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1.
Annals of Translational Medicine ; 10(9), 2022.
Article in English | EuropePMC | ID: covidwho-1971008

ABSTRACT

Background New England Journal of Medicine (NEJM), Lancet, Journal of the American Medical Association (JAMA), and British Medical Journal (BMJ) are collectively known as “the Top Four Medical Journals (TFMJ)” in China. Through the analysis of Chinese scholars’ publications in the TFMJ in the recent 10 years, this study aimed to clarify the current situation of high-quality medical research conducted by Chinese scholars and institutions. Methods Data were retrieved and downloaded manually from PubMed (2011–2020). Information on the publication year, journal, author, affiliation, and citation, etc. were extracted and analyzed using R software. Results A total of 761 articles were involved in the final analysis. The number of articles published by Chinese scholars in the TFMJ was 135/29,942 (0.45%) in BMJ, 124/14,033 (0.88%) in JAMA, 314/16,117 (1.94%) in Lancet, and 188/15,242 (1.23%) in NEJM (P<0.001). Besides, the letter was the main research type, which was up to 44.54%, and the original research only accounted for 17.47%. The most popular subspecialty and subject were infectious diseases and COVID-19, respectively. The most productive researcher was Chen Wang, and Bin Cao was the most cited Chinese scholar. The most productive institute was Chinese Academy of Medical Sciences and Peking Union Medical College. The most cited study was “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China”. Conclusions The presence of Chinese scholars in the TFMJ has grown, but there is still much room to improve. A Matthew effect in China's high-level scientific research was demonstrated.

2.
Ann Transl Med ; 10(9): 505, 2022 May.
Article in English | MEDLINE | ID: covidwho-1822668

ABSTRACT

Background: New England Journal of Medicine (NEJM), Lancet, Journal of the American Medical Association (JAMA), and British Medical Journal (BMJ) are collectively known as "the Top Four Medical Journals (TFMJ)" in China. Through the analysis of Chinese scholars' publications in the TFMJ in the recent 10 years, this study aimed to clarify the current situation of high-quality medical research conducted by Chinese scholars and institutions. Methods: Data were retrieved and downloaded manually from PubMed (2011-2020). Information on the publication year, journal, author, affiliation, and citation, etc. were extracted and analyzed using R software. Results: A total of 761 articles were involved in the final analysis. The number of articles published by Chinese scholars in the TFMJ was 135/29,942 (0.45%) in BMJ, 124/14,033 (0.88%) in JAMA, 314/16,117 (1.94%) in Lancet, and 188/15,242 (1.23%) in NEJM (P<0.001). Besides, the letter was the main research type, which was up to 44.54%, and the original research only accounted for 17.47%. The most popular subspecialty and subject were infectious diseases and COVID-19, respectively. The most productive researcher was Chen Wang, and Bin Cao was the most cited Chinese scholar. The most productive institute was Chinese Academy of Medical Sciences and Peking Union Medical College. The most cited study was "Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China". Conclusions: The presence of Chinese scholars in the TFMJ has grown, but there is still much room to improve. A Matthew effect in China's high-level scientific research was demonstrated.

3.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-334394

ABSTRACT

Efficient identification of microbe-drug associations is critical for drug development and solving problem of antimicrobial resistance. Traditional wet-lab method requires a lot of money and labor in identifying potential microbe-drug associations. With development of machine learning and large amounts of biological data, computational methods become feasible. In this paper, we proposed a computational model of Neighborhood-based Inference (NI) and Restricted Boltzmann Machine (RBM) to predict potential Microbe-Drug Association (NIRBMMDA) by using multisource data. First, NI was used to predict potential microbe-drug associations by using different thresholds to find similar neighbors for drug or microbe. Then, RBM was also employed to predict potential microbe-drug associations based on contrastive divergence algorithm and sigmoid function. Because generalization ability of individual method is poor, we used an ensemble learning to integrate the two predicted microbe-drug associations. Especially, NI can fully utilize similar (neighbor) information of drug or microbe and RBM can learn potential probability distribution hid in known microbe-drug associations. Finally, ensemble learning was used to integrate individual predictor for obtaining a stronger predictor. To evaluate the performance of NIRBMMDA, global leave-one-out cross validation (LOOCV), local LOOCV and five-fold cross validations were implemented to evaluate the performance of NIRBMMDA based on three datasets of DrugVirus, MDAD and aBiofilm. In global LOOCV, NIRBMMDA gained the area under the receiver operating characteristics curve (AUC) of 0.8666, 0.9413 and 0.9557 for datasets of DrugVirus, MDAD and aBiofilm, respectively. In local LOOCV, AUCs of 0.8512, 0.9204 and 0.9414 were obtained for NIRBMMDA based on datasets of DrugVirus, MDAD and aBiofilm, respectively. For five-fold cross validation, NIRBMMDA acquired AUC and standard deviation of 0.8569 0.0027, 0.9248 0.0014 and 0.9369 0.0020 on the basis of datasets of DrugVirus, MDAD and aBiofilm, respectively. Moreover, case study for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) showed that 13 out of the top 20 predicted drugs were verified by searching literature. The other two case studies indicated that 17 and 15 out of the top 20 predicted microbes for the drug of ciprofloxacin and minocycline were confirmed by published literature, respectively. The results demonstrated NIRBMMDA is an effective model in predicting microbe-drug associations.

4.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-292669

ABSTRACT

Recently, the association prediction between viruses and drugs has drawn more and more attention. A growing number of studies have shown that the problem of antiviral drug resistance is increasing and has become a major problem plaguing the medical community. Moreover, the development cycle of new drugs is long and requires a lot of funds. If new viruses emerge, effective antiviral drugs are urgently needed. Therefore, effective calculation methods are required to predict potential antiviral drugs. In this paper, we developed a computational model of Matrix Decomposition and Heterogeneous Graph based Inference for Drug-Virus Association (MDHGIVDA) to predict potential drug-virus associations. MDHGIVDA integrated virus sequence similarity, drug chemical structure similarity, drug side effect similarity, Gaussian interaction profile kernel similarity for drugs and viruses, new drug-virus associations matrix obtained by matrix decomposition to discover new drug-virus associations. Due to the use of matrix factorization and heterogeneous graphs, our model has a high prediction accuracy compared with the previous four models. In the global and local leave-one-out cross validation (LOOCV), MDHGIVDA obtained area under the receiver operating characteristics curve (AUC) of 0.8528 and AUC of 0.8532, respectively. In addition, in the five-fold cross validation, the AUC and the standard deviation is 0.8299 0.0037, which shows that MDHGIVDA has stability and high prediction accuracy. In the case studies of three important viruses, 18, 14, and 16 out of the top 20 predicted drugs for Zika virus (ZIKV), Severe Acute Respiratory Syndrome Coronavirus 2 ( SARS-COV-2 ), Human Immunodeficiency Virus-1 (HIV-1) were verified respectively by searching the literature on PubMed. These results showed that MDHGIVDA is effective in predicting potential drug-virus associations.

5.
J Virol ; 94(6)2020 02 28.
Article in English | MEDLINE | ID: covidwho-824860

ABSTRACT

Porcine reproductive and respiratory syndrome virus (PRRSV), an important pathogen that affects the pig industry, is a highly genetically diverse RNA virus. However, the phylogenetic and genomic recombination properties of this virus have not been completely elucidated. In this study, comparative analyses of all available genomic sequences of North American (NA)-type PRRSVs (n = 355, including 138 PRRSV genomes sequenced in this study) in China and the United States during 2014-2018 revealed a high frequency of interlineage recombination hot spots in nonstructural protein 9 (NSP9) and the GP2 to GP3 regions. Lineage 1 (L1) PRRSV was found to be susceptible to recombination among PRRSVs both in China and the United States. The recombinant major parent between the 1991-2013 data and the 2014-2018 data showed a trend from complex to simple. The major recombination pattern changed from an L8 to L1 backbone during 2014-2018 for Chinese PRRSVs, whereas L1 was always the major backbone for US PRRSVs. Intralineage recombination hot spots were not as concentrated as interlineage recombination hot spots. In the two main clades with differential diversity in L1, NADC30-like PRRSVs are undergoing a decrease in population genetic diversity, NADC34-like PRRSVs have been relatively stable in population genetic diversity for years. Systematic analyses of insertion and deletion (indel) polymorphisms of NSP2 divided PRRSVs into 25 patterns, which could generate novel references for the classification of PRRSVs. The results of this study contribute to a deeper understanding of the recombination of PRRSVs and indicate the need for coordinated epidemiological investigations among countries.IMPORTANCE Porcine reproductive and respiratory syndrome (PRRS) is one of the most significant swine diseases. However, the phylogenetic and genomic recombination properties of the PRRS virus (PRRSV) have not been completely elucidated. In this study, we systematically compared differences in the lineage distribution, recombination, NSP2 polymorphisms, and evolutionary dynamics between North American (NA)-type PRRSVs in China and in the United States. Strikingly, we found high frequency of interlineage recombination hot spots in nonstructural protein 9 (NSP9) and in the GP2 to GP3 region. Also, intralineage recombination hot spots were scattered across the genome between Chinese and US strains. Furthermore, we proposed novel methods based on NSP2 indel patterns for the classification of PRRSVs. Evolutionary dynamics analysis revealed that NADC30-like PRRSVs are undergoing a decrease in population genetic diversity, suggesting that a dominant population may occur and cause an outbreak. Our findings offer important insights into the recombination of PRRSVs and suggest the need for coordinated international epidemiological investigations.


Subject(s)
Polymorphism, Genetic , Porcine respiratory and reproductive syndrome virus/genetics , Recombination, Genetic , Viral Proteins/genetics , Animals , China/epidemiology , Phylogeography , Porcine Reproductive and Respiratory Syndrome/epidemiology , Porcine Reproductive and Respiratory Syndrome/genetics , Swine , United States/epidemiology
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