Objective:Qu Du Qiang Fei 1 Hao Fang(QDQF1)is a novel Chinese herbal medicine formula used to treat coronavirus disease 2019(COVID-19).However,the pharmacological mechanisms of action of QDQF1 remain unclear.The objec...Objective:Qu Du Qiang Fei 1 Hao Fang(QDQF1)is a novel Chinese herbal medicine formula used to treat coronavirus disease 2019(COVID-19).However,the pharmacological mechanisms of action of QDQF1 remain unclear.The objective of this study was to identify the effective ingredients and biological targets of QDQF1 for COVID-19 treatment.Materials and Methods:The effective ingredients and mechanisms of action of QDQF1 were analyzed by using network pharmacology methods,which included an analysis of the effective ingredients and corresponding targets,COVID-19-related target acquisition,compound-target network analyses,protein-protein interaction network analysis,Kyoto Encyclopedia of Genes and Genomes(KEGG)and Gene Ontology(GO)enrichment analyses,and molecular docking studies.Results:In total,288 effective QDQF1 ingredients were identified.We identified 51 core targets from the 148 targets through an overlap between putative QDQF1 targets and COVID-19-related targets.Six key components,including formononetin,kaempferol,luteolin,naringenin,quercetin,and wogonin were identified through component-target network analyses.GO functional enrichment analysis of the core targets revealed 1296 items,while KEGG pathway enrichment analysis identified 148 signaling pathways.Nine central targets(CCL2,CXCL8,IL1B,IL6,MAPK1,MAPK3,MAPK8,STAT3,and TNF)related to the COVID-19 pathway were identified in the KEGG pathway enrichment analysis.Furthermore,molecular docking analysis suggested that the docking scores of the six key components to the nine central targets were better than those to remdesivir.Conclusions:QDQF1 may regulate multiple immune-and inflammation-related targets to inhibit the progression of severe acute respiratory syndrome coronavirus 2,and thus,may be suitable for the treatment of COVID-19.展开更多
This paper considers the modeling and convergence of hyper-networked evolutionary games （HNEGs）. In an HNEG the network graph is a hypergraph, which allows the fundamental network game to be a multi-player one. Usin...This paper considers the modeling and convergence of hyper-networked evolutionary games （HNEGs）. In an HNEG the network graph is a hypergraph, which allows the fundamental network game to be a multi-player one. Using semi-tensor product of matrices and the fundamental evolutionary equation, the dynamics of an HNEG is obtained and we extend the results about the networked evolutionary games to show whether an HNEG is potential and how to calculate the potential. Then we propose a new strategy updating rule, called the cascading myopic best response adjustment rule （MBRAR）, and prove that under the cascading MBRAR the strategies of an HNEG will converge to a pure Nash equilibrium. An example is presented and discussed in detail to demonstrate the theoretical and numerical results.展开更多
基金funded by the National Natural Science Foundation of China(81973543)the Shanghai Sailing Program(No.22YF1449800),China。
文摘Objective:Qu Du Qiang Fei 1 Hao Fang(QDQF1)is a novel Chinese herbal medicine formula used to treat coronavirus disease 2019(COVID-19).However,the pharmacological mechanisms of action of QDQF1 remain unclear.The objective of this study was to identify the effective ingredients and biological targets of QDQF1 for COVID-19 treatment.Materials and Methods:The effective ingredients and mechanisms of action of QDQF1 were analyzed by using network pharmacology methods,which included an analysis of the effective ingredients and corresponding targets,COVID-19-related target acquisition,compound-target network analyses,protein-protein interaction network analysis,Kyoto Encyclopedia of Genes and Genomes(KEGG)and Gene Ontology(GO)enrichment analyses,and molecular docking studies.Results:In total,288 effective QDQF1 ingredients were identified.We identified 51 core targets from the 148 targets through an overlap between putative QDQF1 targets and COVID-19-related targets.Six key components,including formononetin,kaempferol,luteolin,naringenin,quercetin,and wogonin were identified through component-target network analyses.GO functional enrichment analysis of the core targets revealed 1296 items,while KEGG pathway enrichment analysis identified 148 signaling pathways.Nine central targets(CCL2,CXCL8,IL1B,IL6,MAPK1,MAPK3,MAPK8,STAT3,and TNF)related to the COVID-19 pathway were identified in the KEGG pathway enrichment analysis.Furthermore,molecular docking analysis suggested that the docking scores of the six key components to the nine central targets were better than those to remdesivir.Conclusions:QDQF1 may regulate multiple immune-and inflammation-related targets to inhibit the progression of severe acute respiratory syndrome coronavirus 2,and thus,may be suitable for the treatment of COVID-19.
基金supported partly by National Natural Science Foundation of China(Nos.61074114 and 61273013)
文摘This paper considers the modeling and convergence of hyper-networked evolutionary games （HNEGs）. In an HNEG the network graph is a hypergraph, which allows the fundamental network game to be a multi-player one. Using semi-tensor product of matrices and the fundamental evolutionary equation, the dynamics of an HNEG is obtained and we extend the results about the networked evolutionary games to show whether an HNEG is potential and how to calculate the potential. Then we propose a new strategy updating rule, called the cascading myopic best response adjustment rule （MBRAR）, and prove that under the cascading MBRAR the strategies of an HNEG will converge to a pure Nash equilibrium. An example is presented and discussed in detail to demonstrate the theoretical and numerical results.
