With the rapid global spread of the COVID-19 pandemic, researchers have contributed several important advances. The WHO and countries with severe outbreaks have developed diagnosis and treatment guidelines. Here, we analyze the current transformation and application of scientific research to global epidemic prevention and control. We described and analyzed current COVID-19 research from the perspectives of international cooperation, interdisciplinary cooperation, and research hotspots using a bibliometric clustering algorithm. Using the diagnosis and treatment guidelines of the WHO and the United States and China as examples, we evaluate the transformation of scientific results from basic research to applications. Scientific research results that have not yet been incorporated into these guidelines are summarized to encourage updates and improvements by applying scientific research to prevention and control. COVID-19 has fostered interdisciplinary cooperative research, and the current results are mainly focused on the origin, epidemiological characteristics, clinical research, and diagnosis and treatment methods for the virus. Due to the ongoing publication of new research, diagnosis and treatment guidelines are constantly improving. However, some research gaps still exist, and some results have not yet been incorporated into the guidelines. The current research is still in the preliminary exploratory stage, and some problems, such as weak international cooperation, unbalanced interdisciplinary cooperation, and the lack of coordination between research and applications, exist. Therefore, countries around the world must improve the International Public Health Emergency Management System and prepare for major public health emergencies in the future.
Isatin is an important building block in organic synthesis. The electron-deficient carbonyl in C-3 site is similar to aldehyde. C-3 carbonyl is also able to function as the Onucleophile. Devise methodologies about three-component reactions of isatin have been reported in this decade. This review highlights these multicomponent reactions by triggered mechanisms. We hope it will promote future research in this area.
Background Coronavirus disease 2019 (COVID-19) has spurred a boom in uncovering repurposable existing drugs. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. Motivation Current works of drug repurposing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are mostly limited to only focusing on chemical medicines, analysis of single drug targeting single SARS-CoV-2 protein, one-size-fits-all strategy using the same treatment (same drug) for different infected stages of SARS-CoV-2. To dilute these issues, we initially set the research focusing on herbal medicines. We then proposed a heterogeneous graph embedding method to signaled candidate repurposing herbs for each SARS-CoV-2 protein, and employed the variational graph convolutional network approach to recommend the precision herb combinations as the potential candidate treatments against the specific infected stage. Method We initially employed the virtual screening method to construct the ‘Herb-Compound’ and ‘Compound-Protein’ docking graph based on 480 herbal medicines, 12,735 associated chemical compounds and 24 SARS-CoV-2 proteins. Sequentially, the ‘Herb-Compound-Protein’ heterogeneous network was constructed by means of the metapath-based embedding approach. We then proposed the heterogeneous-information-network-based graph embedding method to generate the candidate ranking lists of herbs that target structural, nonstructural and accessory SARS-CoV-2 proteins, individually. To obtain precision synthetic effective treatments forvarious COVID-19 infected stages, we employed the variational graph convolutional network method to generate candidate herb combinations as the recommended therapeutic therapies. Results There were 24 ranking lists, each containing top-10 herbs, targeting 24 SARS-CoV-2 proteins correspondingly, and 20 herb combinations were generated as the candidate-specific treatment to target the four infected stages. The code and supplementary materials are freely available at https://github.com/fanyang-AI/TCM-COVID19.
Purpose This study aims to reveal the factors that drive researchers to share data and to provide reference for promoting open scientific data. Design/methodology/approach Based on the theory of social capital and the theory of planned behaviour, hypotheses were proposed and the model was developed. The authors acquired 479 valid samples of Chinese researchers through questionnaires and conducted an empirical analysis via AMOS 23.0. Findings Attitudes towards data sharing are significantly and positively correlated with trust, reciprocity and social interaction, but not with a shared vision; willingness to share data is significantly and positively correlated with attitudes and perceived behavioural control, but not with subjective norms; furthermore, data quality, which performed the function of a moderating variable, was found to play a facilitating role in the above correlations. Based on the findings, suggestions for relevant entities were specified. Originality/value The study developed and validated an integrated theoretical framework, clarified the mechanism by which social capital and planned behaviour affect willingness to share data, hoping to provide reference and empirical support for subsequent studies as well as new ideas for data management and sharing.
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