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Ablikim M, Achasov MN, Adlarson P, Ahmed S, Albrecht M, Aliberti R, Amoroso A, An MR, An Q, Bai XH, Bai Y, Bakina O, Ferroli RB, Balossino I, Ban Y, Begzsuren K, Berger N, Bertani M, Bettoni D, Bianchi F, Bloms J, Bortone A, Boyko I, Briere RA, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang WL, Chelkov G, Chen DY, Chen G, Chen HS, Chen ML, Chen SJ, Chen XR, Chen YB, Chen ZJ, Cheng WS, Cibinetto G, Cossio F, Cui XF, Dai HL, Dai XC, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding Y, Dong C, Dong J, Dong LY, Dong MY, Dong X, Du SX, Fan YL, Fang J, Fang SS, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fritsch M, Fu CD, Gao Y, Gao Y, Gao YG, Garzia I, Ge PT, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Greco M, Gu LM, Gu MH, Guan CY, Guo AQ, Guo LB, Guo RP, Guo YP, Guskov A, Han TT, Han WY, Hao XQ, Harris FA, He KL, Heinsius FH, Heinz CH, Heng YK, Herold C, Himmelreich M, Holtmann T, Hou GY, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang LQ, Huang XT, Huang YP, Huang Z, Hussain T, Hüsken N, Andersson WI, Imoehl W, Irshad M, Jaeger S, Janchiv S, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jiang HB, Jiang XS, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, Kalantar-Nayestanaki N, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Keshk IK, Khoukaz A, Kiese P, Kiuchi R, Kliemt R, Koch L, Kolcu OB, Kopf B, Kuemmel M, Kuessner M, Kupsc A, Kurth MG, Kühn W, Lane JJ, Lange JS, Larin P, Lavania A, Lavezzi L, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li H, Li HB, Li HJ, Li HN, Li JL, Li JQ, Li JS, Li K, Li LK, Li L, Li PR, Li SY, Li WD, Li WG, Li XH, Li XL, Li X, Li ZY, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Libby J, Lin CX, Lin T, Liu BJ, Liu CX, Liu D, Liu FH, Liu F, Liu F, Liu GM, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu L, Liu MH, Liu PL, Liu Q, Liu Q, Liu SB, Liu T, Liu WM, Liu X, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JD, Lu JG, Lu XL, Lu Y, Lu YP, Luo CL, Luo MX, Luo PW, Luo T, Luo XL, Lyu XR, Ma FC, Ma HL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XX, Ma XY, Maas FE, Maggiora M, Maldaner S, Malde S, Malik QA, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Muramatsu H, Nakhoul S, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Pathak A, Patteri P, Pelizaeus M, Peng HP, Peters K, Pettersson J, Ping JL, Ping RG, Pogodin S, Poling R, Prasad V, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qian Z, Qiao CF, Qin JJ, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Rashid KH, Ravindran K, Redmer CF, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Rump M, Sang HS, Sarantsev A, Schelhaas Y, Schnier C, Schoenning K, Scodeggio M, Shan W, Shan XY, Shangguan JF, Shao M, Shen CP, Shen HF, Shen XY, Shi HC, Shi RS, Shi X, Shi XD, Song JJ, Song JJ, Song WM, Song YX, Sosio S, Spataro S, Su KX, Su PP, Sui FF, Sun GX, Sun HK, Sun JF, Sun L, Sun SS, Sun T, Sun WY, Sun X, Sun YJ, Sun YZ, Sun ZT, Tan YH, Tan YX, Tang CJ, Tang GY, Tang J, Teng JX, Thoren V, Tian WH, Tian YT, Uman I, Wang B, Wang CW, Wang DY, Wang HJ, Wang HP, Wang K, Wang LL, Wang M, Wang MZ, Wang M, Wang S, Wang W, Wang WH, Wang WP, Wang X, Wang XF, Wang XL, Wang Y, Wang YD, Wang YF, Wang YQ, Wang YY, Wang Z, Wang ZY, Wang Z, Wang Z, Wei DH, Weidner F, Wen SP, White DJ, Wiedner U, Wilkinson G, Wolke M, Wollenberg L, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Z, Xia L, Xiao H, Xiao SY, Xiao ZJ, Xie XH, Xie YG, Xie YH, Xing TY, Xu GF, Xu QJ, Xu W, Xu XP, Xu YC, Yan F, Yan L, Yan WB, Yan WC, Yang HJ, Yang HX, Yang L, Yang SL, Yang YX, Yang Y, Yang Z, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yuan CZ, Yuan L, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng XZ, Zeng Y, Zhang AQ, Zhang BX, Zhang G, Zhang H, Zhang HH, Zhang HH, Zhang HY, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang S, Zhang SF, Zhang S, Zhang XD, Zhang XY, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao Q, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng YH, Zhong B, Zhong C, Zhou LP, Zhou Q, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhu AN, Zhu J, Zhu K, Zhu KJ, Zhu SH, Zhu TJ, Zhu WJ, Zhu WJ, Zhu YC, Zhu ZA, Zou BS, Zou JH. Measurement of the Cross Section for e^{+}e^{-}→Hadrons at Energies from 2.2324 to 3.6710 GeV. PHYSICAL REVIEW LETTERS 2022; 128:062004. [PMID: 35213186 DOI: 10.1103/physrevlett.128.062004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/21/2022] [Indexed: 06/14/2023]
Abstract
Based on electron-positron collision data collected with the BESIII detector operating at the Beijing Electron-Positron Collider II storage rings, the value of R≡σ(e^{+}e^{-}→hadrons)/σ(e^{+}e^{-}→μ^{+}μ^{-}) is measured at 14 center-of-mass energies from 2.2324 to 3.6710 GeV. The resulting uncertainties are less than 3.0% and are dominated by systematic uncertainties.
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Liu S, Chen H, Wang C, Xu Q, Feng S, Wang Y, Yao J, Zhou Q, Tong C, Yang B, Chen J, Jiang H. POS-340 MAPK1 MEDIATES HIGH GLUCOSE INDUCED RENAL TUBULAR INJURY THROUGH DISRUPTING THE INTEGRITY OF MAM. Kidney Int Rep 2022. [DOI: 10.1016/j.ekir.2022.01.361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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103
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Zhou Q, Tan YL, How CH, Yang LY. Breastfeeding woes: a family physician’s approach. Singapore Med J 2022; 63:68-73. [DOI: 10.11622/smedj.2022036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Wu Z, Zhou Q, Li Y, Zhang N, Liu HS, Chen C, Pang GF, Liang QH, Hu CY, Yuan HP, Yang Z, Sun L. [Assessment of cognitive function of the elderly by serum metabolites of brain-gut axis]. ZHONGHUA YI XUE ZA ZHI 2022; 102:125-129. [PMID: 35012301 DOI: 10.3760/cma.j.cn112137-20210702-01496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To investigate the feasibility of assessing cognitive function of the elderly by serum metabolites of brain-gut axis. Methods: Convenience sampling was used to select 100 and 60 participants from the healthy population cohort and microecological balance cohort of the longevity population in Guangxi, to constitute subset of healthy population and longevity population, respectively. A questionnaire was used to investigate the demographic characteristics of the subjects, 2-5 ml of fasting venous blood was collected from the subjects, and the serum untargeted metabolomics was determined by liquid chromatography tandem mass spectrometry. The biomarkers related to the brain-gut axis were collected through literature retrieval, and the results were intersected with the untargeted metabolites and annotated. Spearman correlation analysis was used to screen serum metabolites of brain-gut axis associated with aging, and multiple linear regression method was used to construct biological age model. The mini mental status examination was used to evaluate the cognitive function of longevity population subsets. The differences of biological age and chronological age of longevity population subsets with different cognitive function were compared. Results: The M (Q1, Q3) of subset of healthy population and longevity population were 64 (38, 72) and 97 (95, 99) years old, respectively, and there were 50 (50.0%) and 44 (73.3%) females, respectively. Nine serum metabolites of brain-gut axis were obtained by initial screening, which were propionic acid, glutamic acid, γ-aminobutyric acid (GABA), lactic acid, 5-hydroxytryptamine (5-HT), tryptophan, trimethylamine oxide, dopamine and canine urea. Spearman correlation analysis showed that glutamic acid and dopamine were positively correlated with aging (r values were 0.208 and 0.524, respectively, all P values<0.05), and tryptophan, 5-HT and GABA were negatively correlated with aging (r values were -0.308, -0.533 and -0.213, respectively, all P values<0.05). The biological age model was constructed as: y=49.81-1.18×10-5× GABA-1.82×10-4×5-HT+1.99×10-3×dopamine+1.65×10-6×glutamic acid -2.04×10-6×tryptophan+2.36×gender, where y was the biological age (years), the items on the right were the intercept item, the relative concentration of each metabolite, and gender (male=1, female=2). The coefficient of determination of model was 0.50 (P<0.001). The M (Q1, Q3) of the chronological age of the subset of longevity population with poor, moderate and good cognitive function were 97 (94, 100), 97 (93, 101) and 96 (94, 101) years old, respectively, and there was no statistical significance in pairwise comparison (all P values>0.05). The M (Q1, Q3) of the biological age of the subjects with better cognitive function was 51 (38, 54) years old, which was lower than that of the subjects with poor cognitive function [57 (47, 61)] (P=0.040). Conclusion: The biological age model can be constructed based on serum metabolites of brain-gut axis and used to evaluate the cognitive function of the elderly.
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Ablikim M, Achasov MN, Adlarson P, Ahmed S, Albrecht M, Aliberti R, Amoroso A, An MR, An Q, Bai XH, Bai Y, Bakina O, Baldini Ferroli R, Balossino I, Ban Y, Begzsuren K, Berger N, Bertani M, Bettoni D, Bianchi F, Bloms J, Bortone A, Boyko I, Briere RA, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang WL, Chelkov G, Chen DY, Chen G, Chen HS, Chen ML, Chen SJ, Chen XR, Chen YB, Chen ZJ, Cheng WS, Cibinetto G, Cossio F, Cui XF, Dai HL, Dai XC, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding Y, Dong C, Dong J, Dong LY, Dong MY, Dong X, Du SX, Fan YL, Fang J, Fang SS, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fritsch M, Fu CD, Gao Y, Gao Y, Gao Y, Gao YG, Garzia I, Ge PT, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Greco M, Gu LM, Gu MH, Gu S, Gu YT, Guan CY, Guo AQ, Guo LB, Guo RP, Guo YP, Guskov A, Han TT, Han WY, Hao XQ, Harris FA, He KL, Heinsius FH, Heinz CH, Held T, Heng YK, Herold C, Himmelreich M, Holtmann T, Hou GY, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang LQ, Huang XT, Huang YP, Huang Z, Hussain T, Hüsken N, Ikegami Andersson W, Imoehl W, Irshad M, Jaeger S, Janchiv S, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jiang HB, Jiang XS, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, Kalantar-Nayestanaki N, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Keshk IK, Khoukaz A, Kiese P, Kiuchi R, Kliemt R, Koch L, Kolcu OB, Kopf B, Kuemmel M, Kuessner M, Kupsc A, Kurth MG, Kühn W, Lane JJ, Lange JS, Larin P, Lavania A, Lavezzi L, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li H, Li HB, Li HJ, Li JL, Li JQ, Li JS, Li K, Li LK, Li L, Li PR, Li SY, Li WD, Li WG, Li XH, Li XL, Li X, Li ZY, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Libby J, Lin CX, Liu BJ, Liu CX, Liu D, Liu FH, Liu F, Liu F, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu L, Liu MH, Liu PL, Liu Q, Liu Q, Liu SB, Liu S, Liu T, Liu WM, Liu X, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JD, Lu JG, Lu XL, Lu Y, Lu YP, Luo CL, Luo MX, Luo PW, Luo T, Luo XL, Lyu XR, Ma FC, Ma HL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XX, Ma XY, Maas FE, Maggiora M, Maldaner S, Malde S, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Min TJ, Mitchell RE, Mo XH, Mo YJ, Muchnoi NY, Muramatsu H, Nakhoul S, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pelizaeus M, Peng HP, Peters K, Pettersson J, Ping JL, Ping RG, Pogodin S, Poling R, Prasad V, Qi H, Qi HR, Qi KH, Qi M, Qi TY, Qian S, Qian WB, Qian Z, Qiao CF, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Rashid KH, Ravindran K, Redmer CF, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Rump M, Sang HS, Sarantsev A, Schelhaas Y, Schnier C, Schoenning K, Scodeggio M, Shan DC, Shan W, Shan XY, Shangguan JF, Shao M, Shen CP, Shen HF, Shen PX, Shen XY, Shi HC, Shi RS, Shi X, Shi XD, Song JJ, Song WM, Song YX, Sosio S, Spataro S, Su KX, Su PP, Sui FF, Sun GX, Sun HK, Sun JF, Sun L, Sun SS, Sun T, Sun WY, Sun WY, Sun X, Sun YJ, Sun YK, Sun YZ, Sun ZT, Tan YH, Tan YX, Tang CJ, Tang GY, Tang J, Teng JX, Thoren V, Tian WH, Tian YT, Uman I, Wang B, Wang CW, Wang DY, Wang HJ, Wang HP, Wang K, Wang LL, Wang M, Wang MZ, Wang M, Wang W, Wang WH, Wang WP, Wang X, Wang XF, Wang XL, Wang Y, Wang Y, Wang YD, Wang YF, Wang YQ, Wang YY, Wang Z, Wang ZY, Wang Z, Wang Z, Wei DH, Weidner F, Wen SP, White DJ, Wiedner U, Wilkinson G, Wolke M, Wollenberg L, Wu JF, Wu LH, Wu LJ, Wu X, Wu Z, Xia L, Xiao H, Xiao SY, Xiao ZJ, Xie XH, Xie YG, Xie YH, Xing TY, Xu GF, Xu QJ, Xu W, Xu XP, Xu YC, Yan F, Yan L, Yan WB, Yan WC, Yan X, Yang HJ, Yang HX, Yang L, Yang SL, Yang YX, Yang Y, Yang Z, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yuan CZ, Yuan L, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Yuncu A, Zafar AA, Zeng Y, Zhang AQ, Zhang BX, Zhang G, Zhang H, Zhang HH, Zhang HH, Zhang HY, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang S, Zhang SF, Zhang S, Zhang XD, Zhang XY, Zhang Y, Zhang YH, Zhang YT, Zhang Y, Zhang Y, Zhang ZH, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao Q, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng Y, Zheng YH, Zhong B, Zhong C, Zhou LP, Zhou Q, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhu AN, Zhu J, Zhu K, Zhu KJ, Zhu SH, Zhu TJ, Zhu WJ, Zhu WJ, Zhu YC, Zhu ZA, Zou BS, Zou JH. First Measurement of Polarizations in the Decay D^{0}→ωφ. PHYSICAL REVIEW LETTERS 2022; 128:011803. [PMID: 35061485 DOI: 10.1103/physrevlett.128.011803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 12/14/2021] [Indexed: 06/14/2023]
Abstract
Using a data sample corresponding to an integrated luminosity of 2.93 fb^{-1} collected at a center-of-mass energy sqrt[s]=3.773 GeV by the BESIII detector, the decay D^{0}→ωϕ is observed for the first time. The branching fraction is measured to be (6.48±0.96±0.40)×10^{-4} with a significance of 6.3σ, where the first and second uncertainties are statistical and systematic, respectively. An angular analysis reveals that the ϕ and ω mesons from the D^{0}→ωϕ decay are transversely polarized. The 95% confidence level upper limit on longitudinal polarization fraction is set to be less than 0.24, which is inconsistent with current theoretical expectations and challenges our understanding of the underlying dynamics in charm meson decays.
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Wang K, Zhou Q. Risk Estimates of Dementia and Alzheimer's Disease among Different Whole Grain Food Consumption Categories: A Pilot Study. J Prev Alzheimers Dis 2022; 10:133-136. [PMID: 36641618 DOI: 10.14283/jpad.2022.91] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
OBJECTIVES Whole grains (WG) have been widely recognized as healthy foods but few prospective studies have examined WG foods consumption and all-cause dementia and Alzheimer's disease (AD) dementia. This pilot study aimed to investigate the relationship between WG and dementia. METHODS 2958 subjects from the Framingham Offspring cohort were included with the Food Frequency Questionnaire (FFQ) to assess their diet intake. And multivariate Cox proportional regression was conducted to examine the relations. RESULTS After an average follow-up of 12.6 years, 322 all-cause dementia were documented, including 247 AD dementia. In the fully adjusted model, participants in the highest vs. the lowest quintiles of WG consumption had lower risks of all-cause dementia (HR, 0.72; 95% CI, 0.53-0.84; P for trend <0.001) and AD dementia (HR, 0.64; 95% CI, 0.47-0.80; P for trend <0.001). CONCLUSIONS High consumption of WG foods is associated with decreased risks of all-cause dementia and AD dementia.t disease mortality. Our findings are from a preliminary study and need to be confirmed in comprehensive settings and integrated statistical methods.
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Wang K, Guo K, Ji Z, Liu Y, Chen F, Wu S, Zhang Q, Yao Y, Zhou Q. Association of Preeclampsia with Incident Dementia and Alzheimer’s Disease among Women in the Framingham Offspring Study. J Prev Alzheimers Dis 2022; 9:725-730. [DOI: 10.14283/jpad.2022.62] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Lin R, Guan Z, Zhou Q, Zhong J, Zheng C, Zhang Z. Effects of 7,12-Dimethylbenz(a)anthracene on Apoptosis of Breast Cancer Cells through Regulating Expressions of Fas Ligand and B-Cell Lymphoma 2. Indian J Pharm Sci 2022. [DOI: 10.36468/pharmaceutical-sciences.spl.443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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Zhang H, Chen Y, Lin J, Jiang X, Zhou Q. A POEMS syndrome patient with idiopathic non-cirrhotic portal hypertension received the transjugular intrahepatic portosystemic shunt: a case report and literature review. Niger J Clin Pract 2022; 25:1939-1944. [DOI: 10.4103/njcp.njcp_360_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Ablikim M, Achasov M, Adlarson P, Ahmed S, Albrecht M, Aliberti R, Amoroso A, An M, An Q, Bai X, Bai Y, Bakina O, Baldini Ferroli R, Balossino I, Ban Y, Begzsuren K, Berger N, Bertani M, Bettoni D, Bianchi F, Bloms J, Bortone A, Boyko I, Briere R, Cai H, Cai X, Calcaterra A, Cao G, Cao N, Cetin S, Chang J, Chang W, Chelkov G, Chen D, Chen G, Chen H, Chen M, Chen S, Chen X, Chen Y, Chen Z, Cheng W, Cibinetto G, Cossio F, Cui X, Dai H, Dai X, Dbeyssi A, de Boer R, Dedovich D, Deng Z, Denig A, Denysenko I, Destefanis M, De Mori F, Ding Y, Dong C, Dong J, Dong L, Dong M, Dong X, Du S, Fan Y, Fang J, Fang S, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng C, Feng J, Fritsch M, Fu C, Gao Y, Gao Y, Gao Y, Gao Y, Garzia I, Ge P, Geng C, Gersabeck E, Gilman A, Goetzen K, Gong L, Gong W, Gradl W, Greco M, Gu L, Gu M, Gu S, Gu Y, Guan C, Guo A, Guo L, Guo R, Guo Y, Guskov A, Han T, Han W, Hao X, Harris F, Hüsken N, He K, Heinsius F, Heinz C, Held T, Heng Y, Herold C, Himmelreich M, Holtmann T, Hou Y, Hou Z, Hu H, Hu J, Hu T, Hu Y, Huang G, Huang L, Huang X, Huang Y, Huang Z, Hussain T, Ikegami Andersson W, Imoehl W, Irshad M, Jaeger S, Janchiv S, Ji Q, Ji Q, Ji X, Ji X, Ji Y, Jiang H, Jiang X, Jiao J, Jiao Z, Jin S, Jin Y, Johansson T, Kalantar-Nayestanaki N, Kang X, Kappert R, Kavatsyuk M, Ke B, Keshk I, Khoukaz A, Kiese P, Kiuchi R, Kliemt R, Koch L, Kolcu O, Kopf B, Kuemmel M, Kuessner M, Kupsc A, Kurth M, Kühn W, Lane J, Lange J, Larin P, Lavania A, Lavezzi L, Lei Z, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li C, Li D, Li F, Li G, Li H, Li H, Li H, Li H, Li J, Li J, Li J, Li K, Li L, Li L, Li P, Li S, Li W, Li W, Li X, Li X, Li X, Li Z, Liang H, Liang H, Liang H, Liang Y, Liang Y, Liao G, Liao L, Libby J, Lin C, Liu B, Liu C, Liu D, Liu F, Liu F, Liu F, Liu H, Liu H, Liu H, Liu H, Liu J, Liu J, Liu J, Liu K, Liu K, Liu K, Liu L, Liu M, Liu P, Liu Q, Liu Q, Liu S, Liu S, Liu T, Liu W, Liu X, Liu Y, Liu Y, Liu Z, Liu Z, Lou X, Lu F, Lu F, Lu H, Lu J, Lu J, Lu X, Lu Y, Lu Y, Luo C, Luo M, Luo P, Luo T, Luo X, Lusso S, Lyu X, Ma F, Ma H, Ma L, Ma M, Ma Q, Ma R, Ma R, Ma X, Ma X, Maas F, Maggiora M, Maldaner S, Malde S, Malik Q, Mangoni A, Mao Y, Mao Z, Marcello S, Meng Z, Messchendorp J, Mezzadri G, Min T, Mitchell R, Mo X, Mo Y, Muchnoi N, Muramatsu H, Nakhoul S, Nefedov Y, Nerling F, Nikolaev I, Ning Z, Nisar S, Olsen S, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pelizaeus M, Peng H, Peters K, Pettersson J, Ping J, Ping R, Poling R, Prasad V, Qi H, Qi H, Qi K, Qi M, Qi T, Qi T, Qian S, Qian W, Qian Z, Qiao C, Qin L, Qin X, Qin X, Qin Z, Qiu J, Qu S, Rashid K, Ravindran K, Redmer C, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Rump M, Sang H, Sarantsev A, Schelhaas Y, Schnier C, Schoenning K, Scodeggio M, Shan D, Shan W, Shan X, Shangguan J, Shao M, Shen C, Shen P, Shen X, Shi H, Shi R, Shi X, Shi X, Song J, Song W, Song Y, Sosio S, Spataro S, Su K, Su P, Sui F, Sun G, Sun H, Sun J, Sun L, Sun S, Sun T, Sun W, Sun W, Sun X, Sun Y, Sun Y, Sun Y, Sun Z, Tan Y, Tan Y, Tang C, Tang G, Tang J, Teng J, Thoren V, Tian W, Uman I, Wang B, Wang C, Wang D, Wang H, Wang H, Wang K, Wang L, Wang M, Wang M, Wang M, Wang W, Wang W, Wang W, Wang X, Wang X, Wang X, Wang Y, Wang Y, Wang Y, Wang Y, Wang Y, Wang Y, Wang Z, Wang Z, Wang Z, Wang Z, Wei D, Weidenkaff P, Weidner F, Wen S, White D, Wiedner U, Wilkinson G, Wolke M, Wollenberg L, Wu J, Wu L, Wu L, Wu X, Wu Z, Xia L, Xiao H, Xiao S, Xiao Z, Xie X, Xie Y, Xie Y, Xing T, Xu G, Xu Q, Xu W, Xu X, Xu Y, Yan F, Yan L, Yan W, Yan W, Yan X, Yang H, Yang H, Yang L, Yang S, Yang Y, Yang Y, Yang Z, Ye M, Ye M, Yin J, You Z, Yu B, Yu C, Yu G, Yu J, Yu T, Yuan C, Yuan L, Yuan X, Yuan Y, Yuan Z, Yue C, Yuncu A, Zafar A, Zeng Y, Zhang B, Zhang G, Zhang H, Zhang H, Zhang H, Zhang H, Zhang J, Zhang J, Zhang J, Zhang J, Zhang J, Zhang J, Zhang J, Zhang J, Zhang L, Zhang L, Zhang S, Zhang S, Zhang S, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang Y, Zhang Y, Zhang Y, Zhang Z, Zhang Z, Zhao G, Zhao J, Zhao J, Zhao J, Zhao L, Zhao L, Zhao M, Zhao Q, Zhao S, Zhao Y, Zhao Y, Zhao Z, Zhemchugov A, Zheng B, Zheng J, Zheng Y, Zheng Y, Zhong B, Zhong C, Zhou L, Zhou Q, Zhou X, Zhou X, Zhou X, Zhu A, Zhu J, Zhu K, Zhu K, Zhu S, Zhu T, Zhu W, Zhu W, Zhu Y, Zhu Z, Zou B, Zou J. Cross sections for the reactions
e+e−→K+K−π+π−(π0)
,
K+K−K+K−(π0)
,
π+π−π+π−(π0)
,
pp¯π+π−(π0)
in the energy region between 3.773 and 4.600 GeV. Int J Clin Exp Med 2021. [DOI: 10.1103/physrevd.104.112009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Chen N, Zhou Q, Cai LB. [A case report about curing chronic hepatitis C in children by direct-acting antiviral drugs]. ZHONGHUA GAN ZANG BING ZA ZHI = ZHONGHUA GANZANGBING ZAZHI = CHINESE JOURNAL OF HEPATOLOGY 2021; 29:1196-1197. [PMID: 35045637 DOI: 10.3760/cma.j.cn501113-20200814-00455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
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Li RZ, Zhu JX, Wang YY, Zhao SY, Peng CF, Zhou Q, Sun RQ, Hao AM, Li S, Wang Y, Xia B. [Development of a deep learning based prototype artificial intelligence system for the detection of dental caries in children]. ZHONGHUA KOU QIANG YI XUE ZA ZHI = ZHONGHUA KOUQIANG YIXUE ZAZHI = CHINESE JOURNAL OF STOMATOLOGY 2021; 56:1253-1260. [PMID: 34915661 DOI: 10.3760/cma.j.cn112144-20210712-00323] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To develop a prototype artificial intelligence image recognition system for detecting dental caries, especially those without cavities, in children. Methods: Seven hundred and twelve intraoral photos, which were taken by dental professionals using a digital camera from October 2013 to June 2020 in the Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology, were collected from the children who received dental treatment under general anesthesia. The well-documented post-treatment electronic dental record of each child was identified as label standard to determine whether the teeth were carious and the type of caries types such as caries that had become cavities (caries with cavities), pit and fissure caries that had not become cavities (pit and fissure caries) and proximal caries which the marginal ridge enamel had not been destroyed (proximal caries). The various teeth and caries types were labeled by pediatric dentists using VoTT software (Windows 2.1.0, Microsoft, U S A). There were five labeled groups: pit and fissure caries, approximal caries, non-carious approximal surfaces, caries with cavities and teeth without caries (including intact fillings). Each group was randomly divided into training dataset, validation dataset and test dataset at a ratio of 6.4∶1.6∶2.0 by using random number table. After using the labeled training dataset for deep learning training, a deep learning-based artificial intelligence (AI) image recognition system for detecting dental caries was established, with the caries probability greater than 50.0% as the criterion for determining caries. Sensitivity and accuracy were used as indicators of recognition specificity. Results: Seven hundred and twelve single-jaw intraoral photographs were segmented and annotated into 953 pit and fissure caries, 1 002 approximal caries, 3 008 caries with cavities, 3 189 teeth without caries and 862 non-carious approximal surfaces, totaly 9 014 labels. The sensitivities and specificities of the test set were 96.0% and 97.0% for caries with cavities, 95.8% and 99.0% for pit and fissure caries and 88.1% and 97.1% for approximal caries. Conclusions: The current AI system developed based on deep learning of the intra-oral photos in the present study showed the ability to detect dental caries. Furthermore, the AI system could accurately verify different types of dental caries such as caries with cavities, pit and fissure caries and proximal caries.
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Ablikim M, Achasov M, Adlarson P, Ahmed S, Albrecht M, Aliberti R, Amoroso A, An M, An Q, Bai X, Bai Y, Bakina O, Baldini Ferroli R, Balossino I, Ban Y, Begzsuren K, Berger N, Bertani M, Bettoni D, Bianchi F, Bloms J, Bortone A, Boyko I, Briere R, Cai H, Cai X, Calcaterra A, Cao G, Cao N, Cetin S, Chang J, Chang W, Chelkov G, Chen D, Chen G, Chen H, Chen M, Chen S, Chen X, Chen Y, Chen ZJ, Cheng W, Cibinetto G, Cossio F, Cui X, Dai H, Dai J, Dai X, Dbeyssi A, de Boer R, Dedovich D, Deng Z, Denig A, Denysenko I, Destefanis M, De Mori F, Ding Y, Dong C, Dong J, Dong L, Dong M, Dong X, Du S, Fan Y, Fang J, Fang S, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng C, Feng J, Fritsch M, Fu C, Gao Y, Gao Y, Gao Y, Gao Y, Garzia I, Ge P, Geng C, Gersabeck E, Gilman A, Goetzen K, Gong L, Gong W, Gradl W, Greco M, Gu L, Gu M, Gu Y, Guan CY, Guo A, Guo L, Guo R, Guo Y, Guskov A, Han T, Han W, Hao X, Harris F, He K, Heinsius F, Heinz C, Heng Y, Herold C, Himmelreich M, Holtmann T, Hou G, Hou Y, Hou Z, Hu H, Hu J, Hu T, Hu Y, Huang G, Huang L, Huang X, Huang Y, Huang Z, Hussain T, Hüsken N, Ikegami Andersson W, Imoehl W, Irshad M, Jaeger S, Janchiv S, Ji Q, Ji Q, Ji X, Ji X, Ji Y, Jiang H, Jiang X, Jiao J, Jiao Z, Jin S, Jin Y, Jing M, Johansson T, Kalantar-Nayestanaki N, Kang X, Kappert R, Kavatsyuk M, Ke B, Keshk I, Khoukaz A, Kiese P, Kiuchi R, Kliemt R, Koch L, Kolcu O, Kopf B, Kuemmel M, Kuessner M, Kupsc A, Kurth M, Kühn W, Lane J, Lange J, Larin P, Lavania A, Lavezzi L, Lei Z, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li C, Li D, Li F, Li G, Li H, Li H, Li H, Li H, Li J, Li J, Li J, Li K, Li L, Li L, Li P, Li S, Li W, Li W, Li X, Li X, Li X, Li Z, Liang H, Liang H, Liang H, Liang Y, Liang Y, Liao G, Liao L, Libby J, Lin C, Liu B, Liu C, Liu D, Liu F, Liu F, Liu F, Liu H, Liu H, Liu H, Liu H, Liu J, Liu J, Liu J, Liu K, Liu K, Liu K, Liu L, Liu M, Liu P, Liu Q, Liu Q, Liu S, Liu S, Liu T, Liu W, Liu X, Liu Y, Liu Y, Liu Z, Liu Z, Lou X, Lu F, Lu H, Lu J, Lu J, Lu X, Lu Y, Lu Y, Luo C, Luo M, Luo P, Luo T, Luo X, Lyu X, Ma F, Ma H, Ma L, Ma M, Ma Q, Ma R, Ma R, Ma X, Ma X, Maas F, Maggiora M, Maldaner S, Malde S, Malik Q, Mangoni A, Mao Y, Mao Z, Marcello S, Meng Z, Messchendorp J, Mezzadri G, Min T, Mitchell R, Mo X, Muchnoi N, Muramatsu H, Nakhoul S, Nefedov Y, Nerling F, Nikolaev I, Ning Z, Nisar S, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Pathak A, Patteri P, Pelizaeus M, Peng H, Peters K, Pettersson J, Ping J, Ping R, Pogodin S, Poling R, Prasad V, Qi H, Qi H, Qi K, Qi M, Qi T, Qian S, Qian W, Qian Z, Qiao C, Qin L, Qin X, Qin X, Qin Z, Qiu J, Qu S, Rashid K, Ravindran K, Redmer C, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Rump M, Sang H, Sarantsev A, Schelhaas Y, Schnier C, Schoenning K, Scodeggio M, Shan D, Shan W, Shan X, Shangguan J, Shao M, Shen C, Shen H, Shen P, Shen X, Shi H, Shi R, Shi X, Shi XD, Song J, Song W, Song Y, Sosio S, Spataro S, Su K, Su P, Sui F, Sun G, Sun H, Sun J, Sun L, Sun S, Sun T, Sun W, Sun W, Sun X, Sun Y, Sun Y, Sun Y, Sun Z, Tan Y, Tan Y, Tang C, Tang G, Tang J, Teng J, Thoren V, Tian W, Tian Y, Uman I, Wang B, Wang C, Wang D, Wang H, Wang H, Wang K, Wang L, Wang M, Wang M, Wang M, Wang S, Wang W, Wang W, Wang W, Wang X, Wang X, Wang X, Wang Y, Wang Y, Wang Y, Wang Y, Wang Y, Wang Y, Wang Z, Wang Z, Wang Z, Wang Z, Wei D, Weidner F, Wen S, White D, Wiedner U, Wilkinson G, Wolke M, Wollenberg L, Wu J, Wu L, Wu L, Wu X, Wu Z, Xia L, Xiao H, Xiao S, Xiao Z, Xie X, Xie Y, Xie Y, Xing T, Xu C, Xu G, Xu Q, Xu W, Xu X, Xu Y, Yan F, Yan L, Yan W, Yan W, Yan X, Yang H, Yang H, Yang L, Yang S, Yang Y, Yang Y, Yang Z, Ye M, Ye M, Yin J, You Z, Yu B, Yu C, Yu G, Yu J, Yu T, Yuan C, Yuan L, Yuan X, Yuan Y, Yuan Z, Yue C, Zafar A, Zeng XZ, Zeng Y, Zhang A, Zhang B, Zhang G, Zhang H, Zhang H, Zhang H, Zhang H, Zhang J, Zhang J, Zhang J, Zhang J, Zhang J, Zhang J, Zhang J, Zhang L, Zhang L, Zhang L, Zhang S, Zhang S, Zhang S, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang Y, Zhang Y, Zhang Z, Zhao G, Zhao J, Zhao J, Zhao J, Zhao L, Zhao L, Zhao M, Zhao Q, Zhao S, Zhao Y, Zhao Y, Zhao Z, Zhemchugov A, Zheng B, Zheng J, Zheng Y, Zhong B, Zhong C, Zhou L, Zhou Q, Zhou X, Zhou X, Zhou X, Zhou X, Zhu A, Zhu J, Zhu K, Zhu K, Zhu S, Zhu T, Zhu W, Zhu W, Zhu Y, Zhu Z, Zou B, Zou J. Measurement of the cross section for
e+e−→ΛΛ¯
and evidence of the decay
ψ(3770)→ΛΛ¯. Int J Clin Exp Med 2021. [DOI: 10.1103/physrevd.104.l091104] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Zhou Q, Huang X, Xie Y, Liu X, Li S, Zhou J. Role of quantitative energy spectrum CT parameters in differentiating thymic epithelial tumours and thymic cysts. Clin Radiol 2021; 77:136-141. [PMID: 34857380 DOI: 10.1016/j.crad.2021.10.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 11/01/2021] [Indexed: 11/30/2022]
Abstract
AIM To explore the utility of multiple energy spectrum computed tomography (CT) parameters in distinguishing thymic epithelial tumours (TETs) from thymic cysts among lesions <5 cm in diameter. MATERIALS AND METHODS Data pertaining to 56 patients with TETs and thymic cysts <5 cm in diameter were assessed retrospectively. All patients underwent surgical resection and the diagnosis was confirmed histopathologically. Thirty-five patients with TETs (average age, 51.97 years) and 21 patients with thymic cysts (average age, 50.54 years) were included. The region of interest for the lesion on the energy spectrum CT was delineated on the post-processing workstation, and multiple parameters of the energy spectrum CT were obtained. The diagnostic efficacies of the parameters were analysed using receiver operating characteristic (ROC) curves. RESULTS To distinguish small TETs from thymic cysts, a single-energy CT value of 60 keV showed good differential diagnostic performance in the arterial phase (cut-off value = 68.42 HU; area under the curve [AUC] = 0.978), a single-energy CT value of 70 keV showed good differential diagnostic performance in the venous phase (cut-off value = 59.77 HU; AUC = 0.956). In the arterial and venous phases, effective atomic numbers of 8.065 and 8.175, respectively, were used as cut-off values to distinguish small TETs from thymic cysts (AUC = 0.972 and AUC = 0.961, respectively). Iodine concentrations of 10.99 and 11.05 were used as cut-off values to distinguish small TETs from thymic cysts (AUC = 0.956 and AUC = 0.924, respectively). CONCLUSION According to the present study, energy spectrum CT parameters may have clinical value in the differential diagnosis of TETs and thymic cysts.
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Lu Y, Zhou Q, Wang LN, He T, Zhao HY, Cao XQ. [Application effects of failure mode and effect analysis on the limb posture positioning nursing of extremely severe burn patients]. ZHONGHUA SHAO SHANG ZA ZHI = ZHONGHUA SHAOSHANG ZAZHI = CHINESE JOURNAL OF BURNS 2021; 37:1078-1084. [PMID: 34794260 DOI: 10.3760/cma.j.cn501120-20210412-00126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To explore the application effects of risk assessment method of failure mode and effect analysis (FMEA) on the limb posture positioning nursing of extremely severe burn patients. Methods: A retrospective observational study was conducted. According to the different limb posture positioning methods, 30 extremely severe burn patients who met the inclusion criteria and underwent routine limb posture positioning in the First Affiliated Hospital of Air Force Medical University from January 2018 to June 2019 were included into routine limb positioning group (19 males and 11 females, aged (40±10) years), and 30 extremely severe burn patients who met the inclusion criteria and underwent limb posture positioning with FMEA risk assessment from July 2019 to December 2020 in the department were included into FMEA limb positioning group (20 males and 10 females, aged (38±10) years). Patients in routine limb positioning group received only routine limb posture positioning by rehabilitation therapists with bare hand every day from the time when their limb wounds healed until they were discharged from hospital. Patients in FMEA limb positioning group received FMEA risk assessment by physicians, rehabilitation therapists, and nurses within 24 hours after admission to analyze the potential failure modes of limb posture positioning, and target-directed limb posture positioning measures were adopted until they were discharged. The risk priority numbers (RPNs) of six major failure modes of patients in FMEA limb positioning group before and after intervention were compared. The range of motion (ROM) of shoulder abduction, elbow extension, wrist dorsiflexion, ankle plantarflexion, total action motion of hand, and modified Barthel index scores of the patients in two groups before and after intervention were also assessed. Data were statistically analyzed with independent sample t test, chi-square test, and paired sample t test. Results: The RPNs of 6 main potential failure modes of patients in FMEA limb positioning group i.e. untimely interference of limb posture positioning, not strong awareness of limb posture positioning of nurses, inconsistent of evaluation standards of limb posture positioning, nurses' lacking knowledge about limb posture positioning, nurses' lacking active participation, unsatisfying effects of patients' limb posture positioning were respectively (146±31), (140±22), (125±34), (136±23), (110±28), and (110±5) points after intervention, which were significantly lower than (578±64), (543±57), (419±89), (269±64), (240±41), and (222±48) points before intervention (t=18.441, 23.681, 10.035, 5.362, 9.438, 7.171, P<0.01). After intervention, the ROMs of shoulder abduction, elbow extension, wrist dorsiflexion, and ankle plantarflexion of patients in FMEA limb positioning group were significantly better than those in routine limb positioning group (t=-4.250, 11.400, -15.928, 10.963, -7.470, P<0.01); the ROMs of shoulder abduction, elbow extension, wrist dorsiflexion, and ankle plantarflexion of patients in FMEA limb positioning group and routine limb positioning group were significantly better than those before intervention (t=-35.573, 33.670, -31.090, 32.902, -19.647, -14.952, 11.411, -33.462, -12.818, -13.672, P<0.01). After intervention, the Barthel index score of patients in FMEA limb positioning group (78±9) was significantly higher than 57±9 in routine limb positioning group (t=-9.055, P<0.01), and the Barthel index scores of patients in FMEA limb positioning group and routine limb positioning group were significantly higher than those before intervention (35±5 and 34±4, t=-22.964, -12.329, P<0.01). Conclusions: In the limb posture positioning nursing of extremely severe burn patients, risk assessment method of FMEA can effectively avoid the high risk factors in the limb posture positioning of patients, thus maintain the effects of limb posture positioning and improve the ROM of patients, as well as increase the daily living ability of patients in prognosis.
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Cheng W, Ge H, Li Z, Li D, Zhou Q, Li B. An Investigation of AI Algorithms on Esophageal Gross Tumor Volume Segmentation. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Shan N, Xiang Z, Sun J, Zhu Q, Xiao Y, Wang P, Chen X, Zhou Q, Gan Z. Genome-wide analysis of valine-glutamine motif-containing proteins related to abiotic stress response in cucumber (Cucumis sativus L.). BMC PLANT BIOLOGY 2021; 21:492. [PMID: 34696718 PMCID: PMC8546950 DOI: 10.1186/s12870-021-03242-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/20/2021] [Indexed: 05/06/2023]
Abstract
BACKGROUND Cucumber (Cucumis sativus L.) is one of the most important economic crops and is susceptible to various abiotic stresses. The valine-glutamine (VQ) motif-containing proteins are plant-specific proteins with a conserved "FxxhVQxhTG" amino acid sequence that regulates plant growth and development. However, little is known about the function of VQ proteins in cucumber. RESULTS In this study, a total of 32 CsVQ proteins from cucumber were confirmed and characterized using comprehensive genome-wide analysis, and they all contain a conserved motif with 10 variations. Phylogenetic tree analysis revealed that these CsVQ proteins were classified into nine groups by comparing the CsVQ proteins with those of Arabidopsis thaliana, melon and rice. CsVQ genes were distributed on seven chromosomes. Most of these genes were predicted to be localized in the nucleus. In addition, cis-elements in response to different stresses and hormones were observed in the promoters of the CsVQ genes. A network of CsVQ proteins interacting with WRKY transcription factors (CsWRKYs) was proposed. Moreover, the transcripts of CsVQ gene were spatio-temporal specific and were induced by abiotic adversities. CsVQ4, CsVQ6, CsVQ16-2, CsVQ19, CsVQ24, CsVQ30, CsVQ32, CsVQ33, and CsVQ34 were expressed in the range of organs and tissues at higher levels and could respond to multiple hormones and different stresses, indicating that these genes were involved in the response to stimuli. CONCLUSIONS Together, our results reveal novel VQ resistance gene resources, and provide critical information on CsVQ genes and their encoded proteins, which supplies important genetic basis for VQ resistance breeding of cucumber plants.
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Zhou Q. Comment on: Laparoscopic and open liver resection for hepatocellular carcinoma with Child-Pugh B cirrhosis: multicentre propensity score-matched study. Br J Surg 2021; 108:e350. [PMID: 34227652 DOI: 10.1093/bjs/znab234] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 05/31/2021] [Indexed: 11/14/2022]
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Ablikim M, Achasov MN, Adlarson P, Ahmed S, Albrecht M, Aliberti R, Amoroso A, An MR, An Q, Bai XH, Bai Y, Bakina O, Baldini Ferroli R, Balossino I, Ban Y, Begzsuren K, Berger N, Bertani M, Bettoni D, Bianchi F, Bloms J, Bortone A, Boyko I, Briere RA, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang WL, Chelkov G, Chen DY, Chen G, Chen HS, Chen ML, Chen SJ, Chen XR, Chen YB, Chen ZJ, Cheng WS, Cibinetto G, Cossio F, Cui XF, Dai HL, Dai XC, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding Y, Dong C, Dong J, Dong LY, Dong MY, Dong X, Du SX, Fan YL, Fang J, Fang SS, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fritsch M, Fu CD, Gao Y, Gao Y, Gao Y, Gao YG, Garzia I, Ge PT, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Greco M, Gu LM, Gu MH, Gu S, Gu YT, Guan CY, Guo AQ, Guo LB, Guo RP, Guo YP, Guskov A, Han TT, Han WY, Hao XQ, Harris FA, He KL, Heinsius FH, Heinz CH, Held T, Heng YK, Herold C, Himmelreich M, Holtmann T, Hou GY, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang LQ, Huang XT, Huang YP, Huang Z, Hussain T, Hüsken N, Ikegami Andersson W, Imoehl W, Irshad M, Jaeger S, Janchiv S, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jiang HB, Jiang XS, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, Kalantar-Nayestanaki N, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Keshk IK, Khoukaz A, Kiese P, Kiuchi R, Kliemt R, Koch L, Kolcu OB, Kopf B, Kuemmel M, Kuessner M, Kupsc A, Kurth MG, Kühn W, Lane JJ, Lange JS, Larin P, Lavania A, Lavezzi L, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li H, Li HB, Li HJ, Li JL, Li JQ, Li JS, Li K, Li LK, Li L, Li PR, Li SY, Li WD, Li WG, Li XH, Li XL, Li X, Li ZY, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Libby J, Lin CX, Liu BJ, Liu CX, Liu D, Liu FH, Liu F, Liu F, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu L, Liu MH, Liu PL, Liu Q, Liu Q, Liu SB, Liu S, Liu T, Liu WM, Liu X, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JD, Lu JG, Lu XL, Lu Y, Lu YP, Luo CL, Luo MX, Luo PW, Luo T, Luo XL, Lyu XR, Ma FC, Ma HL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XX, Ma XY, Maas FE, Maggiora M, Maldaner S, Malde S, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Min TJ, Mitchell RE, Mo XH, Mo YJ, Muchnoi NY, Muramatsu H, Nakhoul S, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pelizaeus M, Peng HP, Peters K, Pettersson J, Ping JL, Ping RG, Poling R, Prasad V, Qi H, Qi HR, Qi KH, Qi M, Qi TY, Qian S, Qian WB, Qian Z, Qiao CF, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Rashid KH, Ravindran K, Redmer CF, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Rump M, Sang HS, Sarantsev A, Schelhaas Y, Schnier C, Schoenning K, Scodeggio M, Shan DC, Shan W, Shan XY, Shangguan JF, Shao M, Shen CP, Shen HF, Shen PX, Shen XY, Shi HC, Shi RS, Shi X, Shi XD, Song JJ, Song WM, Song YX, Sosio S, Spataro S, Su KX, Su PP, Sui FF, Sun GX, Sun HK, Sun JF, Sun L, Sun SS, Sun T, Sun WY, Sun WY, Sun X, Sun YJ, Sun YK, Sun YZ, Sun ZT, Tan YH, Tan YX, Tang CJ, Tang GY, Tang J, Teng JX, Thoren V, Tian WH, Tian YT, Uman I, Wang B, Wang CW, Wang DY, Wang HJ, Wang HP, Wang K, Wang LL, Wang M, Wang MZ, Wang M, Wang W, Wang WH, Wang WP, Wang X, Wang XF, Wang XL, Wang Y, Wang Y, Wang YD, Wang YF, Wang YQ, Wang YY, Wang Z, Wang ZY, Wang Z, Wang Z, Wei DH, Weidner F, Wen SP, White DJ, Wiedner U, Wilkinson G, Wolke M, Wollenberg L, Wu JF, Wu LH, Wu LJ, Wu X, Wu Z, Xia L, Xiao H, Xiao SY, Xiao ZJ, Xie XH, Xie YG, Xie YH, Xing TY, Xu GF, Xu QJ, Xu W, Xu XP, Xu YC, Yan F, Yan L, Yan WB, Yan WC, Yan X, Yang HJ, Yang HX, Yang L, Yang SL, Yang YX, Yang Y, Yang Z, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yuan CZ, Yuan L, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Yuncu A, Zafar AA, Zeng Y, Zeng Y, Zhang AQ, Zhang BX, Zhang G, Zhang H, Zhang HH, Zhang HH, Zhang HY, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang S, Zhang SF, Zhang S, Zhang XD, Zhang XY, Zhang Y, Zhang YH, Zhang YT, Zhang Y, Zhang Y, Zhang Y, Zhang ZH, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao Q, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng Y, Zheng YH, Zhong B, Zhong C, Zhou LP, Zhou Q, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhu AN, Zhu J, Zhu K, Zhu KJ, Zhu SH, Zhu TJ, Zhu WJ, Zhu WJ, Zhu YC, Zhu ZA, Zou BS, Zou JH. Measurement of the Absolute Branching Fraction of D_{s}^{+}→τ^{+}ν_{τ} via τ^{+}→e^{+}ν_{e}ν[over ¯]_{τ}. PHYSICAL REVIEW LETTERS 2021; 127:171801. [PMID: 34739288 DOI: 10.1103/physrevlett.127.171801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 08/26/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
Using a dataset of 6.32 fb^{-1} of e^{+}e^{-} annihilation data collected with the BESIII detector at center-of-mass energies between 4178 and 4226 MeV, we have measured the absolute branching fraction of the leptonic decay D_{s}^{+}→τ^{+}ν_{τ} via τ^{+}→e^{+}ν_{e}ν[over ¯]_{τ}, and find B_{D_{s}^{+}→τ^{+}ν_{τ}}=(5.27±0.10±0.12)×10^{-2}, where the first uncertainty is statistical and the second is systematic. The precision is improved by a factor of 2 compared to the previous best measurement. Combining with f_{D_{s}^{+}} from lattice quantum chromodynamics calculations or the |V_{cs}| from the CKMfitter group, we extract |V_{cs}|=0.978±0.009±0.012 and f_{D_{s}^{+}}=(251.1±2.4±3.0) MeV, respectively. Combining our result with the world averages of B_{D_{s}^{+}→τ^{+}ν_{τ}} and B_{D_{s}^{+}→μ^{+}ν_{μ}}, we obtain the ratio of the branching fractions B_{D_{s}^{+}→τ^{+}ν_{τ}}/B_{D_{s}^{+}→μ^{+}ν_{μ}}=9.72±0.37, which is consistent with the standard model prediction of lepton flavor universality.
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Blum S, Lampart M, Zhou Q, Mueller C, Osswald S, Kuster GM, Twerenbold R. Antihypertensive medication and outcome in patients with COVID-19 compared to non-COVID respiratory infections. Eur Heart J 2021. [PMCID: PMC8767616 DOI: 10.1093/eurheartj/ehab724.2395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Background Recent reports suggested no adverse effects of antihypertensive medication including inhibitors of the renin-angiotensin system on outcome of patients with coronavirus disease 19 (COVID-19). However, most of these studies lack adequate control groups, and regional and socio-economic differences may additionally affect clinical course and outcome of COVID-19. Methods In the prospective observational cohort COrona VIrus surviVAl (COVIVA) study at our university hospital, we consecutively enrolled patients presenting to the emergency department with symptoms suggestive of COVID-19 between March and June 2020. Patients tested positive for COVID-19 (cases) were compared with patients tested negative, who had a respiratory infection (respiratory control). Primary outcome measure was the composite of ICU admission, 3'-day mortality or rehospitalization for respiratory symptoms. Results The final analysis consisted of 191 patients with COVID-19 and 323 respiratory controls. Sixty cases (31.4%) and 87 (26.9%) respiratory control patients were on ACE inhibitors (ACE-I) or angiotensin II receptor blockers (ARB). In unadjusted models the hazard ratio [95% CI] for the composite outcome for patients on ACE-I/ARBs was 2.36 [1.34; 4.16], p=0.003 and 2.05 [1.03; 4.09], p=0.04 among patients with COVID-19 and respiratory controls, respectively. The corresponding multivariable adjusted HRs were 1.32 [0.68; 2.55], p=0.41 and 1.20 [0.58; 2.48], p=0.62. Furthermore, we did not observe an increased risk for the outcome when assessing ACE-Is and ARBs separately or other antihypertensive agents, both in COVID-19 patients and respiratory controls (Table). Conclusions In a Swiss cohort of patients with COVID-19 or non-COVID respiratory controls treatment with ACE-I/ARBs or other antihypertensive medication was not associated with adverse events after accounting for comorbidities and risk factors. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Foundation for Cardiovascular Research Basel (FCVR Basel)Swiss Heart Foundation
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Yang R, Yin D, Yang D, Liu X, Zhou Q, Pan Y, Li J, Li S. Xinnaokang improves cecal microbiota and lipid metabolism to target atherosclerosis. Lett Appl Microbiol 2021; 73:779-792. [PMID: 34596907 DOI: 10.1111/lam.13573] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/09/2021] [Accepted: 09/27/2021] [Indexed: 12/15/2022]
Abstract
This study aims to explore the potential mechanisms of Xinnaokang in atherosclerosis treatment. Firstly, the active components of Xinnaokang were analysed by HPLC, which contains ginsenoside Rg1, puerarin, tanshinone, notoginsenoside R1, ammonium glycyrrhizate and glycyrrhizin. Network pharmacology analysis showed there were 145 common targets of Xinnaokang, including the chemical stress, lipid metabolite, lipopolysaccharide, molecules of bacterial origin, nuclear receptor and fluid shear stress pathways. Then, the animal experiment showed that Xinnaokang reduced the body weight and blood lipid levels of atherosclerotic mice. Vascular plaque formation was increased in atherosclerotic mice, which was markedly reversed by Xinnaokang. In addition, Xinnaokang reduced CAV-1 expression and increased ABCA1, SREBP-1 and LXR expressions in the vasculature. Xinnaokang promoted SREBP-2 and LDLR expressions in the liver but decreased IDOL and PCSK9 expressions, indicating that Xinnaokang regulated lipid transport-related protein expression. Cecal microbiota diversity was reduced in atherosclerotic mice but increased after Xinnaokang treatment. Xinnaokang treatment also improved gut microbiota communities by enriching Actinobacteria, Bifidobacteriales and Bifidobacteriaceae abundances. Metabolic profile showed that Xinnaokang significantly reduced homogentisate, phenylacetylglycine, alanine and methionine expressions in the liver of atherosclerotic mice. Xinnaokang effectively alleviated atherosclerosis, and this effect might be linked with the altered features of the liver metabolite profiles and cecal microbiota.
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Zheng M, Li Y, Tu H, Sun H, Yin K, Yang J, Zhang X, Zhou Q, Wu Y. OA16.03 Matched Targeted Therapy by cfDNA of CSF Beyond Leptomeningeal Metastases Progression Upon Osimertinib in EGFR-Mutated NSCLC Patients. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.08.088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Feng H, Chen Y, Xie Z, Jiang J, Zhong Y, Gao L, Zhou W, Guo W, Yan W, Lv Z, Lu D, Liang H, Xu F, Yang J, Yang X, Zhou Q, Zhang D, Zhang Z, Chuai S, Zhang H, Wu Y, Zhang X. P52.02 High SHP2 Expression Determines the Efficacy of PD-1/PD-L1 Inhibitors in Advanced KRAS Mutant Non-Small Cell Lung Cancer. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.08.546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Deng J, Yang X, Yang M, Zhou Q. P57.15 Safety and Efficacy of Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer Patients With Low Creatinine Clearance Rate. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.08.587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Dong S, Wang Z, Zhou Q, Yang L, Zhang J, Chen Y, Liu S, Lin J, Liao R, Tu H, Xu C, Yang X, Zhong W, Yang J, Wu Y. P49.01 Drug Holiday Based on Minimal Residual Disease Status After Local Therapy Following EGFR-TKI Treatment for Patients With Advanced NSCLC. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.08.529] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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