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| 1 | The role played by traditional Chinese medicine in preventing and treating COVID-19 in China显示文摘Traditional Chinese medicine(TCM),an ancient system of alternative medicine,played an active role in the prevention and control of COVID-19 in China.It improved the clinical symptoms of patients,reduced the mortality rate,improved the recovery rate,and effectively relieved the operating pressure on the national medical system during critical conditions.In light of the current global pandemic,TCM-related measures might open up a new channel in the control of COVID-19 in other countries and regions.Here,we summarize the TCM-related measures that were widely used in China,including TCM guidelines,the Wuchang pattern,mobile cabin hospitals,integrated treatment of TCM and modern medicine for critical patients,and non-medicine therapy for convalescent patients,and describe how TCM effectively treated patients afflicted with the COVID-19.Effective TCM therapies could,therefore,be recommended and practiced based on the existing medical evidence from increased scientific studies. | Qingwei Li Han Wang Xiuyang Li Yujiao Zheng Yu Wei Pei Zhang Qiyou Ding Jiaran Lin Shuang Tang Yikun Zhao Linhua Zhao Xiaolin Tong | 2020 | Frontiers of Medicine2020，14，5： | 10 |
| 2 | Interpretation of the Traditional Chinese Medicine portion of the diagnosis and treatment protocol for corona virus disease 2019(Trial Version 7)显示文摘The TCM protocol in the Diagnosis and Treatment Protocol for corona virus disease 2019(COVID-19)(Trial Version 7) has been updated from previous versions. The protocol was formulated under the direct leadership of the National Administration of Traditional Chinese Medicine, based on the experience of a panel of experts, supported by evidence from fever clinics and from the outcomes of emergency(EM) observation rooms and inpatients throughout China(especially in Wuhan, Hubei Province) in combination with the latest scientific research results and data. The present interpretation of the TCM protocol is based on an overall understanding of the revised content, and aims to guide and standardize its clinical application to provide a reference for clinicians. | Song Ping Zhao Linhua Li Xiuyang Su Jiansen Jiang Zhaoyuan Song Bin Liu Wenke Tang Shuang Lei Ye Ding Qiyou Yang Ze Lin Jiaran Wei Yu Tong Xiaolin | 2020 | Journal of Traditional Chinese Medicine2020，40，3： | 5 |
| 3 | Microbiome and metabolome dysbiosis analysis in impaired glucose tolerance for the prediction of progression to diabetes mellitus显示文摘Gut microbiota and circulating metabolite dysbiosis predate important pathological changes in glucose metabolic disorders;however,comprehensive studies on impaired glucose tolerance(IGT),a diabetes mellitus(DM)precursor,are lacking.Here,we perform metagenomic sequencing and metabolomics on 47 pairs of individuals with IGT and newly diagnosed DM and 46 controls with normal glucose tolerance(NGT);patients with IGT are followed up after 4 years for progression to DM.Analysis of baseline data reveals significant differences in gut microbiota and serum metabolites among the IGT,DM,and NGT groups.In addition,13 types of gut microbiota and 17 types of circulating metabolites showed significant differences at baseline before IGT progressed to DM,including higher levels of Eggerthella unclassified,Coprobacillus unclassified,Clostridium ramosum,L-valine,L-norleucine,and L-isoleucine,and lower levels of Eubacterium eligens,Bacteroides faecis,Lachnospiraceae bacterium 3_1_46FAA,Alistipes senegalensis,Megaspaera elsdenii,Clostridium perfringens,α-linolenic acid,10E,12Z-octadecadienoic acid,and dodecanoic acid.A random forest model based on differential intestinal microbiota and circulating metabolites can predict the progression from IGT to DM(AUC=0.87).These results suggest that microbiome and metabolome dysbiosis occur in individuals with IGT and have important predictive values and potential for intervention in preventing IGT from progressing to DM. | Boxun Zhang Xuan Zhang Zhen Luo Jixiang Ren Xiaotong Yu Haiyan Zhao Yitian Wang Wenhui Zhang Weiwei Tian Xiuxiu Wei Qiyou Ding Haoyu Yang Zishan Jin Xiaolin Tong Jun Wang Linhua Zhao | 2024 | Journal of Genetics and Genomics2024，51，1： | 0 |
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