Objective: This study aims to investigate the potential targets of diosgenin for the treatment of Alzheimer's disease (AD) and Coronavirus Disease 2019 (COVID-19) through the utilization of bioinformatics, network...Objective: This study aims to investigate the potential targets of diosgenin for the treatment of Alzheimer's disease (AD) and Coronavirus Disease 2019 (COVID-19) through the utilization of bioinformatics, network pharmacology, and molecular docking techniques. Methods: Differential expression genes (DEGs) shared by AD and COVID-19 were enriched by bioinformatics. Additionally, regulatory networks were analyzed to identify key genes in the Transcription Factor (TF) of both diseases. The networks were visualized using Cytoscape. Utilizing the DGIdb database, an investigation was conducted to identify potential drugs capable of treating both Alzheimer's disease (AD) and COVID-19. Subsequently, a Venn diagram analysis was performed using the drugs associated with AD and COVID-19 in the CTD database, leading to the identification of diosgenin as a promising candidate for the treatment of both AD and COVID-19.SEA, SuperPred, Swiss Target Prediction and TCMSP were used to predict the target of diosgenin in the treatment of AD and COVID-19, and the target of diosgenin in the treatment of AD and COVID-19 was determined by Wayne diagram intersection analysis with the differentially expressed genes of AD and COVID- 19. Their Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed jointly. Genomes The Protein Protein Interaction (PPI) network of these drug targets was constructed, and core targets with the highest correlation were screened out. The binding of diosgenin to these core targets was analyzed by molecular docking. Results: Through enrichment and cluster analysis, it was found that the biological processes, pathways and diseases enriched by DEGs in AD and COVID-19 were all related to inflammation and immune regulation. These common DEGs and Trust databases were used to construct AD and COVID-19 TFs regulatory networks. Diosgenin was predicted as a potential drug for the treatment of AD and COVID-19 by network pharmacology, and 36 targets of diosgenin for the treatment of AD and 27 targets for COVID-19 were revealed. The six core targets with the highest correlation were selected for molecular docking with diosgenin using CytohHubba to calculate the scores. Conclusions: This study firstly revealed that the common TFs regulatory network of AD and COVID-19, and predicted and verified diosgenin as a potential drug for the treatment of AD and COVID-19. The binding of diosgenin to the core pharmacological targets for the treatment of AD and COVID-19 was determined by molecular docking, which provides a theoretical basis for developing a new approach to clinical treatment of AD and COVID-19.展开更多
为了充分考虑介质运动对声传播的影响,建立了一种利用高斯波束法求解亚音速运动介质中的声传播问题的模型.该模型基于高频近似任意马赫数的速度势函数亥姆霍兹方程,采用波束追踪方法,推导了运动介质中的动态射线方程组,进而将偏微分方...为了充分考虑介质运动对声传播的影响,建立了一种利用高斯波束法求解亚音速运动介质中的声传播问题的模型.该模型基于高频近似任意马赫数的速度势函数亥姆霍兹方程,采用波束追踪方法,推导了运动介质中的动态射线方程组,进而将偏微分方程转换成常微分方程组的形式.理论表明运动介质中波束的扩展更为复杂,并且声线管束内的能量不一定守恒.将该模型应用于标准问题、水平分层大气次声三维远距离传播问题和墨西哥湾流流域的声传播问题,仿真结果表明,相比于常用的N×2D近似计算方法,运动介质中的高斯波束追踪法充分考虑了介质运动的影响,特别是横风的作用,可以更加精确地计算运动介质中的三维声场;尽管海流的马赫数很小,但是同样会定量地改变声传播,影响会聚区位置,在一些区域考虑海流和不考虑海流的计算结果相差5 d B以上.展开更多
基金Research and Development and Industrialization Demonstration of Xinjiang Special Medicinal Materials,Antiinfective Drugs and Disinfection Products-Construction of Xinjiang Special Resource Antiinfective Drug Research and Development Platform(No.2021A03002-4)。
文摘Objective: This study aims to investigate the potential targets of diosgenin for the treatment of Alzheimer's disease (AD) and Coronavirus Disease 2019 (COVID-19) through the utilization of bioinformatics, network pharmacology, and molecular docking techniques. Methods: Differential expression genes (DEGs) shared by AD and COVID-19 were enriched by bioinformatics. Additionally, regulatory networks were analyzed to identify key genes in the Transcription Factor (TF) of both diseases. The networks were visualized using Cytoscape. Utilizing the DGIdb database, an investigation was conducted to identify potential drugs capable of treating both Alzheimer's disease (AD) and COVID-19. Subsequently, a Venn diagram analysis was performed using the drugs associated with AD and COVID-19 in the CTD database, leading to the identification of diosgenin as a promising candidate for the treatment of both AD and COVID-19.SEA, SuperPred, Swiss Target Prediction and TCMSP were used to predict the target of diosgenin in the treatment of AD and COVID-19, and the target of diosgenin in the treatment of AD and COVID-19 was determined by Wayne diagram intersection analysis with the differentially expressed genes of AD and COVID- 19. Their Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed jointly. Genomes The Protein Protein Interaction (PPI) network of these drug targets was constructed, and core targets with the highest correlation were screened out. The binding of diosgenin to these core targets was analyzed by molecular docking. Results: Through enrichment and cluster analysis, it was found that the biological processes, pathways and diseases enriched by DEGs in AD and COVID-19 were all related to inflammation and immune regulation. These common DEGs and Trust databases were used to construct AD and COVID-19 TFs regulatory networks. Diosgenin was predicted as a potential drug for the treatment of AD and COVID-19 by network pharmacology, and 36 targets of diosgenin for the treatment of AD and 27 targets for COVID-19 were revealed. The six core targets with the highest correlation were selected for molecular docking with diosgenin using CytohHubba to calculate the scores. Conclusions: This study firstly revealed that the common TFs regulatory network of AD and COVID-19, and predicted and verified diosgenin as a potential drug for the treatment of AD and COVID-19. The binding of diosgenin to the core pharmacological targets for the treatment of AD and COVID-19 was determined by molecular docking, which provides a theoretical basis for developing a new approach to clinical treatment of AD and COVID-19.
文摘为了充分考虑介质运动对声传播的影响,建立了一种利用高斯波束法求解亚音速运动介质中的声传播问题的模型.该模型基于高频近似任意马赫数的速度势函数亥姆霍兹方程,采用波束追踪方法,推导了运动介质中的动态射线方程组,进而将偏微分方程转换成常微分方程组的形式.理论表明运动介质中波束的扩展更为复杂,并且声线管束内的能量不一定守恒.将该模型应用于标准问题、水平分层大气次声三维远距离传播问题和墨西哥湾流流域的声传播问题,仿真结果表明,相比于常用的N×2D近似计算方法,运动介质中的高斯波束追踪法充分考虑了介质运动的影响,特别是横风的作用,可以更加精确地计算运动介质中的三维声场;尽管海流的马赫数很小,但是同样会定量地改变声传播,影响会聚区位置,在一些区域考虑海流和不考虑海流的计算结果相差5 d B以上.
