be higher in poorly differentiated lung cancer, squamous cell carcinoma, or solid nodules. Tumor markers combined with thin-slice CT showed limited improvement in diagnostic efficiency for early lung cancer.
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Affiliation(s)
- Q Peng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - N Wu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Huang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S J Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - W Tang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - M Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y L Ran
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - T Xiao
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory for Carcinogenesis and Cancer Prevention, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Yang
- Department of Pathology Diagnosis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - X Liang
- Medical Statistics Office, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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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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Westwood SJ, Conti AA, Tang W, Xue S, Cortese S, Rubia K. Clinical and cognitive effects of external trigeminal nerve stimulation (eTNS) in neurological and psychiatric disorders: a systematic review and meta-analysis. Mol Psychiatry 2023; 28:4025-4043. [PMID: 37674019 PMCID: PMC10827664 DOI: 10.1038/s41380-023-02227-4] [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: 03/20/2023] [Revised: 07/27/2023] [Accepted: 08/08/2023] [Indexed: 09/08/2023]
Abstract
This pre-registered (CRD42022322038) systematic review and meta-analysis investigated clinical and cognitive outcomes of external trigeminal nerve stimulation (eTNS) in neurological and psychiatric disorders. PubMed, OVID, Web of Science, Chinese National Knowledge Infrastructure, Wanfang, and VIP database for Chinese technical periodicals were searched (until 16/03/2022) to identify trials investigating cognitive and clinical outcomes of eTNS in neurological or psychiatric disorders. The Cochrane Risk of Bias 2.0 tool assessed randomized controlled trials (RCTs), while the Risk of Bias of Non-Randomized Studies (ROBINS-I) assessed single-arm trials. Fifty-five peer-reviewed articles based on 48 (27 RCTs; 21 single-arm) trials were included, of which 12 trials were meta-analyzed (N participants = 1048; of which ~3% ADHD, ~3% Epilepsy, ~94% Migraine; age range: 10-49 years). The meta-analyses showed that migraine pain intensity (K trials = 4, N = 485; SMD = 1.03, 95% CI[0.84-1.23]) and quality of life (K = 2, N = 304; SMD = 1.88, 95% CI[1.22-2.53]) significantly improved with eTNS combined with anti-migraine medication. Dimensional measures of depression improved with eTNS across 3 different disorders (K = 3, N = 111; SMD = 0.45, 95% CI[0.01-0.88]). eTNS was well-tolerated, with a good adverse event profile across disorders. eTNS is potentially clinically relevant in other disorders, but well-blinded, adequately powered RCTs must replicate findings and support optimal dosage guidance.
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Affiliation(s)
- Samuel J Westwood
- Department of Psychology, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, London, UK.
- Department of Psychology, School of Social Science, University of Westminster, London, UK.
| | - Aldo Alberto Conti
- Department of Child and Adolescent Psychiatry; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Wanjie Tang
- Department of Child and Adolescent Psychiatry; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Sociology and Psychology, School of Public Administration, Sichuan University, Chengdu, China
- Department of Psychiatry, West China Hospital, Sichuan University, Chengdu, China
| | - Shuang Xue
- Department of Sociology and Psychology, School of Public Administration, Sichuan University, Chengdu, China
| | - Samuele Cortese
- Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK
- Solent NHS Trust, Southampton, UK
- Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York City, NY, USA
- Division of Psychiatry and Applied Psychology, School of Medicine, University of Nottingham, Nottingham, UK
| | - Katya Rubia
- Department of Child and Adolescent Psychiatry; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Child & Adolescent Psychiatry, Technical University Dresden, Dresden, Germany
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Tang W, Guo Q, Chen J, Wu Q, Zhang T, Wang Q, Zhang X, Xie P. The Predictive Value of Circulating Exosomal PD-L1 in Cervical Cancer Immunotherapy. Int J Radiat Oncol Biol Phys 2023; 117:e548-e549. [PMID: 37785688 DOI: 10.1016/j.ijrobp.2023.06.1851] [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) Programmed death ligand 1 (PD-L1) expression was wildly used as a predictor of immune Check-Point Inhibitors (ICIs) efficiency. However, emerging results showed that PD-L1 was of great heterogeneity in sampling time and site. Recently, some studies found that exosomal PD-L1(ExoPD-L1) was related to ICIs response. In this study, we aimed to explore the predictive value of ExoPD-L1 in ICIs treatment of cervical cancer (CC) for the first time. MATERIALS/METHODS A total of 40 primarily diagnosed CC patients who accepted radical radiotherapy (RT) from March 2021 to October 2022 were included. The consecutive tumor sample were collected before and during RT. Another 37 advanced CC patients who accepted ICIs combination therapy from June 2020 to October 2022 were enrolled in this study. Blood samples were collected from each participant before and during treatment. Exosomes were derived by differential centrifugation, which was further identified by Western blot (WB) (CD9/TSG101/Calnexin), transmission electron microscope analysis and nanoparticle tracking analysis. ExoPD-L1 detection was conducted by enzyme-linked immuno-sorbent assay (ELISA). The knockout of PD-L1 was conducted via CRISPR/Cas9 assay and the overexpress of PD-L1 was conducted by lentiviral transfection. CD8+ T cells were extracted from murine spleen by CD8+ T Cell Isolation Kit. Immune cells and cytokines markers were detected by multicolor flow cytometry. RESULTS The consecutive detection of PD-L1 showed a dynamic change during RT. Compared with the level before RT, PD-L1 expression elevated in most patients (87.5%, 35/40) after RT. And the responders (n = 18) had elevated ExoPD-L1 level at the first two circles in the ICIs combination therapy (P<0.001). Whereas the level of pre-treatment ExoPD-L1 couldn't stratified clinical responders and non-responders (P = 0.181). The median follow-up time was 14.13 months. The mPFS in increased group vs. decreased group: not reach vs.11.02 months (P = 0.025, HR: 0.218, 0.052-0.913). Continuous blood sampling of mice models also found that effective therapeutic intervention could increase ExoPD-L1 in the early stage. The combination of exosome inhibitor GW4869 and anti-PD-1 further inhibited tumor growth. Mice were injected with external ExoPD-L1OE and ExoPD-L1KO. The results showed that ExoPD-L1OE suppressed body immunity and promoted tumor growth. The results of flow cytometry showed that ExoPD-L1OE inhibited CD8+ T cells from releasing interferon-and granzyme B. And ExoPD-L1OE also suppressed the CD8+ T cells proliferation in murine spleen. The coculture of CD8+ T cells and exosomes in vitro also confirmed the above conclusion. CONCLUSION Compared with unstable and impressionable tumoral PD-L1, ExoPD-L1 seems to be better predictor for the efficacy of immunotherapy in CC, which was with easy accessibility and continuation. Exosome PD-L1 played an immunosuppressive role by inhibiting the proliferation and functional factor release of CD8+ T cell.
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Affiliation(s)
- W Tang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Q Guo
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - J Chen
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Q Wu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - T Zhang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Q Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - X Zhang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - P Xie
- Shandong Cancer Hospital and Institute, Jinan, Shandong, China
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Wang S, Tang W, Luo H, Jin F. Incidence and Risk Factors for Brain Metastases in Patients with Lung Cancer: A Systematic Review and Meta-Analysis. Int J Radiat Oncol Biol Phys 2023; 117:e71-e72. [PMID: 37786078 DOI: 10.1016/j.ijrobp.2023.06.804] [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) Brain metastases (BM) are a very common metastatic site in lung cancer, but the exact rate of metastasis is still controversial. Risk factors for BM development are also largely lacking, hampering personalized treatment strategies. This study aimed to identify the incidence and possible risk factors for BM in lung cancer. MATERIALS/METHODS A systematic review, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guide-lines, was conducted using PubMed, Medline databases and Cochrane Library databases from inception until February 2023. Two investigators independently searched and selected literature, included in randomized controlled trials and cohort studies. Heterogeneity was assessed using the χ2 test and the I2 statistic. Significant heterogeneity was indicated by P <0.05 in Cochrane Q tests and a ratio greater than 40% in I2 statistics. The review is registered on PROSPERO, number: CRD42022370173. RESULTS Forty-nine studies were included in the meta-analysis. The results showed that the incidence rate of BM in non-small cell lung cancer (NSCLC) was 0.24 (95% confidence interval [CI]: 0.23-0.25; I2 = 97.1%). The incidence rate in early NSCLC was 0.11 (95% CI: 0.10-0.13), locally advanced NSCLC was 0.32 (95% CI: 0.29-0.34), and advanced NSCLC was 0.37 (95% CI: 0.35-0.38). Lung adenocarcinoma was more prone to BM in NSCLC (risk ratio [RR] = 3.59, 95% CI: 1.97-6.54; P<0.001). The BM rate of NSCLC with EGFR mutation was also higher (hazard ratio [HR] = 1.49, 95% CI: 1.14-1.94; P = 0.004). Sex and smoking had no significant effect on the incidence of BM in NSCLC. Prophylactic Cranial Irradiation (PCI) could significantly reduce BM in NSCLC (HR = 0.36, 95% CI: 0.23-0.56; P<0.001), but chemotherapy had no obvious effect on decreasing the rate of BM (HR = 0.91, 95% CI: 0.54-1.54; P = 0.73). The incidence rate of BM in small cell lung cancer (SCLC) was 0.28 (95% CI: 0.27-0.30; I2 = 95.9%), and 0.23 (95% CI: 0.20-0.25) in the limited-stage SCLC. Older age (≥65) (HR = 0.70, 95% CI: 0.54-0.92; P = 0.01) were associated with less BM in SCLC. A higher T stage (≥T3) (HR = 1.72, 95% CI: 1.16-2.56; P = 0.007) was a significant risk factor for BM, while sex, smoking dose were not. PCI could also significantly decreased BM in SCLC (HR = 0.47, 95% CI: 0.38-0.58; P<0.001). CONCLUSION This study is the first meta-analysis of BM incidence rate in lung cancer, and further explores the factors affecting BM, providing some suggestions for clinical decision-making of BM prevention in patients with lung cancer.
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Affiliation(s)
- S Wang
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
| | - W Tang
- Department of Rehabilitation, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - H Luo
- Department of Radiation Oncology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, China
| | - F Jin
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
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Abratenko P, Alterkait O, Andrade Aldana D, Anthony J, Arellano L, Asaadi J, Ashkenazi A, Balasubramanian S, Baller B, Barr G, Barrow J, Basque V, Benevides Rodrigues O, Berkman S, Bhanderi A, Bhattacharya M, Bishai M, Blake A, Bogart B, Bolton T, Book JY, Camilleri L, Caratelli D, Caro Terrazas I, Cavanna F, Cerati G, Chen Y, Cohen EO, Conrad JM, Convery M, Cooper-Troendle L, Crespo-Anadón JI, Del Tutto M, Dennis SR, Detje P, Devitt A, Diurba R, Djurcic Z, Dorrill R, Duffy K, Dytman S, Eberly B, Ereditato A, Evans JJ, Fine R, Finnerud OG, Foreman W, Fleming BT, Foppiani N, Franco D, Furmanski AP, Garcia-Gamez D, Gardiner S, Ge G, Gollapinni S, Goodwin O, Gramellini E, Green P, Greenlee H, Gu W, Guenette R, Guzowski P, Hagaman L, Hen O, Hicks R, Hilgenberg C, Horton-Smith GA, Irwin B, Itay R, James C, Ji X, Jiang L, Jo JH, Johnson RA, Jwa YJ, Kalra D, Kamp N, Karagiorgi G, Ketchum W, Kirby M, Kobilarcik T, Kreslo I, Leibovitch MB, Lepetic I, Li JY, Li K, Li Y, Lin K, Littlejohn BR, Louis WC, Luo X, Mariani C, Marsden D, Marshall J, Martinez N, Martinez Caicedo DA, Mason K, Mastbaum A, McConkey N, Meddage V, Miller K, Mills J, Mogan A, Mohayai T, Mooney M, Moor AF, Moore CD, Mora Lepin L, Mousseau J, Mulleriababu S, Naples D, Navrer-Agasson A, Nayak N, Nebot-Guinot M, Nowak J, Oza N, Palamara O, Pallat N, Paolone V, Papadopoulou A, Papavassiliou V, Parkinson HB, Pate SF, Patel N, Pavlovic Z, Piasetzky E, Ponce-Pinto ID, Pophale I, Prince S, Qian X, Raaf JL, Radeka V, Rafique A, Reggiani-Guzzo M, Ren L, Rochester L, Rodriguez Rondon J, Rosenberg M, Ross-Lonergan M, Rudolf von Rohr C, Scanavini G, Schmitz DW, Schukraft A, Seligman W, Shaevitz MH, Sharankova R, Shi J, Snider EL, Soderberg M, Söldner-Rembold S, Spitz J, Stancari M, John JS, Strauss T, Sword-Fehlberg S, Szelc AM, Tang W, Taniuchi N, Terao K, Thorpe C, Torbunov D, Totani D, Toups M, Tsai YT, Tyler J, Uchida MA, Usher T, Viren B, Weber M, Wei H, White AJ, Williams Z, Wolbers S, Wongjirad T, Wospakrik M, Wresilo K, Wright N, Wu W, Yandel E, Yang T, Yates LE, Yu HW, Zeller GP, Zennamo J, Zhang C. First Double-Differential Measurement of Kinematic Imbalance in Neutrino Interactions with the MicroBooNE Detector. Phys Rev Lett 2023; 131:101802. [PMID: 37739352 DOI: 10.1103/physrevlett.131.101802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 05/09/2023] [Accepted: 07/14/2023] [Indexed: 09/24/2023]
Abstract
We report the first measurement of flux-integrated double-differential quasielasticlike neutrino-argon cross sections, which have been made using the Booster Neutrino Beam and the MicroBooNE detector at Fermi National Accelerator Laboratory. The data are presented as a function of kinematic imbalance variables which are sensitive to nuclear ground-state distributions and hadronic reinteraction processes. We find that the measured cross sections in different phase-space regions are sensitive to different nuclear effects. Therefore, they enable the impact of specific nuclear effects on the neutrino-nucleus interaction to be isolated more completely than was possible using previous single-differential cross section measurements. Our results provide precision data to help test and improve neutrino-nucleus interaction models. They further support ongoing neutrino-oscillation studies by establishing phase-space regions where precise reaction modeling has already been achieved.
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Affiliation(s)
- P Abratenko
- Tufts University, Medford, Massachusetts 02155, USA
| | - O Alterkait
- Tufts University, Medford, Massachusetts 02155, USA
| | - D Andrade Aldana
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - J Anthony
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - L Arellano
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - J Asaadi
- University of Texas, Arlington, Texas 76019, USA
| | - A Ashkenazi
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - S Balasubramanian
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - B Baller
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Barr
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - J Barrow
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - V Basque
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - O Benevides Rodrigues
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
- Syracuse University, Syracuse, New York 13244, USA
| | - S Berkman
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - A Bhanderi
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - M Bhattacharya
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Bishai
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - A Blake
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - B Bogart
- University of Michigan, Ann Arbor, Michigan 48109, USA
| | - T Bolton
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - J Y Book
- Harvard University, Cambridge, Massachusetts 02138, USA
| | - L Camilleri
- Columbia University, New York, New York 10027, USA
| | - D Caratelli
- University of California, Santa Barbara, California 93106, USA
| | - I Caro Terrazas
- Colorado State University, Fort Collins, Colorado 80523, USA
| | - F Cavanna
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Cerati
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - Y Chen
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - E O Cohen
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - J M Conrad
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - M Convery
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - L Cooper-Troendle
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - J I Crespo-Anadón
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid E-28040, Spain
| | - M Del Tutto
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - S R Dennis
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - P Detje
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - A Devitt
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - R Diurba
- Universität Bern, Bern CH-3012, Switzerland
| | - Z Djurcic
- Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
| | - R Dorrill
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - K Duffy
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - S Dytman
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - B Eberly
- University of Southern Maine, Portland, Maine 04104, USA
| | | | - J J Evans
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - R Fine
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - O G Finnerud
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - W Foreman
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - B T Fleming
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - N Foppiani
- Harvard University, Cambridge, Massachusetts 02138, USA
| | - D Franco
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - A P Furmanski
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | | | - S Gardiner
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Ge
- Columbia University, New York, New York 10027, USA
| | - S Gollapinni
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
- University of Tennessee, Knoxville, Tennessee 37996, USA
| | - O Goodwin
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - E Gramellini
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - P Green
- The University of Manchester, Manchester M13 9PL, United Kingdom
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - H Greenlee
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - W Gu
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - R Guenette
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - P Guzowski
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - L Hagaman
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - O Hen
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - R Hicks
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - C Hilgenberg
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | | | - B Irwin
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - R Itay
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - C James
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - X Ji
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - L Jiang
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - J H Jo
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - R A Johnson
- University of Cincinnati, Cincinnati, Ohio 45221, USA
| | - Y-J Jwa
- Columbia University, New York, New York 10027, USA
| | - D Kalra
- Columbia University, New York, New York 10027, USA
| | - N Kamp
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - G Karagiorgi
- Columbia University, New York, New York 10027, USA
| | - W Ketchum
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Kirby
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Kobilarcik
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - I Kreslo
- Universität Bern, Bern CH-3012, Switzerland
| | - M B Leibovitch
- University of California, Santa Barbara, California 93106, USA
| | - I Lepetic
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - J-Y Li
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - K Li
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - Y Li
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - K Lin
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - B R Littlejohn
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - W C Louis
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - X Luo
- University of California, Santa Barbara, California 93106, USA
| | - C Mariani
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - D Marsden
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - J Marshall
- University of Warwick, Coventry CV4 7AL, United Kingdom
| | - N Martinez
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - D A Martinez Caicedo
- South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA
| | - K Mason
- Tufts University, Medford, Massachusetts 02155, USA
| | - A Mastbaum
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - N McConkey
- The University of Manchester, Manchester M13 9PL, United Kingdom
- University College London, London WC1E 6BT, United Kingdom
| | - V Meddage
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - K Miller
- University of Chicago, Chicago, Illinois 60637, USA
| | - J Mills
- Tufts University, Medford, Massachusetts 02155, USA
| | - A Mogan
- Colorado State University, Fort Collins, Colorado 80523, USA
| | - T Mohayai
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Mooney
- Colorado State University, Fort Collins, Colorado 80523, USA
| | - A F Moor
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - C D Moore
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - L Mora Lepin
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - J Mousseau
- University of Michigan, Ann Arbor, Michigan 48109, USA
| | | | - D Naples
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - A Navrer-Agasson
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - N Nayak
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - M Nebot-Guinot
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - J Nowak
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - N Oza
- Columbia University, New York, New York 10027, USA
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - O Palamara
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - N Pallat
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - V Paolone
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - A Papadopoulou
- Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - V Papavassiliou
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - H B Parkinson
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - S F Pate
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - N Patel
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - Z Pavlovic
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - E Piasetzky
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - I D Ponce-Pinto
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - I Pophale
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - S Prince
- Harvard University, Cambridge, Massachusetts 02138, USA
| | - X Qian
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - J L Raaf
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - V Radeka
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - A Rafique
- Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
| | - M Reggiani-Guzzo
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - L Ren
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - L Rochester
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - J Rodriguez Rondon
- South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA
| | - M Rosenberg
- Tufts University, Medford, Massachusetts 02155, USA
| | - M Ross-Lonergan
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | | | - G Scanavini
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - D W Schmitz
- University of Chicago, Chicago, Illinois 60637, USA
| | - A Schukraft
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - W Seligman
- Columbia University, New York, New York 10027, USA
| | - M H Shaevitz
- Columbia University, New York, New York 10027, USA
| | - R Sharankova
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J Shi
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - E L Snider
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Soderberg
- Syracuse University, Syracuse, New York 13244, USA
| | | | - J Spitz
- University of Michigan, Ann Arbor, Michigan 48109, USA
| | - M Stancari
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J St John
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Strauss
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - S Sword-Fehlberg
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - A M Szelc
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - W Tang
- University of Tennessee, Knoxville, Tennessee 37996, USA
| | - N Taniuchi
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - K Terao
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - C Thorpe
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - D Torbunov
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - D Totani
- University of California, Santa Barbara, California 93106, USA
| | - M Toups
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - Y-T Tsai
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - J Tyler
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - M A Uchida
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - T Usher
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - B Viren
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - M Weber
- Universität Bern, Bern CH-3012, Switzerland
| | - H Wei
- Louisiana State University, Baton Rouge, Louisiana 70803, USA
| | - A J White
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - Z Williams
- University of Texas, Arlington, Texas 76019, USA
| | - S Wolbers
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Wongjirad
- Tufts University, Medford, Massachusetts 02155, USA
| | - M Wospakrik
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - K Wresilo
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - N Wright
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - W Wu
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - E Yandel
- University of California, Santa Barbara, California 93106, USA
| | - T Yang
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - L E Yates
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - H W Yu
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - G P Zeller
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J Zennamo
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - C Zhang
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
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Abratenko P, Andrade Aldana D, Anthony J, Arellano L, Asaadi J, Ashkenazi A, Balasubramanian S, Baller B, Barr G, Barrow J, Basque V, Benevides Rodrigues O, Berkman S, Bhanderi A, Bhattacharya M, Bishai M, Blake A, Bogart B, Bolton T, Book JY, Camilleri L, Caratelli D, Caro Terrazas I, Cavanna F, Cerati G, Chen Y, Conrad JM, Convery M, Cooper-Troendle L, Crespo-Anadón JI, Del Tutto M, Dennis SR, Detje P, Devitt A, Diurba R, Djurcic Z, Dorrill R, Duffy K, Dytman S, Eberly B, Ereditato A, Evans JJ, Fine R, Finnerud OG, Foreman W, Fleming BT, Foppiani N, Franco D, Furmanski AP, Garcia-Gamez D, Gardiner S, Ge G, Gollapinni S, Goodwin O, Gramellini E, Green P, Greenlee H, Gu W, Guenette R, Guzowski P, Hagaman L, Hen O, Hicks R, Hilgenberg C, Horton-Smith GA, Irwin B, Itay R, James C, Ji X, Jiang L, Jo JH, Johnson RA, Jwa YJ, Kalra D, Kamp N, Karagiorgi G, Ketchum W, Kirby M, Kobilarcik T, Kreslo I, Leibovitch MB, Lepetic I, Li JY, Li K, Li Y, Lin K, Littlejohn BR, Louis WC, Luo X, Mariani C, Marsden D, Marshall J, Martinez N, Martinez Caicedo DA, Mason K, Mastbaum A, McConkey N, Meddage V, Miller K, Mills J, Mogan A, Mohayai T, Mooney M, Moor AF, Moore CD, Mora Lepin L, Mousseau J, Mulleriababu S, Naples D, Navrer-Agasson A, Nayak N, Nebot-Guinot M, Nowak J, Nunes M, Oza N, Palamara O, Pallat N, Paolone V, Papadopoulou A, Papavassiliou V, Parkinson HB, Pate SF, Patel N, Pavlovic Z, Piasetzky E, Ponce-Pinto ID, Pophale I, Prince S, Qian X, Raaf JL, Radeka V, Rafique A, Reggiani-Guzzo M, Ren L, Rochester L, Rodriguez Rondon J, Rosenberg M, Ross-Lonergan M, Rudolf von Rohr C, Scanavini G, Schmitz DW, Schukraft A, Seligman W, Shaevitz MH, Sharankova R, Shi J, Snider EL, Soderberg M, Söldner-Rembold S, Spitz J, Stancari M, John JS, Strauss T, Sword-Fehlberg S, Szelc AM, Tang W, Taniuchi N, Terao K, Thorpe C, Torbunov D, Totani D, Toups M, Tsai YT, Tyler J, Uchida MA, Usher T, Viren B, Weber M, Wei H, White AJ, Williams Z, Wolbers S, Wongjirad T, Wospakrik M, Wresilo K, Wright N, Wu W, Yandel E, Yang T, Yates LE, Yu HW, Zeller GP, Zennamo J, Zhang C. First Measurement of Quasielastic Λ Baryon Production in Muon Antineutrino Interactions in the MicroBooNE Detector. Phys Rev Lett 2023; 130:231802. [PMID: 37354393 DOI: 10.1103/physrevlett.130.231802] [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: 12/16/2022] [Revised: 04/07/2023] [Accepted: 04/28/2023] [Indexed: 06/26/2023]
Abstract
We present the first measurement of the cross section of Cabibbo-suppressed Λ baryon production, using data collected with the MicroBooNE detector when exposed to the neutrinos from the main injector beam at the Fermi National Accelerator Laboratory. The data analyzed correspond to 2.2×10^{20} protons on target running in neutrino mode, and 4.9×10^{20} protons on target running in anti-neutrino mode. An automated selection is combined with hand scanning, with the former identifying five candidate Λ production events when the signal was unblinded, consistent with the GENIE prediction of 5.3±1.1 events. Several scanners were employed, selecting between three and five events, compared with a prediction from a blinded Monte Carlo simulation study of 3.7±1.0 events. Restricting the phase space to only include Λ baryons that decay above MicroBooNE's detection thresholds, we obtain a flux averaged cross section of 2.0_{-1.7}^{+2.2}×10^{-40} cm^{2}/Ar, where statistical and systematic uncertainties are combined.
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Affiliation(s)
- P Abratenko
- Tufts University, Medford, Massachusetts 02155, USA
| | - D Andrade Aldana
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - J Anthony
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - L Arellano
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - J Asaadi
- University of Texas, Arlington, Texas 76019, USA
| | - A Ashkenazi
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - S Balasubramanian
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - B Baller
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Barr
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - J Barrow
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - V Basque
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | | | - S Berkman
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - A Bhanderi
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - M Bhattacharya
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Bishai
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - A Blake
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - B Bogart
- University of Michigan, Ann Arbor, Michigan 48109, USA
| | - T Bolton
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - J Y Book
- Harvard University, Cambridge, Massachusetts 02138, USA
| | - L Camilleri
- Columbia University, New York, New York 10027, USA
| | - D Caratelli
- University of California, Santa Barbara, California 93106, USA
| | - I Caro Terrazas
- Colorado State University, Fort Collins, Colorado 80523, USA
| | - F Cavanna
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Cerati
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - Y Chen
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - J M Conrad
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - M Convery
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - L Cooper-Troendle
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - J I Crespo-Anadón
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid E-28040, Spain
| | - M Del Tutto
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - S R Dennis
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - P Detje
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - A Devitt
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - R Diurba
- Universität Bern, Bern CH-3012, Switzerland
| | - Z Djurcic
- Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
| | - R Dorrill
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - K Duffy
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - S Dytman
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - B Eberly
- University of Southern Maine, Portland, Maine 04104, USA
| | | | - J J Evans
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - R Fine
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - O G Finnerud
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - W Foreman
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - B T Fleming
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - N Foppiani
- Harvard University, Cambridge, Massachusetts 02138, USA
| | - D Franco
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - A P Furmanski
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | | | - S Gardiner
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Ge
- Columbia University, New York, New York 10027, USA
| | - S Gollapinni
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
- University of Tennessee, Knoxville, Tennessee 37996, USA
| | - O Goodwin
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - E Gramellini
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - P Green
- The University of Manchester, Manchester M13 9PL, United Kingdom
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - H Greenlee
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - W Gu
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - R Guenette
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - P Guzowski
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - L Hagaman
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - O Hen
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - R Hicks
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - C Hilgenberg
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | | | - B Irwin
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - R Itay
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - C James
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - X Ji
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - L Jiang
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - J H Jo
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - R A Johnson
- University of Cincinnati, Cincinnati, Ohio 45221, USA
| | - Y-J Jwa
- Columbia University, New York, New York 10027, USA
| | - D Kalra
- Columbia University, New York, New York 10027, USA
| | - N Kamp
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - G Karagiorgi
- Columbia University, New York, New York 10027, USA
| | - W Ketchum
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Kirby
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Kobilarcik
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - I Kreslo
- Universität Bern, Bern CH-3012, Switzerland
| | - M B Leibovitch
- University of California, Santa Barbara, California 93106, USA
| | - I Lepetic
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - J-Y Li
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - K Li
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - Y Li
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - K Lin
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - B R Littlejohn
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - W C Louis
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - X Luo
- University of California, Santa Barbara, California 93106, USA
| | - C Mariani
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - D Marsden
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - J Marshall
- University of Warwick, Coventry CV4 7AL, United Kingdom
| | - N Martinez
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - D A Martinez Caicedo
- South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA
| | - K Mason
- Tufts University, Medford, Massachusetts 02155, USA
| | - A Mastbaum
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - N McConkey
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - V Meddage
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - K Miller
- University of Chicago, Chicago, Illinois 60637, USA
| | - J Mills
- Tufts University, Medford, Massachusetts 02155, USA
| | - A Mogan
- Colorado State University, Fort Collins, Colorado 80523, USA
| | - T Mohayai
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Mooney
- Colorado State University, Fort Collins, Colorado 80523, USA
| | - A F Moor
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - C D Moore
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - L Mora Lepin
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - J Mousseau
- University of Michigan, Ann Arbor, Michigan 48109, USA
| | | | - D Naples
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - A Navrer-Agasson
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - N Nayak
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - M Nebot-Guinot
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - J Nowak
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - M Nunes
- Syracuse University, Syracuse, New York 13244, USA
| | - N Oza
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - O Palamara
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - N Pallat
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - V Paolone
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - A Papadopoulou
- Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - V Papavassiliou
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - H B Parkinson
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - S F Pate
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - N Patel
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - Z Pavlovic
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - E Piasetzky
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - I D Ponce-Pinto
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - I Pophale
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - S Prince
- Harvard University, Cambridge, Massachusetts 02138, USA
| | - X Qian
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - J L Raaf
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - V Radeka
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - A Rafique
- Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA
| | - M Reggiani-Guzzo
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - L Ren
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - L Rochester
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - J Rodriguez Rondon
- South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA
| | - M Rosenberg
- Tufts University, Medford, Massachusetts 02155, USA
| | - M Ross-Lonergan
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | | | - G Scanavini
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - D W Schmitz
- University of Chicago, Chicago, Illinois 60637, USA
| | - A Schukraft
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - W Seligman
- Columbia University, New York, New York 10027, USA
| | - M H Shaevitz
- Columbia University, New York, New York 10027, USA
| | - R Sharankova
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J Shi
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - E L Snider
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Soderberg
- Syracuse University, Syracuse, New York 13244, USA
| | | | - J Spitz
- University of Michigan, Ann Arbor, Michigan 48109, USA
| | - M Stancari
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J St John
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Strauss
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - S Sword-Fehlberg
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - A M Szelc
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - W Tang
- University of Tennessee, Knoxville, Tennessee 37996, USA
| | - N Taniuchi
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - K Terao
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - C Thorpe
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - D Torbunov
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - D Totani
- University of California, Santa Barbara, California 93106, USA
| | - M Toups
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - Y-T Tsai
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - J Tyler
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - M A Uchida
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - T Usher
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - B Viren
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - M Weber
- Universität Bern, Bern CH-3012, Switzerland
| | - H Wei
- Louisiana State University, Baton Rouge, Louisiana 70803, USA
| | - A J White
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - Z Williams
- University of Texas, Arlington, Texas 76019, USA
| | - S Wolbers
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Wongjirad
- Tufts University, Medford, Massachusetts 02155, USA
| | - M Wospakrik
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - K Wresilo
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - N Wright
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - W Wu
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - E Yandel
- University of California, Santa Barbara, California 93106, USA
| | - T Yang
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - L E Yates
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - H W Yu
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - G P Zeller
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J Zennamo
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - C Zhang
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
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Zeng W, Zhou SL, Guo JX, Tang W. [Metal artifact reduction and clinical verification in oral and maxillofacial region based on deep learning]. Zhonghua Kou Qiang Yi Xue Za Zhi 2023; 58:542-548. [PMID: 37271998 DOI: 10.3760/cma.j.cn112144-20230302-00067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Objective: To construct a kind of neural network for eliminating the metal artifacts in CT images by training the generative adversarial networks (GAN) model, so as to provide reference for clinical practice. Methods: The CT data of patients treated in the Department of Radiology, West China Hospital of Stomatology, Sichuan University from January 2017 to June 2022 were collected. A total of 1 000 cases of artifact-free CT data and 620 cases of metal artifact CT data were obtained, including 5 types of metal restorative materials, namely, fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies. Four hundred metal artifact CT data and 1 000 artifact-free CT data were utilized for simulation synthesis, and 1 000 pairs of simulated artifacts and metal images and simulated metal images (200 pairs of each type) were constructed. Under the condition that the data of the five metal artifacts were equal, the entire data set was randomly (computer random) divided into a training set (800 pairs) and a test set (200 pairs). The former was used to train the GAN model, and the latter was used to evaluate the performance of the GAN model. The test set was evaluated quantitatively and the quantitative indexes were root-mean-square error (RMSE) and structural similarity index measure (SSIM). The trained GAN model was employed to eliminate the metal artifacts from the CT data of the remaining 220 clinical cases of metal artifact CT data, and the elimination results were evaluated by two senior attending doctors using the modified LiKert scale. Results: The RMSE values for artifact elimination of fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies in test set were 0.018±0.004, 0.023±0.007, 0.015±0.003, 0.019±0.004, 0.024±0.008, respectively (F=1.29, P=0.274). The SSIM values were 0.963±0.023, 0.961±0.023, 0.965±0.013, 0.958±0.022, 0.957±0.026, respectively (F=2.22, P=0.069). The intra-group correlation coefficient of 2 evaluators was 0.972. For 220 clinical cases, the overall score of the modified LiKert scale was (3.73±1.13), indicating a satisfactory performance. The scores of modified LiKert scale for fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies were (3.68±1.13), (3.67±1.16), (3.97±1.03), (3.83±1.14), (3.33±1.12), respectively (F=1.44, P=0.145). Conclusions: The metal artifact reduction GAN model constructed in this study can effectively remove the interference of metal artifacts and improve the image quality.
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Affiliation(s)
- W Zeng
- Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University & State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Chengdu 610041, China
| | - S L Zhou
- Department of Oral and Maxillofacial Surgery, School of Stomatology, The Fourth Military Medical University & State Key Laboratory of Military Stomatology & National Clinical Research Center for Oral Diseases & Shaanxi Clinical Research Center for Oral Diseases, Xi'an 710032, China
| | - J X Guo
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610041, China
| | - W Tang
- Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University & State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Chengdu 610041, China
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Tang W, Zhu D, Wu F, Xu JF, Yang JP, Deng ZP, Chen XB, Papi A, Qu JM. Intravenous N-acetylcysteine in respiratory disease with abnormal mucus secretion. Eur Rev Med Pharmacol Sci 2023; 27:5119-5127. [PMID: 37318485 DOI: 10.26355/eurrev_202306_32628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
OBJECTIVE Evidence for the mucolytic and expectorant efficacy of intravenous (IV) N-acetylcysteine (NAC) is limited. This study aimed to evaluate in a large, multicenter, randomized, controlled, subject, and rater-blinded study whether IV NAC is superior to placebo and non-inferior to ambroxol in improving sputum viscosity and expectoration difficulty. PATIENTS AND METHODS A total of 333 hospitalized subjects from 28 centers in China with respiratory disease (such as acute bronchitis, chronic bronchitis and exacerbations, emphysema, mucoviscidosis, and bronchiectasis) and abnormal mucus secretion were randomly allocated in a 1:1:1 ratio to receive NAC 600 mg, ambroxol hydrochloride 30 mg, or placebo as an IV infusion twice daily for 7 days. Mucolytic and expectorant efficacy was assessed by ordinal categorical 4-point scales and analyzed by stratified and modified Mann-Whitney U statistics. RESULTS NAC showed consistent and statistically significant superiority to placebo and non-inferiority to ambroxol in change from baseline to day 7 in both sputum viscosity scores [mean (SD) difference 0.24 (0.763), p<0.001 vs. placebo] and expectoration difficulty score [mean (SD) difference 0.29 (0.783), p=0.002 vs. placebo]. Safety findings confirm the good tolerability profile of IV NAC reported from previous small studies, and no new safety concerns were identified. CONCLUSIONS This is the first large, robust study of the efficacy of IV NAC in respiratory diseases with abnormal mucus secretion. It provides new evidence for IV NAC administration in this indication in clinical situations where the IV route is preferred.
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Affiliation(s)
- W Tang
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Dong Z, Zhou J, Conti A, Westwood SJ, Fu X, Liu Q, Yuan Y, Huang X, Qiu C, Zhang X, Tang W. Association between alexithymia and non-suicidal self-injury in psychiatric patients: the mediating role of self-esteem and the moderating role of emotional intelligence. J Psychiatr Res 2023; 162:57-64. [PMID: 37088044 DOI: 10.1016/j.jpsychires.2023.04.006] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/09/2023] [Accepted: 04/05/2023] [Indexed: 04/25/2023]
Abstract
BACKGROUND Raffagnato's theory claims that if people have no words to express their emotions (alexithymia), they express themselves by venting or through non-suicidal self-injury (NSSI). However, these associations have not been confirmed in psychiatric patients. This study explored the relationship between alexithymia and NSSI in psychiatric patients and the potential underlying psychological mechanisms. METHODS This retrospective study involved face-to-face interviews with 449 outpatients consecutively recruited from West China Hospital. Alexithymia, self-esteem, NSSI, and emotional intelligence (EI) were measured. The moderating role of EI and the mediating role of self-esteem between alexithymia and NSSI were also explored. Logistic regressions were used to examine whether sociodemographic, clinical variables and alexithymia were independently associated with NSSI. RESULTS The DSM-5 NSSI disorder and alexithymia prevalences were found to be 32.5% and 45.2%. When the other covariables were controlled for, the alexithymic patients were found to be at increased odds (OR 2.76) of engaging in NSSI behaviors. These results confirmed the strong associations between alexithymia, low self-esteem, and NSSI risk. Lower EI was found to be related to the connections between alexithymia and NSSI. Except for the lower risk in anxiety patients, the risk of NSSI was similar for patients with other mental disorders, CONCLUSION: This study revealed the psychological mechanisms through which alexithymia increases the risk of NSSI. Therefore, to reduce NSSI risk, screening for alexithymia should be emphasized. Self-esteem as a targeted psychological intervention could also assist in mitigating the process from alexithymia to NSSI behaviors, and EI training for psychiatric patients could weaken the relationship between alexithymia and NSSI.
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Affiliation(s)
- Zaiquan Dong
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Zhou
- Department of Psychosomatic Medicine, Leshan People's Hospital, Leshan, Sichuan, China
| | - Aldo Conti
- Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Samuel J Westwood
- Department of Psychology, School of Social Science, University of Westminster, London, UK
| | - Xia Fu
- Out-patient Department of West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qi Liu
- Out-patient Department of West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yanling Yuan
- Department of Pharmacy of West China Hospital, Sichuan University, Chengdu, China
| | - Xia Huang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Changjian Qiu
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaohui Zhang
- State Key Laboratory of Oral Diseases, National Clinical Research Centre for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Wanjie Tang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, China; Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
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Tang W, Chen M, Wang N, Deng R, Tang H, Xu W, Xu J. Bullying victimization and internalizing and externalizing problems in school-aged children: The mediating role of sleep disturbance and the moderating role of parental attachment. Child Abuse Negl 2023; 138:106064. [PMID: 36731288 DOI: 10.1016/j.chiabu.2023.106064] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 12/15/2022] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Studies have shown that bullying victimization may be related to internalizing and externalizing problems; however, the mechanism underlying this relationship remains unknown. This study explored the mediating role of sleep disturbance and the moderating role of parental attachment. METHODS A total of 1543 Chinese primary school students (M age = 8.92 years, SD1.7 years; range, 6-12) completed bullying victimization, sleep disturbance, and parental attachment measures, and provided information on their parents' occupations. The parents or guardians (n = 1995) also completed ratings on their children's internalizing and externalizing problems. RESULTS It was found that bullying victimization directly affected internalizing and externalizing problems and also influenced sleep disturbance. Regardless of the parent's socioeconomic status, parental attachment was found to moderate the relationship between bullying victimization and internalizing problems. CONCLUSIONS These findings contribute to understanding the partial mediating mechanism of sleep disturbance in the association between bullying victimization and internalizing and externalizing problems. The protective role of parental attachment proved central to preventing internalizing problems in bullied children. Intervention programs that enhance parental attachment and improve sleep quality could assist in mitigating the impact of bullying victimization on internalizing or externalizing problems.
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Affiliation(s)
- Wanjie Tang
- Mental Health Centre, Sichuan University, Chengdu, China; Department of child and adolescent psychiatry, Institute of psychiatry, psychology and neuroscience, King's College London, London, United Kingdom; Institute of Emergency Management and Post-disaster Reconstruction, Business School, Sichuan University, Chengdu, China
| | - Mingxia Chen
- Experimental Primary School affiliated to Sichuan University, Chengdu, China
| | - Ning Wang
- Experimental Primary School affiliated to Sichuan University, Chengdu, China
| | - Renyu Deng
- Experimental Primary School affiliated to Sichuan University, Chengdu, China
| | - Huai Tang
- Experimental Primary School affiliated to Sichuan University, Chengdu, China
| | - Wenjian Xu
- School of Public Administration, Sichuan University, Chengdu, China.
| | - Jiuping Xu
- Institute of Emergency Management and Post-disaster Reconstruction, Business School, Sichuan University, Chengdu, China.
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23
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Hofmeyer M, Haas G, Kransdorf E, Ewald G, Morris A, Owens A, Lowes B, Stoller D, Tang W, Garg S, Trachtenberg B, Shah P, Pamboukian S, Sweitzer N, Wheeler M, Wilcox J, Katz S, Pan S, Jimenez J, Smart F, Wang J, Gottlieb S, Judge D, Moore C, Huggins G, Jordan E, Kinnamon D, Ni H, Hershberger R. Genetic Signature of Dilated Cardiomyopathy Severity: The DCM Precision Medicine Study. J Heart Lung Transplant 2023. [DOI: 10.1016/j.healun.2023.02.1674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
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24
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Longinow J, Il'Giovine Z, Martens P, Higgins A, Soltesz E, Tong M, Estep J, Starling R, Tang W, Hanna M, Lee R. Hemodynamic Response after Intra-Aortic Balloon Counter-Pulsation in Cardiac Amyloidosis and Cardiogenic Shock. J Heart Lung Transplant 2023. [DOI: 10.1016/j.healun.2023.02.821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
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25
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Jiang DH, Tang W. [The theory of unresponsive pulse by Wang Ji : The historical position of his Yun Qi Yi Lan]. Zhonghua Yi Shi Za Zhi 2023; 53:67-73. [PMID: 37183619 DOI: 10.3760/cma.j.cn112155-20221025-00153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Wang Ji (1463-1539) was a well-known doctor of the Xin An Medical School in the Ming Dynasty. He and his representative masterpiece Yun Qi Yi Lan are particularly important in the medical history of Yunqi, which refers to the principles of Air (Qi) regulation, influencing almost all life in nature. In terms of the theory "nonresponsive pulse matching the South and the North in the ten Stem years" (Nan Bei Zheng Bu Ying Mai), Wang Ji differentiated and analysed the changes of this theory after the Jin and Yuan Dynasties and traced it back to the classics the Inner canon of Huangdi (Huang Di Nei Jing), based on Su Wen Ru Shi Yun Qi Lun Ao, Huang Di Nei Jing and other relevant reference materials. This paper examined the evolution of the theory of unresponsive pulse in the ancient and modern literature. It was found that after the Song Dynasty, the theory of nonresponsive pulse in the South-North in the ten Stem years was developed into two main schools. One was represented by Cheng Wuji and Liu Wansu, followed with Zhang Jingyue, Li Yanshi, Yao Zhian, Lu Guanquan, Wu Qian, Huang Yuanyu, Xue Fuchen and Zhou Xuehai, who argued that the nonresponsive pulse was determined by the position of Shaoyin. Another was represented by Liu Wenshu, followed with Wang Ji, Li Zhongzi, Zhang Zhicong and Ren Yingqiu, who believed that Shaoyin always stands in the middle, Jueyin and Taiyin are always on the two sides of Shaoyin.
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Affiliation(s)
- D H Jiang
- College of Acupuncture and Moxibustion, Anhui University of Chinese Medicine, Hefei 230038, China
| | - W Tang
- College of Acupuncture and Moxibustion, Anhui University of Chinese Medicine, Hefei 230038, China
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26
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Liang M, Zhao SJ, Zhou LN, Xu XJ, Wang YW, Niu L, Wang HH, Tang W, Wu N. [The performance of digital chest radiographs in the detection and diagnosis of pulmonary nodules and the consistency among readers]. Zhonghua Zhong Liu Za Zhi 2023; 45:265-272. [PMID: 36944548 DOI: 10.3760/cma.j.cn112152-20220304-00150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Objective: To investigate the detection and diagnostic efficacy of chest radiographs for ≤30 mm pulmonary nodules and the factors affecting them, and to compare the level of consistency among readers. Methods: A total of 43 patients with asymptomatic pulmonary nodules who consulted in Cancer Hospital, Chinese Academy of Medical Sciences from 2012 to 2014 and had chest CT and X-ray chest radiographs during the same period were retrospectively selected, and one nodule ≤30 mm was visible on chest CT images in the whole group (total 43 nodules in the whole group). One senior radiologist with more than 20 years of experience in imaging diagnosis reviewed CT images and recording the size, morphology, location, and density of nodules was selected retrospectively. Six radiologists with different levels of experience (2 residents, 2 attending physicians and 2 associate chief physicians independently reviewed the chest images and recorded the time of review, nodule detection, and diagnostic opinion. The CT imaging characteristics of detected and undetected nodules on X images were compared, and the factors affecting the detection of nodules on X-ray images were analyzed. Detection sensitivity and diagnosis accuracy rate of 6 radiologists were calculated, and the level of consistency among them was compared to analyze the influence of radiologists' seniority and reading time on the diagnosis results. Results: The number of nodules detected by all 6 radiologists was 17, with a sensitivity of detection of 39.5%(17/43). The number of nodules detected by ≥5, ≥4, ≥3, ≥2, and ≥1 physicians was 20, 21, 23, 25, and 28 nodules, respectively, with detection sensitivities of 46.5%, 48.8%, 53.5%, 58.1%, and 65.1%, respectively. Reasons for false-negative result of detection on X-ray images included the size, location, density, and morphology of the nodule. The sensitivity of detecting ≤30 mm, ≤20 mm, ≤15 mm, and ≤10 mm nodules was 46.5%-58.1%, 45.9%-54.1%, 36.0%-44.0%, and 36.4% for the 6 radiologists, respectively; the diagnosis accuracy rate was 19.0%-85.0%, 16.7%-6.5%, 18.2%-80.0%, and 0%-75.0%, respectively. The consistency of nodule detection among 6 doctors was good (Kappa value: 0.629-0.907) and the consistency of diagnostic results among them was moderate or poor (Kappa value: 0.350-0.653). The higher the radiologist's seniority, the shorter the time required to read the images. The reading time and the seniority of the radiologists had no significant influence on the detection and diagnosis results (P>0.05). Conclusions: The ability of radiographs to detect lung nodules ≤30 mm is limited, and the ability to determine the nature of the nodules is not sufficient, and the increase in reading time and seniority of the radiologists will not improve the diagnostic accuracy. X-ray film exam alone is not suitable for lung cancer diagnosis.
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Affiliation(s)
- M Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - S J Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L N Zhou
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - X J Xu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y W Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Niu
- Radiology Department, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
| | - H H Wang
- Radiology Department, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
| | - W Tang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - N Wu
- Department of Nuclear Medicine (PET-CT Center), National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Langfang 065001, China
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Jiang C, Tang W, Hou X, Li H. Recurrent syncope in an 84-year-old man. J Postgrad Med 2023; 69:111-113. [PMID: 36861546 DOI: 10.4103/jpgm.jpgm_414_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
Abstract
An 84-year-old man with hypertension and type 2 diabetes presented with recurrent transient loss of consciousness within 2 hours after dinner at home. Physical examination, electrocardiogram, and laboratory studies were unremarkable except hypotension. Blood pressures were measured in different postures and within 2 hours after meal, but neither orthostatic hypotension nor postprandial hypotension was detected. Further, history taking revealed that the patient was tube-fed with a fluid food pump with an inappropriate rapid infusion rate of 1500 mL per minute at home. He was eventually diagnosed as having syncope due to postprandial hypotension, which was caused by the inappropriate way of tube feeding. The family was educated about appropriate way of tube-feeding and the patient did not develop any episode of syncope during a two-year follow-up. This case highlights the importance of careful history taking in the diagnostic evaluation of syncope and the increased risk of syncope due to postprandial hypotension in the elderly.
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Affiliation(s)
- C Jiang
- Department of Internal Medicine and Geriatrics, Beijing Friendship Hospital, Capital Medical University, Beijing, India
| | - W Tang
- Department of Internal Medicine and Geriatrics, Beijing Friendship Hospital, Capital Medical University, Beijing, India
| | - X Hou
- Department of Internal Medicine and Geriatrics, Beijing Friendship Hospital, Capital Medical University, Beijing, India
| | - H Li
- Department of Internal Medicine and Geriatrics, Beijing Friendship Hospital, Capital Medical University, Beijing, India
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28
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Yu YX, Wu ZJ, Tang W, Liao R. [A comparison of current guidelines for the management of intrahepatic cholangiocarcinoma worldwide]. Zhonghua Wai Ke Za Zhi 2023; 61:297-304. [PMID: 36822586 DOI: 10.3760/cma.j.cn112139-20221125-00495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Intrahepatic cholangiocarcinoma (ICC) is the second most common human liver malignancy and its incidence rate has been gradually increasing worldwide over the past decades. Surgical resection (R0 resection) is the preferred potentially curative treatment for ICC patients. However, due to its conceal clinical features and high invasiveness, most patients have lost the opportunity for surgical resection at the time of diagnosis. In recent years, with the rapid development of targeted therapy and immunotherapy, which is represented by immune checkpoint inhibitors, clinicians are expected to provide more effective treatment options for patients with mid-stage or advanced ICC. At present, there are still controversial opinions on different guidelines regarding preoperative biliary drainage, the extent of hepatectomy, the definition of R0 resection, the width of the resection margin, lymph node dissection, postoperative recurrence, adjuvant therapy, etc. In this review, 12 guidelines or expert consensus published worldwide from 2012 to 2022 (including 4 Chinese guidelines, 4 European guidelines, 2 American guidelines and 2 Japanese guidelines) were retrieved. Focusing on sorting and comparing the current views on clinical management of ICC in different guidelines, this review aims to provide reference information for ICC clinical management and decision-making.
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Affiliation(s)
- Y X Yu
- Department of Hepatobiliary Surgery, the First Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Z J Wu
- Department of Hepatobiliary Surgery, the First Hospital of Chongqing Medical University, Chongqing 400016, China
| | - W Tang
- National Center for Global Health and Medicine, Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, the University of Tokyo Hospital, Tokyo 162-8655, Japan
| | - R Liao
- Department of Hepatobiliary Surgery, the First Hospital of Chongqing Medical University, Chongqing 400016, China
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29
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Zhao X, Lukito S, Huang X, Qiu C, Tang W. Longitudinal associations between pandemic post-traumatic stress symptoms and subsequent non-suicidal self-injury in adolescents: A multiple mediation model. J Affect Disord 2023; 323:707-715. [PMID: 36529405 DOI: 10.1016/j.jad.2022.12.040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 10/06/2022] [Accepted: 12/10/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUNDS PTSD is one of the most common conditions after people have experienced trauma. While previous studies have found a link between PTSD and non-suicidal self-injury, (NSSI), few studies have longitudinally explored this relationship and the underlying mechanisms. AIMS This study explored adolescent NSSI frequency after COVID-19 lockdown experiences, the relationship with early PTSD symptoms, and the mediating role of depression and sleep problems. METHODS A cohort of 1609 adolescents completed two surveys during and after the national lockdown in China; one month into the lockdown and six months later; which assessed demographic and pandemic-related exposure variables; PTSD, depression, sleep, and NSSI. Mediation analyses and hierarchical regression were employed to examine the relationships and the paths between these variables. RESULTS The NSSI rate was found to be 31.9 % after the three-month lockdown, with 20.6 % of adolescent participants reporting sleeping disorders, and 33.9 % indicating probable depression. Adolescents who had earlier PTSD symptoms, often smoked and/or drank, and had current depression and sleep disorders reported greater NSSI. Early PTSD symptoms were found to predict later NSSI and were mediated by sleep problems and depressive symptoms. Specifically, PTSD avoidance and numbing symptoms were significantly associated with NSSI above and beyond the depressive symptoms, sleeping problems, and the other covariables. CONCLUSION It is necessary to be vigilant about the increased risk of NSSI in adolescents who have experienced extended pandemic lockdowns. Preventing early adolescent PTSD symptoms, especially avoidance and numbness, and helping teenagers quit smoking and drinking could reduce the risk of sleep disorders, depression, and NSSI.
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Affiliation(s)
- Xingkui Zhao
- School of Teacher Education, Yangtze Normal University, Chongqing, China; Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Steve Lukito
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Xia Huang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Changjian Qiu
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Wanjie Tang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
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30
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Ji MM, Shen YG, Gong JC, Tang W, Xu XQ, Zheng Z, Chen SY, He Y, Zheng X, Zhao LD, Zhao WL, Wu W. [Efficiency and safety analysis of Plerixafor combined with granulocyte colony-stimulating factor on autologous hematopoietic stem cell mobilization in lymphoma]. Zhonghua Xue Ye Xue Za Zhi 2023; 44:112-117. [PMID: 36948864 PMCID: PMC10033277 DOI: 10.3760/cma.j.issn.0253-2727.2023.02.005] [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] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Objective: To evaluate the advantages and safety of Plerixafor in combination with granulocyte colony-stimulating factor (G-CSF) in autologous hematopoietic stem cell mobilization of lymphoma. Methods: Lymphoma patients who received autologous hematopoietic stem cell mobilization with Plerixafor in combination with G-CSF or G-CSF alone were obtained. The clinical data, the success rate of stem cell collection, hematopoietic reconstitution, and treatment-related adverse reactions between the two groups were evaluated retrospectively. Results: A total of 184 lymphoma patients were included in this analysis, including 115 cases of diffuse large B-cell lymphoma (62.5%) , 16 cases of classical Hodgkin's lymphoma (8.7%) , 11 cases of follicular non-Hodgkin's lymphoma (6.0%) , 10 cases of angioimmunoblastic T-cell lymphoma (5.4%) , 6 cases of mantle cell lymphoma (3.3%) , and 6 cases of anaplastic large cell lymphoma (3.3%) , 6 cases of NK/T-cell lymphoma (3.3%) , 4 cases of Burkitt's lymphoma (2.2%) , 8 cases of other types of B-cell lymphoma (4.3%) , and 2 cases of other types of T-cell lymphoma (1.1%) ; 31 patients had received radiotherapy (16.8%) . The patients in the two groups were recruited with Plerixafor in combination with G-CSF or G-CSF alone. The baseline clinical characteristics of the two groups were basically similar. The patients in the Plerixafor in combination with the G-CSF mobilization group were older, and the number of recurrences and third-line chemotherapy was higher. 100 patients were mobilized with G-CSF alone. The success rate of the collection was 74.0% for one day and 89.0% for two days. 84 patients in the group of Plerixafor combined with G-CSF were recruited successfully with 85.7% for one day and 97.6% for two days. The success rate of mobilization in the group of Plerixafor combined with G-CSF was substantially higher than that in the group of G-CSF alone (P=0.023) . The median number of CD34(+) cells obtained in the mobilization group of Plerixafor combined with G-CSF was 3.9×10(6)/kg. The median number of CD34(+) cells obtained in the G-CSF Mobilization group alone was 3.2×10(6)/kg. The number of CD34(+) cells collected by Plerixafor combined with G-CSF was considerably higher than that in G-CSF alone (P=0.001) . The prevalent adverse reactions in the group of Plerixafor combined with G-CSF were grade 1-2 gastrointestinal reactions (31.2%) and local skin redness (2.4%) . Conclusion: The success rate of autologous hematopoietic stem cell mobilization in lymphoma patients treated with Plerixafor combined with G-CSF is significantly high. The success rate of collection and the absolute count of CD34(+) stem cells were substantially higher than those in the group treated with G-CSF alone. Even in older patients, second-line collection, recurrence, or multiple chemotherapies, the combined mobilization method also has a high success rate of mobilization.
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Affiliation(s)
- M M Ji
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Y G Shen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - J C Gong
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - W Tang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - X Q Xu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Z Zheng
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - S Y Chen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Y He
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - X Zheng
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - L D Zhao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - W L Zhao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - W Wu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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Abratenko P, Andrade Aldana D, Anthony J, Arellano L, Asaadi J, Ashkenazi A, Balasubramanian S, Baller B, Barr G, Barrow J, Basque V, Bathe-Peters L, Benevides Rodrigues O, Berkman S, Bhanderi A, Bhattacharya M, Bishai M, Blake A, Bogart B, Bolton T, Book JY, Camilleri L, Caratelli D, Caro Terrazas I, Cavanna F, Cerati G, Chen Y, Conrad JM, Convery M, Cooper-Troendle L, Crespo-Anadón JI, Del Tutto M, Dennis SR, Detje P, Devitt A, Diurba R, Dorrill R, Duffy K, Dytman S, Eberly B, Ereditato A, Evans JJ, Fine R, Finnerud OG, Foreman W, Fleming BT, Foppiani N, Franco D, Furmanski AP, Garcia-Gamez D, Gardiner S, Ge G, Gollapinni S, Goodwin O, Gramellini E, Green P, Greenlee H, Gu W, Guenette R, Guzowski P, Hagaman L, Hen O, Hicks R, Hilgenberg C, Horton-Smith GA, Irwin B, Itay R, James C, Ji X, Jiang L, Jo JH, Johnson RA, Jwa YJ, Kalra D, Kamp N, Karagiorgi G, Ketchum W, Kirby M, Kobilarcik T, Kreslo I, Leibovitch MB, Lepetic I, Li JY, Li K, Li Y, Lin K, Littlejohn BR, Louis WC, Luo X, Manivannan K, Mariani C, Marsden D, Marshall J, Martinez N, Martinez Caicedo DA, Mason K, Mastbaum A, McConkey N, Meddage V, Miller K, Mills J, Mogan A, Mohayai T, Mooney M, Moor AF, Moore CD, Mora Lepin L, Mousseau J, Mulleriababu S, Naples D, Navrer-Agasson A, Nayak N, Nebot-Guinot M, Nowak J, Nunes M, Oza N, Palamara O, Pallat N, Paolone V, Papadopoulou A, Papavassiliou V, Parkinson HB, Pate SF, Patel N, Pavlovic Z, Piasetzky E, Ponce-Pinto ID, Pophale I, Prince S, Qian X, Raaf JL, Radeka V, Reggiani-Guzzo M, Ren L, Rochester L, Rodriguez Rondon J, Rosenberg M, Ross-Lonergan M, Rudolf von Rohr C, Scanavini G, Schmitz DW, Schukraft A, Seligman W, Shaevitz MH, Sharankova R, Shi J, Smith A, Snider EL, Soderberg M, Söldner-Rembold S, Spitz J, Stancari M, St John J, Strauss T, Sword-Fehlberg S, Szelc AM, Tang W, Taniuchi N, Terao K, Thorpe C, Torbunov D, Totani D, Toups M, Tsai YT, Tyler J, Uchida MA, Usher T, Viren B, Weber M, Wei H, White AJ, Williams Z, Wolbers S, Wongjirad T, Wospakrik M, Wresilo K, Wright N, Wu W, Yandel E, Yang T, Yates LE, Yu HW, Zeller GP, Zennamo J, Zhang C. First Constraints on Light Sterile Neutrino Oscillations from Combined Appearance and Disappearance Searches with the MicroBooNE Detector. Phys Rev Lett 2023; 130:011801. [PMID: 36669216 DOI: 10.1103/physrevlett.130.011801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
We present a search for eV-scale sterile neutrino oscillations in the MicroBooNE liquid argon detector, simultaneously considering all possible appearance and disappearance effects within the 3+1 active-to-sterile neutrino oscillation framework. We analyze the neutrino candidate events for the recent measurements of charged-current ν_{e} and ν_{μ} interactions in the MicroBooNE detector, using data corresponding to an exposure of 6.37×10^{20} protons on target from the Fermilab booster neutrino beam. We observe no evidence of light sterile neutrino oscillations and derive exclusion contours at the 95% confidence level in the plane of the mass-squared splitting Δm_{41}^{2} and the sterile neutrino mixing angles θ_{μe} and θ_{ee}, excluding part of the parameter space allowed by experimental anomalies. Cancellation of ν_{e} appearance and ν_{e} disappearance effects due to the full 3+1 treatment of the analysis leads to a degeneracy when determining the oscillation parameters, which is discussed in this Letter and will be addressed by future analyses.
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Affiliation(s)
- P Abratenko
- Tufts University, Medford, Massachusetts 02155, USA
| | - D Andrade Aldana
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - J Anthony
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - L Arellano
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - J Asaadi
- University of Texas, Arlington, Texas 76019, USA
| | - A Ashkenazi
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - S Balasubramanian
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - B Baller
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Barr
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - J Barrow
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - V Basque
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | | | | | - S Berkman
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - A Bhanderi
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - M Bhattacharya
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Bishai
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - A Blake
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - B Bogart
- University of Michigan, Ann Arbor, Michigan 48109, USA
| | - T Bolton
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - J Y Book
- Harvard University, Cambridge, Massachusetts 02138, USA
| | - L Camilleri
- Columbia University, New York, New York 10027, USA
| | - D Caratelli
- University of California, Santa Barbara, California 93106, USA
| | - I Caro Terrazas
- Colorado State University, Fort Collins, Colorado 80523, USA
| | - F Cavanna
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Cerati
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - Y Chen
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - J M Conrad
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - M Convery
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - L Cooper-Troendle
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - J I Crespo-Anadón
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid E-28040, Spain
| | - M Del Tutto
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - S R Dennis
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - P Detje
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - A Devitt
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - R Diurba
- Universität Bern, Bern CH-3012, Switzerland
| | - R Dorrill
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - K Duffy
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - S Dytman
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - B Eberly
- University of Southern Maine, Portland, Maine 04104, USA
| | | | - J J Evans
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - R Fine
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - O G Finnerud
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - W Foreman
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - B T Fleming
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - N Foppiani
- Harvard University, Cambridge, Massachusetts 02138, USA
| | - D Franco
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - A P Furmanski
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | | | - S Gardiner
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - G Ge
- Columbia University, New York, New York 10027, USA
| | - S Gollapinni
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
- University of Tennessee, Knoxville, Tennessee 37996, USA
| | - O Goodwin
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - E Gramellini
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - P Green
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - H Greenlee
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - W Gu
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - R Guenette
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - P Guzowski
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - L Hagaman
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - O Hen
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - R Hicks
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - C Hilgenberg
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | | | - B Irwin
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - R Itay
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - C James
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - X Ji
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - L Jiang
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - J H Jo
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - R A Johnson
- University of Cincinnati, Cincinnati, Ohio 45221, USA
| | - Y-J Jwa
- Columbia University, New York, New York 10027, USA
| | - D Kalra
- Columbia University, New York, New York 10027, USA
| | - N Kamp
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - G Karagiorgi
- Columbia University, New York, New York 10027, USA
| | - W Ketchum
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Kirby
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Kobilarcik
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - I Kreslo
- Universität Bern, Bern CH-3012, Switzerland
| | - M B Leibovitch
- University of California, Santa Barbara, California 93106, USA
| | - I Lepetic
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - J-Y Li
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - K Li
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - Y Li
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - K Lin
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - B R Littlejohn
- Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA
| | - W C Louis
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - X Luo
- University of California, Santa Barbara, California 93106, USA
| | - K Manivannan
- Syracuse University, Syracuse, New York 13244, USA
| | - C Mariani
- Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - D Marsden
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - J Marshall
- University of Warwick, Coventry CV4 7AL, United Kingdom
| | - N Martinez
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - D A Martinez Caicedo
- South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA
| | - K Mason
- Tufts University, Medford, Massachusetts 02155, USA
| | - A Mastbaum
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - N McConkey
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - V Meddage
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - K Miller
- University of Chicago, Chicago, Illinois 60637, USA
| | - J Mills
- Tufts University, Medford, Massachusetts 02155, USA
| | - A Mogan
- Colorado State University, Fort Collins, Colorado 80523, USA
| | - T Mohayai
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Mooney
- Colorado State University, Fort Collins, Colorado 80523, USA
| | - A F Moor
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - C D Moore
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - L Mora Lepin
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - J Mousseau
- University of Michigan, Ann Arbor, Michigan 48109, USA
| | | | - D Naples
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - A Navrer-Agasson
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - N Nayak
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - M Nebot-Guinot
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - J Nowak
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - M Nunes
- Syracuse University, Syracuse, New York 13244, USA
| | - N Oza
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | - O Palamara
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - N Pallat
- University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - V Paolone
- University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - A Papadopoulou
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - V Papavassiliou
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - H B Parkinson
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - S F Pate
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - N Patel
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - Z Pavlovic
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - E Piasetzky
- Tel Aviv University, Tel Aviv, Israel, 69978
| | - I D Ponce-Pinto
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - I Pophale
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - S Prince
- Harvard University, Cambridge, Massachusetts 02138, USA
| | - X Qian
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - J L Raaf
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - V Radeka
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - M Reggiani-Guzzo
- The University of Manchester, Manchester M13 9PL, United Kingdom
| | - L Ren
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - L Rochester
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - J Rodriguez Rondon
- South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA
| | - M Rosenberg
- Tufts University, Medford, Massachusetts 02155, USA
| | - M Ross-Lonergan
- Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA
| | | | - G Scanavini
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - D W Schmitz
- University of Chicago, Chicago, Illinois 60637, USA
| | - A Schukraft
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - W Seligman
- Columbia University, New York, New York 10027, USA
| | - M H Shaevitz
- Columbia University, New York, New York 10027, USA
| | - R Sharankova
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J Shi
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - A Smith
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - E L Snider
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - M Soderberg
- Syracuse University, Syracuse, New York 13244, USA
| | | | - J Spitz
- University of Michigan, Ann Arbor, Michigan 48109, USA
| | - M Stancari
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J St John
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Strauss
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - S Sword-Fehlberg
- New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA
| | - A M Szelc
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - W Tang
- University of Tennessee, Knoxville, Tennessee 37996, USA
| | - N Taniuchi
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - K Terao
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - C Thorpe
- Lancaster University, Lancaster LA1 4YW, United Kingdom
| | - D Torbunov
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - D Totani
- University of California, Santa Barbara, California 93106, USA
| | - M Toups
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - Y-T Tsai
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - J Tyler
- Kansas State University (KSU), Manhattan, Kansas 66506, USA
| | - M A Uchida
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - T Usher
- SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA
| | - B Viren
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - M Weber
- Universität Bern, Bern CH-3012, Switzerland
| | - H Wei
- Louisiana State University, Baton Rouge, Louisiana 70803, USA
| | - A J White
- Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA
| | - Z Williams
- University of Texas, Arlington, Texas 76019, USA
| | - S Wolbers
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - T Wongjirad
- Tufts University, Medford, Massachusetts 02155, USA
| | - M Wospakrik
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - K Wresilo
- University of Cambridge, Cambridge CB3 0HE, United Kingdom
| | - N Wright
- Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - W Wu
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - E Yandel
- University of California, Santa Barbara, California 93106, USA
| | - T Yang
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - L E Yates
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - H W Yu
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
| | - G P Zeller
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - J Zennamo
- Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA
| | - C Zhang
- Brookhaven National Laboratory (BNL), Upton, New York 11973, USA
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Ni T, Zhang Y, Xue S, Xu W, Tang W. PTSD and depressive symptoms in Chinese adolescents exposed to multiple stressors from natural disasters, stressful life events, and maltreatment: A dose-response effect. Front Psychol 2022; 13:1050260. [PMID: 36591085 PMCID: PMC9794843 DOI: 10.3389/fpsyg.2022.1050260] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/29/2022] [Indexed: 12/15/2022] Open
Abstract
Objectives Little is known about the effects and the extent that childhood adversity has on post-traumatic stress disorder (PTSD) and depression. Study design A population-based, epidemiological study from the Wenchuan earthquake. Methods A total of 5,195 Wenchuan Earthquake adolescent survivors aged 11-18 years from nine high schools in southwest China completed questionnaires that assessed their PTSD and depression symptoms due to childhood maltreatment, stressful life events, and childhood earthquake exposure. Results The PTSD and depression prevalences were 7.1 and 32.4%. After controlling for age and gender, the multiple linear regressions revealed that stressful life events had the most significant direct effect on depression (β = 0.491), followed by childhood emotional abuse (β = 0.085), and earthquake exposure (β = 0.077). Similarly, stressful life events (β = 0.583) were found to have more significant direct effects on PSTD, followed by earthquake exposure (β = 0.140); however, childhood emotional abuse was not found to have an effect. The structural equation modeling (SEM) revealed that there were interactions between the three childhood adversities, with all three concurrently affecting both PTSD and depression. Conclusion These findings add weight to the supposition that psychological maltreatment, negative life events, and earthquake exposure contribute to PTSD and depression. In particular, the identification of subgroups that have a high prevalence of these childhood adversities could assist professionals to target populations that are at high risk of mental health problems.
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Affiliation(s)
- Ting Ni
- College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu, China
| | - Yi Zhang
- School of Economics and Business Administration, Yibin University, Yibin, China
| | - Shuang Xue
- Department of Sociology and Psychology, School of Public Administration, Sichuan University, Chengdu, China
| | - Wenjian Xu
- Department of Sociology and Psychology, School of Public Administration, Sichuan University, Chengdu, China
| | - Wanjie Tang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, China,King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom,*Correspondence: Wanjie Tang,
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Tang W. Doctor's bag belonging to Dr Wai-cheung Chau. Hong Kong Med J 2022; 28:504-505. [PMID: 36523126 DOI: 10.12809/hkmj202212-hkmms] [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: 12/23/2022] Open
Affiliation(s)
- W Tang
- Member, Educational and Research Committee, Hong Kong Museum of Medical Sciences Society
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Xu W, Xue S, Huang Y, Zhang X, Tang W, Kaufman MR. Childhood abuse, left-behind status and mental health among lesbian, gay, and bisexual young adults in China. Child Abuse Negl 2022; 134:105936. [PMID: 36327763 DOI: 10.1016/j.chiabu.2022.105936] [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] [Received: 02/19/2022] [Revised: 08/12/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Lesbian, gay, and bisexual (LGB) young adults are at increased risk of mental distress in China. To better carry out psychological intervention, it is essential to understand unique patterns of mental distress and their association with childhood abuse/neglect, including experiencing being left behind by migrating parents. OBJECTIVE In a sample of Chinese LGB young adults, we examined: (1) associations between childhood abuse and left-behind status and mental distress; (2) latent profiles of mental distress; and (3) associations between childhood abuse and left-behind status and latent profiles of mental distress. PARTICIPANTS AND SETTING A sample of 630 Chinese LGB young adults aged 18-30 years was recruited to complete an online survey. METHODS Participants provided demographic information and completed validated measures of childhood abuse experience and mental distress. Latent profile analysis (LPA) was used to identify patterns of mental distress, and logistic regression analysis was used to examine the relationships among these variables. RESULTS Results showed that all forms of childhood abuse and left-behind status were associated with all dimensions of adulthood mental distress. The LPA suggested a 3-group solution as optimal (no mental distress, mild mental distress, and moderate/severe mental distress). Participants who experienced any forms of childhood abuse were more likely to be members of both the mild mental distress and moderate/severe mental distress groups (all p's < 0.001). Also, participants who had left-behind status were more likely to be in the moderate/severe mental distress group (AOR = 1.61, p < .05). CONCLUSIONS Our findings highlight the need for interventions aimed at addressing childhood abuse/neglect among Chinese LGB young adults, as these experiences increase the risk for mental health issues in adulthood.
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Affiliation(s)
- Wenjian Xu
- Department of Sociology and Psychology, School of Public Administration, Sichuan University, Chengdu, China; Social Development and Risk Control Research Center, Sichuan University, Chengdu, China.
| | - Shuang Xue
- Department of Sociology and Psychology, School of Public Administration, Sichuan University, Chengdu, China
| | - Yuxia Huang
- Department of Sociology and Psychology, School of Public Administration, Sichuan University, Chengdu, China
| | - Xing Zhang
- School of Psychology, Jiangxi Normal University, Nanchang, China
| | - Wanjie Tang
- Center for Educational and Health Psychology, Sichuan University, Chengdu, China.
| | - Michelle R Kaufman
- Department of Health, Behavior & Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
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Wang X, Lu X, Hu T, Xue S, Xu W, Tang W. Optimism and friendship quality as mediators between trait emotional intelligence and life satisfaction in Chinese adolescents: A two-wave longitudinal study. Curr Psychol 2022; 42:1-10. [PMID: 36468160 PMCID: PMC9684851 DOI: 10.1007/s12144-022-03931-0] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/25/2022] [Indexed: 11/25/2022]
Abstract
Using a convenience sample of adolescents (N = 1609; 63.5% female; M age = 16.54), this study explored whether EI predicted adolescent life satisfaction and whether friendship quality and optimism mediated this relationship during the COVID-19 pandemic. The structural equation modeling revealed that EI predicted adolescent life satisfaction, friendship quality, and optimism, friendship quality partially mediated the relationship between EI and life satisfaction, and optimism partially mediated the relationship between EI and friendship quality. These findings prove that psychological or educative approaches focused on EI could increase life satisfaction in adolescents during difficult times such as COVID-19, but EI may be linked with life satisfaction via friendship quality only. Training in optimism approaches and friendship quality enhancement programs could also effectively promote life satisfaction.
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Affiliation(s)
- Xiaobo Wang
- Department of Education and Psychology, Chengdu Normal University, Chengdu, China
| | - Xiong Lu
- Department of Education and Psychology, Chengdu Normal University, Chengdu, China
| | - Tao Hu
- Department of Education and Psychology, Chengdu Normal University, Chengdu, China
| | - Shuang Xue
- Faculty of Public Administration, Sichuan University, Chengdu, China
| | - Wenjian Xu
- Faculty of Public Administration, Sichuan University, Chengdu, China
| | - Wanjie Tang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, China
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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36
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Zhang Y, Zhao Y, Ni T, Chen J, Tang W. Alexithymia and post-traumatic stress disorder symptoms in Chinese undergraduate students during the COVID-19 national lockdown: The mediating role of sleep problems and the moderating role of self-esteem. Front Psychol 2022; 13:1040935. [PMID: 36438324 PMCID: PMC9691979 DOI: 10.3389/fpsyg.2022.1040935] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/21/2022] [Indexed: 01/25/2023] Open
Abstract
OBJECTIVE This study examined whether sleep disturbance was a mediator between alexithymic traits and post-traumatic stress disorder (PTSD) COVID-19 pandemic-related stress symptoms, and explored whether self-esteem moderated the alexithymic contribution to poor sleep and PTSD symptoms. METHOD A representative sample of young adults (N = 2,485) from six universities in Southwest China completed online self-report surveys on alexithymia, sleep, PTSD, self-esteem, sociodemographic information, and health-related behaviors. RESULTS High alexithymic young adults were found to be more likely to have higher sleep problems and higher PTSD symptoms. The moderated mediation model showed that sleep problems mediated the associations between alexithymia and PTSD symptoms. Alexithymic people with lower self-esteem were more likely to have elevated PTSD symptoms and sleep problems than those with higher self-esteem. CONCLUSION Targeted psychological interventions for young people who have difficulty expressing and identifying emotions are recommended as these could assist in reducing their post-traumatic psychophysical and psychological problems. Improving self-esteem could also offer some protection for trauma-exposed individuals.
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Affiliation(s)
- Yi Zhang
- School of Economics and Business Administration, Yibin University, Yibin, China,School of Business, Sichuan University, Chengdu, Sichuan Province, China
| | - Yijin Zhao
- Mental Health Center, Sichuan University, Chengdu, China,Division of Accounting, Sichuan University, Chengdu, China
| | - Ting Ni
- School of Business, Sichuan University, Chengdu, Sichuan Province, China,College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu, China
| | - Jing Chen
- Department of Psychology and Education, Chengdu Normal University, Chengdu, China,*Correspondence: Jing Chen,
| | - Wanjie Tang
- Mental Health Center, Sichuan University, Chengdu, China,Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom,*Correspondence: Jing Chen,
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Chang W, Zhou S, Sun D, Liu Y, Mao W, Cen W, Tang W, Ye L, Wang L, Xu J. 53P Baseline PET/CT deep radiomics signature apply for identifying bevacizumab sensitivity of RAS-mutant colorectal cancer liver metastases patients. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.10.085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
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38
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Xing J, Fu YH, Song Z, Wang Q, Ma T, Li M, Zhuang Y, Li Z, Zhu YJ, Tang W, Wang SG, Yang N, Wang PF, Zhang K. Predictive model for deep venous thrombosis caused by closed lower limb fracture after thromboprophylactic treatment. Eur Rev Med Pharmacol Sci 2022; 26:8508-8522. [PMID: 36459032 DOI: 10.26355/eurrev_202211_30387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
OBJECTIVE Currently, there are still no convincing clinical models predicting closed lower extremity fracture-associated deep vein thrombosis in patients treated through thromboprophylactic methods. We aimed at using two retrospective cohorts to develop and externally verify a clinical prediction model for deep vein thrombosis in patients treated with anticoagulants after suffering closed lower extremity fractures. PATIENTS AND METHODS We evaluated the patients' pre- and post-operatively, to accurately determine the predictive power of the biomarkers and clinical risk factors. Two retrospective cohorts were used for the development and external verification of a pre-operative clinical prediction model (development: n = 2,253; verification: n = 833) and post-operative clinical prediction model (development: n = 1,422; verification: n = 449), respectively. RESULTS The C-indices were used to show the predicted incidence of objective thrombosis at the pre- and post-operative stage, which were then compared with the observed incidence of thrombosis in both cohorts. Biomarkers and clinical indicators were included in pre- and post-operative nomograms, which were adequately calibrated in both cohorts. The cross-validated C-indices of the pre- and post-operative clinical prediction models in the verification cohort were 0.706 (95% Cl, 0.67-0.74) and 0.875 (95% Cl, 0.84-0.91), respectively. CONCLUSIONS We present our findings of novel pre- and post-operative nomograms for the prediction of deep venous thrombosis in patients who received thromboprophylaxis after suffering closed lower extremity fractures.
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Affiliation(s)
- J Xing
- Department of Orthopedics and Traumatology, Honghui Hospital, Xi'an Jiaotong University, Shaanxi, China.
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Li B, Hu T, Tang W. The effects of peer bullying and poverty on suicidality in Chinese left behind adolescents: The mediating role of psychotic-like experiences. Early Interv Psychiatry 2022; 16:1217-1229. [PMID: 35192219 DOI: 10.1111/eip.13271] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 11/05/2021] [Accepted: 01/18/2022] [Indexed: 02/05/2023]
Abstract
AIM This study investigated the influence of childhood adversity, such as peer bullying and socioeconomic status, on the suicidal behaviour of left-behind Chinese adolescents to determine whether psychotic-like experiences (PLEs) mediated the associations between these childhood adversities and suicidality; suicidal ideation (serious thoughts about taking one's own life), suicide plans, and suicide attempts. METHODS A representative group of rural adolescents (n = 3346) was recruited from 16 rural high schools in China. Suicidality was assessed using the suicide module from the Mini International Neuropsychiatric Interview Kid. Participants also completed questionnaires on bullying, socioeconomic status, left-behind characteristics, and PLEs. Structural equation modelling was then employed to explore the relationships between these variables. RESULTS Peer bullying, poverty, and left-behind status were all found to significantly increase adolescent suicide risk, the relationships between which were mediated by PLEs. Peer bullying was found to play the most significant role in the PLEs and suicidality, with the risk of suicide increasing with the length of time a child had been left behind. CONCLUSION Adverse life events can lead to a high risk of PLEs, which in turn can increase the risk of suicide. These results could assist in identifying individuals at risk of suicidality and the design of appropriate interventions. The results also highlighted the role PLEs play in suicidality and highlighted the need for further research in this area.
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Affiliation(s)
- Bin Li
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Tao Hu
- Department of Psychology, Chengdu Normal University, Chengdu, China
| | - Wanjie Tang
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.,Centre for Educational and Health Psychology, Sichuan University, Chengdu, China
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Lin Q, Ding K, Zhao R, Wang H, Ren L, Wei Y, Ye Q, Cui Y, He G, Tang W, Feng Q, Zhu D, Chang W, Lv Y, Mao Y, Wang X, Liang L, Zhou G, Liang F, Xu J. 43O Preoperative chemotherapy prior to primary tumor resection for colorectal cancer patients with asymptomatic resectable primary lesion and synchronous unresectable liver-limited metastases (RECUT): A prospective, randomized, controlled, multicenter clinical trial. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.10.075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
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41
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Zhang ZW, Jin YJ, Zhao SJ, Zhou LN, Huang Y, Wang JW, Tang W, Wu N. [Prevalence and risk factors of coronary artery calcification on lung cancer screening with low-dose CT]. Zhonghua Zhong Liu Za Zhi 2022; 44:1112-1118. [PMID: 36319457 DOI: 10.3760/cma.j.cn112152-20201114-00986] [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] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To investigate the prevalence and risk factors of coronary artery calcification (CAC) on lung cancer screening with low-dose computed tomography (LDCT). Methods: A total of 4 989 asymptomatic subjects (2 542 males and 2 447 females) who underwent LDCT lung cancer screening were recruited at Cancer Hospital, Chinese Academy of Medical Sciences from 2014 to 2017. The visual scoring method was used to assess coronary artery calcification score. χ(2) test or independent t-test was used to compare the difference of CAC positive rate among different groups. Multivariate logistic regression was used to analyze risk factors associated with CAC in the study. Results: Of the 4 989 asymptomatic subjects, CAC occurred in 1 018 cases. The positive rate was 20.4%, of which mild, moderate and severe calcification accounted for 86.3%, 11.4% and 2.3%, respectively. Gender, age, BMI, education level, occupation, smoking history, diabetes, hypertension and hyperlipidemia had statistically significant differences in CAC positive rates among groups. Multivariate logistic regression analysis showed that gender, age, diabetes, hypertension, hyperlipidemia and smoking history were risk factors for CAC. Age, diabetes, hypertension and smoking history were statistically significant risk factors between the mild and moderate CAC group. A total of 1 730 coronary arteries in 1 018 CAC positive cases had calcification, CAC positive rate of left anterior descending was the highest(51.3%); 568 cases (55.8%) were single vessel calcification, 450 cases (44.2%) were multiple vessel calcification. Conclusions: LDCT can be used for the 'one-stop' early detection of lung cancer and coronary atherosclerosis. Gender, age, diabetes, hypertension, hyperlipidemia and smoking are related risk factors for coronary atherosclerosis.
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Affiliation(s)
- Z W Zhang
- Department of Nuclear Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021 China
| | - Y J Jin
- Department of Nuclear Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021 China
| | - S J Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L N Zhou
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Huang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - J W Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - W Tang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - N Wu
- Department of Nuclear Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021 China
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Xiong YT, Xu L, Zeng W, Liu C, Guo JX, Tang W. [Virtual reconstruction and clinical verification of maxillary defect based on deep learning]. Zhonghua Kou Qiang Yi Xue Za Zhi 2022; 57:1029-1035. [PMID: 36266076 DOI: 10.3760/cma.j.cn112144-20220714-00384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To construct a virtual reconstruction method including midspan maxillary defects and provide clinical reference by training a generative adversarial network (GAN) model. Methods: The CT data of middle-aged Han patients with oral diseases who visited the Department of Radiology, West China Hospital of Stomatology, Sichuan University from June 2015 to June 2022 were collected, where the CT data of 100 healthy maxilla and 15 maxillary defects (5 simple unilateral defects, 5 unilateral defects involving zygomatic bone, 5 midspan defects) were selected. Mimics was used to create spherical phantom and simulate bone defects around the healthy maxillas, including simple unilateral defects, unilateral defects involving zygomatic bone and midspan defects. The original image was set as the correct reference for the reconstruction: artificial defects paired with the correct reference were divided into training set (n=70), validation set (n=20) and test set (n=10), where the first two were used to train the GAN model, and the test set was used to evaluate the GAN performance. Data from 15 clinical defects were imported into the trained GAN model for reconstruction, with mirroring and GAN-based virtual reconstruction for unilateral clinical defects, and only the latter method was adopted for midspan defects. The reconstruction results were divided into mirror reconstruction group (n=10), unilateral defect GAN reconstruction group (n=10) and midspan defect GAN reconstruction group (n=5). The test set, mirror reconstruction group, and unilateral defect GAN reconstruction group were quantitatively evaluated, whose quantitative indicators were Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95), and the group results were subjected to one-way ANOVA and Tukey test. The test set, mirror reconstruction group, unilateral defect GAN reconstruction group and midspan defect GAN reconstruction group were qualitatively scored, and Kruskal-Wallis test and Bonferroni correction were used for the total score of each group. Results: The total differences in the test set, mirror reconstruction group, unilateral defect GAN reconstruction group DCS (0.891±0.049, 0.721±0.047, 0.778±0.057, respectively) and HD95 [(3.58±1.51), (5.19±1.38), (4.51±1.10) mm, respectively] were statistically significant (F=28.08, P<0.001; F=3.62, P=0.041); among them, the test set DSC was significantly larger than the mirror reconstruction group (P<0.05), and the test set HD95 was significantly less than the mirror reconstruction group (P<0.05). Overall difference in qualitative total scores [8 (1), 6 (2), 6 (2), and 4 (2) points, respectively] in the test set, mirror reconstruction group, unilateral defect GAN reconstruction group, and midspan defect GAN reconstruction group were statistical significance (H=18.13, P<0.001); pairwise comparison showed that the total score of the test set was significantly higher than that of the mirror reconstruction group (P<0.05). Conclusions: The virtual reconstruction method based on GAN proposed in this study has better virtual reconstruction effect of unilateral defect than mirror technique, and can also realize virtual reconstruction of maxillary midspan defect.
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Affiliation(s)
- Y T Xiong
- Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University & State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Chengdu 610041, China
| | - L Xu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610041, China
| | - W Zeng
- Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University & State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Chengdu 610041, China
| | - C Liu
- Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University & State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Chengdu 610041, China
| | - J X Guo
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610041, China
| | - W Tang
- Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University & State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Chengdu 610041, China
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Greene S, Spertus JA, Tang W, Kang A, Zhong Y, Myers M, Shen S, Jiang J, Liu X, Steffen DR, Viola M, Felker GM. Heart failure across the range of preserved ejection fraction in United States clinical practice. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.863] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Introduction
Recent clinical trials of heart failure with preserved ejection fraction (HFpEF) have observed varying patient profiles by ejection fraction (EF), with attenuation of treatment benefits as EF increases. In routine clinical practice, the degree to which patients hospitalized for HF with EF≥60% may differ from those with lower EF is unknown.
Purpose
To compare patient characteristics, treatment patterns, and clinical outcomes across the range of EF among patients hospitalized for HFpEF.
Methods
Using the Humedica electronic medical records database between Jan 2010 and Dec 2020, patients hospitalized for a primary diagnosis of HF with EF>40% and who were haemodynamically stable at admission, without concurrent acute coronary syndrome or end-stage renal disease, and treated with intravenous (IV) diuretic agents within 48 h of admission were identified. Patient characteristics, treatment patterns, and clinical outcomes were compared by EF ranges of 41–49%, 50–59%, and ≥60%.
Results
Of 47,026 patients hospitalized with HFpEF, 6,335 (13%) had EF 41–49%, 18,603 (40%) had EF 50–59%, and 22,088 (47%) had EF≥60%. Across all 3 groups, patients were similar with respect to age (median 77 years for each group), race (83–84% White, 12–13% Black), systolic blood pressure (137–138 mmHg at admission), and eGFR (63–64 mL/min/1.73 m2 at admission). With progressively higher EF group, the proportion of women increased (45% vs 54% vs 65%) and median NT-proBNP decreased (4,221 vs 2,945 vs 2,234 pg/mL). Patients with EF ≥60% had the lowest rates of coronary artery disease and atrial fibrillation, and the highest rates of chronic pulmonary disease (Figure 1, Panel A). Discharge medications were generally similar, with exception of less beta-blocker use and more calcium channel blocker use among those with EF ≥60% (Figure 1, Panel B). Discharge use of angiotensin receptor-neprilysin inhibitor and sodium glucose cotransporter-2 inhibitor therapies were each <1% in all groups. Hospital length of stay (median 4 days for each group) and in-hospital mortality (1.1–1.3%) were similar across groups, but rates of in-hospital acute respiratory failure were higher among patients with EF ≥60% (27% vs 230-25% for lower EF groups). Rates of 30-day and 12-month post-discharge clinical events were high irrespective of EF, without meaningful differences between groups (Figure 2).
Conclusion
In a contemporary real-world population of US patients hospitalized for HF with EF >40%, nearly half had an EF≥60%. While clinical profiles and discharge medications varied, post-discharge outcomes were similarly poor irrespective of EF. There remain important opportunities to improve the care and outcomes for patients with HF across the range of preserved ejection fraction.
Funding Acknowledgement
Type of funding sources: Private company. Main funding source(s): MyoKardia, Inc., a wholly owned subsidiary of Bristol Myers Squibb
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Affiliation(s)
- S Greene
- Duke Clinical Research Institute , Durham , United States of America
| | - J A Spertus
- St. Luke's Mid America Heart Institute , Kansas City , United States of America
| | - W Tang
- Duke Clinical Research Institute , Durham , United States of America
| | - A Kang
- Bristol-Myers Squibb Company , Lawrenceville , United States of America
| | - Y Zhong
- Bristol-Myers Squibb Company , Lawrenceville , United States of America
| | - M Myers
- Bristol-Myers Squibb Company , Lawrenceville , United States of America
| | - S Shen
- Bristol-Myers Squibb Company , Lawrenceville , United States of America
| | - J Jiang
- Bristol-Myers Squibb Company , Lawrenceville , United States of America
| | - X Liu
- Bristol-Myers Squibb Company , Lawrenceville , United States of America
| | - D R Steffen
- Analysis Group Inc. , New York , United States of America
| | - M Viola
- Analysis Group Inc. , New York , United States of America
| | - G M Felker
- Duke Clinical Research Institute , Durham , United States of America
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Hughes D, Wilson R, Saijo Y, Chan N, Kumar A, Grimm R, Griffin B, Tang W, Nissen S, Aminian A, Xu B. Impact of weight loss on cardiac function: improvement in left ventricular global longitudinal strain following metabolic surgery. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.030] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Introduction
Obesity leads to an increased risk of cardiovascular disease (CVD) morbidity and mortality and is associated with the metabolic risk factors such as hypertension, diabetes mellitus, hyperlipidemia [1]. Metabolic surgery has been proven to be the most effective long term weight management tool and has known benefits in CVD prevention [2]. Global longitudinal strain (GLS) is an effective quantitative measurement of left ventricular (LV) function that is also a powerful predictor of future CVD events and mortality [3]. The impact of metabolic surgery on LV structure and function is unknown.
Purpose
This study investigated the changes in cardiac structure and function after metabolic surgery, including GLS. To our knowledge there has not been a study investigating this relationship previously reported.
Methods
Consecutive patients undergoing metabolic surgery at our center between March 2005 and February 2019 were recruited. Patients with transthoracic echocardiographic imaging (TTE) pre and post metabolic surgery (May 2005 to January 2019) were included. Electronic medical records were searched to obtain demographic, surgical and clinical data. GLS was calculated with Velocity Vector Imaging (VVI, Siemens, v2.0, Pennsylvania, USA). Averaged GLS values were derived from 4 chamber, 2 chamber and 3 chamber calculations.
Results
398 patients with pre- and post-operative cardiac imaging were included. Please see Table 1 for the baseline demographics of our study population. The mean age was 60.0 years with 70% being female. There were significant rates of CVD risk factors such as: hypertension (76.4%), diabetes mellitus (58.8%) and hyperlipidemia (76.4%).
The clinical and echocardiographic changes noted post metabolic surgery are detailed in Table 2. Along with decreases in weight post operatively, there were significant improvements in the markers of CVD risk factors such as mean blood pressure (134/75 to 129/72 mmHg, p value <0.001), mean gylcated hemoglobin levels (7.0 to 6.1%, p value <0.001) and mean low density lipoprotein (LDL) levels (97.7 to 88.2 mg/dl, p value <0.001).
There were a number of statistically significant positive changes in the left ventricular structure and function. The mean LV ejection fraction increased from 56.3% to 57.4% (p=0.008); left ventricular mass decreased from 238.2 g to 179.3 g (p value <0.001), and both septal and posterior wall thicknesses decreased significantly (p value <0.001). The LV mass indexed to body surface area (BSA) also decreased from 93.5 g/m2 to 83.1 g/m2.
The average global LV GLS was −15.7% pre-operatively, improving significantly to −17.9% post-operatively (p<0.001).
Conclusion
Our study has shown for the first time the impact of metabolic surgery on ventricular structure and function, with reduction in LV mass and improvement in LV GLS. These novel findings lends further support to the cardiovascular benefits of metabolic surgery.
Funding Acknowledgement
Type of funding sources: None.
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Affiliation(s)
- D Hughes
- Cleveland Clinic, Heart and Vascular Institute , Cleveland , United States of America
| | - R Wilson
- Cleveland Clinic, Bariatric and Metabolic Institute , Cleveland , United States of America
| | - Y Saijo
- Cleveland Clinic, Heart and Vascular Institute , Cleveland , United States of America
| | - N Chan
- Cleveland Clinic, Heart and Vascular Institute , Cleveland , United States of America
| | - A Kumar
- Cleveland Clinic, Heart and Vascular Institute , Cleveland , United States of America
| | - R Grimm
- Cleveland Clinic, Heart and Vascular Institute , Cleveland , United States of America
| | - B Griffin
- Cleveland Clinic, Heart and Vascular Institute , Cleveland , United States of America
| | - W Tang
- Cleveland Clinic, Heart and Vascular Institute , Cleveland , United States of America
| | - S Nissen
- Cleveland Clinic, Heart and Vascular Institute , Cleveland , United States of America
| | - A Aminian
- Cleveland Clinic, Bariatric and Metabolic Institute , Cleveland , United States of America
| | - B Xu
- Cleveland Clinic, Heart and Vascular Institute , Cleveland , United States of America
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Hutt E, Vega Brizneda M, Aguilera J, Wang TKM, Taimeh Z, Culver D, Callahan T, Tang W, Jaber WA, Cremer P, Ribeiro M, Jellis C. Multimodality imaging predictors of appropriate ICD shock and mortality in adults with cardiac sarcoidosis. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.351] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Identifying patients with cardiac sarcoidosis (CS) who are at increased risk of sudden cardiac death (SCD) is imperative. Current guideline recommendations for implantable cardioverter-defibrillator (ICD) implantation in patients with CS are based on small observational studies and have not been validated in contemporary cohorts using multimodality cardiac imaging.
Purpose
The aim of this study was to characterize a cohort of patients with tissue-proven cardiac sarcoidosis who underwent multimodality cardiac imaging and identify predictors of appropriate ICD shock and mortality.
Methods
We retrospectively identified subjects with a diagnosis of CS established by clinical/imaging criteria, and tissue biopsy (N=273) seen at our tertiary care center between 2001 and 2021. Clinical characteristics and outcomes were collected from electronic medical records. The primary endpoint of interest was a composite of appropriate ICD shock and all-cause mortality. Secondary endpoints were individual rates of appropriate ICD shock and all-cause mortality. Cox proportional hazard regression analysis was used to identify independent predictors of the outcomes.
Results
Mean age was 59±11 years and 40% were female. Isolated CS was found in 49 subjects (17.9%). The prevalence of traditional cardiovascular risk factors was low. Atrial fibrillation prevalence was high (41%). After a median follow-up of 7.9 years, the rate of appropriate ICD shock and all-cause mortality was 29% (N=79). The 5-year overall survival rate of 97.5%. Age, left ventricular ejection fraction (LVEF), and delayed gadolinium enhancement (DGE) in cardiac magnetic resonance (CMR) were independent predictors of the primary composite endpoint; LVEF and DGE in CMR were independent predictors of appropriate ICD-shock; and LVEF and baseline serum NT proBNP were independent predictors of overall mortality. An LVEF of 47% was identified as the optimal cutoff in predicting the primary composite endpoint. Presence of scar, inflammation or mismatch pattern in positron emission tomography were not significant predictors of the outcomes.
Conclusion
In this large cohort of subjects with CS, we found that the presence of DGE in CMR was the strongest independent predictor of the composite endpoint of appropriate ICD-shock and mortality and of appropriate ICD-shock individually; LVEF by echocardiogram was an independent predictor of the primary and secondary endpoints with an optimal LVEF cutoff for predicting the composite endpoint of 47%.
Funding Acknowledgement
Type of funding sources: None.
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Affiliation(s)
- E Hutt
- Cleveland Clinic , Cleveland , United States of America
| | | | - J Aguilera
- Cleveland Clinic , Cleveland , United States of America
| | - T K M Wang
- Cleveland Clinic , Cleveland , United States of America
| | - Z Taimeh
- Cleveland Clinic , Cleveland , United States of America
| | - D Culver
- Cleveland Clinic , Cleveland , United States of America
| | - T Callahan
- Cleveland Clinic , Cleveland , United States of America
| | - W Tang
- Cleveland Clinic , Cleveland , United States of America
| | - W A Jaber
- Cleveland Clinic , Cleveland , United States of America
| | - P Cremer
- Cleveland Clinic , Cleveland , United States of America
| | - M Ribeiro
- Cleveland Clinic , Cleveland , United States of America
| | - C Jellis
- Cleveland Clinic , Cleveland , United States of America
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Xu W, Huang Y, Tang W, Kaufman MR. Heterosexual Marital Intention: The Influences of Confucianism and Stigma Among Chinese Sexual Minority Women and Men. Arch Sex Behav 2022; 51:3529-3540. [PMID: 35900678 DOI: 10.1007/s10508-021-02229-9] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 11/06/2021] [Accepted: 11/09/2021] [Indexed: 06/15/2023]
Abstract
In traditional Confucianist culture in China, marriage and offspring are highly valued, placing sexual minority adults under tremendous pressure to marry an opposite sex partner. This study explored how Confucianism and stigma were associated with the intention to pursue a heterosexual marriage among Chinese sexual minority individuals as well as the moderating mechanisms of gender and age. Cross-sectional data were collected from 747 participants via online social networks from March to June 2020. Items assessed Confucianism values (communalism, filial piety, traditional gender roles); stigma (rejection sensitivity, social discrimination); and heterosexual marital intention (HMI). A total of 1.7% (n = 12) participants had ever been married, 11.6% (n = 87) planned to marry a different-sex partner, 60.4% (n = 451) had no intention to pursue a heterosexual marriage, and 26.4% (n = 197) had no specific marital plan. Bisexual participants scored significantly higher than homosexual individuals in HMI. Sexual minority adults with high levels of Confucianism and stigma were more likely to intend to marry. Importantly, both individual stigma (rejection sensitivity) and interpersonal stigma (social discrimination) partially mediated the relationship between Confucianism and HMI. Confucianism had a stronger impact on HMI for men than women, and age moderated the influence of Confucianism (including communalism and filial piety) on HMI, with a stronger impact for younger than older generations. This study contributes to a better understanding of how Confucianism and stigma may be connected to the intention to pursue a heterosexual marriage, suggesting culture-modified theories of stigma and sexual minority stress are needed to explain the experiences of sexual minority people in contemporary China.
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Affiliation(s)
- Wenjian Xu
- Department of Sociology and Psychology, School of Public Administration, Sichuan University, Chengdu, China
| | - Yuxia Huang
- Department of Sociology and Psychology, School of Public Administration, Sichuan University, Chengdu, China
| | - Wanjie Tang
- Center for Educational and Health Psychology, Sichuan University, Chengdu, 610065, China.
| | - Michelle R Kaufman
- Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Haringa C, Tang W, Noorman H. Analyzing bioprocess heterogeneity from the microbial viewpoint: Recent developments. CHEM-ING-TECH 2022. [DOI: 10.1002/cite.202255018] [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: 11/06/2022]
Affiliation(s)
- C. Haringa
- Delft University of Technology Biotechnology Van der Maasweg 9 2629HZ Delft The Netherlands
| | - W. Tang
- Delft University of Technology Biotechnology Van der Maasweg 9 2629HZ Delft The Netherlands
- Royal DSM Alexander Fleminglaan 1 2613AX Delft The Netherlands
| | - H. J. Noorman
- Delft University of Technology Biotechnology Van der Maasweg 9 2629HZ Delft The Netherlands
- Royal DSM Alexander Fleminglaan 1 2613AX Delft The Netherlands
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48
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Li G, Conti AA, Qiu C, Tang W. Adolescent mobile phone addiction during the COVID-19 pandemic predicts subsequent suicide risk: a two-wave longitudinal study. BMC Public Health 2022; 22:1537. [PMID: 35962376 PMCID: PMC9372972 DOI: 10.1186/s12889-022-13931-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/02/2022] [Indexed: 11/10/2022] Open
Abstract
Both the rate of mobile phone addiction and suicidality among adolescents have increased during the pandemic lockdown. However, the relationship between mobile phone addiction and suicide risk and the underlying psychological mechanisms remains unknown. This study examined the associations between mobile phone addiction in adolescents during the first month of lockdown and the suicide risk in the subsequent five months. A two-wave short-term longitudinal web-based survey was conducted on 1609 senior high school students (mean age = 16.53 years, SD = 0.97 years; 63.5% female). At Time 1 (T1), the severity of mobile phone addiction and basic demographic information was collected from Feb 24 to 28, 2020 in Sichuan Province, China (at the pandemic’s peak). Five months later, between July 11 and July 23 (Time 2, T2), mobile phone addiction, daytime sleepiness, depression, and suicidality were measured within the past five months. The regression analysis revealed that mobile phone addiction during quarantine directly predicted suicidality within the next five months, even after controlling for the effect of depression and daytime sleepiness. Meanwhile, mobile phone addiction at T1 also indirectly predicted suicidality at T2, with depression and daytime sleepiness mediating this association. Programs targeting improvement of daytime sleepiness and depressive symptoms may be particularly effective in reducing suicide risk among adolescents with mobile phone addiction.
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Affiliation(s)
- Gangqin Li
- Department of Forensic Psychiatry, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Aldo Alberto Conti
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Changjian Qiu
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, China.
| | - Wanjie Tang
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK. .,Mental Health Centre, West China Hospital, Sichuan University, Chengdu, China.
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49
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Yang YY, Tang SW, Tang W, Fan JL, Li Z, Yang JW, Ren J, Li CS. [Antibody levels of measles, rubella and mumps viruses in healthy population in Shanghai from 2010 to 2020]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:1095-1100. [PMID: 35922237 DOI: 10.3760/cma.j.cn112150-20211116-01057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Objective: To determine IgG antibody levels of measles, rubella, mumps in healthy population in Shanghai from 2010 to 2020 and analyze the trend of antibody changes in different age groups. Methods: 10 828 healthy people without measles, rubella and mumps in Shanghai were included in the study from 2010 to 2020. Serum samples were collected from 12 age groups, and the serum IgG antibody of measles, rubella and mumps were detected by ELISA. The difference of antibody positive rates and antibody levels were analyzed. Results: The median age M (Q1, Q3) of 10 828 objects were 8 years old (9 months old, 20 years old). Males accounted for 48.34% (5 234/10 828) and females accounted for 50.92% (5 514/10 828). Unknown gender information accounted for 0.74% (80/10 828), and 27.03% (2 927/10 828) of participants had unknown MMR immunization history. The total positive rates of measles, rubella and mumps IgG antibody were 76.78%, 64.46% and 64.29% and their GMCs were 541.45 mIU/ml, 31.76 IU/ml and 133.73 U/ml respectively. There were significant differences in serum IgG antibody GMC of measles, rubella and mumps in each year (Fmeasles=180.74, P<0.001; Frubella=189.95, P<0.001; Fmumps=122.40, P<0.001). The positive rate of measles antibody was higher than that of rubella and mumps, and the difference was statistically significant (χ²=518.09, P<0.001). Conclusion: The level of measles IgG antibody in healthy people in Shanghai is higher, while the level of rubella and mumps IgG antibody is slightly lower.
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Affiliation(s)
- Y Y Yang
- Department of Pathogen Biological Detection, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China
| | - S W Tang
- Department of Pathogen Biological Detection, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China
| | - W Tang
- Department of Pathogen Biological Detection, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China
| | - J L Fan
- Department of Infectious Disease Prevention and Control, Shanghai Minhang District Municipal Center for Disease Control and Prevention, Shanghai 201101, China
| | - Z Li
- Department of Pathogen Biological Detection, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China
| | - J W Yang
- Department of Pathogen Biological Detection, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China
| | - J Ren
- Department of Pathogen Biological Detection, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China
| | - C S Li
- Shanghai Institute of Infectious Disease and Biosecurity, Shanghai 200032, China
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Tang W, Yan Z, Lu Y, Xu J. Prospective examination of adolescent emotional intelligence and post-traumatic growth during and after COVID-19 lockdown. J Affect Disord 2022; 309:368-374. [PMID: 35472475 PMCID: PMC9035660 DOI: 10.1016/j.jad.2022.04.129] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 04/17/2022] [Accepted: 04/19/2022] [Indexed: 02/08/2023]
Abstract
OBJECTIVES While there have been some studies examining the post-traumatic growth (PTG) responses to the COVID-19 pandemic, few have been longitudinal studies exploring the changes over time or examining the underlying psychological PTG mechanisms. This study examined whether baseline perceived emotional intelligence (EI) predicted PTG through self-esteem and emotional regulation (ER) in a five-month follow-up study conducted on Chinese adolescents during the COVID-19 pandemic. METHODS Validated measures were completed by 2090 participants, which assessed both the perceived EI and the PTG 1 month after a nationwide lockdown in China, with 1609 of these participating in the follow-up five months later. Structural equation models (SEM) were then used to explore the paths between the variables. RESULTS As hypothesized, the follow-up survey found that the baseline perceived EI predicted PTG, ER, and self-esteem outcomes. The SEM analyses also revealed that self-esteem and ER significantly mediated the association between EI and PTG. LIMITATIONS Studies of three or more waves may be more suitable for longitudinal mediation analyses. Self-assessment reports may have subjective effects. CONCLUSIONS It was concluded that perceived EI might improve PTG in adolescents following the COVID-19 pandemic, and self-esteem and ER program training could be helpful in promoting PTG.
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Affiliation(s)
- Wanjie Tang
- Mental Health Centre, West China Hospital, Sichuan University, Chengdu, China,Institute of Emergency Management and Post-disaster Reconstruction, Business School, Sichuan University, Chengdu, China
| | | | - Yi Lu
- Institute of Emergency Management and Post-disaster Reconstruction, Business School, Sichuan University, Chengdu, China
| | - Jiuping Xu
- Institute of Emergency Management and Post-disaster Reconstruction, Business School, Sichuan University, Chengdu, China.
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