| 1 | Dynamics of the SARS-CoV-2 antibody response up to 10 months after infection显示文摘COVID-19 caused by SARS-CoV-2 infection has caused substantial morbidity and mortality worldwide and paralyzed the international economy.Understanding the magnitude and duration of the antibody response to SARS-CoV-2 is important to achieve a balance between curbing the pandemic and minimizing adverse effects on society.1 Although the antibody response to SARS-CoV-2 within 9 months has been extensively studied,2,3,4,5,6 little is known about the magnitude and kinetics of antibody responses for over 9 months.Moreover,with limited observations over 9 months(n<100),2,7,8 several studies have produced inconsistent conclusions about antibody dynamics,suggesting different rates of antiviral antibody positivity at the last follow-up.2,7,8 These studies have been limited by a lack of measurement of neutralizing antibodies(NAbs),7 of inclusion of mild or asymptomatic cases,2,8 and of further exploration of potential predisposing factors for antibody dynamics.2,7 Considering the individual heterogeneity(such as disease severity)8 and time-dependent nature1 of the immune response,in-depth characterization of SARS-CoV-2 antibody kinetics across disease severity groups over a long period is urgently needed.Therefore,we repeatedly tested IgM,IgG,viral spike protein receptor-binding dom(anti-RBD)IgG,and NAb titers in COVID-19 patients during a follow-up period of up to 10 months and explored potential predisposing factors of antibody titers during follow-up. | Hao Wang Yu Yuan Mingzhong Xiao Li Chen Youyun Zhao Haiwei Zhang Pinpin Long Yana Zhou Xi Xu Yanshou Lei Bihao Wu Tingyue Diao Hao Cai Li Liu Zuoyu Shao Jingzhi Wang Yansen Bai Kai Wang Miao Peng Linlin Liu Shi Han Fanghua Mei Kun Cai Yake Lei An Pan Chaolong Wang Rui Gong Xiaodong Li Tangchun Wu | 2021 | Cellular & Molecular Immunology2021，18，7： | 1 |
| 2 | Mining intrinsic information of convalescent patients after suffering coronavirus disease 2019 in Wuhan显示文摘OBJECTIVE:To summarize the potential characteristics of convalescent patients with coronavirus disease 2019(COVID-19)in China based on emerging clinical tongue data and guide the treatment and recovery of COVID-19 patients from the perspective of Traditional Chinese Medicine tongue diagnosis.METHODS:In this study,we developed and validated radiomics-based and lab-based methods as a novel approach to provide individualized pretreatment evaluation by analyzing different features to mine the orderliness behind tongue data of convalescent patients.In addition,this study analyzed the tongue features of convalescent patients from clinical tongue qualitative values,including thick and thin,fur,peeling,fat and lean,tooth marks and cracked,and greasy and putrid fur.RESULTS:We included 2164 tongue images in total(34%from day 0,35.4%from day 14 and 30.6%from day 28)from convalescent patients.The significance results are shown as follows.Firstly,as the recovery time prolongs,the L average values of tongue and coat decrease from 60.21 to 57.18 and from 60.06 to 57.03 respectively.Secondly,the decrease of abnormality rate of tongue coat,included greasy tongue fur,putrid fur,teeth-mark,thick-thin fur,are of significant statistical difference(P<0.05).Thirdly,the average value of gray-level cooccurrence matrices increases from 0.173 to 0.194,the average value of entropy increases from 0.606 to 0.665,the average value of inverse difference normalized decrease from 0.981 to 0.979,and the average value of dissimilarity decrease from 0.1576 to 0.1828.The details of other radiomics features are describe in results section.CONCLUSIONS:Our experiment shows that patients in different recovery periods have a relationship with quantitative values of tongue images,including L color space of the tongue and coat radiomics features analysis.This relationship can help clinical doctors master the recovery and health of patients as soon as possible and improve their understanding of the potential mechanisms underlying the dynamic changes and mechanisms underlying COVID-19. | YAN Shixing LüYi LIU Ziqing REN Meng HE Haiyang XIAO Li GUO Feng PENG Miao LI Xiaoxia WANG Yong XU Xi YANG Tao SHAO Zuoyu HUANG Jingjing XIAO Mingzhong | 2022 | Journal of Traditional Chinese Medicine2022，42，2： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员