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Jing S, Dai Z, Wu Y, Liu X, Ren T, Liu X, Zhang L, Fu J, Chen X, Xiao W, Wang H, Huang Y, Qu Y, Wang W, Gu X, Ma L, Zhang S, Yu Y, Li L, Han Z, Su X, Qiao Y, Wang C. Prevalence and influencing factors of depressive and anxiety symptoms among hospital-based healthcare workers during the surge period of the COVID-19 pandemic in the Chinese mainland: a multicenter cross-sectional study. QJM 2023; 116:911-922. [PMID: 37561096 DOI: 10.1093/qjmed/hcad188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/06/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND From November 2022 to February 2023, the Chinese mainland experienced a surge in COVID-19 infection and hospitalization, and the hospital-based healthcare workers (HCWs) might suffer serious psychological crisis during this period. This study aims to assess the depressive and anxiety symptoms among HCWs during the surge of COVID-19 pandemic and to provide possible reference on protecting mental health of HCWs in future infectious disease outbreaks. METHODS A multicenter cross-sectional study was carried out among hospital-based HCWs in the Chinese mainland from 5 January to 9 February 2023. The PHQ-9 (nine-item Patient Health Questionnaire) and GAD-7 (seven-item Generalized Anxiety Disorder Questionnaire) were used to measure depressive and anxiety symptoms. Ordinal logistic regression analysis was performed to identify influencing factors. RESULTS A total of 6522 hospital-based HCWs in the Chinse mainland were included in this survey. The prevalence of depressive symptoms among the HCWs was 70.75%, and anxiety symptoms was 47.87%. The HCWs who perceived higher risk of COVID-19 infection and those who had higher work intensity were more likely to experience depressive and anxiety symptoms. Additionally, higher levels of mindfulness, resilience and perceived social support were negatively associated with depressive and anxiety symptoms. CONCLUSION This study revealed that a high proportion of HCWs in the Chinese mainland suffered from mental health disturbances during the surge of the COVID-19 pandemic. Resilience, mindfulness and perceived social support are important protective factors of HCWs' mental health. Tailored interventions, such as mindfulness practice, should be implemented to alleviate psychological symptoms of HCWs during the COVID-19 pandemic or other similar events in the future.
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Li ZX, Liu XB, Li Y, Liang GH, Wang ZF, Zheng Y, Sun HB, Wang W, Song T, Xing WQ. [Application value of CT examination of lymph node short diameter in evaluating cardia-left gastric lymph node metastasis in thoracic esophageal squamous cell carcinoma]. ZHONGHUA ZHONG LIU ZA ZHI [CHINESE JOURNAL OF ONCOLOGY] 2023; 45:962-966. [PMID: 37968082 DOI: 10.3760/cma.j.cn112152-20220313-00173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/17/2023]
Abstract
Objective: To investigate the application value of computed tomography (CT) examination of lymph node short diameter in evaluating cardia-left gastric lymph node metastasis in thoracic esophageal squamous cell carcinoma (ESCC). Methods: A total of 477 patients with primary thoracic ESCC who underwent surgical treatment in the Affiliated Cancer Hospital of Zhengzhou University from January 2013 to December 2017 were collected. All of them underwent McKeown esophagectomy plus complete two-field or three-field lymph node dissection. Picture archiving and communication system were used to measure the largest cardia-left gastric lymph node short diameter in preoperative CT images. The postoperative pathological diagnosis results of cardia-left gastric lymph node were used as the gold standard. Receiver operating characteristic (ROC) curve was used to evaluate the efficacy of CT lymph node short diameter in detecting the metastasis of cardia-left gastric lymph node in thoracic ESCC, and determine the optimal cut-off value. Results: The median short diameter of the largest cardia-left gastric lymph node was 4.1 mm in 477 patients, and the largest cardia-left gastric lymph node short diameter was less than 3 mm in 155 cases (32.5%). Sixty-eight patients had cardia-left gastric lymph node metastases, of which 38 had paracardial node metastases and 41 had left gastric node metastases. The lymph node ratios of paracardial node and left gastric node were 4.0% (60/1 511) and 3.3% (62/1 887), respectively. ROC curve analysis showed that the area under the curve of CT lymph node short diameter for evaluating cardia-left gastric lymph node metastasis was 0.941 (95% CI: 0.904-0.977; P<0.05). The optimal cut-off value of CT examination of the cardia-left gastric lymph node short diameter was 6 mm, and the corresponding sensitivity, specificity and accuracy were 85.3%, 91.7%, and 90.8%, respectively. Conclusion: CT examination of lymph node short diameter can be a good evaluation of cardia-left gastric lymph node metastasis in thoracic ESCC, and the optimal cut-off value is 6 mm.
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Liu J, Zhao H, Wang W, Zhou H, Lu F, Wan L, Luo X, Teng L. CO Diffusion Study and Spatial and Temporal Variation Modeling during the Construction Period of the Plateau Railroad Tunnel. ACS OMEGA 2023; 8:42565-42575. [PMID: 38024665 PMCID: PMC10652820 DOI: 10.1021/acsomega.3c05280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 08/21/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023]
Abstract
In order to investigate the diffusion law of CO gas in the vicinity of the tunnel boring face of the plateau long tunnel, to improve the efficiency of tunnel smoke exhaust, and to derive the spatial-temporal variation model of CO concentration for predicting the concentration of CO at different times and in different cross sections under specific environments, a CO diffusion model of a tunnel in Yunnan was established by using Ansys Fluent Fluid Simulation Software, and the CO transport characteristics under different conditions were simulated by taking the ventilation time, wind speed, and location of the air ducts as the influencing factors. The results show that the wind flows from the mouth of the wind pipe after the wind speed decreases, the diffusion area increases and arrives at the face of the direction of the rebound in the jet stream of new wind, and the return wind under the joint action of the vortex produced obviously, to reach the wind pipe mouth after the tunnel wind flow field, basically tends to stabilize. When the wind pipe mouth was arranged in the arch waist, 20 m away from the boring face, the inlet wind speed was 9 m/s and the ventilation time was 30 min; the CO concentration in the tunnel was reduced to below the maximum allowable concentration value. Moreover, the concentration of CO in the tunnel at the moment of 15 min of ventilation has a nonlinear positive correlation with the change of distance L from the boring face, while at the cross section of the air outlet of the wind pipe L = 20 m, the ventilation time is from 1 to 30 min and the concentration of CO at the cross section has a nonlinear decreasing trend with the ventilation time, which can be deduced according to the different space-time change models.
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Wang W, Jiang K, Liu X, Li J, Zhou W, Wang C, Cui J, Liang T. FBXW7 and human tumors: mechanisms of drug resistance and potential therapeutic strategies. Front Pharmacol 2023; 14:1278056. [PMID: 38027013 PMCID: PMC10680170 DOI: 10.3389/fphar.2023.1278056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 10/11/2023] [Indexed: 12/01/2023] Open
Abstract
Drug therapy, including chemotherapy, targeted therapy, immunotherapy, and endocrine therapy, stands as the foremost therapeutic approach for contemporary human malignancies. However, increasing drug resistance during antineoplastic therapy has become a substantial barrier to favorable outcomes in cancer patients. To enhance the effectiveness of different cancer therapies, an in-depth understanding of the unique mechanisms underlying tumor drug resistance and the subsequent surmounting of antitumor drug resistance is required. Recently, F-box and WD Repeat Domain-containing-7 (FBXW7), a recognized tumor suppressor, has been found to be highly associated with tumor therapy resistance. This review provides a comprehensive summary of the underlying mechanisms through which FBXW7 facilitates the development of drug resistance in cancer. Additionally, this review elucidates the role of FBXW7 in therapeutic resistance of various types of human tumors. The strategies and challenges implicated in overcoming tumor therapy resistance by targeting FBXW7 are also discussed.
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Zhang MZ, Zhang RY, Liu J, Wang W. [Advances in the role of autoimmune mechanisms in chronic obstructive pulmonary disease]. ZHONGHUA JIE HE HE HU XI ZA ZHI = ZHONGHUA JIEHE HE HUXI ZAZHI = CHINESE JOURNAL OF TUBERCULOSIS AND RESPIRATORY DISEASES 2023; 46:1131-1136. [PMID: 37914427 DOI: 10.3760/cma.j.cn112147-20230731-00037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) is a common chronic respiratory disease whose pathogenesis mainly involves airway remodelling and alveolar destruction caused by inflammation, protease-antiprotease imbalance, oxidative stress, and imbalance between apoptosis and compensatory repair of lung tissue structure cells. In recent years, the role of the autoimmune response in COPD has attracted widespread attention, but there is still some controversy. This article reviewed the role of autoimmunity in COPD from different perspectives, starting with the relationship between autoimmunity and the pathogenesis of COPD.
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Ablikim M, Achasov MN, Adlarson P, Ai XC, Aliberti R, Amoroso A, An MR, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bloms J, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang TT, Chang WL, Che GR, Chelkov G, Chen C, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Cheng WS, Choi SK, Chu X, Cibinetto G, Coen SC, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du SX, Duan ZH, Egorov P, Fan YL, Fang J, Fang SS, Fang WX, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fischer K, Fritsch M, Fritzsch C, Fu CD, Fu JL, Fu YW, Gao H, Gao YN, Gao Y, Garbolino S, Garzia I, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Gu YT, Guan CY, Guan ZL, Guo AQ, Guo LB, Guo MJ, Guo RP, Guo YP, Guskov A, Han TT, Han WY, Hao XQ, Harris FA, He KK, He KL, Heinsius FHH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou XT, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hüsken N, Imoehl W, Irshad M, Jackson J, Jaeger S, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jia XQ, Jia ZK, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Khoukaz A, Kiuchi R, Kliemt R, Kolcu OB, Kopf B, Kuessner MK, Kui X, Kupsc A, Kühn W, Lane JJ, Larin P, Lavania A, Lavezzi L, Lei TT, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li JW, Li KL, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li QX, Li SX, Li T, Li WD, Li WG, Li XH, Li XL, Li X, Li YG, Li ZJ, Li ZX, Liang C, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Libby J, Limphirat A, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu FH, Liu F, Liu F, Liu GM, Liu H, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu Y, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XY, Ma Y, Ma YM, Maas FE, Maggiora M, Malde S, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pei YP, Pelizaeus M, Peng HP, Peters K, Ping JL, Ping RG, Plura S, Pogodin S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin JJ, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Redmer CF, Ren KJ, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi HC, Shi JL, Shi JY, Shi QQ, Shi RS, Shi X, Song JJ, Song TZ, Song WM, Song YJ, Song YX, Sosio S, Spataro S, Stieler F, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZT, Tan YX, Tang CJ, Tang GY, Tang J, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian WH, Tian Y, Tian ZF, Uman I, Wang SJ, Wang B, Wang BL, Wang B, Wang CW, Wang DY, Wang F, Wang HJ, Wang HP, Wang JP, Wang K, Wang LL, Wang M, Wang M, Wang S, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang Y, Wang YD, Wang YF, Wang YH, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei D, Wei DH, Weidner F, Wen SP, Wenzel CW, Wiedner UW, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YJ, Wu Z, Xia L, Xian XM, Xiang T, Xiao D, Xiao GY, Xiao H, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu QJ, Xu QN, Xu W, Xu WL, Xu XP, Xu YC, Xu ZP, Xu ZS, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YX, Yang Y, Yang ZW, Yao ZP, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yu XD, Yuan CZ, Yuan L, Yuan SC, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng X, Zeng Y, Zeng YJ, Zhai XY, Zhai YC, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang P, Zhang QY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang XY, Zhang Y, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou LP, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WJ, Zhu YC, Zhu ZA, Zou JH, Zu J. Test of CP Symmetry in Hyperon to Neutron Decays. PHYSICAL REVIEW LETTERS 2023; 131:191802. [PMID: 38000397 DOI: 10.1103/physrevlett.131.191802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 10/03/2023] [Indexed: 11/26/2023]
Abstract
The quantum entangled J/ψ→Σ^{+}Σ[over ¯]^{-} pairs from (1.0087±0.0044)×10^{10} J/ψ events taken by the BESIII detector are used to study the nonleptonic two-body weak decays Σ^{+}→nπ^{+} and Σ[over ¯]^{-}→n[over ¯]π^{-}. The CP-odd weak decay parameters of the decays Σ^{+}→nπ^{+} (α_{+}) and Σ[over ¯]^{-}→n[over ¯]π^{-} (α[over ¯]_{-}) are determined to be 0.0481±0.0031_{stat}±0.0019_{syst} and -0.0565±0.0047_{stat}±0.0022_{syst}, respectively. The decay parameter α[over ¯]_{-} is measured for the first time, and the accuracy of α_{+} is improved by a factor of 4 compared to the previous results. The simultaneously determined decay parameters allow the first precision CP symmetry test for any hyperon decay with a neutron in the final state with the measurement of A_{CP}=(α_{+}+α[over ¯]_{-})/(α_{+}-α[over ¯]_{-})=-0.080±0.052_{stat}±0.028_{syst}. Assuming CP conservation, the average decay parameter is determined as ⟨α_{+}⟩=(α_{+}-α[over ¯]_{-})/2=-0.0506±0.0026_{stat}±0.0019_{syst}, while the ratios α_{+}/α_{0} and α[over ¯]_{-}/α[over ¯]_{0} are -0.0490±0.0032_{stat}±0.0021_{syst} and -0.0571±0.0053_{stat}±0.0032_{syst}, where α_{0} and α[over ¯]_{0} are the decay parameters of the decays Σ^{+}→pπ^{0} and Σ[over ¯]^{-}→p[over ¯]π^{0}, respectively.
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Ablikim M, Achasov MN, Adlarson P, Ai XC, Aliberti R, Amoroso A, An MR, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang TT, Chang WL, Che GR, Chelkov G, Chen C, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Cheng WS, Choi SK, Chu X, Cibinetto G, Coen SC, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du MC, Du SX, Duan ZH, Egorov P, Fan YHY, Fan YL, Fang J, Fang SS, Fang WX, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fischer K, Fritsch M, Fritzsch C, Fu CD, Fu JL, Fu YW, Gao H, Gao YN, Gao Y, Garbolino S, Garzia I, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Guan CY, Guan ZL, Guo AQ, Guo LB, Guo MJ, Guo RP, Guo YP, Guskov A, Han TT, Han WY, Hao XQ, Harris FA, He KK, He KL, Heinsius FHH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou XT, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hüsken N, Imoehl W, Jackson J, Jaeger S, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jia XQ, Jia ZK, Jiang HJ, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Khoukaz A, Kiuchi R, Kliemt R, Kolcu OB, Kopf B, Kuessner M, Kupsc A, Kühn W, Lane JJ, Larin P, Lavania A, Lavezzi L, Lei TT, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li JW, Li KL, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li QX, Li SX, Li T, Li WD, Li WG, Li XH, Li XL, Li X, Li YG, Li ZJ, Liang C, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Liao YP, Libby J, Limphirat A, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu FH, Liu F, Liu F, Liu GM, Liu H, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu Y, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XY, Ma Y, Ma YM, Maas FE, Maggiora M, Malde S, Malik QA, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Muskalla J, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pei YP, Pelizaeus M, Peng HP, Peters K, Ping JL, Ping RG, Plura S, Pogodin S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qiao XK, Qin JJ, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Redmer CF, Ren KJ, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi HC, Shi JL, Shi JY, Shi QQ, Shi RS, Shi X, Song JJ, Song TZ, Song WM, Song YJ, Song YX, Sosio S, Spataro S, Stieler F, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZT, Tan YX, Tang CJ, Tang GY, Tang J, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian WH, Tian Y, Tian ZF, Uman I, Wang SJ, Wang B, Wang BL, Wang B, Wang CW, Wang DY, Wang F, Wang HJ, Wang HP, Wang JP, Wang K, Wang LL, Wang M, Wang M, Wang S, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang Y, Wang YD, Wang YF, Wang YH, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei D, Wei DH, Weidner F, Wen SP, Wenzel CW, Wiedner U, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YJ, Wu Z, Xia L, Xian XM, Xiang T, Xiao D, Xiao GY, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu QJ, Xu QN, Xu W, Xu WL, Xu XP, Xu YC, Xu ZP, Xu ZS, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YX, Yang Y, Yang ZW, Yao ZP, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yu XD, Yuan CZ, Yuan L, Yuan SC, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng X, Zeng Y, Zeng YJ, Zhai XY, Zhai YC, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang P, Zhang QY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang X, Zhang Y, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou LP, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WJ, Zhu YC, Zhu ZA, Zou JH, Zu J. Measurement of Energy-Dependent Pair-Production Cross Section and Electromagnetic Form Factors of a Charmed Baryon. PHYSICAL REVIEW LETTERS 2023; 131:191901. [PMID: 38000396 DOI: 10.1103/physrevlett.131.191901] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/29/2023] [Accepted: 09/15/2023] [Indexed: 11/26/2023]
Abstract
We study the process e^{+}e^{-}→Λ_{c}^{+}Λ[over ¯]_{c}^{-} at twelve center-of-mass energies from 4.6119 to 4.9509 GeV using data samples collected by the BESIII detector at the BEPCII collider. The Born cross sections and effective form factors (|G_{eff}|) are determined with unprecedented precision after combining the single and double-tag methods based on the decay process Λ_{c}^{+}→pK^{-}π^{+}. Flat cross sections around 4.63 GeV are obtained and no indication of the resonant structure Y(4630), as reported by Belle, is found. In addition, no oscillatory behavior is discerned in the |G_{eff}| energy dependence of Λ_{c}^{+}, in contrast to what is seen for the proton and neutron cases. Analyzing the cross section together with the polar-angle distribution of the Λ_{c}^{+} baryon at each energy point, the moduli of electric and magnetic form factors (|G_{E}| and |G_{M}|) are extracted and separated. For the first time, the energy dependence of the form factor ratio |G_{E}/G_{M}| is observed, which can be well described by an oscillatory function.
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Robertson O, Rolan E, Wang W, Shirtcliff E, Marceau K. Within-person associations of cortisol, dehydroepiandrosterone, and testosterone hair hormone concentrations and psychological distress in pregnant and non-pregnant women. COMPREHENSIVE PSYCHONEUROENDOCRINOLOGY 2023; 16:100214. [PMID: 37928315 PMCID: PMC10623163 DOI: 10.1016/j.cpnec.2023.100214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 11/07/2023] Open
Abstract
Prenatal hair cortisol concentration is inconsistently associated with maternal psychological distress. However, prior studies have not often measured hair cortisol and maternal psychological distress prospectively over time, examined within-person associations, nor concurrently considered the complex hormonal milieu in which cortisol operates during pregnancy. We addressed these limitations and tested associations against a similar non-pregnant comparison group. Participants included 68 women (34 pregnant and 34 non-pregnant; Mage = 29.14 and 83 % White) from the Midwestern United States. Pregnant women were assessed each trimester, at 12, 26, and 38 weeks and non-pregnant women were assessed three times on the same schedule. At each assessment, participants completed measures of psychological distress and provided hair samples. The first 3 cm (from the scalp) of hair was assayed using enzyme immune-assay kits to reflect cumulative levels within the given trimester/3-month time period of cortisol, dehydroepiandrosterone (DHEA) and testosterone. Within-person associations of hair cortisol and ratio of hair cortisol-to-DHEA and cortisol-to-testosterone with psychological distress were assessed using multilevel models. There were positive within-person associations of hair cortisol with cumulative psychological distress (γ = 0.01, s.e. = 0.003, p = .049), anxiety (γ = 0.09, s.e. = 0.04, p = .046), and pregnancy-related anxiety symptoms (γ = 0.10, s.e. = 0.05, p = .041) in the pregnant sample such that on occasions when hair cortisol was higher than average so were psychological distress symptoms. No within-person associations of hair cortisol were supported in non-pregnant women although there was a negative within-person association, such that on occasions of having lower testosterone level than typical, depression symptoms were higher. There were no within-person associations of psychological distress and cortisol-to-DHEA ratio or cortisol-to-testosterone ratio in either the pregnant or non-pregnant sample. At the between person-level for pregnant women, lower cortisol levels were associated with higher perceived stress (γ = -0.28, s.e. = 0.09, p = .003) and depression symptoms (γ = -0.11, s.e. = 0.06, p = .039), whereas higher cortisol levels were associated with higher psychological distress (γ = 0.03, s.e. = 0.01, p = .010), state anxiety (γ = 0.33, s.e. = 0.13, p = .010), and depression symptoms (γ = 0.23, s.e. = 0.09, p = .017) in non-pregnant women. Modeling hair cortisol at the within-person and between-person level revealed differential findings in pregnant and non-pregnant women. Hair cortisol concentration, psychological distress, pregnancy, hormone coupling, within-person associations.
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Tang WY, Wang W, Yang JX, Duan XP, Yao W, Lv QM, Ding WB, He RJ. Development and validation of a nomogram for urinary tract infection in geriatric patients with hip fracture: a retrospective study. EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES 2023; 27:10884-10898. [PMID: 38039018 DOI: 10.26355/eurrev_202311_34456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
OBJECTIVE This study aims to develop and validate a risk nomogram for urinary tract infections (UTIs) in geriatric patients with hip fractures. PATIENTS AND METHODS A total of 900 geriatric patients who underwent hip fracture surgery at Dandong Central Hospital between June 2017 and June 2023 were systematically collected. The cohort was randomly divided into a training set (70%, n=632) and a validation set (30%, n=268) for model development and validation, respectively. Univariate and multivariate logistic regression analyses were conducted to identify the independent risk factors associated with UTIs. Based on the results of the multivariate analysis, a UTI nomogram prediction model was developed and evaluated in the training and validation sets using the C-index, ROC curve, calibration curve, and decision curve analysis to assess discrimination, calibration, and clinical utility, respectively. RESULTS Out of the 900 participants, 24.6% were diagnosed with UTIs. The nomogram was developed based on 9 predictors that were found to be independently associated with UTI. The area under the curve (AUC) for predicting UTI in geriatric patients with hip fractures was 0.829 in the training set and 0.803 in the validation set. Following internal verification, the modified C-index remained at 0.829. Furthermore, the nomogram's calibration plot and decision curve analysis demonstrated good performance in both the training and validation sets. CONCLUSIONS The established and validated nomogram provides a reliable and convenient tool for predicting UTI risk in geriatric patients with hip fractures. This model facilitates the early identification of high-risk patients and offers guidance for implementing targeted preventive interventions.
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Ma LJ, Li C, Cai YC, Zhou YQ, Wang W, Sun RH, Wang X, Shui CY. [Da Vinci robot-assisted surgery for parotid benign tumor via retroauricular hairline approach: a case report]. ZHONGHUA ER BI YAN HOU TOU JING WAI KE ZA ZHI = CHINESE JOURNAL OF OTORHINOLARYNGOLOGY HEAD AND NECK SURGERY 2023; 58:1131-1134. [PMID: 37932137 DOI: 10.3760/cma.j.cn115330-20230726-00021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
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Yao W, Tang WY, Wang W, Lv QM, Ding WB. Development and validation of preoperative proximal and distal lower limb deep vein thrombosis nomograms in geriatric hip fracture patients. EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES 2023; 27:10269-10283. [PMID: 37975352 DOI: 10.26355/eurrev_202311_34303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
OBJECTIVE This study aimed to develop and validate a risk nomogram for preoperative proximal and distal deep vein thrombosis (DVT) in geriatric patients with hip fractures. PATIENTS AND METHODS The 970 collected geriatric hip fracture patients were randomly divided into a training set (70%, n=682) and a validation set (30%, n=288). Multivariate logistic regression analyses were used to optimize the predictive risk variables for proximal and distal preoperative lower extremity DVT in the training set, respectively, and the selected variables were finally incorporated to establish preoperative DVT nomogram prediction models. Receiver operating characteristic curves (ROC), calibration plots, and decision curve analysis (DCA) were performed to validate the nomograms in the training and validation sets, respectively. RESULTS Among the 970 patients, 125 (12.88%) were diagnosed with preoperative DVT. The area under the curve (AUC) for predicting preoperative proximal DVT was 0.888 in the training and 0.792 in the validation sets. The AUC for predicting preoperative distal DVT was 0.907 in the training and 0.790 in the validation sets. The calibration plots and decision curve analysis for preoperative proximal DVT performed well in the training set and slightly worse in the validation set. The calibration plots and decision curve analysis for preoperative distal DVT performed well in both the training and validation sets. CONCLUSIONS To construct nomograms for predicting the risk of proximal and distal preoperative lower extremity DVT in geriatric hip fracture patients. For patients at high risk, as assessed by this model, clinicians should intervene and treat them promptly before surgery.
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Wang W, Wang K, Qiu J, Li W, Wang X, Zhang Y, Wang X, Wu J. MRI-based radiomics analysis of bladder cancer: prediction of pathological grade and histological variant. Clin Radiol 2023; 78:e889-e897. [PMID: 37633748 DOI: 10.1016/j.crad.2023.07.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/08/2023] [Accepted: 07/26/2023] [Indexed: 08/28/2023]
Abstract
AIM To develop magnetic resonance imaging (MRI)-based radiomics models for the prediction of the pathological grade and histological variant of bladder cancer. MATERIALS AND METHODS A total of 227 patients who underwent bladder MRI and had histopathologically confirmed grades and variants were included retrospectively from January 2017 to March 2022. They were assigned to a training set (n=131) and a testing set (n=96) based on the MRI system. MRI-based radiomics features were extracted from manually segmented volumes of interest from high-b-value DWI images and ADC maps. The radiomics models were trained with all possible pipelines in the training set. One optimal model was selected using the fivefold cross-validation method and verified by the testing set according to the pathological results. Univariate and multivariate analyses were performed to identify the significant clinical and imaging factors for developing clinical-radiomics models. RESULTS The radiomics model for grade prediction had area under the curve (AUC) values of 0.784, 0.786, and 0.733 in the training, cross-validation, and testing sets, respectively. The radiomics model for variant prediction had AUC values of 0.748, 0.757, and 0.789 in the training, cross-validation, and testing sets, respectively. The performance of the clinical-radiomics model was significantly improved compared with the radiomics models alone for the total dataset (AUC for grade: 0.846 versus 0.756; AUC for variant: 0.810 versus 0.757, p<0.05). CONCLUSION MRI-based radiomics models could be used to predict the pathological grade and histological variants of bladder cancer with relatively good performance.
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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, Apyan A, 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, Bruscino N, Buanes T, Buat Q, Buchin D, Buckley AG, Bugge MK, Bulekov O, Bullard BA, Burdin S, Burgard CD, Burger AM, Burghgrave B, Burlayenko O, Burr JTP, Burton CD, Burzynski JC, Busch EL, Büscher V, Bussey PJ, Butler JM, Buttar CM, Butterworth JM, Buttinger W, Buxo Vazquez CJ, Buzykaev AR, Cabras G, Cabrera Urbán S, Cadamuro L, Caforio D, Cai H, Cai Y, Cairo VMM, Cakir O, Calace N, Calafiura P, Calderini G, Calfayan P, Callea G, Caloba LP, Calvet D, Calvet S, Calvet TP, Calvetti M, Camacho Toro R, Camarda S, Camarero Munoz D, Camarri P, Camerlingo MT, Cameron D, Camincher C, Campanelli M, Camplani A, Canale V, Canesse A, Cano Bret M, Cantero J, Cao Y, Capocasa F, Capua M, Carbone A, Cardarelli R, Cardenas JCJ, Cardillo F, Carli T, Carlino G, Carlotto JI, Carlson BT, Carlson EM, Carminati L, Carnelli A, Carnesale M, Caron S, Carquin E, Carrá S, Carratta G, Carrio Argos F, Carter JWS, Carter TM, Casado MP, Caspar M, Castiglia EG, Castillo FL, Castillo Garcia L, Castillo Gimenez V, Castro NF, Catinaccio A, Catmore JR, Cavaliere V, Cavalli N, Cavasinni V, Cekmecelioglu YC, Celebi E, Celli F, Centonze MS, Cerny K, Cerqueira AS, Cerri A, Cerrito L, Cerutti F, Cervato B, Cervelli A, Cesarini G, Cetin SA, Chadi Z, Chakraborty D, Chala M, Chan J, Chan WY, Chapman JD, Chapon E, Chargeishvili B, Charlton DG, Charman TP, Chatterjee M, Chauhan C, Chekanov S, Chekulaev SV, Chelkov GA, Chen A, Chen B, Chen B, Chen H, Chen H, Chen J, Chen J, Chen M, Chen S, Chen SJ, Chen X, Chen X, Chen Y, Cheng CL, Cheng HC, Cheong S, Cheplakov A, Cheremushkina E, Cherepanova E, Cherkaoui El Moursli R, Cheu E, Cheung K, Chevalier L, Chiarella V, Chiarelli G, Chiedde N, Chiodini G, Chisholm AS, Chitan A, Chitishvili M, Chizhov MV, Choi K, Chomont AR, Chou Y, Chow EYS, Chowdhury T, Chu KL, Chu MC, Chu X, Chudoba J, Chwastowski JJ, Cieri D, Ciesla KM, Cindro V, Ciocio A, Cirotto F, Citron ZH, Citterio M, Ciubotaru DA, Ciungu BM, Clark A, Clark PJ, Clavijo Columbie JM, Clawson SE, Clement C, Clercx J, Clissa L, Coadou Y, Cobal M, Coccaro A, Barrue RFC, Coelho Lopes De Sa R, Coelli S, Cohen H, Coimbra AEC, Cole B, Collot J, Conde Muiño P, Connell MP, Connell SH, Connelly IA, Conroy EI, Conventi F, Cooke HG, Cooper-Sarkar AM, Cordeiro Oudot Choi A, Cormier F, Corpe LD, Corradi M, Corriveau F, Cortes-Gonzalez A, Costa MJ, Costanza F, Costanzo D, Cote BM, Cowan G, Cranmer K, Cremonini D, Crépé-Renaudin S, Crescioli F, Cristinziani M, Cristoforetti M, Croft V, Crosby JE, Crosetti G, Cueto A, Cuhadar Donszelmann T, Cui H, Cui Z, Cunningham WR, Curcio F, Czodrowski P, Czurylo MM, De Sousa MJDCS, Da Fonseca Pinto JV, Da Via C, Dabrowski W, Dado T, Dahbi S, Dai T, Dallapiccola C, Dam M, D'amen G, D'Amico V, Damp J, Dandoy JR, Daneri MF, Danninger M, Dao V, Darbo G, Darmora S, Das SJ, D'Auria S, David C, Davidek T, Davis-Purcell B, Dawson I, Day-Hall HA, De K, De Asmundis R, De Biase N, De Castro S, De Groot N, de Jong P, De la Torre H, De Maria A, De Salvo A, De Sanctis U, De Santo A, De Vivie De Regie JB, Dedovich DV, Degens J, Deiana AM, Del Corso F, Del Peso J, Del Rio F, Deliot F, Delitzsch CM, Della Pietra M, Della Volpe D, Dell'Acqua A, Dell'Asta L, Delmastro M, Delsart PA, Demers S, Demichev M, Denisov SP, D'Eramo L, Derendarz D, Derue F, Dervan P, Desch K, Deutsch C, Di Bello FA, Di Ciaccio A, Di Ciaccio L, Di Domenico A, Di Donato C, Di Girolamo A, Di Gregorio G, Di Luca A, Di Micco B, Di Nardo R, Diaconu C, Dias FA, Vale TDD, Diaz MA, Diaz Capriles FG, Didenko M, Diehl EB, Diehl L, Díez Cornell S, Diez Pardos C, Dimitriadi C, Dimitrievska A, Dingfelder J, Dinu IM, Dittmeier SJ, Dittus F, Djama F, Djobava T, Djuvsland JI, Doglioni C, Dolejsi J, Dolezal Z, Donadelli M, Dong B, Donini J, D'Onofrio A, D'Onofrio M, Dopke J, Doria A, Dos Santos Fernandes N, Dova MT, Doyle AT, Draguet MA, Dreyer E, Drivas-Koulouris I, Drobac AS, Drozdova M, Du D, du Pree TA, Dubinin F, Dubovsky M, Duchovni E, Duckeck G, Ducu OA, Duda D, Dudarev A, Duden ER, D'uffizi M, Duflot L, Dührssen M, Dülsen C, Dumitriu AE, Dunford M, Dungs S, Dunne K, Duperrin A, Yildiz HD, Düren M, Durglishvili A, Dwyer BL, Dyckes GI, Dyndal M, Dysch S, Dziedzic BS, Earnshaw ZO, Eberwein GH, Eckerova B, Eggebrecht S, Eggleston MG, Purcino De Souza EE, Ehrke LF, Eigen G, Einsweiler K, Ekelof T, Ekman PA, El Farkh S, El Ghazali Y, El Jarrari H, El Moussaouy A, Ellajosyula V, Ellert M, Ellinghaus F, Elliot AA, Ellis N, Elmsheuser J, Elsing M, Emeliyanov D, Enari Y, Ene I, Epari S, Erdmann J, Erland PA, Errenst M, Escalier M, Escobar C, Etzion E, Evans G, Evans H, Evans LS, Evans MO, Ezhilov A, Ezzarqtouni S, Fabbri F, Fabbri L, Facini G, Fadeyev V, Fakhrutdinov RM, Falciano S, Falda Ulhoa Coelho LF, Falke PJ, Faltova J, Fan C, Fan Y, Fang Y, Fanti M, Faraj M, Farazpay Z, Farbin A, Farilla A, Farooque T, Farrington SM, Fassi F, Fassouliotis D, Faucci Giannelli M, Fawcett WJ, Fayard L, Federic P, Federicova P, Fedin OL, Fedotov G, Feickert M, Feligioni L, Fellers DE, Feng C, Feng M, Feng Z, Fenton MJ, Fenyuk AB, Ferencz L, Ferguson RAM, Fernandez Luengo SI, Fernoux MJV, Ferrando J, Ferrari A, Ferrari P, Ferrari R, Ferrere D, Ferretti C, Fiedler F, Filipčič A, Filmer EK, Filthaut F, Fiolhais MCN, Fiorini L, Fisher WC, Fitschen T, Fitzhugh PM, Fleck I, Fleischmann P, Flick T, Flores L, Flores M, Flores Castillo LR, Flores Sanz De Acedo L, Follega FM, Fomin N, Foo JH, Forland BC, Formica A, Forti AC, Fortin E, Fortman AW, Foti MG, Fountas L, Fournier D, Fox H, Francavilla P, Francescato S, Franchellucci S, Franchini M, Franchino S, Francis D, Franco L, Franconi L, Franklin M, Frattari G, Freegard AC, Freund WS, Frid YY, Fritzsche N, Froch A, Froidevaux D, Frost JA, Fu Y, Fujimoto M, Fullana Torregrosa E, Fung KY, De Simas Filho EF, Furukawa M, Fuster J, Gabrielli A, Gabrielli A, Gadow P, Gagliardi G, Gagnon LG, Gallas EJ, Gallop BJ, Gan KK, Ganguly S, Gao J, Gao Y, Garay Walls FM, Garcia B, García C, Garcia Alonso A, Garcia Caffaro AG, García Navarro JE, Garcia-Sciveres M, Gardner GL, Gardner RW, Garelli N, Garg D, Garg RB, Gargan JM, Garner CA, Gasiorowski SJ, Gaspar P, Gaudio G, Gautam V, Gauzzi P, Gavrilenko IL, Gavrilyuk A, Gay C, Gaycken G, Gazis EN, Geanta AA, Gee CM, Gemme C, Genest MH, Gentile S, George S, George WF, Geralis T, Gessinger-Befurt P, Geyik ME, Ghneimat M, Ghorbanian K, Ghosal A, Ghosh A, Ghosh A, Giacobbe B, Giagu S, Giannetti P, Giannini A, Gibson SM, Gignac M, Gil DT, Gilbert AK, Gilbert BJ, Gillberg D, Gilles G, Gillwald NEK, Ginabat L, Gingrich DM, Giordani MP, Giraud PF, Giugliarelli G, Giugni D, Giuli F, Gkialas I, Gladilin LK, Glasman C, Gledhill GR, Glisic M, Gnesi I, Go Y, Goblirsch-Kolb M, Gocke B, Godin D, Gokturk B, Goldfarb S, Golling T, Gololo MGD, Golubkov D, Gombas JP, Gomes A, Gomes Da Silva G, Gomez Delegido AJ, Gonçalo R, Gonella G, Gonella L, Gongadze A, Gonnella F, Gonski JL, González Andana RY, González de la Hoz S, Gonzalez Fernandez S, Gonzalez Lopez R, Gonzalez Renteria C, Gonzalez Suarez R, Gonzalez-Sevilla S, Gonzalvo Rodriguez GR, Goossens L, Gorbounov PA, Gorini B, Gorini E, Gorišek A, Gosart TC, Goshaw AT, Gostkin MI, Goswami S, Gottardo CA, Gouighri M, Goumarre V, Goussiou AG, Govender N, Grabowska-Bold I, Graham K, Gramstad E, Grancagnolo S, Grandi M, Gratchev V, Gravila PM, Gravili FG, Gray HM, Greco M, Grefe C, Gregor IM, Grenier P, Grieco C, Grillo AA, Grimm K, Grinstein S, Grivaz JF, Gross E, Grosse-Knetter J, Grud C, Grundy JC, Guan L, Guan W, Gubbels C, Guerrero Rojas JGR, Guerrieri G, Guescini F, Gugel R, Guhit JAM, Guida A, Guillemin T, Guilloton E, Guindon S, Guo F, Guo J, Guo L, Guo Y, Gupta R, Gurbuz S, Gurdasani SS, Gustavino G, Guth M, Gutierrez P, Gutierrez Zagazeta LF, Gutschow C, Gwenlan C, Gwilliam CB, Haaland ES, Haas A, Habedank M, Haber C, Hadavand HK, Hadef A, Hadzic S, Hahn JJ, Haines EH, Haleem M, Haley J, Hall JJ, Hallewell GD, Halser L, Hamano K, Hamdaoui H, Hamer M, Hamity GN, Hampshire EJ, Han J, Han K, Han L, Han L, Han S, Han YF, 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Siegert F, Sijacki D, Sikora R, Sili F, Silva JM, Silva Oliveira MV, Silverstein SB, Simion S, Simoniello R, Simpson EL, Simpson H, Simpson LR, Simpson ND, Simsek S, Sindhu S, Sinervo P, Singh S, Sinha S, Sinha S, Sioli M, Siral I, Sitnikova E, 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, Snesarev AA, Snider SR, 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, Sonneveld JM, Sopczak A, Sopio AL, Sopkova F, Sothilingam V, Sottocornola S, Soualah R, Soumaimi Z, South D, Spagnolo S, Spalla M, 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, Stange MV, Stanislaus B, Stanitzki MM, Stapf B, Starchenko EA, Stark GH, Stark J, Starko DM, Staroba P, Starovoitov P, Stärz S, Staszewski R, Stavropoulos G, Steentoft J, Steinberg P, Stelzer B, Stelzer HJ, Stelzer-Chilton O, Stenzel H, Stevenson TJ, Stewart GA, Stewart JR, 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, Sur N, Sutton MR, Suzuki H, Svatos M, Swiatlowski M, Swirski T, Sykora I, Sykora M, Sykora T, Ta D, Tackmann K, Taffard A, Tafirout R, Tafoya Vargas JS, Takashima R, Takeva EP, Takubo Y, Talby M, Talyshev AA, Tam KC, Tamir NM, Tanaka A, Tanaka J, Tanaka R, Tanasini M, 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, Tian Y, Tikhomirov V, Tikhonov YA, Timoshenko S, Timoshyn D, Ting EXL, Tipton P, Tlou SH, Tnourji A, Todome K, Todorova-Nova S, Todt S, Togawa M, Tojo J, Tokár S, Tokushuku K, Toldaiev O, Tombs R, Tomoto M, Tompkins L, Topolnicki KW, Torrence E, Torres H, Torró Pastor E, Toscani M, Tosciri C, Tost M, Tovey DR, Traeet A, Trandafir IS, Trefzger T, Tricoli A, Trigger IM, Trincaz-Duvoid S, Trischuk DA, Trocmé B, 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, Tsuri K, 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, Umaka EN, Unal G, Unal M, Undrus A, Unel G, Urban J, Urquijo P, Usai G, Ushioda R, Usman M, Uysal Z, Vacavant L, Vacek V, Vachon B, Vadla KOH, Vafeiadis T, Vaitkus A, Valderanis C, Valdes Santurio E, Valente M, Valentinetti S, Valero A, Valiente Moreno E, Vallier A, Valls Ferrer JA, Van Arneman DR, Van Daalen TR, Van Der Graaf A, 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, 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, Visibile 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, Voss K, Vossebeld JH, Vozak M, Vozdecky L, Vranjes N, Vranjes Milosavljevic M, Vreeswijk M, Vu NK, Vuillermet R, Vujinovic O, Vukotic I, Wada S, Wagner C, Wagner JM, Wagner W, Wahdan S, Wahlberg H, Wakasa R, Wakida M, Walder J, Walker R, Walkowiak W, Wall A, Wamorkar T, Wang AZ, Wang C, Wang C, Wang H, Wang J, Wang RJ, Wang R, Wang R, Wang SM, Wang S, Wang T, Wang WT, Wang W, 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 H, Watson MF, Watton E, Watts G, Waugh BM, Weber C, Weber HA, Weber MS, Weber SM, Wei C, Wei Y, Weidberg AR, Weik EJ, 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, Wiglesworth C, Wilbern DJ, Wilkens HG, Williams DM, Williams HH, Williams S, Willocq S, Wilson BJ, Windischhofer PJ, Winkel FI, Winklmeier F, Winter BT, Winter JK, Wittgen M, Wobisch M, Wolffs Z, Wölker R, Wollrath J, Wolter MW, Wolters H, Wongel AF, Worm SD, Wosiek BK, Woźniak KW, Wozniewski S, Wraight K, Wu C, Wu J, Wu M, 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, Xin S, Xiong J, Xu D, Xu H, Xu L, Xu R, Xu T, Xu Y, Xu Z, Xu Z, Yabsley B, Yacoob S, Yamaguchi N, Yamaguchi Y, Yamashita E, 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 Y, Yang Z, Yao WM, Yap YC, Ye H, Ye H, Ye J, Ye S, Ye X, Yeh Y, Yeletskikh I, Yeo BK, Yexley MR, Yin P, Yorita 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. 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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Sun C, Niu P, Zhang X, Zhao L, Wang W, Luan X, Han X, Chen Y, Zhao D. Concurrent clinical and pathological response predicts favorable prognosis of patients with gastric cancer after neoadjuvant therapy: a real-world study. BMC Cancer 2023; 23:996. [PMID: 37853387 PMCID: PMC10585908 DOI: 10.1186/s12885-023-11508-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 10/10/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Response of locally advanced gastric cancer (LAGC) to neoadjuvant therapy (NAT) may be associated with prognosis, but which of the clinical or pathological evaluation can accurately predict a favorable prognosis is still controversial. This study aims to compare the effect of clinical and pathological response on the prognosis of patients with gastric cancer. METHODS This study retrospectively analyzed LAGC patients who underwent NAT followed by surgery in the China National Cancer Center from January 2004 to January 2021. Clinical and pathological responses after NAT were evaluated using RECIST 1.1 and Mandard tumor regression grade system (TRG) respectively. Complete response (CR) and partial response (PR) assessed by computed tomography were regarded as clinical response. For histopathology regression assessment, response was defined as Mandard 1, 2, 3 and non-response as Mandard 4, 5. Furthermore, we combined clinical and pathological evaluation results into a variable termed "comprehensive assessment" and divided it into four groups based on the presence or absence of response (concurrent response, only clinical response, only pathological response, both non-response). The association between the prognosis and clinicopathological factors was assessed in univariate and multivariate Cox regression analysis. RESULTS In total, 238 of 1073 patients were included in the study after screening. The postoperative pathological response rate and clinical response rate were 50.84% (121/238) and 39.92% (95/238), respectively. 154 patients got consistent results in clinical and pathological evaluation (66 were concurrent response and 88 were both non-response), while the other 84 patients did not. The kappa value was 0.297(p < 0.001), which showed poor consistency. Multivariate Cox regression analysis revealed that comprehensive assessment (P = 0.03), clinical N stage(P < 0.001), vascular or lymphatic invasion (VOLI) (HR 2.745, P < 0.001), and pre-CA724(HR 1.577, P = 0.047) were independent factors for overall survival in patients with gastric cancer. Among four groups in the comprehensive assessment, concurrent response had significantly better survival (median OS: 103.5 months) than the other groups (P = 0.008). CONCLUSION Concurrent clinical and pathological response might predict a favorable prognosis of patients with gastric cancer after neoadjuvant therapy, further validation is needed in prospective clinical trials with larger samples.
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Ablikim M, Achasov MN, Adlarson P, Aliberti R, Amoroso A, An MR, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bloms J, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang TT, Chang WL, Che GR, Chelkov G, Chen C, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Cheng WS, Choi SK, Chu X, Cibinetto G, Coen SC, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du SX, Duan ZH, Egorov P, Fan YL, Fang J, Fang SS, Fang WX, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fischer K, Fritsch M, Fritzsch C, Fu CD, Fu YW, Gao H, Gao YN, Gao Y, Garbolino S, Garzia I, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Gu YT, Guan CY, Guan ZL, Guo AQ, Guo LB, Guo RP, Guo YP, Guskov A, Hou XT, Han WY, Hao XQ, Harris FA, He KK, He KL, Heinsius FH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hüsken N, Imoehl W, Irshad M, Jackson J, Jaeger S, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jia ZK, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, Kui X, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Khoukaz A, Kiuchi R, Kliemt R, Koch L, Kolcu OB, Kopf B, Kuessner M, Kupsc A, Kühn W, Lane JJ, Lange JS, Larin P, Lavania A, Lavezzi L, Lei TT, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li JW, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li SX, Li T, Li WD, Li WG, Li XH, Li XL, Li X, Li YG, Li ZJ, Li ZX, Li ZY, Liang C, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Libby J, Limphirat A, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu D, Liu FH, Liu F, Liu F, Liu GM, Liu H, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XY, Ma Y, Maas FE, Maggiora M, Maldaner S, Malde S, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Pei YP, Pelizaeus M, Peng HP, Peters K, Ping JL, Ping RG, Plura S, Pogodin S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin JJ, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Redmer CF, Ren KJ, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi HC, Shi JY, Shi QQ, Shi RS, Shi X, Song JJ, Song TZ, Song WM, Song YX, Sosio S, Spataro S, Stieler F, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZT, Tan YX, Tang CJ, Tang GY, Tang J, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian WH, Tian Y, Tian ZF, Uman I, Wang B, Wang BL, Wang B, Wang CW, Wang DY, Wang F, Wang HJ, Wang HP, Wang K, Wang LL, Wang M, Wang M, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WH, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang Y, Wang YD, Wang YF, Wang YH, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei D, Wei DH, Weidner F, Wen SP, Wenzel CW, Wiedner U, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YJ, Wu Z, Xia L, Xian XM, Xiang T, Xiao D, Xiao GY, Xiao H, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu QJ, Xu WL, Xu XP, Xu YC, Xu ZP, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YX, Yang Y, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu T, Yu XD, Yuan CZ, Yuan L, Yuan SC, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng X, Zeng Y, Zeng YJ, Zhai XY, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang P, Zhang QY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang XY, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou LP, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WJ, Zhu YC, Zhu ZA, Zou JH, Zu J. Determination of Spin-Parity Quantum Numbers for the Narrow Structure near the pΛ[over ¯] Threshold in e^{+}e^{-}→pK^{-}Λ[over ¯]+c.c. PHYSICAL REVIEW LETTERS 2023; 131:151901. [PMID: 37897776 DOI: 10.1103/physrevlett.131.151901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 08/22/2023] [Accepted: 09/15/2023] [Indexed: 10/30/2023]
Abstract
A narrow structure in the pΛ[over ¯] system near the mass threshold, named as X(2085), is observed in the process e^{+}e^{-}→pK^{-}Λ[over ¯] with a statistical significance greater than 20σ. Its spin and parity are determined for the first time to be J^{P}=1^{+} in an amplitude analysis, with a statistical significance greater than 5σ over other quantum numbers (0^{-},1^{-} and 2^{+}). The pole positions of X(2085) are measured to be M_{pole}=(2084_{-2}^{+4}±9) MeV and Γ_{pole}=(58_{-3}^{+4}±25) MeV, where the first uncertainties are statistical and the second ones are systematic. The analysis is based on the study of the process e^{+}e^{-}→pK^{-}Λ[over ¯] and uses the data samples collected with the BESIII detector at the center-of-mass energies sqrt[s]=4.008, 4.178, 4.226, 4.258, 4.416, and 4.682 GeV with a total integrated luminosity of 8.35 fb^{-1}.
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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, 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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, Wiglesworth C, Wilbern DJ, Wilkens HG, Williams DM, Williams HH, Williams S, Willocq S, Wilson BJ, Windischhofer PJ, Winkel FI, Winklmeier F, Winter BT, Winter JK, Wittgen M, Wobisch M, Wolffs Z, Wölker R, Wollrath J, Wolter MW, Wolters H, Wongel AF, Worm SD, Wosiek BK, Woźniak KW, Wozniewski S, Wraight K, Wu C, Wu J, Wu M, 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, Xin S, Xiong J, Xu D, Xu H, Xu L, Xu R, Xu T, Xu Y, Xu Z, Xu Z, Yabsley B, Yacoob S, Yamaguchi N, Yamaguchi Y, Yamashita E, 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 Y, Yang Z, Yao WM, Yap YC, Ye H, Ye H, Ye J, Ye S, Ye X, Yeh Y, Yeletskikh I, Yeo BK, Yexley MR, Yin P, Yorita 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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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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Ablikim M, Achasov MN, Adlarson P, Ai XC, Aliberti R, Amoroso A, An MR, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang TT, Chang WL, Che GR, Chelkov G, Chen C, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Cheng WS, Choi SK, Chu X, Cibinetto G, Coen SC, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du MC, Du SX, Duan ZH, Egorov P, Fan YL, Fang J, Fang SS, Fang WX, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fischer K, Fritsch M, Fritzsch C, Fu CD, Fu JL, Fu YW, Gao H, Gao YN, Gao Y, Garbolino S, Garzia I, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Gu YT, Guan CY, Guan ZL, Guo AQ, Guo LB, Guo MJ, Guo RP, Guo YP, Guskov A, Han TT, Han WY, Hao XQ, Harris FA, He KK, He KL, Heinsius FHH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou XT, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hüsken N, Imoehl W, Irshad M, Jackson J, Jaeger S, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jia XQ, Jia ZK, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, X K, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Khoukaz A, Kiuchi R, Kliemt R, Kolcu OB, Kopf B, Kuessner MK, Kupsc A, Kühn W, Lane JJ, Larin P, Lavania A, Lavezzi L, Lei TT, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li JW, Li KL, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li QX, Li SX, Li T, Li WD, Li WG, Li XH, Li XL, Li X, Li YG, Li ZJ, Li ZX, Liang C, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Liao YP, Libby J, Limphirat A, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu FH, Liu F, Liu F, Liu GM, Liu H, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu Y, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XY, Ma Y, Ma YM, Maas FE, Maggiora M, Malde S, Malik QA, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pei YP, Pelizaeus M, Peng HP, Peters K, Ping JL, Ping RG, Plura S, Pogodin S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin JJ, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Redmer CF, Ren KJ, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi HC, Shi JL, Shi JY, Shi QQ, Shi RS, Shi X, Song JJ, Song TZ, Song WM, Song YJ, Song YX, Sosio S, Spataro S, Stieler F, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZT, Tan YX, Tang CJ, Tang GY, Tang J, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian WH, Tian Y, Tian ZF, Uman I, Wang SJ, Wang B, Wang BL, Wang B, Wang CW, Wang DY, Wang F, Wang HJ, Wang HP, Wang JP, Wang K, Wang LL, Wang M, Wang M, Wang S, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang Y, Wang YD, Wang YF, Wang YH, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei D, Wei DH, Weidner F, Wen SP, Wenzel CW, Wiedner UW, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YJ, Wu Z, Xia L, Xian XM, Xiang T, Xiao D, Xiao GY, Xiao H, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu QJ, Xu QN, Xu W, Xu WL, Xu XP, Xu YC, Xu ZP, Xu ZS, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YX, Yang Y, Yang ZW, Yao ZP, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yu XD, Yuan CZ, Yuan L, Yuan SC, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng X, Zeng Y, Zeng YJ, Zhai XY, Zhai YC, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang P, Zhang QY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang XY, Zhang Y, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou LP, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WJ, Zhu YC, Zhu ZA, Zou JH, Zu J. Precise Measurement of the e^{+}e^{-}→D_{s}^{*+}D_{s}^{*-} Cross Sections at Center-of-Mass Energies from Threshold to 4.95 GeV. PHYSICAL REVIEW LETTERS 2023; 131:151903. [PMID: 37897771 DOI: 10.1103/physrevlett.131.151903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/16/2023] [Accepted: 08/29/2023] [Indexed: 10/30/2023]
Abstract
The process e^{+}e^{-}→D_{s}^{*+}D_{s}^{*-} is studied with a semi-inclusive method using data samples at center-of-mass energies from threshold to 4.95 GeV collected with the BESIII detector operating at the Beijing Electron Positron Collider. The Born cross sections of the process are measured for the first time with high precision in this energy region. Two resonance structures are observed in the energy-dependent cross sections around 4.2 and 4.4 GeV. By fitting the cross sections with a coherent sum of three Breit-Wigner amplitudes and one phase-space amplitude, the two significant structures are assigned masses of (4186.8±8.7±30) and (4414.6±3.4±6.1) MeV/c^{2}, widths of (55±15±53) and (122.5±7.5±8.1) MeV, where the first errors are statistical and the second ones are systematic. The inclusion of a third Breit-Wigner amplitude is necessary to describe a structure around 4.79 GeV.
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Wang W, Jiang ZZ, Gong GL, Feng JY, Lin XP. [Diagnosis and orthodontic treatment strategy of complete canine transposition]. ZHONGHUA KOU QIANG YI XUE ZA ZHI = ZHONGHUA KOUQIANG YIXUE ZAZHI = CHINESE JOURNAL OF STOMATOLOGY 2023; 58:1091-1096. [PMID: 37818546 DOI: 10.3760/cma.j.cn112144-20230802-00053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
Tooth transposition is a challenge for orthodontists, especially in correcting the order of teeth. At present, the literature on transposition canines mainly focuses on epidemiological studies and case reports, and no systematic treatment guidance has been formed. In this article, the definition and classification, epidemiology and etiology, imaging diagnosis, treatment and risk control of transposed canines are systematically described in order to provide reference for clinical practice.
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Wang W, Zhou R, Chen C, Feng X, Zhang W, Li HJ, Jin RH. [Development of auxiliary early predicting model for human brucellosis using machine learning algorithm]. ZHONGHUA YU FANG YI XUE ZA ZHI [CHINESE JOURNAL OF PREVENTIVE MEDICINE] 2023; 57:1601-1607. [PMID: 37859377 DOI: 10.3760/cma.j.cn112150-20221013-00991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Using machine learning algorithms to construct an early prediction model of brucellosis to improve the diagnosis efficiency of Brucellosis. This study was a case-control study. 2 381 brucellosis patients from Beijing Ditan Hospital affiliated to Capital Medical University were retrospectively collected as case group, and healthy people from Beijing Chaoyang Hospital affiliated to Capital Medical University were collected as control group from May 9, 2011 to November 29, 2021. The relevant clinical information and full blood count results of 13 257 data were collected and five algorithms of machine learning were used to construct an early predication model of brucellosis by using machine learning: random forest, Naive Bayes, decision tree, logistic regression and support vector machine;14 074 data (2 143 cases incase group and 11 931 cases in control group) were used to establish the early predication model of brucellosis, and 1 564 (238 cases in case group and 1 326 cases in control group) data were used to test the predication efficiency of the brucellosis model. The results showed that the support vector machine algorithm has the best predication performance by comparing the five machine learning models. The area under receiver curve (AUC) of receiver operating characteristic (ROC) was 0.991, and the accuracy, precision, specificity and Recall were 95.6%, 95.5%, 95.4% and 95.9%, respectively. Based on the SHAP plot, platelet distribution width (PDW) and basophil relative value (BASO%) results were low, and men with high coefficient of variation (R-CV), erythrocyte hemoglobin concentration (MCHC), and platelet volume (MPV) were predicted to be at high risk of brucellosis. Platelet distribution width (PDW) contributed the most to the prediction model, followed by red blood cell distribution width coefficient of variation (R-CV). In conclusion, the establishment of a high-precision early predication method of brucellosis based on machine learning may be of great significance for the early detection and treatment of brucellosis patients.
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Ablikim M, Achasov MN, Adlarson P, Aliberti R, Amoroso A, An MR, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bloms J, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang TT, Chang WL, Che GR, Chelkov G, Chen C, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Cheng WS, Choi SK, Chu X, Cibinetto G, Coen SC, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du SX, Duan ZH, Egorov P, Fan YL, Fang J, Fang SS, Fang WX, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fischer K, Fritsch M, Fritzsch C, Fu CD, Fu JL, Fu YW, Gao H, Gao YN, Gao Y, Garbolino S, Garzia I, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Gu YT, Guan CY, Guan ZL, Guo AQ, Guo LB, Guo RP, Guo YP, Guskov A, Hou XT, Han TT, Han WY, Hao XQ, Harris FA, He KK, He KL, Heinsius FHH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hüsken N, Imoehl W, Irshad M, Jackson J, Jaeger S, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jia ZK, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, Kui X, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Khoukaz A, Kiuchi R, Kliemt R, Koch L, Kolcu OB, Kopf B, Kuessner MK, Kupsc A, Kühn W, Lane JJ, Lange JS, Larin P, Lavania A, Lavezzi L, Lei TT, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li JW, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li SX, Li T, Li WD, Li WG, Li XH, Li XL, Li X, Li YG, Li ZJ, Li ZX, Li ZY, Liang C, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Libby J, Limphirat A, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu D, Liu FH, Liu F, Liu F, Liu GM, Liu H, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XY, Ma Y, Maas FE, Maggiora M, Maldaner S, Malde S, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pei YP, Pelizaeus M, Peng HP, Peters K, Ping JL, Ping RG, Plura S, Pogodin S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin JJ, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Redmer CF, Ren KJ, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi HC, Shi JL, Shi JY, Shi QQ, Shi RS, Shi X, Song JJ, Song TZ, Song WM, Song YJ, Song YX, Sosio S, Spataro S, Stieler F, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZT, Tan YX, Tang CJ, Tang GY, Tang J, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian WH, Tian ZF, Uman I, Wang B, Wang BL, Wang B, Wang CW, Wang DY, Wang F, Wang HJ, Wang HP, Wang K, Wang LL, Wang M, Wang M, Wang S, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WH, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang Y, Wang YD, Wang YF, Wang YH, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei D, Wei DH, Weidner F, Wen SP, Wenzel CW, Wiedner UW, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YJ, Wu Z, Xia L, Xian XM, Xiang T, Xiao D, Xiao GY, Xiao H, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu QJ, Xu QN, Xu W, Xu WL, Xu XP, Xu YC, Xu ZP, Xu ZS, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YX, Yang Y, Yang ZW, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu T, Yu XD, Yuan CZ, Yuan L, Yuan SC, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng X, Zeng Y, Zeng YJ, Zhai XY, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang P, Zhang QY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang XY, Zhang Y, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou LP, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WJ, Zhu YC, Zhu ZA, Zou JH, Zu J. First Experimental Study of the Purely Leptonic Decay D_{s}^{*+}→e^{+}ν_{e}. PHYSICAL REVIEW LETTERS 2023; 131:141802. [PMID: 37862669 DOI: 10.1103/physrevlett.131.141802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/25/2023] [Accepted: 09/05/2023] [Indexed: 10/22/2023]
Abstract
Using 7.33 fb^{-1} of e^{+}e^{-} collision data taken with the BESIII detector at the BEPCII collider, we report the first experimental study of the purely leptonic decay D_{s}^{*+}→e^{+}ν_{e}. Our data contain a signal of this decay with a statistical significance of 2.9σ. The branching fraction of D_{s}^{*+}→e^{+}ν_{e} is measured to be (2.1_{-0.9_{stat}}^{+1.2}±0.2_{syst})×10^{-5}, corresponding to an upper limit of 4.0×10^{-5} at the 90% confidence level. Taking the total width of the D_{s}^{*+} [(0.070±0.028) keV] predicted with the radiative D_{s}^{*+} decay from the lattice QCD calculation as input, the decay constant of the D_{s}^{*+} is determined to be f_{D_{s}^{*+}}=(214_{-46_{stat}}^{+61}±44_{syst}) MeV, corresponding to an upper limit of 354 MeV at the 90% confidence level.
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Zhao L, Fu Y, Niu P, Zhang F, Jiao F, Zhou X, Wu Z, Wang W, Luan X, Han X, He M, Guan Q, Li Y, Zhao D, Gao J, Chen Y. Perioperative Chemotherapy Could Not Improve the Prognosis of Gastric Cancer Patients With Mismatch Repair Deficiency: A Multicenter, Real-World Study. Oncologist 2023; 28:e891-e901. [PMID: 37104872 PMCID: PMC10546834 DOI: 10.1093/oncolo/oyad108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 03/21/2023] [Indexed: 04/29/2023] Open
Abstract
INTRODUCTION To date, the role of deficient mismatch repair (dMMR) remains to be proven in gastric cancer, and it is difficult to judge its value in clinical application. Our study aimed to investigate how MMR status affected the prognosis in patients with gastrectomy, as well as the efficacy of neoadjuvant chemotherapy and adjuvant chemotherapy in patients with dMMR with gastric cancer. MATERIALS AND METHODS Patients with gastric cancer with certain pathologic diagnosis of dMMR or proficient MMR (pMMR) using immunohistochemistry from 4 high-volume hospitals in China were included. Propensity score matching was used to match patients with dMMR or pMMR in 1:2 ratios. Overall survival (OS) and progression-free survival (PFS) curves were plotted using the Kaplan-Meier method and compared statistically using the log-rank test. Univariate and multivariate Cox proportional hazards models based on hazard ratios (HRs) and 95% confidence intervals (CIs) were used to determine the risk factors for survival. RESULTS In total, data from 6176 patients with gastric cancer were ultimately analyzed, and loss of expression of one or more MMR proteins was observed in 293 patients (293/6176, 4.74%). Compared to patients with pMMR, patients with dMMR are more likely to be older (≥66, 45.70% vs. 27.94%, P < .001), distal location (83.51% vs. 64.19%, P < .001), intestinal type (42.21% vs. 34.46%, P < .001), and in the earlier pTNM stage (pTNM I, 32.79% vs. 29.09%, P = .009). Patients with gastric cancer with dMMR showed better OS than those with pMMR before PSM (P = .002); however, this survival advantage was not observed for patients with dMMR after PSM (P = .467). As for perioperative chemotherapy, results of multivariable Cox regression analysis showed that perioperative chemotherapy was not an independent prognostic factor for PFS and OS in patients with dMMR with gastric cancer (HR = 0.558, 95% CI, 0.270-1.152, P = .186 and HR = 0.912, 95% CI, 0.464-1.793, P = .822, respectively). CONCLUSION In conclusion, perioperative chemotherapy could not prolong the OS and PFS of patients with dMMR with gastric cancer.
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Bao Y, Men Y, Yang X, Sun S, Yuan M, Ma Z, Liu Y, Wang J, Deng L, Wang W, Zhai Y, Bi N, Lv J, Liang J, Feng Q, Chen D, Xiao Z, Zhou Z, Wang L, Hui Z. Efficacy of Postoperative Radiotherapy for Patients with New N2 Descriptors of Subclassification in Completely Resected Non-Small Cell Lung Cancer: A Real-World Study. Int J Radiat Oncol Biol Phys 2023; 117:e5. [PMID: 37785570 DOI: 10.1016/j.ijrobp.2023.06.657] [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) Patients with N2 non-small cell lung cancer (NSCLC) were heterogeneous groups and required further stratification. The International Society for the Study of Lung Cancer (IASLC) added new descriptors of three sub-stages for stage N2 NSCLC: N2 at a single station without N1 involvement (N2a1), N2 at a single station with N1 involvement (N2a2), and N2 at multiple stations (N2b). This study aimed to investigate the efficacy of postoperative radiotherapy (PORT) for patients with these N2 descriptors. MATERIALS/METHODS Patients with histologically confirmed NSCLC after complete resection and divided into PORT group and non-PORT group. The primary endpoint was DFS. The second endpoints were overall survival (OS) and locoregional recurrence-free survival (LRFS). Propensity-score matching (PSM) of baseline characteristics between the PORT and non-PORT groups was used for validation. RESULTS Totally 1832 patients were enrolled, including 308 N2a1 patients, 682 N2a2 patients, and 842 N2b patients. The median follow-up time was 50.1 months. The survival outcomes of the PORT and non-PORT groups before PSM were shown in Table 1. For patients with N2a1, PORT could not improve the DFS (median DFS of the PORT group and the non-PORT group: not reached vs. 46.8 months, P = 0.41), OS (P = 0.85), or LRFS (P = 0.32), which were consistent with the multivariate analysis and data after the PSM. For patients with N2a2, PORT significantly improved the DFS (median DFS 29.7 vs. 22.2 months, P = 0.02), OS (P = 0.03), and LRFS (P = 0.01). The multivariate analysis and data after the PSM confirmed the benefits in DFS and LRFS, but no benefit was observed in OS (multivariate analysis: HR 0.79, P = 0.18; median OS after PSM: 103.7 vs. 63.1 months, P = 0.34). For patients with N2b, PORT could not improve the DFS (median DFS 20.6 vs. 21.2 months, P = 0.39) but significantly improved the OS (P<0.001) and LRFS (P<0.001). However, the multivariate analysis showed that PORT significantly improved DFS (HR 0.81, P = 0.03), consistent with the data after the PSM (median DFS 20.6 and 17.6 months, P = 0.04). CONCLUSION PORT significantly improved the DFS and LRFS in patients with N2a2 and significantly improved the DFS, LRFS, and OS in patients with N2b. Patients with N2a1 could not benefit from PORT.
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Yan Z, Wang W, Hou X, Ren K, Hu K, Zhang F. Timing of Postoperative Radiation Therapy for Early-Stage Endometrial Carcinoma: A 20-Year Single-Center Retrospective Cohort Experience in China. Int J Radiat Oncol Biol Phys 2023; 117:e555-e556. [PMID: 37785706 DOI: 10.1016/j.ijrobp.2023.06.1867] [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 investigate the appropriate timing of radiotherapy (RT) after hysterectomy for women with early-stage endometrial carcinoma (EC). MATERIALS/METHODS Between 1999 and 2020, 1080 patients with I and II endometrial cancer received postoperative RT at our hospital. All patients underwent hysterectomy followed by RT. The optimal cut-off values for the surgery-RT interval (SRI) based on overall survival (OS) were determined using the R software. The disease-free survival (DFS), OS, locoregional recurrence free survival (LRFS), and distant metastasis free survival (DMFS) rates were estimated using the Kaplan-Meier method. Multivariate analyses were performed using Cox proportional hazards regression. RESULTS Median follow-up time was 52 months. Median SRI were 46 days. The optimal cut-off value for the surgery-RT interval (SRI) based on overall survival (OS) is 40 days. The group initiated RT within 40 days following surgery has higher OS (P = 0.004), higher LRFS (P = 0.002) and higher DMFS (P = 0.039). An SRI of ≤ 40 days was independently associated with higher OS (HR 0.454, 95% CI:0.261-0.788), higher LRFS (HR 0.487, 95% CI:0.304-0.779), and higher DMFS (HR 0.643, 95% CI:0.421-0.982) than SRI of >40 days. However, SRI had no significant effect on DFS. CONCLUSION The surgery-RT interval affects the patients' survival endpoints. Based on this analysis, the timing of the initiation of RT after hysterectomy is crucial for patients with early-stage endometrial carcinoma (EC). The postoperative radiation therapy for endometrial cancer should be initiated within 40 days following surgery.
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Zhang T, Zhao L, Wang Z, Sun XH, Wang W, Duan J, Chen LT. Dosimetric Validation of 3D-Printed Bolus at Different Printing Infill Percentage in VMAT Plan. Int J Radiat Oncol Biol Phys 2023; 117:e746. [PMID: 37786163 DOI: 10.1016/j.ijrobp.2023.06.2286] [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 3D printed bolus technology is rapidly evolving in external beam radiotherapy and printing parameters can have a significant impact on absorbed dose. In this study, a novel 3D printed bolus was designed to evaluate the time and material cost effects, dosimetry differences, and surface dose modulation capabilities in the volumetric-modulated arc therapy (VMAT) plan at different print filling percentages. MATERIALS/METHODS A hollow-type bolus, the middle 2.36 mm of 5 mm thickness infilled with different ratio, was designed and printed with polylactic acid (PLA). The ratio of printed material was defined by the infill percentage parameter ranging from 10% to 90%. For each bolus, two treatment plans were designed with AAA algorithm, considering the real computed tomography (CT) scan of the 3D printed bolus and modeling the 3D printed bolus as a virtual bolus structure. Percentage depth dose (PDD) profiles were calculated to build up the mapping equivalent CT value in treatment plan system (TPS). Measurement dose was performed by radiographic films. The PDD profiles were then compared between measured and calculated. A simulation VMAT treatment plan with planning target volume (PTV) close to the body surface was designed on a water-equivalent phantom, and the modulation capabilities of epidermal dose under different filling percentage was compared. RESULTS Compared with 100% percent infill 3D printed bolus, The maximum printing time could be reduced by 47.8% and material consumption could be reduced by 42.5%. The surface dose at single field irradiation can reach 69.6% to 85.8% of the maximum dose in different filling boluses. The PDD measurement and mapping equivalent CT calculation deviation was less than 3% when the infill percentage of the middle region is greater than 30%. The dose distribution of the VMAT plan is satisfying for infill percentages greater than 30%. CONCLUSION Using the 3D printing technology is possible to modulate the amount of shift of the build-up region by tuning the infill percentage of the 3D printed bolus. Patients could undergo CT simulation without bolus. Appropriate bolus could be selected according to the location of the PTV region and dose requirement.
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