Dataset Information

Traditional Chinese Medicine JingYinGuBiao Formula Therapy Improves the Negative Conversion Rate of SARS-CoV2 in Patients with Mild COVID-19.

ABSTRACT: Background: Traditional Chinese Medicine (TCM) JingYinGuBiao formula (JYGB) was recommended by the Expert consensus on Traditional Chinese Medicine diagnosis and treatment of COVID-19 infection in Shanghai. We evaluated the safety and efficacy of JYGB in treating mild COVID-19 patients. Methods: A prospective, double-blind, randomized, controlled trial was conducted (ClinicalTrial.gov registration number: ChiCTR2200058695). A total of 885 patients were randomized into the treatment group (administration of JYGB,n=508) or the control group (administration of TCM placebo, n=377) with 7-day treatment. The primary outcomes were the negative conversion rate and negative conversion time of SARS-CoV2 RNA. Secondary outcomes included the hospitalized days and symptom improvement. Results: A total of 490 and 368 patients in the treatment and control groups completed the study. The cumulative negative conversion rates at 2 days, 3 days, 4 days, and 6 days post randomization in the treatment group were all markedly higher than those in the control group (13.88% vs. 9.24%, P=0.04; 32.24% vs. 16.58%, P<0.001; 51.43% vs. 36.14%, P <0.001; 77.76% vs. 69.84%, P=0.008). Compared with the control group, after JYGB treatment, the median negative conversion time (4.0 [3.0-6.0] vs. 5.0 [4.0-7.0] days, P<0.001) and hospitalized days (6.0 [4.0-8.0] vs. 7.0 [5.0-9.0] days, P<0.001) were reduced. While the symptoms were improved, there were no significant differences in symptom disappearance rates between both groups. In addition, further sub-group analysis showed that for patients with interval time ≤4 days or patients≤ 60 years, the clinical effects of JYGB were more remarkable with an increase in cumulative negative conversion rates, a decrease in negative conversion time and hospitalized days. JYGB was well tolerated without any severe side effects. Conclusion: JYGB, a TCM prescription, improves the negative conversion rate of SARS-CoV2 in mild COVID-19 patients.

SUBMITTER: Chen B

PROVIDER: S-EPMC9576522 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Traditional Chinese Medicine JingYinGuBiao Formula Therapy Improves the Negative Conversion Rate of SARS-CoV2 in Patients with Mild COVID-19.

Chen Bowu B Geng Peihua P Shen Jiaojiao J Liangpunsakul Suthat S Lyu Hua H Zhang Jue J Yang Yanbing Y Zhang Lei L Xu Yuping Y Dong Chunling C Wang Yanping Y Xue Yan Y Zhang Wei W Liu Hua H Li Man M Gao Yueqiu Y

International journal of biological sciences 20220901 15

<b>Background:</b> Traditional Chinese Medicine (TCM) JingYinGuBiao formula (JYGB) was recommended by the Expert consensus on Traditional Chinese Medicine diagnosis and treatment of COVID-19 infection in Shanghai. We evaluated the safety and efficacy of JYGB in treating mild COVID-19 patients. Methods: A prospective, double-blind, randomized, controlled trial was conducted (ClinicalTrial.gov registration number: ChiCTR2200058695). A total of 885 patients were randomized into the treatment group ...[more]

PMID: 36263182

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Project description:Purpose: This study proposes an S-TextBLCNN model for the efficacy of traditional Chinese medicine (TCM) formula classification. This model uses deep learning to analyze the relationship between herb efficacy and formula efficacy, which is helpful in further exploring the internal rules of formula combination. Methods: First, for the TCM herbs extracted from Chinese Pharmacopoeia, natural language processing (NLP) is used to learn and realize the quantitative expression of different TCM herbs. Three features of herb name, herb properties, and herb efficacy are selected to encode herbs and to construct formula-vector and herb-vector. Then, based on 2,664 formulae for stroke collected in TCM literature and 19 formula efficacy categories extracted from Yifang Jijie, an improved deep learning model TextBLCNN consists of a bidirectional long short-term memory (Bi-LSTM) neural network and a convolutional neural network (CNN) is proposed. Based on 19 formula efficacy categories, binary classifiers are established to classify the TCM formulae. Finally, aiming at the imbalance problem of formula data, the over-sampling method SMOTE is used to solve it and the S-TextBLCNN model is proposed. Results: The formula-vector composed of herb efficacy has the best effect on the classification model, so it can be inferred that there is a strong relationship between herb efficacy and formula efficacy. The TextBLCNN model has an accuracy of 0.858 and an F1-score of 0.762, both higher than the logistic regression (acc = 0.561, F1-score = 0.567), SVM (acc = 0.703, F1-score = 0.591), LSTM (acc = 0.723, F1-score = 0.621), and TextCNN (acc = 0.745, F1-score = 0.644) models. In addition, the over-sampling method SMOTE is used in our model to tackle data imbalance, and the F1-score is greatly improved by an average of 47.1% in 19 models. Conclusion: The combination of formula feature representation and the S-TextBLCNN model improve the accuracy in formula efficacy classification. It provides a new research idea for the study of TCM formula compatibility.

Project description:High-fat diet (HFD)-induced obesity is a risk factor for many metabolic disorders including cardiovascular diseases, diabetes, and fatty liver disease. Although there are accumulating evidences supporting the assumption that regulating gut microbiota as well as its metabolic status is able to mitigate obesity, the inner relationship between the obesity-related gut microbiota and the relevant metabolites are not well defined. In current study, we applied a traditional herbal formula Kang Shuai Lao Pian (KSLP) to HFD-fed mice and evaluated its effect against obesity. Emphases were addressed on identifying profiles of gut microbiota and fecal metabolites with the aid of 16S rRNA gene sequencing and non-target fecal metabolomics techniques. We showed that KSLP could improve HFD-induced obesity, glucose tolerance disorder, as well as gut dysbiosis. In the gut, KSLP corrected the increased abundance of Firmicutes and Proteobacteria, increased ratio of Firmicutes/Bacteroidetes, and decreased abundance of Bacteroidetes caused by HFD. KSLP also reversed HFD-induced significant changes in the abundance of certain genus including Intestinimonas, Oscillibacter, Christensenellaceae_R-7_group, Ruminococcaceae_UCG-010, and Aliihoeflea. Pearson correlation analysis indicated that except for Ruminococcaceae_UCG-010, other four genera had positive correlations with obesity. In addition, 22 key fecal metabolites responding to KSLP treatment were identified. Pearson correlation analysis showed that those metabolites are intimately related to KSLP effective genera of Intestinimonas, Oscillibacter, and Christensenellaceae_R-7_group. Our results indicate that KSLP is a promising traditional Chinese medicine (TCM) applicable for individuals with HFD habit. Intestinimonas, Oscillibacter, and Christensenellaceae_R-7_group might be responsible for the regulatory effect of KSLP. Linking of obesity phenotypes with gut microbiota as well as fecal metabolites is therefore a powerful research strategy to reveal the mechanism of obesity and the targets of intervention.

Dataset Information

Traditional Chinese Medicine JingYinGuBiao Formula Therapy Improves the Negative Conversion Rate of SARS-CoV2 in Patients with Mild COVID-19.

Publications

Traditional Chinese Medicine JingYinGuBiao Formula Therapy Improves the Negative Conversion Rate of SARS-CoV2 in Patients with Mild COVID-19.

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