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10.3760/cma.j.cn121430-20200225-00200

http://scihub22266oqcxt.onion/10.3760/cma.j.cn121430-20200225-00200
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32385990!ä!32385990

suck abstract from ncbi

pmid32385990
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  • Visualization analysis on treatment of coronavirus based on knowledge graph #MMPMID32385990
  • Yi W; Wang Y; Tang J; Xiong X; Zhang Y; Yan S
  • Zhonghua Wei Zhong Bing Ji Jiu Yi Xue 2020[Mar]; 32 (3): 279-286 PMID32385990show ga
  • OBJECTIVE: To discuss the research progress in the field of coronavirus (CoVs) treatment based on the visualization analysis of knowledge graph. METHODS: The related literatures in the field of CoVs treatment were retrieved from the establishment of Web of Science core collection database to February 15th, 2020, and the literature analysis tool of Web of Science database was used to count the annual trend of published literatures. The VOSviewer software was used to analyze the relationship among countries, institutions, authors, clustering and density of subject words. The HistCite software was used to screen important documents and to draw the evolution process of hot spots. The CiteSpace software was used to analyze the breakout points of subject words, so as to find the front and hot spots in this field. RESULTS: A total of 1 747 data were retrieved, with the exception of 17 duplicate data, and 1 730 data were retained for visualization analysis. In terms of literature volume, the literatures on CoVs therapy rose after 2003 and 2012, and the number of published literatures had remained high since 2014. In terms of countries, the main countries that carried out the research on the treatment of CoVs were the United States (n = 613), China (n = 582), Germany (n = 122), Canada (n = 99), etc., and the cooperation among countries was close. In terms of institutions, the number of papers issued by Chinese Academy of Sciences in the field of CoVs treatment ranked first (n = 82), followed by University of Hong Kong of China (n = 74) and Chinese University of Hong Kong of China (n = 58), followed by National Institute of Allergy and Infectious Diseases (n = 37), and the cooperation among various institutions was close. In terms of literature authors, there were two high-yielding authors in the United States [Ralph S. Baric (n = 21) and Kuochen Chou (n = 17)], two Chinese authors [Yuen Kwok-yung (n = 17) and Jiang Shibo (n = 16)] and one Dutch author [Eric J. Snijder (n = 17)]. In terms of the cluster analysis of authors, the authors were closely related in reverse genetics, respiratory infection, receptor binding domain, etc., and the 15 top-cited papers came mainly from China, the United States, Netherlands and other countries, and the literature content represented the frontiers and hot spots in different periods. The treatment hot spots focused on preventing virus adsorption, inhibiting the virus gene nucleic acid replication, transcription and translation. The main subject words were divided into three main categories, namely, CoVs epidemiology, basic research and drug development, in which basic research and drug development were strongly correlated. In the subject words breakthrough analysis, there were time-related breakthrough points in 1991, 1996 and 2002, and the "diagnosis" and "sequence" were continuous hot spots. CONCLUSIONS: Through the visualization analysis of knowledge graph, the development trend and hot spots of CoVs therapy research could be well observed. In this study, the degree of attention in the field of CoVs treatment showed periodic changes, related to the outbreak of new CoVs, and the country, institutions and the author were closely related. The treatment hot spots focused on preventing virus adsorption, inhibiting the virus gene nucleic acid replication, transcription and translation in order to develop new targets of drug.
  • |*Coronavirus[MESH]
  • |Bibliometrics[MESH]
  • |China[MESH]
  • |Coronavirus Infections/*therapy[MESH]
  • |Databases, Factual[MESH]
  • |Humans[MESH]
  • |Pandemics[MESH]
  • |Pattern Recognition, Automated[MESH]
  • |Pneumonia, Viral[MESH]
  • |Publications[MESH]
  • |United States[MESH]


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  • suck abstract from ncbi

    279 3.32 2020