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Zhong HY, Chen Y, Du XY, Wang Q, Wang M, Zou ML, Yuan SM. [Selection strategy of pedicled axial flaps for repairing high-voltage electric burn wounds in foot and ankle]. ZHONGHUA SHAO SHANG YU CHUANG MIAN XIU FU ZA ZHI 2023; 39:939-946. [PMID: 37899559 DOI: 10.3760/cma.j.cn501225-20230613-00212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
Objective: To explore the selection strategy of pedicled axial flaps for repairing high-voltage electric burn wounds in foot and ankle. Methods: The retrospective observational research method was used. From January 2017 to December 2022, 16 patients with skin and soft tissue defects in foot and ankle after high-voltage electric burns were treated in General Hospital of Eastern Theater Command, including 11 cases of unilateral defect and 5 cases of bilateral defect. All patients were male, aged from 25 to 75 years. After thorough debridement, the area of the defect to be repaired with the flap was 5.0 cm×4.0 cm to 12.0 cm×8.0 cm. Before operation, the color Doppler ultrasound, computed tomography angiography, or digital subtraction angiography was used to fully evaluate the degree of vascular injury in the affected limb and to identify the distribution and traffic anastomosis of vascular network. Pedicled axial flaps with reliable blood supply were used to repair the wounds as soon as possible, and the area of flaps ranged from 3.0 cm×2.0 cm to 13.0 cm×8.0 cm. The wound in the donor area of flaps was repaired with split-thickness skin graft from head or medium-thickness skin graft from thigh. The flap repair of wounds in various areas of the ankle and foot was recorded. The postoperative survivals of the flaps and skin grafts were observed after surgery. The postoperative appearance of flaps and walking function of patients were followed up. At the last follow-up, the foot and ankle function was evaluated and rated using the American Association of Foot and Ankle Surgeons Ankle Posterior Foot Scoring System. Results: Two wounds in toe area were repaired with reverse dorsal pedis flaps, 3 wounds in medial ankle area and 2 wounds in heel area were repaired with medial plantar flaps, 2 wounds in anterior plantar area combined with toe area were repaired with reverse medial plantar flaps, 2 wounds in anterior plantar area combined with toe area and 5 wounds in anterior plantar area were repaired with reverse medial pedis flaps, 1 wound in toe area was combined with proper plantar digital artery flap, 1 dorsal pedis wound and 1 lateral malleolus wound were repaired with lateral supramalleolar perforator flaps, and 1 lateral malleolus wound and 1 dorsal pedis wound were repaired with sural neurovascular flap. One flap had venous reflux disorder after surgery and survived after treatment, while the other flaps and skin grafts survived completely after surgery. During the follow-up of 6 to 24 months after operation, the appearance of the flaps was good, and the walking function of patients was normal. At the last follow-up, the functional score of foot and ankle was 76 to 95, which was evaluated as excellent in 11 cases and good in 5 cases. Conclusions: According to the condition of high-voltage electric burn in foot and ankle, early and thorough debridement, preoperative imaging examination to evaluate blood vessels of the affected limb, and selection of pedicled axial flap with reliable blood supply are good methods for wound repair and related functional reconstruction of high-voltage electric burn in foot and ankle.
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Zabinski B, Zaid E, Zakareishvili T, Zakharchuk N, Zambito S, Zamora Saa JA, Zang J, Zanzi D, Zaplatilek O, Zeitnitz C, Zeng H, Zeng JC, Zenger DT, Zenin O, Ženiš T, Zenz S, Zerradi S, Zerwas D, Zhai M, Zhang B, Zhang DF, Zhang J, Zhang J, Zhang K, Zhang L, Zhang P, Zhang R, Zhang S, Zhang T, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Z, Zhang Z, Zhao H, Zhao P, Zhao T, Zhao Y, Zhao Z, Zhemchugov A, Zheng K, Zheng X, Zheng Z, Zhong D, Zhou B, Zhou H, Zhou N, Zhou Y, Zhu CG, Zhu J, Zhu Y, Zhu Y, Zhuang X, Zhukov K, Zhulanov V, Zimine NI, Zinsser J, Ziolkowski M, Živković L, Zoccoli A, Zoch K, Zorbas TG, Zormpa O, Zou W, Zwalinski L. Measurement of the Sensitivity of Two-Particle Correlations in pp Collisions to the Presence of Hard Scatterings. PHYSICAL REVIEW LETTERS 2023; 131:162301. [PMID: 37925689 DOI: 10.1103/physrevlett.131.162301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/18/2023] [Accepted: 08/09/2023] [Indexed: 11/07/2023]
Abstract
A key open question in the study of multiparticle production in high-energy pp collisions is the relationship between the "ridge"-i.e., the observed azimuthal correlations between particles in the underlying event that extend over all rapidities-and hard or semihard scattering processes. In particular, it is not known whether jets or their soft fragments are correlated with particles in the underlying event. To address this question, two-particle correlations are measured in pp collisions at sqrt[s]=13 TeV using data collected by the ATLAS experiment at the LHC, with an integrated luminosity of 15.8 pb^{-1}, in two different configurations. In the first case, charged particles associated with jets are excluded from the correlation analysis, while in the second case, correlations are measured between particles within jets and charged particles from the underlying event. Second-order flow coefficients, v_{2}, are presented as a function of event multiplicity and transverse momentum. These measurements show that excluding particles associated with jets does not affect the measured correlations. Moreover, particles associated with jets do not exhibit any significant azimuthal correlations with the underlying event, ruling out hard processes contributing to the ridge.
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Koeth T, Lai Y, Lascio S, Mignerey AC, Nabili S, Palmer C, Papageorgakis C, Wang L, Wong K, Busza W, Cali IA, Chen Y, D’Alfonso M, Eysermans J, Freer C, Gomez-Ceballos G, Goncharov M, Harris P, Kovalskyi D, Krupa J, Lee YJ, Long K, Mironov C, Paus C, Rankin D, Roland C, Roland G, Shi Z, Stephans GSF, Wang J, Wang Z, Wyslouch B, Yang TJ, Chatterjee RM, Crossman B, Hiltbrand J, Joshi BM, Kapsiak C, Krohn M, Kubota Y, Mahon D, Mans J, Revering M, Rusack R, Saradhy R, Schroeder N, Strobbe N, Wadud MA, Cremaldi LM, Bloom K, Bryson M, Claes DR, Fangmeier C, Finco L, Golf F, Joo C, Kamalieddin R, Kravchenko I, Reed I, Siado JE, Snow GR, Tabb W, Wightman A, Yan F, Zecchinelli AG, Agarwal G, Bandyopadhyay H, Hay L, Iashvili I, Kharchilava A, McLean C, Morris M, Nguyen D, Pekkanen J, Rappoccio S, Williams A, Alverson G, Barberis E, Haddad Y, Han Y, Krishna A, Li J, Lidrych J, Madigan G, Marzocchi B, Morse DM, Nguyen V, Orimoto T, Parker A, Skinnari L, Tishelman-Charny A, Wamorkar T, Wang B, 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Fernández Manteca PJ, Freed S, Gardner P, Geurts FJM, Kumar A, Li W, Padley BP, Redjimi R, Rotter J, Yang S, Yigitbasi E, Zhang Y, Bodek A, de Barbaro P, Demina R, Dulemba JL, Fallon C, Garcia-Bellido A, Hindrichs O, Khukhunaishvili A, Parygin P, Popova E, Taus R, Van Onsem GP, Goulianos K, Chiarito B, Chou JP, Gershtein Y, Halkiadakis E, Hart A, Heindl M, Jaroslawski D, Karacheban O, Laflotte I, Lath A, Montalvo R, Nash K, Osherson M, Routray H, Salur S, Schnetzer S, Somalwar S, Stone R, Thayil SA, Thomas S, Wang H, Acharya H, Delannoy AG, Fiorendi S, Holmes T, Nibigira E, Spanier S, Bouhali O, Dalchenko M, Delgado A, Eusebi R, Gilmore J, Huang T, Kamon T, Kim H, Luo S, Malhotra S, Mueller R, Overton D, Rathjens D, Safonov A, Akchurin N, Damgov J, Hegde V, Lamichhane K, Lee SW, Mengke T, Muthumuni S, Peltola T, Volobouev I, Whitbeck A, Appelt E, Greene S, Gurrola A, Johns W, Melo A, Romeo F, Sheldon P, Tuo S, Velkovska J, Viinikainen J, Cardwell B, Cox B, Cummings G, Hakala J, Hirosky 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A, Ovtin I, Palichik V, Perelygin V, Perfilov M, Petrushanko S, Popov V, Radchenko O, Rusinov V, Savina M, Savrin V, Shalaev V, Shmatov S, Shulha S, Skovpen Y, Slabospitskii S, Smirnov V, Sosnov D, Sulimov V, Tcherniaev E, Terkulov A, Teryaev O, Tlisova I, Toropin A, Uvarov L, Uzunian A, Vorobyev A, Voytishin N, Yuldashev BS, Zarubin A, Zhizhin I, Zhokin A. A search for decays of the Higgs boson to invisible particles in events with a top-antitop quark pair or a vector boson in proton-proton collisions at s=13TeV. THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS 2023; 83:933. [PMID: 37855556 PMCID: PMC10579171 DOI: 10.1140/epjc/s10052-023-11952-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 08/23/2023] [Indexed: 10/20/2023]
Abstract
A search for decays to invisible particles of Higgs bosons produced in association with a top-antitop quark pair or a vector boson, which both decay to a fully hadronic final state, has been performed using proton-proton collision data collected at s = 13 Te V by the CMS experiment at the LHC, corresponding to an integrated luminosity of 138fb - 1 . The 95% confidence level upper limit set on the branching fraction of the 125Ge V Higgs boson to invisible particles, B ( H → inv ) , is 0.54 (0.39 expected), assuming standard model production cross sections. The results of this analysis are combined with previous B ( H → inv ) searches carried out at s = 7 , 8, and 13Te V in complementary production modes. The combined upper limit at 95% confidence level on B ( H → inv ) is 0.15 (0.08 expected).
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Grants
- Austrian Federal Ministry of Education, Science and Research
- Austrian Science Fund
- Belgian Fonds de la Recherche Scientifique
- Belgian Fonds voor Wetenschappelijk Onderzoek
- CNPq
- CAPES
- FAPERJ
- FAPERGS
- FAPESP
- Bulgarian Ministry of Education and Science
- Bulgarian National Science Fund
- CERN
- Chinese Academy of Sciences
- Ministry of Science and Technology
- Chinese National Natural Science Foundation of China
- Colombian Funding Agency (MINICIENCIAS)
- Croatian Ministry of Science, Education and Sport
- Croatian Science Foundation
- Research and Innovation Foundation
- SENESCYT
- Ministry of Education and Research
- Estonian Research Council via PRG780, PRG803, and PRG445
- European Regional Development Fund
- Academy of Finland
- Finnish Ministry of Education and Culture
- Helsinki Institute of Physics
- Institut National de Physique Nucléaire et de Physique des Particules
- Centre National de la Recherche Scientifique
- Commissariat à l’Énergie Atomique et aux Énergies Alternatives
- Bundesministerium für Bildung und Forschung
- Deutsche Forschungsgemeinschaft
- Helmholtz-Gemeinschaft Deutscher Forschungszentren
- General Secretariat for Research and Innovation
- National Research, Development and Innovation Office
- Department of Atomic Energy
- Department of Science and Technology
- Institute for Research in Fundamental Studies
- Science Foundation
- Istituto Nazionale di Fisica Nucleare
- Korean Ministry of Education, Science and Technology
- National Research Foundation of Korea (NRF)
- MES
- Lithuanian Academy of Sciences
- Ministry of Education
- University of Malaya
- BUAP
- CINVESTAV
- CONACYT
- LNS
- SEP
- UASLP
- MOS
- Ministry of Business, Innovation and Employment
- Pakistan Atomic Energy Commission
- Ministry of Educaton and Science
- National Science Centre
- Fundação para a Ciência e a Tecnologia, CERN/FIS-PAR/0025/2019 and CERN/FIS-INS/0032/2019
- Ministry of Education, Science and Technological Development of Serbia
- MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”
- Fondo Europeo de Desarrollo Regional, Spain
- Plan de Ciencia, Tecnología e Innovación del Principado de Asturias
- MOSTR
- ETH Board
- ETH Zurich
- PSI
- SNF
- UniZH
- Canton Zurich
- SER
- Thailand Center of Excellence in Physics
- Institute for the Promotion of Teaching Science and Technology of Thailand
- Special Task Force for Activating Research
- National Science and Technology Development Agency of Thailand
- Scientific and Technical Research Council of Turkey
- Turkish Atomic Energy Authority
- National Academy of Sciences of Ukraine
- Science and Technology Facilities Council
- US Department of Energy
- US National Science Foundation
- Marie-Curie programme
- European Research Council and EPLANET (European Union)
- European Research Council/European Cooperation in Science and Technology), Action CA16108
- Individual
- Leventis Foundation
- Alfred P. Sloan Foundation
- Alexander von Humboldt Foundation
- Belgian Federal Science Policy Office
- Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium)
- Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)
- Belgian Fonds de la Recherche Scientifique, “Excellence of Science - EOS” - be.h project n. 30820817
- Belgian Fonds voor Wetenschappelijk Onderzoek, “Excellence of Science - EOS” - be.h project n. 30820817
- Beijing Municipal Science & Technology Commission, No. Z191100007219010
- Ministry of Education, Youth and Sports (MEYS) of the Czech Republic
- Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306
- Deutsche Forschungsgemeinschaft (DFG), project number 400140256 - GRK2497
- Hellenic Foundation for Research and Innovation, Project Number 2288
- Hungarian Academy of Sciences
- New National Excellence Program - ÚNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64
- Council of Scientific and Industrial Research, India
- Latvian Council of Science
- Ministy of Education and Science, project no. 2022/WK/14
- National Science Center, Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552
- Fundação para a Ciência e a Tecnologia, CEECIND/01334/2018
- National Priorities Research Program by Qatar National Research Fund
- Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and projects PID2020-113705RB, PID2020-113304RB, PID2020-116262RB and PID2020-113341RB-I00
- Programa Severo Ochoa del Principado de Asturias
- Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand)
- CUAASC
- Kavli Foundation
- Nvidia Corporation
- Welch Foundation, contract C-1845
- Weston Havens Foundation
- Institut für Hochenergiephysik (HEPHY) using the Cloud Infrastructure Platform (CLIP), Vienna
- Inter-University Institute for High Energies, Brussels
- Université Catholique de Louvain, Louvain-la-Neuve
- São Paulo Research and Analysis Center, São Paulo
- Universidade do Estado do Rio de Janeiro, Rio de Janeiro
- University of Sofia, Sofia
- Institute of High Energy Physics of the Chinese Academy of Sciences, Beijing
- National Institute of Chemical Physics and Biophysics, Tallinn
- Helsinki Institute of Physics, Helsinki
- Grille de Recherche d’Ile de France (GRIF), Institut de recherche sur les lois fondamentales de l’Univers, CEA, Université Paris-Saclay, Gif-sur-Yvette, France and Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris
- Institut de recherche sur les lois fondamentales de l’Univers, CEA, Université Paris-Saclay, Gif-sur-Yvette
- Institut national de physique nucléaire et de physique des particules, IN2P3, Villeurbanne
- Institut Pluridisciplinaire Hubert Curien (IPHC), Strasbourg
- Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau
- Deutsches Elektronen-Synchrotron, Hamburg
- Karlsruher Institut für Technologie, Karlsruhe
- RWTH Aachen University, Aachen
- University of Ioánnina, Ioánnina
- Wigner Research Centre for Physics, Budapest
- Tata Institute of Fundamental Research, Mumbai
- INFN CNAF, Bologna
- INFN Sezione di Bari, Università di Bari, Politecnico di Bari, Bari
- INFN Sezione di Pisa, Università di Pisa, Scuola Normale Superiore di Pisa, Pisa
- INFN Sezione di Roma, Sapienza Università di Roma, Rome
- INFN Sezione di Trieste, Università di Trieste, Trieste
- Laboratori Nazionali di Legnaro, Legnaro
- Kyungpook National University, Daegu
- National Centre for Physics, Quaid-I-Azam University, Islamabad
- Akademickie Centrum Komputerowe Cyfronet AGH, Krakow
- National Centre for Nuclear Research, Swierk
- Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa
- Korea Institute of Science and Technology Information (KISTI), Daejeon
- Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid
- Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander
- Port d’Informació Científica, Bellaterra
- CERN, European Organization for Nuclear Research, Geneva
- CSCS - Swiss National Supercomputing Centre, Lugano
- National Center for High-performance Computing (NCHC), Hsinchu City
- Middle East Technical University, Physics Department, Ankara
- National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov
- GridPP, Brunel University, Uxbridge
- GridPP, Imperial College, London
- GridPP, Queen Mary University of London, London
- GridPP, Royal Holloway, University of London, London
- GridPP, Rutherford Appleton Laboratory, Didcot
- GridPP, University of Bristol, Bristol
- GridPP, University of Glasgow, Glasgow
- Baylor University, Waco
- California Institute of Technology, Pasadena
- Fermi National Accelerator Laboratory, Batavia
- Massachusetts Institute of Technology, Cambridge
- National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility, Berkeley
- Open Science Grid (OSG) Consortium
- Pittsburgh Supercomputing Center (PSC), Pittsburgh
- Purdue University, West Lafayette
- Texas Advanced Computing Center (TACC), Austin
- University of California, San Diego, La Jolla
- University of Colorado Boulder, Boulder
- University of Florida, Gainesville
- University of Nebraska-Lincoln, Lincoln
- University of Wisconsin-Madison, Madison
- Vanderbilt University, Nashville
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N, Alexander J, Bright-Thonney S, Chen X, Cranshaw DJ, Fan J, Fan X, Gadkari D, Hogan S, Monroy J, Patterson JR, Quach D, Reichert J, Reid M, Ryd A, Thom J, Wittich P, Zou R, Albrow M, Alyari M, Apollinari G, Apresyan A, Bauerdick LAT, Berry D, Berryhill J, Bhat PC, Burkett K, Butler JN, Canepa A, Cerati GB, Cheung HWK, Chlebana F, Di Petrillo KF, Dickinson J, Elvira VD, Feng Y, Freeman J, Gandrakota A, Gecse Z, Gray L, Green D, Grünendahl S, Gutsche O, Harris RM, Heller R, Herwig TC, Hirschauer J, Horyn L, Jayatilaka B, Jindariani S, Johnson M, Joshi U, Klijnsma T, Klima B, Kwok KHM, Lammel S, Lincoln D, Lipton R, Liu T, Madrid C, Maeshima K, Mantilla C, Mason D, McBride P, Merkel P, Mrenna S, Nahn S, Ngadiuba J, Noonan D, Papadimitriou V, Pastika N, Pedro K, Pena C, Ravera F, Reinsvold Hall A, Ristori L, Sexton-Kennedy E, Smith N, Soha A, Spiegel L, Strait J, Taylor L, Tkaczyk S, Tran NV, Uplegger L, Vaandering EW, Weber HA, Zoi I, Avery P, Bourilkov D, Cadamuro L, Cherepanov V, Field RD, Guerrero D, Kim M, Koenig E, Konigsberg J, Korytov A, Lo KH, Matchev K, Menendez N, Mitselmakher G, Muthirakalayil Madhu A, Rawal N, Rosenzweig D, Rosenzweig S, Shi K, Wang J, Wu Z, Adams T, Askew A, Habibullah R, Hagopian V, Kolberg T, Martinez G, Prosper H, Schiber C, Viazlo O, Yohay R, Zhang J, Baarmand MM, Butalla S, Elkafrawy T, Hohlmann M, Kumar Verma R, Rahmani M, Yumiceva F, Adams MR, Becerril Gonzalez H, Cavanaugh R, Dittmer S, Evdokimov O, Gerber CE, Hofman DJ, Lemos DS, Merrit AH, Mills C, Oh G, Roy T, Rudrabhatla S, Tonjes MB, Varelas N, Wang X, Ye Z, Yoo J, Alhusseini M, Dilsiz K, Emediato L, Gandrajula RP, Karaman G, Köseyan OK, Merlo JP, Mestvirishvili A, Nachtman J, Neogi O, Ogul H, Onel Y, Penzo A, Snyder C, Tiras E, Amram O, Blumenfeld B, Corcodilos L, Davis J, Gritsan AV, Kang L, Kyriacou S, Maksimovic P, Roskes J, Sekhar S, Swartz M, Vámi TÁ, Abreu A, Alcerro Alcerro LF, Anguiano J, Baringer P, Bean A, Flowers Z, Isidori T, Khalil S, King J, Krintiras G, Lazarovits M, Le Mahieu C, Lindsey C, Marquez J, Minafra N, Murray M, Nickel M, Rogan C, Royon C, Salvatico R, Sanders S, Smith C, Wang Q, Williams J, Wilson G, Allmond B, Duric S, Gujju Gurunadha R, Ivanov A, Kaadze K, Kim D, Maravin Y, Mitchell T, Modak A, Nam K, Natoli J, Roy D, Rebassoo F, Wright D, Adams E, Baden A, Baron O, Belloni A, Bethani A, Eno SC, Hadley NJ, Jabeen S, Kellogg RG, Koeth T, Lai Y, Lascio S, Mignerey AC, Nabili S, Palmer C, Papageorgakis C, Wang L, Wong K, Abercrombie D, Busza W, Cali IA, Chen Y, D'Alfonso M, Eysermans J, Freer C, Gomez-Ceballos G, Goncharov M, Harris P, Hu M, Kovalskyi D, Krupa J, Lee YJ, Long K, Mironov C, Paus C, Rankin D, Roland C, Roland G, Shi Z, Stephans GSF, Wang J, Wang Z, Wyslouch B, Chatterjee RM, Crossman B, Evans A, Hiltbrand J, Jain S, Joshi BM, Kapsiak C, Krohn M, Kubota Y, Mans J, Revering M, Rusack R, Saradhy R, Schroeder N, Strobbe N, Wadud MA, Cremaldi LM, Bloom K, Bryson M, Claes DR, Fangmeier C, Finco L, Golf F, Joo C, Kravchenko I, Reed I, Siado JE, Snow GR, Tabb W, Wightman A, Yan F, Zecchinelli AG, Agarwal G, Bandyopadhyay H, Hay L, Iashvili I, Kharchilava A, McLean C, Morris M, Nguyen D, Pekkanen J, Rappoccio S, Williams A, Alverson G, Barberis E, Haddad Y, Han Y, Krishna A, Li J, Lidrych J, Madigan G, Marzocchi B, Morse DM, Nguyen V, Orimoto T, Parker A, Skinnari L, Tishelman-Charny A, Wamorkar T, Wang B, Wisecarver A, Wood D, Bhattacharya S, Bueghly J, Chen Z, Gilbert A, Hahn KA, Liu Y, Odell N, Schmitt MH, Velasco M, Band R, Bucci R, Castells S, Cremonesi M, Das A, Goldouzian R, Hildreth M, Hurtado Anampa K, Jessop C, Lannon K, Lawrence J, Loukas N, Lutton L, Mariano J, Marinelli N, Mcalister I, McCauley T, Mcgrady C, Mohrman K, Moore C, Musienko Y, Nelson H, Ruchti R, Townsend A, Wayne M, Yockey H, Zarucki M, Zygala L, Bylsma B, Carrigan M, Durkin LS, Francis B, Hill C, Lesauvage A, Nunez Ornelas M, Wei K, Winer BL, Yates BR, Addesa FM, Das P, Dezoort G, Elmer P, Frankenthal A, Greenberg B, Haubrich N, Higginbotham S, Kalogeropoulos A, Kopp G, Kwan S, Lange D, Marlow D, Mei K, Ojalvo I, Olsen J, Stickland D, Tully C, Malik S, Norberg S, Bakshi AS, Barnes VE, Chawla R, Das S, Gutay L, Jones M, Jung AW, Kondratyev D, Koshy AM, Liu M, Negro G, Neumeister N, Paspalaki G, Piperov S, Purohit A, Schulte JF, Stojanovic M, Thieman J, Wang F, Xiao R, Xie W, Dolen J, Parashar N, Acosta D, Baty A, Carnahan T, Decaro M, Dildick S, Ecklund KM, Fernández Manteca PJ, Freed S, Gardner P, Geurts FJM, Kumar A, Li W, Padley BP, Redjimi R, Rotter J, Shi W, Yang S, Yigitbasi E, Zhang L, Zhang Y, Zuo X, Bodek A, de Barbaro P, Demina R, Dulemba JL, Fallon C, Ferbel T, Galanti M, Garcia-Bellido A, Hindrichs O, Khukhunaishvili A, Ranken E, Taus R, Van Onsem GP, Goulianos K, Chiarito B, Chou JP, Gershtein Y, Halkiadakis E, Hart A, Heindl M, Jaroslawski D, Karacheban O, Laflotte I, Lath A, Montalvo R, Nash K, Osherson M, Salur S, Schnetzer S, Somalwar S, Stone R, Thayil SA, Thomas S, Wang H, Acharya H, Delannoy AG, Fiorendi S, Holmes T, Nibigira E, Spanier S, Bouhali O, Dalchenko M, Delgado A, Eusebi R, Gilmore J, Huang T, Kamon T, Kim H, Luo S, Malhotra S, Mueller R, Overton D, Rathjens D, Safonov A, Akchurin N, Damgov J, Hegde V, Lamichhane K, Lee SW, Mengke T, Muthumuni S, Peltola T, Volobouev I, Wang Z, Whitbeck A, Appelt E, Greene S, Gurrola A, Johns W, Melo A, Romeo F, Sheldon P, Tuo S, Velkovska J, Viinikainen J, Cardwell B, Cox B, Cummings G, Hakala J, Hirosky R, Joyce M, Ledovskoy A, Li A, Neu C, Perez Lara CE, Tannenwald B, Karchin PE, Poudyal N, Banerjee S, Black K, Bose T, Dasu S, De Bruyn I, Everaerts P, Galloni C, He H, Herndon M, Herve A, Koraka CK, Lanaro A, Loeliger A, Loveless R, Madhusudanan Sreekala J, Mallampalli A, Mohammadi A, Mondal S, Parida G, Pinna D, Savin A, Shang V, Sharma V, Smith WH, Teague D, Tsoi HF, Vetens W, Afanasiev S, Andreev V, Andreev Y, Aushev T, Azarkin M, Babaev A, Belyaev A, Blinov V, Boos E, Borshch V, Budkouski D, Bunichev V, Bychkova O, Chadeeva M, Chekhovsky V, Dermenev A, Dimova T, Dremin I, Epshteyn V, Ershov A, Gavrilov G, Gavrilov V, Gninenko S, Golovtcov V, Golubev N, Golutvin I, Gorbunov I, Gribushin A, Ivanchenko V, Ivanov Y, Kachanov V, Kardapoltsev L, Karjavine V, Karneyeu A, Khein L, Kim V, Kirakosyan M, Kirpichnikov D, Kirsanov M, Kodolova O, Konstantinov D, Korenkov V, Korotkikh V, Kozyrev A, Krasnikov N, Kuznetsova E, Lanev A, Levchenko P, Litomin A, Lychkovskaya N, Makarenko V, Malakhov A, Matveev V, Murzin V, Nikitenko A, Obraztsov S, Okhotnikov V, Oskin A, Ovtin I, Palichik V, Parygin P, Perelygin V, Petrushanko S, Pivovarov G, Popov V, Popova E, Radchenko O, Rusinov V, Savina M, Savrin V, Shalaev V, Shmatov S, Shulha S, Skovpen Y, Slabospitskii S, Smirnov V, Snigirev A, Sosnov D, Stepennov A, Sulimov V, Tcherniaev E, Terkulov A, Teryaev O, Tlisova I, Toms M, Toropin A, Uvarov L, Uzunian A, Vardanyan I, Vlasov E, Vorobyev A, Voytishin N, Yuldashev BS, Zarubin A, Zhizhin I, Zhokin A. Observation of τ Lepton Pair Production in Ultraperipheral Pb-Pb Collisions at sqrt[s_{NN}]=5.02 TeV. PHYSICAL REVIEW LETTERS 2023; 131:151803. [PMID: 37897747 DOI: 10.1103/physrevlett.131.151803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/31/2022] [Accepted: 10/28/2022] [Indexed: 10/30/2023]
Abstract
We present an observation of photon-photon production of τ lepton pairs in ultraperipheral lead-lead collisions. The measurement is based on a data sample with an integrated luminosity of 404 μb^{-1} collected by the CMS experiment at a center-of-mass energy per nucleon pair of sqrt[s_{NN}]=5.02 TeV. The γγ→τ^{+}τ^{-} process is observed for τ^{+}τ^{-} events with a muon and three charged hadrons in the final state. The measured fiducial cross section is σ(γγ→τ^{+}τ^{-})=4.8±0.6(stat)±0.5(syst) μb, where the second (third) term corresponds to the statistical (systematic) uncertainty in σ(γγ→τ^{+}τ^{-}) in agreement with leading-order QED predictions. Using σ(γγ→τ^{+}τ^{-}), we estimate a model-dependent value of the anomalous magnetic moment of the τ lepton of a_{τ}=0.001_{-0.089}^{+0.055}.
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Aguilar M, Ambrosi G, Anderson H, Arruda L, Attig N, Bagwell C, Barao F, Barbanera M, Barrin L, Bartoloni A, Battiston R, Belyaev N, Berdugo J, Bertucci B, Bindi V, Bollweg K, Bolster J, Borchiellini M, Borgia B, Boschini MJ, Bourquin M, Burger J, Burger WJ, Cai XD, Capell M, Casaus J, Castellini G, Cervelli F, Chang YH, Chen GM, Chen GR, Chen H, Chen HS, Chen Y, Cheng L, Chou HY, Chouridou S, Choutko V, Chung CH, Clark C, Coignet G, Consolandi C, Contin A, Corti C, Cui Z, Dadzie K, D'Angelo F, Dass A, Delgado C, Della Torre S, Demirköz MB, Derome L, Di Falco S, Di Felice V, Díaz C, Dimiccoli F, von Doetinchem P, Dong F, Donnini F, Duranti M, Egorov A, Eline A, Faldi F, Feng J, Fiandrini E, Fisher P, Formato V, Gámez C, García-López RJ, Gargiulo C, Gast H, Gervasi M, Giovacchini F, Gómez-Coral DM, Gong J, Goy C, Grandi D, Graziani M, Guracho AN, Haino S, Han KC, Hashmani RK, He ZH, Heber B, Hsieh TH, Hu JY, Huang BW, Ionica M, Incagli M, Jia Y, Jinchi H, Karagöz G, Khan S, Khiali B, Kirn T, Klipfel AP, Kounina O, Kounine A, Koutsenko V, Krasnopevtsev D, Kuhlman A, Kulemzin A, La Vacca G, Laudi E, Laurenti G, LaVecchia G, Lazzizzera I, Lee HT, Lee SC, Li HL, Li JQ, Li M, Li M, Li Q, Li Q, Li QY, Li S, Li SL, Li JH, Li ZH, Liang J, Liang MJ, Lin CH, Lippert T, Liu JH, Lu SQ, Lu YS, Luebelsmeyer K, Luo JZ, Luo SD, Luo X, Mañá C, Marín J, Marquardt J, Martin T, Martínez G, Masi N, Maurin D, Medvedeva T, Menchaca-Rocha A, Meng Q, Molero M, Mott P, Mussolin L, Jozani YN, Negrete J, Nicolaidis R, Nikonov N, Nozzoli F, Ocampo-Peleteiro J, Oliva A, Orcinha M, Ottupara MA, Palermo M, Palmonari F, Paniccia M, Pashnin A, Pauluzzi M, Pensotti S, Plyaskin V, Poluianov S, Qin X, Qu ZY, Quadrani L, Rancoita PG, Rapin D, Conde AR, Robyn E, Rodríguez-García I, Romaneehsen L, Rossi F, Rozhkov A, Rozza D, Sagdeev R, Savin E, Schael S, von Dratzig AS, Schwering G, Seo ES, Shan BS, Siedenburg T, Silvestre G, Song JW, Song XJ, Sonnabend R, Strigari L, Su T, Sun Q, Sun ZT, Tacconi M, Tang XW, Tang ZC, Tian J, Tian Y, Ting SCC, Ting SM, Tomassetti N, Torsti J, Urban T, Usoskin I, Vagelli V, Vainio R, Valencia-Otero M, Valente E, Valtonen E, Vázquez Acosta M, Vecchi M, Velasco M, Vialle JP, Wang CX, Wang L, Wang LQ, Wang NH, Wang QL, Wang S, Wang X, Wang Y, Wang ZM, Wei J, Weng ZL, Wu H, Wu Y, Xiao JN, Xiong RQ, Xiong XZ, Xu W, Yan Q, Yang HT, Yang Y, Yelland A, Yi H, You YH, Yu YM, Yu ZQ, Zhang C, Zhang F, Zhang FZ, Zhang J, Zhang JH, Zhang Z, Zhao F, Zheng C, Zheng ZM, Zhuang HL, Zhukov V, Zichichi A, Zuccon P. Temporal Structures in Positron Spectra and Charge-Sign Effects in Galactic Cosmic Rays. PHYSICAL REVIEW LETTERS 2023; 131:151002. [PMID: 37897756 DOI: 10.1103/physrevlett.131.151002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 08/26/2023] [Accepted: 09/01/2023] [Indexed: 10/30/2023]
Abstract
We present the precision measurements of 11 years of daily cosmic positron fluxes in the rigidity range from 1.00 to 41.9 GV based on 3.4×10^{6} positrons collected with the Alpha Magnetic Spectrometer (AMS) aboard the International Space Station. The positron fluxes show distinctly different time variations from the electron fluxes at short and long timescales. A hysteresis between the electron fluxes and the positron fluxes is observed with a significance greater than 5σ at rigidities below 8.5 GV. On the contrary, the positron fluxes and the proton fluxes show similar time variation. Remarkably, we found that positron fluxes are modulated more than proton fluxes with a significance greater than 5σ for rigidities below 7 GV. These continuous daily positron fluxes, together with AMS daily electron, proton, and helium fluxes over an 11-year solar cycle, provide unique input to the understanding of both the charge-sign and mass dependencies of cosmic rays in the heliosphere.
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Aad G, Abbott B, Abeling K, Abicht NJ, Abidi SH, Aboulhorma A, Abramowicz H, Abreu H, Abulaiti Y, Abusleme Hoffman AC, Acharya BS, Adam Bourdarios C, Adamczyk L, Adamek L, Addepalli SV, Addison MJ, Adelman J, Adiguzel A, Adye T, Affolder AA, Afik Y, Agaras MN, Agarwala J, Aggarwal A, Agheorghiesei C, Ahmad A, Ahmadov F, Ahmed WS, Ahuja S, Ai X, Aielli G, Ait Tamlihat M, Aitbenchikh B, Aizenberg I, Akbiyik M, Åkesson TPA, Akimov AV, Akiyama D, Akolkar NN, Al Khoury K, Alberghi GL, Albert J, Albicocco P, Albouy GL, Alderweireldt S, Aleksa M, Aleksandrov IN, Alexa C, Alexopoulos T, Alfonsi A, Alfonsi F, Algren M, Alhroob M, Ali B, Ali HMJ, Ali S, Alibocus SW, Aliev M, Alimonti G, Alkakhi W, Allaire C, Allbrooke BMM, Allen JF, Allendes Flores CA, Allport PP, Aloisio A, Alonso F, Alpigiani C, Alvarez Estevez M, Alvarez Fernandez A, Alviggi MG, Aly M, Amaral Coutinho Y, Ambler A, Amelung C, Amerl M, Ames CG, Amidei D, Amor Dos Santos SP, Amos KR, Ananiev V, Anastopoulos C, Andeen T, Anders JK, Andrean SY, Andreazza A, Angelidakis S, Angerami A, Anisenkov AV, Annovi A, Antel C, Anthony MT, Antipov E, Antonelli M, Antrim DJA, Anulli F, Aoki M, Aoki T, Aparisi Pozo JA, Aparo MA, Aperio Bella L, Appelt C, Aranzabal N, Arcangeletti C, Arce ATH, Arena E, Arguin JF, Argyropoulos S, Arling JH, Armbruster AJ, Arnaez O, Arnold H, Arrubarrena Tame ZP, Artoni G, Asada H, Asai K, Asai S, Asbah NA, Assahsah J, Assamagan K, Astalos R, Atashi S, Atkin RJ, Atkinson M, Atlay NB, Atmani H, Atmasiddha PA, Augsten K, Auricchio S, Auriol AD, Austrup VA, Avolio G, Axiotis K, Azuelos G, Babal D, Bachacou H, Bachas K, Bachiu A, Backman F, Badea A, Bagnaia P, Bahmani M, Bailey AJ, Bailey VR, Baines JT, Baines L, Bakalis C, Baker OK, Bakos E, Bakshi Gupta D, Balasubramanian R, Baldin EM, Balek P, Ballabene E, Balli F, Baltes LM, Balunas WK, Balz J, Banas E, Bandieramonte M, Bandyopadhyay A, Bansal S, Barak L, Barakat M, Barberio EL, Barberis D, Barbero M, Barbour G, Barends KN, Barillari T, Barisits MS, Barklow T, Baron P, Baron Moreno DA, Baroncelli A, Barone G, Barr AJ, Barr JD, Barranco Navarro L, Barreiro F, Barreiro Guimarães da Costa J, Barron U, Barros Teixeira MG, Barsov S, Bartels F, Bartoldus R, Barton AE, Bartos P, Basan A, Baselga M, Bassalat A, Basso MJ, Basson CR, Bates RL, Batlamous S, Batley JR, Batool B, Battaglia M, Battulga D, Bauce M, Bauer M, Bauer P, Bazzano Hurrell LT, Beacham JB, Beau T, Beauchemin PH, Becherer F, Bechtle P, Beck HP, Becker K, Beddall AJ, Bednyakov VA, Bee CP, Beemster LJ, Beermann TA, Begalli M, Begel M, Behera A, Behr JK, Beirer JF, Beisiegel F, Belfkir M, Bella G, Bellagamba L, Bellerive A, Bellos P, Beloborodov K, Belyaev NL, Benchekroun D, Bendebba F, Benhammou Y, Benoit M, Bensinger JR, Bentvelsen S, Beresford L, Beretta M, Bergeaas Kuutmann E, Berger N, Bergmann B, Beringer J, Bernardi G, Bernius C, Bernlochner FU, Bernon F, Berry T, Berta P, Berthold A, Bertram IA, Bethke S, Betti A, Bevan AJ, Bhamjee M, Bhatta S, Bhattacharya DS, Bhattarai P, Bhopatkar VS, Bi R, Bianchi RM, Bianco G, Biebel O, Bielski R, Biglietti M, Billoud TRV, Bindi M, Bingul A, Bini C, Biondini A, Birch-Sykes CJ, Bird GA, Birman M, Biros M, Bisanz T, Bisceglie E, Biswas D, Bitadze A, Bjørke K, Bloch I, Blocker C, Blue A, Blumenschein U, Blumenthal J, Bobbink GJ, Bobrovnikov VS, Boehler M, Boehm B, Bogavac D, Bogdanchikov AG, Bohm C, Boisvert V, Bokan P, Bold T, Bomben M, Bona M, Boonekamp M, Booth CD, Borbély AG, Bordulev IS, Borecka-Bielska HM, Borgna LS, Borissov G, Bortoletto D, Boscherini D, Bosman M, Bossio Sola JD, Bouaouda K, Bouchhar N, Boudreau J, Bouhova-Thacker EV, Boumediene D, Bouquet R, Boveia A, Boyd J, Boye D, Boyko IR, Bracinik J, Brahimi N, Brandt G, Brandt O, Braren F, Brau B, Brau JE, Brener R, Brenner L, Brenner R, Bressler S, Britton D, Britzger D, Brock I, Brooijmans G, Brooks WK, Brost E, Brown LM, Bruce LE, Bruckler TL, Bruckman de Renstrom PA, Brüers B, Bruncko D, Bruni A, Bruni G, Bruschi M, 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K, Younas S, Young CJS, Young C, Yu Y, Yuan M, Yuan R, Yue L, Zaazoua M, Zabinski B, Zaid E, Zakareishvili T, Zakharchuk N, Zambito S, Zamora Saa JA, Zang J, Zanzi D, Zaplatilek O, Zeitnitz C, Zeng H, Zeng JC, Zenger DT, Zenin O, Ženiš T, Zenz S, Zerradi S, Zerwas D, Zhai M, Zhang B, Zhang DF, Zhang J, Zhang J, Zhang K, Zhang L, Zhang P, Zhang R, Zhang S, Zhang T, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Z, Zhang Z, Zhao H, Zhao P, Zhao T, Zhao Y, Zhao Z, Zhemchugov A, Zheng K, Zheng X, Zheng Z, Zhong D, Zhou B, Zhou H, Zhou N, Zhou Y, Zhu CG, Zhu J, Zhu Y, Zhu Y, Zhuang X, Zhukov K, Zhulanov V, Zimine NI, Zinsser J, Ziolkowski M, Živković L, Zoccoli A, Zoch K, Zorbas TG, Zormpa O, Zou W, Zwalinski L. Observation of an Excess of Dicharmonium Events in the Four-Muon Final State with the ATLAS Detector. PHYSICAL REVIEW LETTERS 2023; 131:151902. [PMID: 37897770 DOI: 10.1103/physrevlett.131.151902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/31/2023] [Accepted: 08/11/2023] [Indexed: 10/30/2023]
Abstract
A search is made for potential ccc[over ¯]c[over ¯] tetraquarks decaying into a pair of charmonium states in the four muon final state using proton-proton collision data at sqrt[s]=13 TeV, corresponding to an integrated luminosity of 140 fb^{-1} recorded by the ATLAS experiment at LHC. Two decay channels, J/ψ+J/ψ→4μ and J/ψ+ψ(2S)→4μ, are studied. Backgrounds are estimated based on a hybrid approach involving Monte Carlo simulations and data-driven methods. Statistically significant excesses with respect to backgrounds dominated by the single parton scattering are seen in the di-J/ψ channel consistent with a narrow resonance at 6.9 GeV and a broader structure at lower mass. A statistically significant excess is also seen in the J/ψ+ψ(2S) channel. The fitted masses and decay widths of the structures are reported.
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Nitschke JE, Nkadimeng EK, Noacco Rosende SJ, Nobe T, Noel DL, Noguchi Y, Nommensen T, Nomura MA, Norfolk MB, Norisam RRB, Norman BJ, Novak J, Novak T, Novgorodova O, Novotny L, Novotny R, Nozka L, Ntekas K, Nurse E, Oakham FG, Ocariz J, Ochi A, Ochoa I, Oerdek S, Ogrodnik A, Oh A, Ohm CC, Oide H, Oishi R, Ojeda ML, Okazaki Y, O'Keefe MW, Okumura Y, Olariu A, Seabra LFO, Olivares Pino SA, Oliveira Damazio D, Oliveira Goncalves D, Oliver JL, Olsson MJR, Olszewski A, Olszowska J, Öncel ÖO, O'Neil DC, O'Neill AP, Onofre A, Onyisi PUE, Oreglia MJ, Orellana GE, Orestano D, Orlando N, Orr RS, O'Shea V, Ospanov R, Otero Y Garzon G, Otono H, Ott PS, Ottino GJ, Ouchrif M, Ouellette J, Ould-Saada F, Owen M, Owen RE, Oyulmaz KY, Ozcan VE, Ozturk N, Ozturk S, Pacalt J, Pacey HA, Pachal K, Pacheco Pages A, Padilla Aranda C, Padovano G, Pagan Griso S, Palacino G, Palazzo A, Palazzo S, Palestini S, Palka M, Pan J, Pan T, Panchal DK, Pandini CE, Panduro Vazquez JG, Pang H, Pani P, Panizzo G, Paolozzi L, Papadatos C, Parajuli S, Paramonov A, Paraskevopoulos C, Paredes Hernandez D, Park TH, Parker MA, Parodi F, Parrish EW, Parrish VA, Parsons JA, Parzefall U, Pascual Dias B, Pascual Dominguez L, Pascuzzi VR, Pasquali F, Pasqualucci E, Passaggio S, Pastore F, Pasuwan P, Pater JR, Patton J, Pauly T, Pearkes J, Pedersen M, Pedro R, Peleganchuk SV, Penc O, Peng C, Peng H, Penski KE, Penzin M, Peralva BS, Peixoto APP, Pereira Sanchez L, Perepelitsa DV, Perez Codina E, Perganti M, Perini L, Pernegger H, Perrella S, Perrevoort A, Perrin O, Peters K, Peters RFY, Petersen BA, Petersen TC, Petit E, Petousis V, Petridou C, Petrukhin A, Pettee M, Pettersson NE, Petukhov A, Petukhova K, Peyaud A, Pezoa R, Pezzotti L, Pezzullo G, Pham T, Phillips PW, Phipps MW, Piacquadio G, Pianori E, Piazza F, Piegaia R, Pietreanu D, Pilkington AD, Pinamonti M, Pinfold JL, Pereira BCP, Pitman Donaldson C, Pizzi DA, Pizzimento L, Pizzini A, Pleier MA, Plesanovs V, Pleskot V, Plotnikova E, Poddar G, Poettgen R, Poggioli L, Pogrebnyak I, Pohl D, Pokharel I, Polacek S, Polesello G, Poley A, Polifka R, Polini A, Pollard CS, Pollock ZB, Polychronakos V, Ponomarenko D, Pontecorvo L, Popa S, Popeneciu GA, Portillo Quintero DM, Pospisil S, Postolache P, Potamianos K, Potrap IN, Potter CJ, Potti H, Poulsen T, Poveda J, Pownall G, Pozo Astigarraga ME, Prades Ibanez A, Prapa MM, Price D, Primavera M, Principe Martin MA, Proffitt ML, Proklova N, Prokofiev K, Proto G, Protopopescu S, Proudfoot J, Przybycien M, Puddefoot JE, Pudzha D, Puzo P, Pyatiizbyantseva D, Qian J, Qin Y, Qiu T, Quadt A, Queitsch-Maitland M, Rabanal Bolanos G, Rafanoharana D, Ragusa F, Rainbolt JL, Raine JA, Rajagopalan S, Ramakoti E, Ran K, Raskina V, Rassloff DF, Rave S, Ravina B, Ravinovich I, Raymond M, Read AL, Readioff NP, Rebuzzi DM, Redlinger G, Reeves K, Reidelsturz JA, Reikher D, Reiss A, Rej A, Rembser C, Renardi A, Renda M, Rendel MB, Rennie AG, Resconi S, Ressegotti M, Resseguie ED, Rettie S, Reynolds B, Reynolds E, Rezaei Estabragh M, Rezanova OL, Reznicek P, Ricci E, Richter R, Richter S, Richter-Was E, Ridel M, Rieck P, Riedler P, Rijssenbeek M, Rimoldi A, Rimoldi M, Rinaldi L, Rinn TT, Rinnagel MP, Ripellino G, Riu I, Rivadeneira P, Rivera Vergara JC, Rizatdinova F, Rizvi E, Rizzi C, Roberts BA, Roberts BR, Robertson SH, Robin M, Robinson D, Robles Gajardo CM, Robles Manzano M, Robson A, Rocchi A, Roda C, Rodriguez Bosca S, Rodriguez Garcia Y, Rodriguez Rodriguez A, Rodríguez Vera AM, Roe S, Roemer JT, Roepe-Gier AR, Roggel J, Røhne O, Rojas RA, Roland B, Roland CPA, Roloff J, Romaniouk A, Romano E, Romano M, Romero Hernandez AC, Rompotis N, Roos L, Rosati S, Rosser BJ, Rossi E, Rossi E, Rossi LP, Rossini L, Rosten R, Rotaru M, Rottler B, Rousseau D, Rousso D, Rovelli G, Roy A, Rozanov A, Rozen Y, Ruan X, Rubio Jimenez A, Ruby AJ, Ruelas Rivera VH, Ruggeri TA, Rühr F, Ruiz-Martinez A, Rummler A, Rurikova Z, Rusakovich NA, Russell HL, Rutherfoord JP, Rüttinger EM, Rybacki K, Rybar M, Rye EB, Ryzhov A, Sabater Iglesias JA, Sabatini P, Sabetta L, Sadrozinski HFW, Safai Tehrani F, Safarzadeh Samani B, Safdari M, Saha S, Sahinsoy M, Saimpert M, Saito M, Saito T, Salamani D, Salamanna G, Salnikov A, Salt J, Salvador Salas A, Salvatore D, Salvatore F, Salzburger A, Sammel D, Sampsonidis D, Sampsonidou D, Sánchez J, Sanchez Pineda A, Sanchez Sebastian V, Sandaker H, Sander CO, Sandesara JA, Sandhoff M, Sandoval C, Sankey DPC, Sansoni A, Santi L, Santoni C, Santos H, Santpur SN, Santra A, Saoucha KA, Saraiva JG, Sardain J, Sasaki O, Sato K, Sauer C, Sauerburger F, Sauvan E, Savard P, Sawada R, Sawyer C, Sawyer L, Sayago Galvan I, Sbarra C, Sbrizzi A, Scanlon T, Schaarschmidt J, Schacht P, Schaefer D, Schäfer U, Schaffer AC, Schaile D, Schamberger RD, Schanet E, Scharf C, Schegelsky VA, Scheirich D, Schenck F, Schernau M, Scheulen C, Schiavi C, Schillaci ZM, Schioppa EJ, Schioppa M, Schlag B, Schleicher KE, Schlenker S, Schmieden K, Schmitt C, Schmitt S, Schoeffel L, Schoening A, Scholer PG, Schopf E, Schott M, Schovancova J, Schramm S, Schroeder F, Schultz-Coulon HC, Schumacher M, Schumm BA, Schune P, Schwartzman A, Schwarz TA, Schwemling P, Schwienhorst R, Sciandra A, Sciolla G, Scuri F, Scutti F, Sebastiani CD, Sedlaczek K, Seema P, Seidel SC, Seiden A, Seidlitz BD, Seiss T, Seitz C, Seixas JM, Sekhniaidze G, Sekula SJ, Selem L, Semprini-Cesari N, Sen S, Sengupta D, Senthilkumar V, Serin L, Serkin L, Sessa M, Severini H, Sevova S, Sforza F, Sfyrla A, Shabalina E, Shaheen R, Shahinian JD, Shaikh NW, Shaked Renous D, Shan LY, Shapiro M, Sharma A, Sharma AS, Sharma P, Sharma S, Shatalov PB, Shaw K, Shaw SM, Shen Q, Sherwood P, Shi L, Shimmin CO, Shimogama Y, Shinner JD, Shipsey IPJ, Shirabe S, Shiyakova M, Shlomi J, Shochet MJ, Shojaii J, Shope DR, Shrestha S, Shrif EM, Shroff MJ, Sicho P, Sickles AM, Sideras Haddad E, Sidiropoulou O, Sidoti A, Siegert F, Sijacki D, Sikora R, Sili F, Silva JM, Silva Oliveira MV, Silverstein SB, Simion S, Simoniello R, Simpson EL, Simpson ND, Simsek S, Sindhu S, Sinervo P, Sinetckii V, Singh S, Singh S, Sinha S, Sinha S, Sioli M, Siral I, Sivoklokov SY, Sjölin J, Skaf A, Skorda E, Skubic P, Slawinska M, Smakhtin V, Smart BH, Smiesko J, Smirnov SY, Smirnov Y, Smirnova LN, Smirnova O, Smith AC, Smith EA, Smith HA, Smith JL, Smith R, Smizanska M, Smolek K, Smykiewicz A, Snesarev AA, Snoek HL, Snyder S, Sobie R, Soffer A, Solans Sanchez CA, Soldatov EY, Soldevila U, Solodkov AA, Solomon S, Soloshenko A, Solovieva K, Solovyanov OV, Solovyev V, Sommer P, Sonay A, Song WY, Sopczak A, Sopio AL, Sopkova F, Sothilingam V, Sottocornola S, Soualah R, Soumaimi Z, South D, Spagnolo S, Spalla M, Spanò F, Sperlich D, Spigo G, Spina M, Spinali S, Spiteri DP, Spousta M, Staats EJ, Stabile A, Stamen R, Stamenkovic M, Stampekis A, Standke M, Stanecka E, Stanislaus B, Stanitzki MM, Stankaityte M, Stapf B, Starchenko EA, Stark GH, Stark J, Starko DM, Staroba P, Starovoitov P, Stärz S, Staszewski R, Stavropoulos G, Steentoft J, Steinberg P, Steinhebel AL, Stelzer B, Stelzer HJ, Stelzer-Chilton O, Stenzel H, Stevenson TJ, Stewart GA, Stockton MC, Stoicea G, Stolarski M, Stonjek S, Straessner A, Strandberg J, Strandberg S, Strauss M, Strebler T, Strizenec P, Ströhmer R, Strom DM, Strom LR, Stroynowski R, Strubig A, Stucci SA, Stugu B, Stupak J, Styles NA, Su D, Su S, Su W, Su X, Sugizaki K, Sulin VV, Sullivan MJ, Sultan DMS, Sultanaliyeva L, Sultansoy S, Sumida T, Sun S, Sun S, Gudnadottir OS, Sutton MR, Svatos M, Swiatlowski M, Swirski T, Sykora I, Sykora M, Sykora T, Ta D, Tackmann K, Taffard A, Tafirout R, Tafoya Vargas JS, Taibah RHM, Takashima R, Takeda K, Takeva EP, Takubo Y, Talby M, Talyshev AA, Tam KC, Tamir NM, Tanaka A, Tanaka J, Tanaka R, Tanasini M, Tang J, Tao Z, Tapia Araya S, Tapprogge S, Tarek Abouelfadl Mohamed A, Tarem S, Tariq K, Tarna G, Tartarelli GF, Tas P, Tasevsky M, Tassi E, Tate AC, Tateno G, Tayalati Y, Taylor GN, Taylor W, Teagle H, Tee AS, Teixeira De Lima R, Teixeira-Dias P, Teoh JJ, Terashi K, Terron J, Terzo S, Testa M, Teuscher RJ, Thaler A, Theiner O, Themistokleous N, Theveneaux-Pelzer T, Thielmann O, Thomas DW, Thomas JP, Thompson EA, Thompson PD, Thomson E, Thorpe EJ, Tian Y, Tikhomirov V, Tikhonov YA, Timoshenko S, Ting EXL, Tipton P, Tisserant S, Tlou SH, Tnourji A, Todome K, Todorova-Nova S, Todt S, Togawa M, Tojo J, Tokár S, Tokushuku K, Tombs R, Tomoto M, Tompkins L, Topolnicki KW, Tornambe P, Torrence E, Torres H, Torró Pastor E, Toscani M, Tosciri C, Tovey DR, Traeet A, Trandafir IS, Trefzger T, Tricoli A, Trigger IM, Trincaz-Duvoid S, Trischuk DA, Trocmé B, Trofymov A, Troncon C, Truong L, Trzebinski M, Trzupek A, Tsai F, Tsai M, Tsiamis A, Tsiareshka PV, Tsigaridas S, Tsirigotis A, Tsiskaridze V, Tskhadadze EG, Tsopoulou M, Tsujikawa Y, Tsukerman II, Tsulaia V, Tsuno S, Tsur O, Tsybychev D, Tu Y, Tudorache A, Tudorache V, Tuna AN, Turchikhin S, Turk Cakir I, Turra R, Turtuvshin T, Tuts PM, Tzamarias S, Tzanis P, Tzovara E, Uchida K, Ukegawa F, Ulloa Poblete PA, Unal G, Unal M, Undrus A, Unel G, Uno K, Urban J, Urquijo P, Usai G, Ushioda R, Usman M, Uysal Z, Vacek V, Vachon B, Vadla KOH, Vafeiadis T, Valderanis C, Valdes Santurio E, Valente M, Valentinetti S, Valero A, Vallier A, Valls Ferrer JA, Van Daalen TR, Van Gemmeren P, Van Rijnbach M, Van Stroud S, Van Vulpen I, Vanadia M, Vandelli W, Vandenbroucke M, Vandewall ER, Vannicola D, Vannoli L, Vari R, Varnes EW, Varni C, Varol T, Varouchas D, Varriale L, Varvell KE, Vasile ME, Vaslin L, Vasquez GA, Vazeille F, Vazquez Schroeder T, Veatch J, Vecchio V, Veen MJ, Veliscek I, Veloce LM, Veloso F, Veneziano S, Ventura A, Verbytskyi A, Verducci M, Vergis C, Verissimo De Araujo M, Verkerke W, Vermeulen JC, Vernieri C, Verschuuren PJ, Vessella M, Vesterbacka ML, Vetterli MC, Vgenopoulos A, Viaux Maira N, Vickey T, Vickey Boeriu OE, Viehhauser GHA, Vigani L, Villa M, Villaplana Perez M, Villhauer EM, Vilucchi E, Vincter MG, Virdee GS, Vishwakarma A, Vittori C, Vivarelli I, Vladimirov V, Voevodina E, Vogel F, Vokac P, Von Ahnen J, Von Toerne E, Vormwald B, Vorobel V, Vorobev K, Vos M, Vossebeld JH, Vozak M, Vozdecky L, Vranjes N, Vranjes Milosavljevic M, Vreeswijk M, Vuillermet R, Vujinovic O, Vukotic I, Wada S, Wagner C, Wagner W, Wahdan S, Wahlberg H, Wakasa R, Wakida M, Walbrecht VM, Walder J, Walker R, Walkowiak W, Wang AM, Wang AZ, Wang C, Wang C, Wang H, Wang J, Wang P, Wang RJ, Wang R, Wang R, Wang SM, Wang S, Wang T, Wang WT, Wang WX, Wang X, Wang X, Wang X, Wang Y, Wang Y, Wang Z, Wang Z, Wang Z, Warburton A, Ward RJ, Warrack N, Watson AT, Watson MF, Watts G, Waugh BM, Webb AF, Weber C, Weber MS, Weber SA, Weber SM, Wei C, Wei Y, Weidberg AR, Weingarten J, Weirich M, Weiser C, Wells CJ, Wenaus T, Wendland B, Wengler T, Wenke NS, Wermes N, Wessels M, Whalen K, Wharton AM, White AS, White A, White MJ, Whiteson D, Wickremasinghe L, Wiedenmann W, Wiel C, Wielers M, Wieseotte N, Wiglesworth C, Wiik-Fuchs LAM, Wilbern DJ, Wilkens HG, Williams DM, Williams HH, Williams S, Willocq S, Windischhofer PJ, Winklmeier F, Winter BT, Wittgen M, Wobisch M, Wolf A, Wölker R, Wollrath J, Wolter MW, Wolters H, Wong VWS, Wongel AF, Worm SD, Wosiek BK, Woźniak KW, Wraight K, Wu J, Wu M, Wu SL, Wu X, Wu Y, Wu Z, Wuerzinger J, Wyatt TR, Wynne BM, Xella S, Xia L, Xia M, Xiang J, Xiao X, Xie M, Xie X, Xiong J, Xiotidis I, Xu D, Xu H, Xu H, Xu L, Xu R, Xu T, Xu W, Xu Y, Xu Z, Xu Z, Yabsley B, Yacoob S, Yamaguchi N, Yamaguchi Y, Yamauchi H, Yamazaki T, Yamazaki Y, Yan J, Yan S, Yan Z, Yang HJ, Yang HT, Yang S, Yang T, Yang X, Yang X, Yang Y, Yang Z, Yao WM, Yap YC, Ye H, Ye J, Ye S, Ye X, Yeh Y, Yeletskikh I, Yexley MR, Yin P, Yorita K, Young CJS, Young C, Yuan M, Yuan R, Yue L, Yue X, Zaazoua M, Zabinski B, Zaid E, Zakareishvili T, Zakharchuk N, Zambito S, Zamora Saa JA, Zang J, Zanzi D, Zaplatilek O, Zeißner SV, Zeitnitz C, Zeng JC, Zenger DT, Zenin O, Ženiš T, Zenz S, Zerradi S, Zerwas D, Zhang B, Zhang DF, Zhang G, Zhang J, Zhang K, Zhang L, Zhang P, Zhang R, Zhang S, Zhang T, Zhang X, Zhang X, Zhang Z, Zhang Z, Zhao H, Zhao P, Zhao T, Zhao Y, Zhao Z, Zhemchugov A, Zheng Z, Zhong D, Zhou B, Zhou C, Zhou H, Zhou N, Zhou Y, Zhu CG, Zhu C, Zhu HL, Zhu H, Zhu J, Zhu Y, Zhu Y, Zhuang X, Zhukov K, Zhulanov V, Zimine NI, Zinsser J, Ziolkowski M, Živković L, Zoccoli A, Zoch K, Zorbas TG, Zormpa O, Zou W, Zwalinski L. Observation of the γγ→ττ Process in Pb+Pb Collisions and Constraints on the τ-Lepton Anomalous Magnetic Moment with the ATLAS Detector. PHYSICAL REVIEW LETTERS 2023; 131:151802. [PMID: 37897746 DOI: 10.1103/physrevlett.131.151802] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 07/07/2022] [Indexed: 10/30/2023]
Abstract
This Letter reports the observation of τ-lepton-pair production in ultraperipheral lead-lead collisions Pb+Pb→Pb(γγ→ττ)Pb and constraints on the τ-lepton anomalous magnetic moment a_{τ}. The dataset corresponds to an integrated luminosity of 1.44 nb^{-1} of LHC Pb+Pb collisions at sqrt[s_{NN}]=5.02 TeV recorded by the ATLAS experiment in 2018. Selected events contain one muon from a τ-lepton decay, an electron or charged-particle track(s) from the other τ-lepton decay, little additional central-detector activity, and no forward neutrons. The γγ→ττ process is observed in Pb+Pb collisions with a significance exceeding 5 standard deviations and a signal strength of μ_{ττ}=1.03_{-0.05}^{+0.06} assuming the standard model value for a_{τ}. To measure a_{τ}, a template fit to the muon transverse-momentum distribution from τ-lepton candidates is performed, using a dimuon (γγ→μμ) control sample to constrain systematic uncertainties. The observed 95% confidence-level interval for a_{τ} is -0.057
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Cao Z, Aharonian F, An Q, Axikegu, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Cai JT, Cao Q, Cao WY, Cao Z, Chang J, Chang JF, Chen AM, Chen ES, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen SH, Chen SZ, Chen TL, Chen Y, Cheng N, Cheng YD, Cui MY, Cui SW, Cui XH, Cui YD, Dai BZ, Dai HL, Dai ZG, Danzengluobu, Della Volpe D, Dong XQ, Duan KK, Fan JH, Fan YZ, Fang J, Fang K, Feng CF, Feng L, Feng SH, Feng XT, Feng YL, Gabici S, Gao B, Gao CD, Gao LQ, Gao Q, Gao W, Gao WK, Ge MM, Geng LS, Giacinti G, Gong GH, Gou QB, Gu MH, Guo FL, Guo XL, Guo YQ, Guo YY, Han YA, He HH, He HN, He JY, He XB, He Y, Heller M, Hor YK, Hou BW, Hou C, Hou X, Hu HB, Hu Q, Hu SC, Huang DH, Huang TQ, Huang WJ, Huang XT, Huang XY, Huang Y, Huang ZC, Ji XL, Jia HY, Jia K, Jiang K, Jiang XW, Jiang ZJ, Jin M, Kang MM, Ke T, Kuleshov D, Kurinov K, Li BB, Li C, Li C, Li D, Li F, Li HB, Li HC, Li HY, Li J, Li J, Li J, Li K, Li WL, Li WL, Li XR, Li X, Li YZ, Li Z, Li Z, Liang EW, Liang YF, Lin SJ, Liu B, Liu C, Liu D, Liu H, Liu HD, Liu J, Liu JL, Liu JY, Liu MY, Liu RY, Liu SM, Liu W, Liu Y, Liu YN, Lu R, Luo Q, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Min Z, Mitthumsiri W, Mu HJ, Nan YC, Neronov A, Ou ZW, Pang BY, Pattarakijwanich P, Pei ZY, Qi MY, Qi YQ, Qiao BQ, Qin JJ, Ruffolo D, Sáiz A, Semikoz D, Shao CY, Shao L, Shchegolev O, Sheng XD, Shu FW, Song HC, Stenkin YV, Stepanov V, Su Y, Sun QN, Sun XN, Sun ZB, Tam PHT, Tang QW, Tang ZB, Tian WW, Wang C, Wang CB, Wang GW, Wang HG, Wang HH, Wang JC, Wang K, Wang LP, Wang LY, Wang PH, Wang R, Wang W, Wang XG, Wang XY, Wang Y, Wang YD, Wang YJ, Wang ZH, Wang ZX, Wang Z, Wang Z, Wei DM, Wei JJ, Wei YJ, Wen T, Wu CY, Wu HR, Wu S, Wu XF, Wu YS, Xi SQ, Xia J, Xia JJ, Xiang GM, Xiao DX, Xiao G, Xin GG, Xin YL, Xing Y, Xiong Z, Xu DL, Xu RF, Xu RX, Xu WL, Xue L, Yan DH, Yan JZ, Yan T, Yang CW, Yang F, Yang FF, Yang HW, Yang JY, Yang LL, Yang MJ, Yang RZ, Yang SB, Yao YH, Yao ZG, Ye YM, Yin LQ, Yin N, You XH, You ZY, Yu YH, Yuan Q, Yue H, Zeng HD, Zeng TX, Zeng W, Zha M, Zhang BB, Zhang F, Zhang HM, Zhang HY, Zhang JL, Zhang LX, Zhang L, Zhang PF, Zhang PP, Zhang R, Zhang SB, Zhang SR, Zhang SS, Zhang X, Zhang XP, Zhang YF, Zhang Y, Zhang Y, Zhao B, Zhao J, Zhao L, Zhao LZ, Zhao SP, Zheng F, Zhou B, Zhou H, Zhou JN, Zhou M, Zhou P, Zhou R, Zhou XX, Zhu CG, Zhu FR, Zhu H, Zhu KJ, Zuo X. Measurement of Ultra-High-Energy Diffuse Gamma-Ray Emission of the Galactic Plane from 10 TeV to 1 PeV with LHAASO-KM2A. PHYSICAL REVIEW LETTERS 2023; 131:151001. [PMID: 37897763 DOI: 10.1103/physrevlett.131.151001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/08/2023] [Accepted: 08/18/2023] [Indexed: 10/30/2023]
Abstract
The diffuse Galactic γ-ray emission, mainly produced via interactions between cosmic rays and the interstellar medium and/or radiation field, is a very important probe of the distribution, propagation, and interaction of cosmic rays in the Milky Way. In this Letter, we report the measurements of diffuse γ rays from the Galactic plane between 10 TeV and 1 PeV energies, with the square kilometer array of the Large High Altitude Air Shower Observatory (LHAASO). Diffuse emissions from the inner (15°10 TeV). The energy spectrum in the inner Galaxy regions can be described by a power-law function with an index of -2.99±0.04, which is different from the curved spectrum as expected from hadronic interactions between locally measured cosmic rays and the line-of-sight integrated gas content. Furthermore, the measured flux is higher by a factor of ∼3 than the prediction. A similar spectrum with an index of -2.99±0.07 is found in the outer Galaxy region, and the absolute flux for 10≲E≲60 TeV is again higher than the prediction for hadronic cosmic ray interactions. The latitude distributions of the diffuse emission are consistent with the gas distribution, while the longitude distributions show clear deviation from the gas distribution. The LHAASO measurements imply that either additional emission sources exist or cosmic ray intensities have spatial variations.
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Tian Y, Shi Z, Wang C, Ke S, Qiu H, Zhao W, Chen J, Gong Y, Wu Y, Zhang W, Xia L, Zhang Y, Chen Y. A Comparison of Clinicopathologic Outcomes and Patterns of Lymphatic Spread across Neoadjuvant Chemotherapy, Neoadjuvant Chemoradiotherapy and Neoadjuvant Immunochemotherapy in Locally Advanced Esophageal Squamous Cell Carcinoma. Int J Radiat Oncol Biol Phys 2023; 117:e345. [PMID: 37785201 DOI: 10.1016/j.ijrobp.2023.06.2412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) To evaluate the differences in pathologic complete response (pCR) rates, TRG score, pathologic T stage and the pattern of lymphatic spread among patients receiving neoadjuvant chemotherapy (NCT) or neoadjuvant chemoradiotherapy (NCRT) or neoadjuvant immunochemotherapy (NICT) prior to esophagectomy for locally advanced esophageal squamous cell carcinoma (ESCC). MATERIALS/METHODS A total of 702 patients with ESCC who completed transthoracic esophagectomy followed neoadjuvant therapy at three cancer centers from January 2017 to December 2022 were enrolled. Among the included patients, 382 patients were treated with NCR, 172 with NCRT, and 148 with NICT. Inverse probability of treatment weighting (IPTW) was performed to control potential confounding factors. Pathological response of primary tumor was evaluated using the Chirieac modified tumor regression grade (TRG) system. The complete regression of primary lesion and nodal metastases were considered pCR. Lymph node classification system used the 8th edition of AJCC. Specimens were assessed for pattern of lymphatic spread. RESULTS After adjusting for baseline characteristics, the R0 resection rate did not significantly differ between the patients receiving NCT or NCRT or NICT (99.48% vs.100% vs.98.65%, P = 0.273). Compared with the NCT group, the NCRT group and NICT group had an advantage in pathological response (P<0.05). The pCR rate was 7.07% in the NCT group, 30.23% in the NCRT group, and 22.30% in the NICT group. Compared to the other two groups, the TRG score (P<0.05) and pathologic T stage (P<0.05) in the NCT group were significantly higher. In the NCT group, 9.97% had ypT0 disease, compared with 35.76% in the NCRT group and 25.68% in the NICT group. And in the NCT group, 9.71% had TRG1 disease, compared with 32.76% in the NCRT group and 25% in the NICT group. Compared with NICT, NCRT can significantly reduce the rate of LNM in station 1R (0 vs 3.38%, P<0.05) and 2R (1.15% vs 6.76%, P<0.05). Subgroup analysis according to the tumor location distribution showed that in upper thoracic cases, there was no statistical difference in LNM rates among stations no matter whether patients received NCT or NCRT or NICT. NICT group had higher LNM rates in station 2R (9.1%) in middle thoracic cases (P<0.05) and in station 18 (7.5%) (P<0.05) in lower thoracic cases, compared with the NCRT group and NCT group. CONCLUSION NCRT or NICT followed by surgery may result in a promising pCR rate and show a better performance in therapeutic response of primary lesion. No matter whether patients received NCT or NCRT or NICT, multiple level and skip node metastases are common, and adequate lymphadenectomy should be achieved to ensure the complete removal of metastatic lymph nodes.
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Hu M, Yang S, Chen Y, Kang J, Xu Y. Application of a Contralateral Esophageal-Sparing Technique to Reduce Radiation Esophagitis in Limited-Stage Small Cell Lung Cancer Treated with Twice-Daily Radiotherapy and Concurrent Chemotherapy. Int J Radiat Oncol Biol Phys 2023; 117:e25. [PMID: 37784973 DOI: 10.1016/j.ijrobp.2023.06.702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Acute esophagitis (AE) is a common radiation-related toxicity after concurrent twice-daily hyperfractionated radiotherapy and chemotherapy in limited-stage small cell lung cancer (LS-SCLC) patients, which could limit dose-escalation of the target and make treatment postponed to decrease local tumor control. More esophageal protective techniques should be proposed to reduce radiation severe esophagitis of LS-SCLC patients. MATERIALS/METHODS We retrospectively applied a contralateral esophagus sparing technique (CEST) in 20 unresectable LS-SCLC patients, who had gross tumor within 1 cm of the esophagus and received a total dose of 45 Gy of concurrent twice-daily radiation and standard chemotherapy regimen. The contralateral esophagus (CE) was contoured as an avoidance structure, and the feasibility of CEST on promoting a steep dose falloff beyond the target volume near esophagus was analyzed. The appropriate dose constraints of CE were also investigated. The AE events were recorded according to the RTOG acute toxicity grading system. RESULTS We performed CEST in 20 LS-SCLC consecutive patients, among whom three patients experienced severe AE after concurrent chemoradiotherapy. Each treatment plan of eligible patients assured high radiation doses delivering, with the planning and gross tumor volume covered by 95% and 100% of the prescription dose. Among these patients, the median maximum esophagus dose declined from 47.9 Gy (range, 46.6-49.7 Gy) to 41.3 Gy (range, 35.9-48.2 Gy), as well as V30 and V36 of esophagus decreased from 9.22 Gy (range, 0.42-17.71 Gy) and 7.39 Gy (range, 0-16.19 Gy) to 2.40 Gy (range, 0-5.68 Gy) and 0.53 Gy (range, 0 -2.69 Gy) after CEST applying, respectively (all p<0.001). The CE's median maximum dose, V30, and V36 were 41.3 Gy, 2.13 cc, and 0.24 cc, respectively. CONCLUSION By using proposed CE dose constraints of Dmax≤42 Gy, V30 ≤3.5 cc and V36 ≤0.5 cc, we confirmed the feasibility and efficacy of CEST to avoid exposing the esophagus cross-section to high prescription doses in LS-SCLC patients receiving twice-daily hyperfractionated IMRT and concurrent chemotherapy. These findings support the clinical practice of CEST in LS-SCLC patients, while more prospective and large-scale studies are warranted.
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Choi W, Nourzadeh H, Chen Y, Ainsley C, Desai V, Kubli A, Vinogradskiy Y, Mooney K, Werner-Wasik M, Mueller A. Novel Deep Learning Segmentation Models for Accurate GTV and OAR Segmentation in MR-Guided Adaptive Radiotherapy for Pancreatic Cancer Patients. Int J Radiat Oncol Biol Phys 2023; 117:e462. [PMID: 37785478 DOI: 10.1016/j.ijrobp.2023.06.1660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) MR-guided adaptive radiotherapy (MRgART) improves target coverage and organ-at-risk (OAR) sparing in pancreatic cancer radiation therapy (RT). Inter-fractional changes in patients undergoing RT require time intensive re-delineation of gross tumor volume (GTV) and OARs prior to adaptive optimization. Accurate automatic segmentation has the potential to significantly improve efficiency of the adaptive workflow. We hypothesized that state-of-the-art deep learning (DL) segmentation models could adequately segment GTV and OARs in both planning and daily fractional MR scans. MATERIALS/METHODS The study included 21 patients with pancreatic cancer treated with MRgART (10 Gy x 5 fractions). The planning MR as well as all daily MR images and registrations were collected (6 image sets per patient and a total of 126 image sets). The planning MR and fraction 1-4 image sets were used as the training set (N = 105), while the test set (N = 21) comprised images for fraction 5, to simulate the last step of incremental learning from planning to final fraction. Evaluated contours included the GTV, Small Bowel, Large Bowel, Duodenum, Left and Right Kidney, Liver, Spinal Cord, and Stomach. To mimic clinical conditions, contour accuracy was evaluated within the ring structure surrounding the PTV, inside of which daily adaptive re-contouring is applied (2 cm expansion in the cradio-caudal direction, 3 cm expansion otherwise). We evaluated three DL model architectures: SegResNet, SegResNet 2D, and SwinUNETR to autosegment GTV and OARs. The segmentation models were trained on the training set using 5-fold cross-validation (CV) and quantitatively analyzed by comparing against clinically used contours with DICE scores. Qualitative analysis was performed by a radiation oncologist using a scoring scale: 1 = perfect, 2 = minor discrepancy, 3 = moderate discrepancy, and 4 = rejected. RESULTS Overall, the DL segmentations were in acceptable agreement with clinical contours. The best performing model was the SwinUNETR model with overall training DICE = 0.88±0.06, test DICE = 0.78±0.11, and qualitative score of 1.6±0.8. The agreement between the DL model and clinical segmentation for the GTV was 0.79±0.08, with a qualitative score of 2.2±0.9. The highest and lowest OAR DICE scores were for the Left Kidney (DICE = 0.93) and Small Bowel (DICE = 0.68), respectively. The highest qualitative OAR scores were for the Kidney, Liver, and Spinal Cord (score = 1.0) and the lowest qualitative score was for the Duodenum (score = 2.3) CONCLUSION: We report here the most comprehensive work on DL segmentation for pancreatic cancer MRgART, including quantitative and clinically-pertinent qualitative evaluations of 126 image sets and 3 DL architectures. Our data show good quantitative agreement between DL and clinical contours, and acceptable clinician evaluations for the majority of GTVs and OARs. The current work has great potential to significantly reduce a major bottleneck in the MRgART workflow for pancreatic cancer patients.
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Dong Z, Hao Y, Laugeman E, Hugo GD, Samson P, Chen Y, Zhao T. Performance of Adaptive Deep Learning Models for Dose Predictions on High-Quality Cone-Beam Computed Tomography Images. Int J Radiat Oncol Biol Phys 2023; 117:e661. [PMID: 37785959 DOI: 10.1016/j.ijrobp.2023.06.2097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Online plan generation remains a patient-specific and time-consuming process that can place a significant burden on clinics strained with staffing shortages. As previous research show that dose-volume histogram (DVH) prediction plays a crucial role in automatic treatment planning, the objective of this study is to assess the capability of adaptive deep learning models in predicting dose information in volumetric modulation radiotherapy plans using the high-quality CBCT images and contour information of organs-at-risk (OARs). MATERIALS/METHODS The relationship between dose-volume histograms (DVHs) in radiotherapy plans and the geometric information of organs-at-risk (OAR) and planning target volume (PTV) has been well established. To evaluate the performance of the current state-of-the-art convolutional neural network (CNN) models including VIT3D and Unet3D, and intuitive machine learning methods (i.e., SVM and MLP), we implemented those models for dose prediction and conducted a comprehensive analysis with treatment plans created from images acquired from patients who consented to participate an IRB-approved imaging study designed to evaluate the imaging performance of the system. In total, 20 plans created by certified medical dosimetrists were employed in this study, with 15 used for training the machine-learning models and the remaining 5 used for performance testing. Two evaluation metrics were used: 1) root mean square error (RMSE) of the predicted dose and true dose and 2) time spent on dose prediction. RESULTS The results of the analysis showed that the ViT-3D (Transformer) model had the lowest RMSE of 3.682 ±0.010, followed by the Unet-3D (CNN) model with an RMSE of. 3.973 ±0.021 The MLP model had an RMSE of 8.007 ±0.019 while the SVM model had the highest RMSE of 9.156 ±0.032. For a fair comparison, we use 4-fold cross validation (each has 15 training plans and 5 testing plans), and report the mean value with standard deviation. All models are optimized with Adam optimizer of a learning rate 0.01, and the training process is stopped after 100 epochs. These findings indicate that the ViT-3D (Transformer) model performed the best in terms of predicting the dose information in volumetric modulation radiotherapy plans based on the CBCT images and contour information of OARs. For tested plan which contains 81 CT images (512 × 512 resolution), the inference time to predict dose information with a general CPU machine (6-Core Intel Core i7) is about 1.5 minutes. With GPU resources, such as NVIDIA A100, the inference process can be finished within seconds. CONCLUSION The study demonstrated that current state-of-the-art machine-learning models can achieve promising accuracy in dose prediction using high-quality CBCT images. A well-trained machine-learning model could offer clinicians a quick and reliable prediction of the true dose to patients in the case of significant anatomical changes or provide patient-specific optimization objectives if replanning is warranted.
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Hu D, Zhang Y, Li W, Zhang W, Reddy K, Chen Y, Gao H. SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data. Int J Radiat Oncol Biol Phys 2023; 117:S178-S179. [PMID: 37784443 DOI: 10.1016/j.ijrobp.2023.06.2523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Limited-angle CBCT (LA-CBCT) is of great clinical interest, because the scanning time and the patient dose are proportional to the scanning range of gantry rotation angles of CBCT. However, the image reconstruction for LA-CBCT remains technically challenging, which suffers from severe wedge artifacts and image distortions. This work aims to improve LA-CBCT by developing deep learning (DL) methods for real clinical CBCT projection data, which is the first feasibility study of clinical-projection-data-based LA-CBCT, to the best of our knowledge. MATERIALS/METHODS Targeting at real clinical projection data, we have explored various DL methods such as image/data/hybrid-domain methods and finally developed a so-called Structure-Enhanced Attention Network (SEA-Net) method that has the best image quality from clinical projection data among the DL methods we have implemented. Specifically, the proposed SEA-Net employs a specialized structure enhancement sub-network to promote texture preservation. Based on the observation that the distribution of wedge artifacts in reconstruction images is non-uniform, the spatial attention module is utilized to emphasize the relevant regions while ignores the irrelevant ones, which leads to more accurate texture restoration. RESULTS SEA-Net was validated in comparison with analytic (FDK), iterative (TV), image-domain DL (DDNet and FED-INet, data-domain DL (DCAR), dual-domain DL (Sam'Net), and various unrolling DL (hdNet, CTNet, FSR-Net, CasRedSCAN) methods. Among all methods, the SEA-Net had the best image reconstruction quality as quantified by root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), for various LA-CBCT problems of 90°-180° projection data. In addition, LA-CBCT via SEA-Net provided comparable accuracy for both patient setup (quantified by image registration accuracy from planning CT (pCT) to CBCT) and dose calculation (see the table), with full-view CBCT. CONCLUSION We explored various DL methods and developed an image-domain-based method termed SEA-Net that provided the best image quality for clinical projection data. To the best of our knowledge, this is the first feasibility study of the real clinical-projection-data-based LA-CBCT. Moreover, LA-CBCT via SEA-Net can potentially provide comparable accuracy for patient setup and dose calculation, with full-view CBCT.
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Chen Y, Liang C, Li J, Ma L, Wang B, Yuan Z, Yang S, Nong X. Effect of artesunate on cardiovascular complications in periodontitis in a type I diabetes rat model and related mechanisms. J Endocrinol Invest 2023; 46:2031-2053. [PMID: 36892740 DOI: 10.1007/s40618-023-02052-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 02/24/2023] [Indexed: 03/10/2023]
Abstract
PURPOSE Both cardiovascular disease and periodontitis are complications of diabetes that have a great impact on human life and health. Our previous research found that artesunate can effectively improve cardiovascular disease in diabetes and has an inhibitory effect on periodontal disease. Therefore, the present study aimed to explore the potential therapeutic possibility of artesunate in the protection against cardiovascular complications in periodontitis with type I diabetes rats and to elucidate the possible underlying mechanisms. METHODS Sprague‒Dawley rats were randomly divided into the healthy, diabetic, periodontitis, diabetic with periodontitis, and artesunate treatment groups (10, 30, and 60 mg/kg, i.g.). After artesunate treatment, oral swabs were collected and used to determine changes in the oral flora. Micro-CT was performed to observe changes in alveolar bone. Blood samples were processed to measure various parameters, while cardiovascular tissues were evaluated by haematoxylin-eosin, Masson, Sirius red, and TUNEL staining to observe fibrosis and apoptosis. The protein and mRNA expression levels in the alveolar bone and cardiovascular tissues were detected using immunohistochemistry and RT‒PCR. RESULTS Diabetic rats with periodontitis and cardiovascular complications maintained heart and body weight but exhibited reduced blood glucose levels, and they were able to regulate blood lipid indicators at normal levels after artesunate treatment. The staining assays suggested that treatment with 60 mg/kg artesunate has a significant therapeutic effect on myocardial apoptotic fibrosis. The high expression of NF-κB, TLR4, VEGF, ICAM-1, p38 MAPK, TGF-β, Smad2, and MMP9 in the alveolar bone and cardiovascular tissue in the type I diabetes and type I diabetes with periodontitis rat models was reduced after treatment with artesunate in a concentration-dependent manner. Micro-CT showed that treatment with 60 mg/kg artesunate effectively alleviated alveolar bone resorption and density reduction. The sequencing results suggested that each model group of rats had vascular and oral flora dysbiosis, but artesunate treatment could correct the dysbacteriosis. CONCLUSIONS Periodontitis-related pathogenic bacteria cause dysbiosis of the oral and intravascular flora in type I diabetes and aggravate cardiovascular complications. The mechanism by which periodontitis aggravates cardiovascular complications involves the NF-κB pathway, which induces myocardial apoptosis, fibrosis, and vascular inflammation.
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Ding S, Yin Y, Liu H, Liu B, Li Y, Wang B, Chen M, Liu M, Li R, Huang X, Chen Y. Inter-fractional Assessment during MR-guided Online Adaptive Radiotherapy for Glioblastoma. Int J Radiat Oncol Biol Phys 2023; 117:e99-e100. [PMID: 37786230 DOI: 10.1016/j.ijrobp.2023.06.867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Magnetic resonance image (MRI) guided radiation therapy has the potential to improve outcomes for glioblastoma by adapting to tumor changes during radiation therapy. This study aimed to assess the feasibility and potential benefits of MR-guided online adaptive radiotherapy (MRgOART) for patients with glioblastoma. MATERIALS/METHODS Twenty consecutive patients with glioblastoma were treated with MRgOART of 60 Gy in 30 fractions by the 1.5 T MR-Linac. The MRgOART fractions employed daily MR scans and the contours were utilized to create each adapted plan. The gross tumor volume (GTV) and clinical target volume (CTV) were delineated on MRI of pre-treatment simulation (Fx0) and all fractions (Fx1, Fx2, Fx3 ... Fx30) to evaluate the inter-fractional changes. These changes were quantified using absolute/relative volume (∆V), Dice similarity coefficient (DSC) and Hausdorff distance (HD) metrics. The reference treatment plans were generated using step-and-shoot IMRT and utilized 7-9 beam groups on original CT. Before the treatment, a synthetic CT (sCT) quality assurance (QA) process was performed to assess the dose accuracy of bulk relative electron density (rED) assignment for online MRI based treatment plan in terms of gamma analysis, point dose comparison and dose volume histogram (DVH) parameters. Then, the online adaptative treatment plans were obtained by re-optimizing based on the contours on daily pre-treatment MRI by "adapt to shape" workflow. Non-adaptive plans for each patient were generated by recalculating the dose from the reference plans on daily online MRI by "adapt to position" workflow. GTV and CTV coverage and organ at risk (OAR) constraints were used to compare non-adaptive and adaptive plans. RESULTS For both criteria, the 1%/1mm (98.58%±0.15%) and 2%/2mm (99.88%±0.18%) gamma passing rate results were always clinically acceptable in sCT QA process. The differences on point dose and DVH parameters between the plans based on sCT and original CT were less than 1%. A total of 20 patients with 600 fractions were evaluated. The results showed that large inter-fractional changes for GTV limited the efficacy of radiation therapy (DSC: 0.78±0.08, HD: 20.94±3.64mm, ∆V: 2.92%±6.36%). The inter-fractional CTV changes were smaller (DSC: 0.91±0.04, HD: 15.31±3.09mm, ∆V: 1.41%±1.29%). GTV coverage of non-adaptive plans was below the prescribed coverage in 228/600 fractions (38%), with 90 (15%) failing by more than 10%. For CTV coverage of non-adaptive plans, the changes were less than 5%. Online adaptative plans improved GTV and CTV coverage significantly (p<0.001) to 99%. The adaptive plans also had lower dose to whole brain than non-adaptive plans (p<0.001). CONCLUSION Significant inter-fractional tumor changes could be found during radiotherapy in patients with glioblastoma treated by the 1.5 T MR-Linac. Daily MR-guided re-optimization of treatment plans corrected for day-to-day anatomical variations and resulted in adequate target coverage in all fractions.
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Wang SX, Yang Y, Xie H, Yang X, Liu Z, Li H, Huang W, Luo WJ, Lei Y, Sun Y, Ma J, Chen Y, Liu LZ, Mao YP. Delta-Radiomics Guides Adaptive De-Intensification after Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma in the IMRT Era. Int J Radiat Oncol Biol Phys 2023; 117:S152-S153. [PMID: 37784386 DOI: 10.1016/j.ijrobp.2023.06.574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) In the setting of intensity-modulated radiotherapy (IMRT) and induction chemotherapy (IC), the benefits from concurrent chemotherapy remained controversial for locoregionally advanced nasopharyngeal carcinoma (LANPC). This study aimed to construct a delta-radiomics model for benefit prediction and patient selection for omitting concurrent chemotherapy. MATERIALS/METHODS Between December 2009 and December 2015, a total of 718 patients with LANPC treated with IC+IMRT or IC+concurrent chemoradiotherapy (CCRT) were retrospectively enrolled and randomly assigned to a training set (n = 503) and a validation set (n = 215). Radiomic features were extracted from magnetic resonance images of pre-IC and post-IC. Interclass correlation coefficients and Pearson correlation coefficients were calculated to select robust radiomic features. After univariate Cox analysis, a delta-radiomics signature was built using the LASSO-Cox regression. A nomogram incorporating the delta-radiomics signature and clinical prognostic factors was then developed and evaluated for calibration and discrimination. Risk stratification by the nomogram was evaluated by Kaplan-Meier methods. The primary outcome was overall survival (OS). RESULTS The delta-radiomics signature, which comprised 19 selected features, was independently associated with prognosis. It yielded an area under the receiver operating characteristic curve (AUC) of 0.77 (95% confidence interval [CI] 0.71 to 0.82) for the training set and 0.71 (95% CI 0.61 to 0.81) for the validation set. The nomogram composed of the delta-radiomic signature, age, T category, N category, pre-treatment Epstein-Barr virus DNA, and treatment showed great calibration and discrimination performance with an AUC of 0.80 (95% CI 0.75 to 0.85) for the training set and 0.75 (95% CI 0.64 to 0.85) for the validation set. Risk stratification by the nomogram excluding the treatment variable resulted in two risk groups with distinct OS. Significant better outcomes were observed in the high-risk patients with IC+CCRT compared to those with IC+IMRT (5-year OS: 73.8% vs. 61.4% in the training set and 85.8% vs. 65.6% in the validation set; all log-rank p < 0.05), while comparable outcomes between IC+CCRT and IC+IMRT were shown for the low-risk patients (95.8% vs. 95.8% in the training set and 92.2% vs. 88.3% in the validation set; all log-rank p > 0.05). CONCLUSION The delta-radiomics signature was identified as an independent indicator of LANPC. Integrating clinical predictors with the delta-radiomics signature, the radiomics-based nomogram could predict individual's survival outcomes and benefits from concurrent chemotherapy after IC for LANPC. Low-risk patients with LANPC determined by the nomogram may be potential candidates for omission of concurrent chemotherapy following IC in the IMRT era.
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Lu S, Wang J, Hu W, Zhu Z, Chen Y, Yang X. Deep Learning-Based Esophageal Tumor Identification on CT Slice. Int J Radiat Oncol Biol Phys 2023; 117:e691-e692. [PMID: 37786030 DOI: 10.1016/j.ijrobp.2023.06.2165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The study aims to use deep learning to improve the accuracy of identifying esophageal tumors on CT slices for radiotherapy planning. The identification of Gross Tumor Volume (GTV) can be challenging due to low contrast with surrounding tissue. Other methods like endoscopy and PET scan can provide additional information, but may not be suitable for radiotherapy due to differences in tissue density and alignment needs. MATERIALS/METHODS A multi-task deep learning network was developed, which perform both segmentation and slice classification, simultaneously. For segmentation, the normal esophagus and tumor were treated as a single structure due to the difficulty in distinguishing between them on CT images. The slices were divided into 3 categories, including tumor, normal esophageal and other. An Unet was used for segmentation and generate the mask to remove irrelevant areas. The masked image will be input into a Resnet to obtain the categories of slices. The performance of classification was accessed by ROC curve, AUC and confusion matrix on a new dataset and PET images. RESULTS The multi-task deep learning network was developed on a dataset of 315 patients' CT images and GTV segmentations, which were reviewed and verified by physicians. The model was then evaluated on an additional validation dataset of 30 patients, resulting in an accuracy of 88%. In terms of sensitivity and specificity, the model showed high performance, with a sensitivity of 97% and specificity of 95% for tumor and normal esophagus in the validation dataset. Meanwhile, the specificity was 85% and the specificity was 80% for tumor and normal esophagus in PET images dataset. CONCLUSION This multi-task deep learning approach effectively combines the benefits of both segmentation and classification techniques, resulting in improved accuracy and efficiency in identifying esophageal tumors on CT slices for radiotherapy planning.
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Mathen P, Chen Y, Lin A, Spivey R, Smart DK. HDAC-Mediated Glial Crosstalk Mediates Radiation Induced Memory Changes from Whole Brain Radiation in a Mouse Model. Int J Radiat Oncol Biol Phys 2023; 117:S11-S12. [PMID: 37784288 DOI: 10.1016/j.ijrobp.2023.06.225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Prior work demonstrated radiation sensitivity can be regulated via Class III HDAC-mediated intracellular DNA damage response coordinated with Wnt-beta catenin signaling. We hypothesized that radiation induced functional alterations in HDAC mediated glial crosstalk in the CNS produce intercellular signaling alterations leading to phenotypic changes in cognition following whole brain radiation exposure. MATERIALS/METHODS Primary human astrocytes, human astrocytoma (U251), and immortalized human microglia (HMC3) were examined in vitro in response to treatment with conditioned media isolated from irradiated glial cells. Prior to treatment with either single-dose or fractionated radiation schedules, cells were transfected with siRNA expression vector for Sirt2, a class III HDAC, versus scrambled controls. Quantitative PCR and immunoblots for neurotransmitters, metabolism, and inflammatory markers were obtained. Cells exposed to conditioned media were examined by immunofluorescence for cytoskeletal alterations and changes in junctional proteins. The findings were correlated with in vivo qPCR and immunohistochemical changes in brain tissue and functional memory changes in Sirt2 knockout versus wild type mice after whole brain radiation using novel object recognition testing at 2 weeks and 6 month post radiation timepoints. Statistical analysis was performed using paired Students T test between treated and control groups. RESULTS Cells treated with conditioned media produced from irradiated U251, astrocytes and microglia produced increased expression of inflammatory cytokines IL-6, IL-8, GM-CSF and VEGF. However, immunofluorescence of cells treated with conditioned media from irradiated Sirt2 knockdown microglia and primary astrocytes demonstrated qualitative disruptions in glutamate neurotransmitter metabolism and actin cytoskeletal alterations with changes in pseudopodia and lamellipodia after staining with phalloidin and neurofilament L, suggesting altered intercellular communication. Connexin 43 in gap junctions was increased >2-fold (p<0.05) after exposure to conditioned media, whereas E-cadherin in adherens junctions was not significantly affected. Novel object recognition testing of Sirt2 knockout mice demonstrated resistance to radiation induced memory decline from whole brain radiation at pre radiation day 5 versus post radiation days 5 and 185 compared to controls (p<0.05). CONCLUSION Glial crosstalk can be mediated via elimination of a Class III HDAC and appears to be a key mediator of radiation induced disruptions of intercellular communication in the CNS, connecting radiation to structural changes on the cell surface to synaptic activity and neurotransmitter metabolism, leading to functional disputations in recognition memory similar to what is experienced by patients receiving brain radiotherapy. These data suggest glial cross talk as a new therapeutic avenue to combat radiation induced cognitive dysfunction.
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Sun L, Zhao W, Lyu T, Chen Y, Xing L, Liu W. An Efficient Transformer Model for Synthesizing Dual Energy CT from Single Energy Scanner. Int J Radiat Oncol Biol Phys 2023; 117:e721-e722. [PMID: 37786104 DOI: 10.1016/j.ijrobp.2023.06.2231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Dual-energy CT can be used to optimize radiation treatment. Recently, deep learning has been demonstrated to synthesize high-energy CT images from low-energy ones for dose reduction and lower CT system burden. As the state-of-the-art deep learning architecture, the computation burden of Transformer increases quadratically with the feature size, making the model training resource-demanding or even infeasible. Here, we introduce an efficient transformer for the balance between CT image synthesis quality and computational burden. MATERIALS/METHODS The model is a U-shape deep neural network with encoders and decoders built by Transformer blocks. The model input is low-energy 100kVp CT image and the output is high-energy 140kVp one. Each block has a Self Channel Correlation Unit (SCCU) and a Self Spatial Attention Unit (SSAU). Local shortcuts are applied for both units. Under-sampling operation achieved by pixel shuffling is used to obtain multi-scale feature maps, and the transformer block is applied on each feature scale. Symmetric skip connection sending features from shallow layers to deep layers, thus an additional 1 × 1 convolution is used for feature fusion in each decoder. In a SCCU, the feature is first mapped to one Query, one Key, and one Value. Then the Query and the Key tensors perform matrix multiplication to compute cross covariance of feature channels. The channel correlation score can be obtained by normalization of the covariance, and it is used to weight the Value tensor. As a result, the model complexity only increases linearly with the feature size. Besides the channel weighting, we enhance spatial information using SSAU, where the feature is mapped to two tensors. One tensor after activation is used to point-wisely calibrate another tensor. Additional Transformer blocks are cascaded to the last decoder for feature refinement. Because of the structure similarity of low- and high-energy CT images, a global shortcut is used to ease model training. Clinical iodine contrast-enhanced dual energy CT image datasets of 19 patients are used in this study. The dual-energy scanning is performed by a SOMATOM Definition Flash DECT scanner. We split the datasets into training dataset of 15 patients, validation dataset of 1 patient, and testing dataset of 3 patients. The image size is 512 × 512 with pixel size 0.5 × 0.5 mm2. RESULTS The U-Net model with 1.95M parameters and 44.87G FLOPS achieved the averaged PSNR value of 44.55 dB (s.t.d. 1.34) and averaged RMSE value of 0.0060 (s.t.d. 0.001). In comparison, our efficient Transformer with 1.408M parameters and 31.375G FLOPS achieved the averaged PSNR value of 44.78 dB (s.t.d. 1.37) and RMSE value of 0.0059 (s.t.d. 0.001), demonstrating our model has better performance with small model size and less computation. CONCLUSION The efficient Transformer model allows high-resolution CT image synthesis with small model scale and computation burden from low-energy CT image.
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Zhang Y, Ye X, Ge J, Guo D, Zheng D, Yu H, Chen Y, Yao G, Lu Z, Yuille A, Lu L, Jin D, Yan S. Deep Learning-Based Multi-Modality Segmentation of Primary Gross Tumor Volume in CT and MRI for Nasopharyngeal Carcinoma. Int J Radiat Oncol Biol Phys 2023; 117:e498. [PMID: 37785566 DOI: 10.1016/j.ijrobp.2023.06.1739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The delineation of primary gross tumor volume (GTV) of nasopharyngeal carcinoma (NPC) is an essential step for radiotherapy planning. In clinical practice, radiation oncologists manually delineate the GTV in planning CT with the help of diagnostic MRI. This is because NPC tumors are closely adjacent to many important anatomic structures, and CT and MRI provide complementary strength to accurately determine the tumor extension boundary. Manual delineation is time-consuming with the potential registration errors between MRI and CT decreasing the delineation accuracy. In this study, we propose a fully automated GTV segmentation method based on CT and MRI by first aligning MRI to CT, and then, segmenting the GTV using a multi-modality deep learning model. MATERIALS/METHODS We collected 104 nasopharyngeal carcinoma patients with both planning CT and diagnostic MRI scans (T1 & T2 phases). An experienced radiation oncologists manually delineated the GTV, which was further examined by another senior radiation oncologist. Then, a coarse to fine cross-modality registration from MRI to CT was conducted as follows: (1) A rigid transformation was performed on MRI to roughly align MRI to CT with similar anatomic position. (2) Then, the region of interest (RoI) on both CT and rigid-transformed MRI were cropped. (3) A leading cross-modality deformable registration algorithm, named DEEDS, was applied on the cropped MRI and CT RoIs to find an accurate local alignment. Next, using CT and registered MRI as the combined input, a multi-modality deep segmentation network based on nnUNet was trained to generate the GTV prediction. 20% patients were randomly selected as the unseen testing set to quantitatively evaluate the performance. RESULTS The quantitative NPC GTV segmentation performance is summarized in Table 1. The deep segmentation model using CT alone achieved reasonable high performance with 76.6% Dice score and 1.34mm average surface distance (ASD). When both CT and registered MRI were used, the segmentation model further improved the performance by 0.9% Dice score increase and 11% relative ASD error reduction, demonstrating the complementary strength of CT and MRI in determining NPC GTV. Notably, the achieved 77.5% Dice score and 1.19mm ASD by the multimodality model is among the top performing results reported in recent automatic NPC GTV segmentation using either CT or MRI modality. CONCLUSION We developed a fully automated multi-modal deep-learning model for NPC GTV segmentation. The developed model can segment the NPC GTV in high accuracy. With further optimization and validation, this automated model has potential to standardize the NPC GTV segmentation and significantly decrease the workload of radiation oncologists in clinical practice.
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Ren G, Wang Y, Wang Y, Chen Y, Chen Q, Wang S. Development and Validation of a Deep Learning-Based Auto-Delineation of Target Volume and Organs at Risk in Pancreatic Cancer Radiotherapy. Int J Radiat Oncol Biol Phys 2023; 117:e482-e483. [PMID: 37785527 DOI: 10.1016/j.ijrobp.2023.06.1706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The delineation of the clinical target volume (CTV), gross target volume (GTV) and organs at risk (OARs) is a crucial and laborious in pancreatic cancer radiotherapy. In this work, we propose and evaluate a three-dimensional (3D) novel convolutional neural network (CNN) for automatic and accurate CTV, GTV and OARs in pancreatic cancer. MATERIALS/METHODS A total of 120 computed tomography (CT) scans patients with pancreatic cancer were collected. A novel 3D CNN network, called ResUNet3D, was developed to achieve auto-delineation. 96 patients chosen randomly were used for training, 12 patients for validation, and 12 patients for testing. Meanwhile, the Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95%) were used to assess the performance. RESULTS The DSC values for the test data were 80.9±8.6%, 77.5±5.6%, 94.5±1.3%, 66.2±13.4%, 73.6±7.6%, 79.0±8.7%, 94.1±1.9%, 94.6±1.4%, 87.3±5.8% for CTV, GTV, liver, duodenum, spinal cord, bowel, kidney left, kidney right, stomach. The corresponding HD95% values were 10.7±6.9mm, 7.8±5.7mm, 11.6±5.6mm, 18.6±5.6mm, 2.7±0.7mm, 17.7±8.6mm, 3.9±1.4mm, 3.7±1.9mm, 13.4±5.7mm, respectively. The average delineation time for one patient's CT images was within 5 seconds. CONCLUSION The experimental results demonstrate that the CTV, GTV and OARs delineated for pancreatic cancer by ResUNet3D achieved a close agreement with the ground truth. ResUNet3D could significantly reduce the radiation oncologists' contouring time.
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Qiu L, Chen Y, Williams TM, Amini A, Sampath S, Glaser SM, Chen YJ, Liu L, Leung D, Liu A, McGee HM. Evaluation of 68Ga-Fibroblast Activation Protein Inhibitor vs. 18F-FDG as a Novel Radiotracer for Biologically Guided Radiation Therapy. Int J Radiat Oncol Biol Phys 2023; 117:e251. [PMID: 37784976 DOI: 10.1016/j.ijrobp.2023.06.1193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Real-time biology guided radiation therapy (BgRT) uses real-time positron emissions from a PET tracer during treatment to guide targeted radiation to cancerous lesions. Fibroblast activation protein alpha (FAP) is highly expressed on cancer-associated fibroblasts in tumors with low expression in normal tissues. While 18F-FDG-PET requires fasting and has background in the liver and brain, 68-Gallium labeled FAP inhibitor (FAPI) does not require fasting and has less background uptake. The goal of this study was to investigate the utility of FAPI as a potential universal fiducial for BgRT. We hypothesized that 68Ga-FAPI would be a better radiotracer than 18F-FDG, as assessed by the Normalized Minimal kBq/mL and the Normal Target Signal (NTS), two parameters used to gauge the suitability of BgRT. MATERIALS/METHODS PET-CTs were obtained for 50 patients with pancreatic, liver, lung, head & neck, and cervical cancer using 18F-FDG and 68Ga-FAPI (n = 10 for each). Four DICOM images were obtained per patient (FDG PET + CT, FAPI PET + CT). Radiation oncologists delineated the gross tumor volume (GTV) on PET images. A separate set of auto-contours were generated from the PET using an auto-threshold of 40% maximum SUV for all tumors. A 1 cm expansion was added to the GTV to create a ring around the physician-generated contours and auto-contours. The following parameters were measured: GTV volume, SUV max of GTV, SUV mean of GTV, Normalized Minimal kBq/mL within the GTV, and NTS (= SUV max/Ring SUV mean). Values were compared using paired t-test. For the BgRT product with similar calculations, the required Normalized Minimal kBq/mL is > 5 kBq/mL; the required NTS is > 2.7 for treatment planning and > 2.0 for BgRT delivery. RESULTS The Normalized Minimal kBq/mL for FAPI was > 5 kBq/mL for all tumors and greater for auto-contoured GTVs compared to physician-contoured GTVs. The mean NTS for the auto-contours for all tumor sites was > 2.0. In addition, there was a statistically significant increase in the NTS for FAPI compared to FDG in pancreatic, liver and head & neck cancers. In pancreatic cancer, there was a statistically significant increase in Normalized Minimal kBq/mL for FAPI compared to FDG (26.0 vs 14.2) (p = 0.01) and the SUVmax of FAPI was almost double that of FDG (15.9 vs 8.2) (p = 0.01). FAPI had no background in the liver, but had high background in the uterus, suggesting it may have a role in liver cancer but not cervical cancer. CONCLUSION This is the first study demonstrating the potential superiority of 68Ga-FAPI compared to 18F-FDG as a biologic fiducial for BgRT when treating pancreatic, liver and head & neck cancers, with a similar efficacy for lung cancer. Our results indicate that auto-contoured GTVs generate a higher NTS than physician-contoured GTVs but all are > 2.0. In addition, the Normalized Minimal kBq/mL for auto-contours is > 5 kBq/mL for all tumors. As hypothesized, FAPI-based BgRT is most likely to be successful when treating tumors with significant desmoplastic stroma, such as pancreatic cancer.
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Damen P, van Rossum PSN, Chen Y, Liao Z, Hofstetter W, Hobbs BP, Mohan R, Lin SH. Comparing 90-Day Post-Operative Mortality after Neoadjuvant Proton-Based vs. Photon-Based Chemoradiotherapy for Esophageal Cancer. Int J Radiat Oncol Biol Phys 2023; 117:e346-e347. [PMID: 37785204 DOI: 10.1016/j.ijrobp.2023.06.2415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Standard of treatment for locally advanced esophageal cancer consists of chemoradiotherapy (CRT) followed by surgery. Evidence suggests that proton beam therapy (PBT) results in lower toxicity and fewer post-operative complications compared to photon-based radiotherapy (RT). Mortality in the first 90 days after surgery is a rare event occurring in 2-8% of patients, with higher reported rates (of up to 17%) in older patients. This 90-day mortality (90DM) rate is an important measure of post-operative (non-oncologic) mortality as a proxy of quality of care. We hypothesize that PBT could reduce the incidence of 90DM compared to photon-based RT. MATERIALS/METHODS From a single-center retrospectively acquired database patients with esophageal cancer treated with neoadjuvant CRT and esophagectomy in 1998-2022 were selected. Univariable logistic regression analyses were used to study the associations of RT modality and other patient- and treatment-related characteristics with 90DM. Subsequently, 3 separate methods were applied to adjust for confounding bias. These included multivariable logistic regression, 1:1 nearest-neighbor propensity score matching (PSM), and inverse probability of treatment weighting (IPTW). Finally, stratified analyses for patient groups aged ≥67 vs. <67 years were performed. RESULTS A total of 894 eligible patients were included (PBT, n = 202; photon-based RT, n = 692). PBT patients had a significantly higher age, better performance score, and a higher number of comorbidities. The 90DM rate was 5 (2.5%) in the PBT group and 29 (4.2%) in the photon-based RT group (p = 0.262). Significant univariable predictors of 90DM included higher age and tumor location. After multivariable adjustment, PBT vs. photon therapy was not significantly associated with 90DM (OR 0.49, 95% CI 0.18-1.31). The 90DM rates in the PSM cohort (n = 181 vs. n = 181) were 2.8% for PBT and 3.3% for photon-based RT (p = 0.379). The 90DM rates in the IPTW cohort were 2.8% for PBT and 4.1% for photon-based RT (p = 0.427). In the full cohort, stratified analysis for age groups revealed that in patients aged ≥67 years, PBT was associated with a decreased risk of 90DM compared to photon-based RT (1.3% vs. 8.8%; p = 0.046), which was not the case in patients aged <67 years. In the PSM cohort, a comparable (but non-significant) difference was observed in favor of PBT in patients aged ≥67 years (i.e., 1.5% vs. 7.5%; p = 0.099). Within-group analyses in the original cohort demonstrated that a higher age significantly increased the risk of 90DM within the photon-based RT group (8.8% vs. 2.7% for age ≥67 vs. <67 years; p = 0.001), but not within the PBT group (1.3% vs. 3.2%; p = 0.398). CONCLUSION Post-operative 90DM after esophagectomy for cancer was not significantly different between PBT and photon-based neoadjuvant CRT. However, among older patients we observed a signal that PBT may reduce the risk of 90DM. Higher age increased the risk of 90DM in patients who underwent photon-based RT, but not in patients who underwent PBT.
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Chen T, Zheng B, Yang P, Zhang Z, Su Y, Chen Y, Luo L, Luo D, Lin Y, Xie R, Zeng L. The Incidence and Prognosis Value of Perineural Invasion in Rectal Carcinoma: From Meta-Analyses and Real-World Clinical Pathological Features. Clin Oncol (R Coll Radiol) 2023; 35:e611-e621. [PMID: 37263883 DOI: 10.1016/j.clon.2023.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 04/13/2023] [Accepted: 05/16/2023] [Indexed: 06/03/2023]
Abstract
AIMS Perineural invasion (PNI) is a special type of metastasis of several cancers and has been reported as being a factor for poor prognosis in colorectal carcinoma. However, investigations of PNI in only rectal cancer and a comprehensive analysis combining meta-analyses with real-world case studies remain lacking. MATERIALS AND METHODS First, articles from 2000 to 2020 concerning the relationship between PNI and rectal cancer prognoses and clinical features were meta-analysed. Subsequently, we carried out a retrospective analysis of 312 rectal cancer cases that underwent radical surgery in the real world. The incidence of PNI and the relationship between PNI and prognosis, as well as clinicopathological factors, were investigated. RESULTS The incidence of PNI was 23.09% and 33.01% in the meta-analysis and clinical cases, respectively. PNI occurred as early as stage I (2.94%). Moreover, neoadjuvant therapy significantly reduced the PNI-positive rate (20.34% versus 26.54%). Both meta-analysis and real-world clinical case studies suggested that PNI-positive patients had poorer prognoses than PNI-negative patients. We established an effective risk model consisting of T stage, differentiation and lymphovascular invasion to predict PNI in rectal cancer. CONCLUSION PNI is a poor prognostic factor for rectal cancer and could occur even in stage I. Additionally, neoadjuvant therapy could sufficiently reduce the PNI-positive rate. T stage, lymphovascular invasion and differentiation grade were independent risk factors for PNI and the risk model that included these factors could predict the probability of PNI.
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Chen LM, Cong Q, Wu D, Chen Y, Qiu LH, Hong ZB, Yang YB, Xu L, Wang LF, Huang LX, Li WR, Tang JP, Cao YG, Sui L. A prospective multicentre controlled study of Gaoweikang (Chinese multiherb extract-based tincture) used in high-risk HPV infections. EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES 2023; 27:8985-8992. [PMID: 37843310 DOI: 10.26355/eurrev_202310_33922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
OBJECTIVE The aim of the study was to investigate the safety and antiviral efficacy of a Chinese multiherb extract-based tincture (GWK) on a population of patients with high-risk human papilloma (hrHPV) infections and hrHPV-caused cervical low-grade squamous intraepithelial lesions (LSILs). PATIENTS AND METHODS Patients with persistent hrHPV infection were enrolled in Group A, including A1 subjects, who received the intervention, and A2 subjects, who received the control. Patients with hrHPV infection causing cervical LSIL were enrolled in Group B, which included B1 subjects, who received the intervention, and B2 subjects, who served as the control. For Groups A1 and B1, hrHPV was tested at 3 months (M3) and 6 months (M6) after the intervention. The side effects were also analyzed. RESULTS At baseline (D0), a total of 99 patients were enrolled in Group A, with 50 subjects in Group A1 and 49 subjects in Group A2. A total of 91 patients were enrolled in Group B, with 45 subjects in Group B1 and 46 subjects in Group B2. There was no significant difference in the characteristics, including average age, age stratification, and HPV genotype. At M6, both Group A1 and Group B1 had a higher hrHPV clearance rate than the control group (A1/A2: 80.0% vs. 20.4%; B1/B2: 64.4% vs. 15.2%, p<0.001). At M6, the effective rates of Group A1 and Group B1 were 84% (42/50) and 68.9% (31/45), respectively. The side effect rates of Groups A1 and B1 were 11.5% (6/52) and 11.1% (5/45), respectively. Most adverse reactions involved local discomfort, including vulvar erythema, vulvar itch, increased vaginal discharge, cervical bleeding, and mild pain in the lower abdomen. Univariate logistic regression analysis showed that the intervention had an OR of 12 (95% CI 4.431-32.50) for clearing persistent HPV infection (p<0.001). For cervical LSIL, the intervention had an OR of 10.1 for clearing persistent HPV infection (95% CI 3.68-27.7) (p<0.001). CONCLUSIONS The results of this study suggest that the Chinese multiherb extract-based tincture GWK is safe and well tolerated. Furthermore, this preliminary study showed that this Chinese multiherb extract-based tincture is helpful for promoting HPV clearance in cases of persistent HPV and HPV-induced LSIL.
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