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The purpose of this paper is to retrieve and study the highly cited papers as well as the correlation between the citation frequency and the download frequency of the 20 traditional Chinese medicine journals in China, in order to provide the guidance for improving the influence and academic quality of these journals. Bibliometric analyses were conducted on 1103 papers of 20 traditional Chinese medicine journals from 2011 to 2020 by retrieving for the China Academic Journal Network Publishing Database (CAJD) in China National Knowledge Infrastructure (CNKI). SPSS 17.0 software was used to analyze the correlation between the citation frequency and the download frequency via conducting regression fitting and establishing the mathematical models. The results showed that the total citations of the 1103 papers were 93051 times and the average citations were 84.36 times per paper. The total downloads of the 1103 papers were 2058442 times, and the average downloads were 1866.22 times per paper. China Journal of Chinese Materia Medica ranked first according to the number of papers, total citations and total downloads. The citations of Journal of Chinese Medicinal Materials ranked first based on the number of citations per paper. One of Li's paper had been cited the most (983 times). There were 629 (57.03%) papers whose first author was from universities. The scopes of the first authors were distributed in 29 regions and 2 special administrative regions (Macao, Hong Kong) in China. The authors from Beijing published 283 (25.66%) papers, ranking number one. The number of papers supported by funds was 882 (79.96%). The research results of correlation showed that the citation frequency and the download frequency of the highly cited papers had a highly positive correlation from both journal and paper level for whether the sample data of journals was normally distributed or nonnormally distributed. The correlation coefficients of the 20 journals at journal level and that at paper level were 0.9765 and 0.6677, respectively. The correlation was better at journal level than that at paper level, while the optimal regression fitting was all cubic polynomial. Among the 1103 papers, there were 684 (62.01%) research papers and 419 (37.99%) review papers. The main citation period of the top 15 papers was from the 2nd year to the 6th year after publication, accounting for 78.39%. Papers on clinical therapeutics research, papers on the pharmacological effects and its mechanism of traditional Chinese medicine, and papers on traditional Chinese medicine and natural medicine were the main source of the highly cited papers of the traditional Chinese medicine journals. Editors of the journals should focus on the above-mentioned research areas to select manuscripts for exploiting the excellent sources extensively, while paying attention to review papers, focusing on national major or key projects, paying attention to network spreading, stabilizing authors with quality services, in order to improve the influence and the academic quality of journals. [ABSTRACT FROM AUTHOR]

This paper discusses the understanding of traditional Chinese medicine, Uyghur medicine, Tibetan medicine and Mongolian medicine on bronchial asthma, and discusses the causes and pathogenesis, syndrome differentiation, treatment principles and treatment drugs, respectively. It compares the similarities and differences of different ethnic groups' traditional medicines on bronchial asthma treatment, so as to help each ethnic group learn from each other and further improve their understanding of asthma. [ABSTRACT FROM AUTHOR]

The quality of Chinese medical herbs depends on their geographical origins. Combining support vector machine (SVM) with Fourier transform infrared (FTIR) spectroscopy is a fast, accurate and nondestructive technique to classify the herbal origins. It is time consuming to tune the key parameters of SVM elaborately. However, a special leave-one-out (LOO) error bound of generalization ability (i.e., the ratio of the number of support vectors to the number of training vectors) can be estimated through training binary SVM once. In this paper, averaging all LOO error bounds over binary SVM classifiers is defined as the LOO error bound for multiclass problem according to one-against-other technique. Using this error bound and grid search, we can estimate the regularization and kernel parameters in SVM efficiently. In our experiments, 269 spectral curves of Dahurian Angelica Root (Baizhi in Chinese) from four provinces in China are examined in detail using polynomial and RBF kernels. On eight testing subsets, the average error rate is 3.53%. For four geographical origins, the highest error rate 6.07% is obtained using Zhejiang's herb. These results demonstrate that for classifying the herbal origins our proposed method can effectively tune SVM parameters and achieve high classification accuracy simultaneously. [ABSTRACT FROM AUTHOR]

Background: COVID-19 outbreak spread in China and is a threat to the world. Traditional Chinese medicine (TCM) has been used to treat various symptoms and diseases in China and has been demonstrated to be effective. Chinese Medicine may be beneficial in the treatment of COVID-19. So we updated a meta-analysis to fully evaluate the strength of evidence supporting Chinese Medicine treatment for COVID-19. Methods: Six databases (PubMed, Web of Science, Cochrane Library, Wanfang, CBM and CNKI) were searched until 12 September 2023. The Cochrane bias risk tool was used for risk evaluation and JADAD scale for assessing the quality. Meta-analysis was carried out by Stata 15.1. Begg's test was used for publication bias. Results: A total of 4128 COVID-19 patients in 38 eligible RCT studies were included. Compared with Western medicine alone, integrated TCM significantly shortened the duration of fever [ WMD = − 1. 1 4 , 95%CI (−1.28, −0.99)] and length of hospitalization [ WMD = − 2. 6 5 , 95%CI (−3.23, −2.06)] and nucleic acid turning negative time [ WMD = − 2. 2 3 , 95%CI (−2.97, −1.50)] and reduced the severity illness rate [ OR = 0. 4 1 , 95%CI (0.29, 0.57)] and improved CT changes [OR = 2.21, 95%CI (1.78, 2.74)] and a series of clinical symptoms including fever [ OR = 1. 9 4 , 95%CI (1.42, 2.65)], cough [ OR = 2. 3 6 , 95%CI (1.71, 3.25)], fatigue [ OR = 2. 0 9 , 95%CI (1.50, 2.90)], muscle pain [ OR = 4. 7 5 , 95%CI(1.65, 13.68)], expectoration [ OR = 2. 8 9 , 95%CI (1.60, 5.20)], breathless [ OR = 6. 3 9 , 95%CI (3.10, 13.17)], chest distress [ OR = 1. 9 5 , 95%CI (1.20, 3.18)], dyspnea [ OR = 5. 2 5 , 95%CI (1.65, 16.76)] of COVID-19. Conclusion: TCM could be a complementary medicine to improve COVID-19. [ABSTRACT FROM AUTHOR]