The objective of this work was to investigate the mechanism of action of Balanophora involucrata polyphenolic compounds in the treatment of myocardial injury.In the present study,Balanophora involucrata was extracted ...The objective of this work was to investigate the mechanism of action of Balanophora involucrata polyphenolic compounds in the treatment of myocardial injury.In the present study,Balanophora involucrata was extracted by refluxing 75%of ethanol.The obtained extract was extracted with petroleum ether,ethyl acetate and n-butanol respectively.And the ethyl acetate layer was separated.The extract was prepared by silica gel column chromatography,sephadex LH-20 elution and thin layer chromatography.After that,the Swiss target prediction database was utilized to obtain the targets of Balanophora involucrata,and the Genecards,OMIM and TTD databases were used to predict and screen the targets of Balanophora involucrata for the treatment of myocardial injury.The active ingredient-target network was constructed using Cytoscape software,and the PPI network was mapped using String database and Cytoscape software.GO bioprocess enrichment analysis and KEGG pathway enrichment analysis were performed by Metascape software to predict the mechanism of action.Molecular docking was performed in Discovery Studio 2016 client software to verify the binding of Balanophora involucrata polyphenols to key targets.In this study,six polyphenolic compounds were isolated from Balanophora involucrata.By GO enrichment analysis,1614 biological processes(BP),127 cellular compositions(CC),and 215 molecular functions(MF)were obtained;a total of 155 cross-targets were involved in the KEGG enrichment analysis.The PPI network showed that quercetin was the main active component of polyphenolic compounds against myocardial injury and that AKT1,EGFR,STAT3,SRC,ESR1,MMP9,HSP90AA1 and other related signals were associated with myocardial injury treatment.Finally,the multi-component-multi-target-multi-pathway action of Balanophora involucrata was concluded,which provided new ideas and methods for further research on the mechanism of action of Balanophora involucrata in myocardial injury.展开更多
The Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST)has been in normal operation for more than 10 yr,and routine maintenance is performed on the fiber positioner every summer.The positioning accuracy ...The Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST)has been in normal operation for more than 10 yr,and routine maintenance is performed on the fiber positioner every summer.The positioning accuracy of the fiber positioner directly affects the observation performance of LAMOST,and incorrect fiber positioner positioning accuracy will not only increase the interference probability of adjacent fiber positioners but also reduces the observation efficiency of LAMOST.At present,during the manual maintenance process of the positioner,the fault cause of the positioner is determined and analyzed when the positioning accuracy does not meet the preset requirements.This causes maintenance to take a long time,and the efficiency is low.To quickly locate the fault cause of the positioner,the repeated positioning accuracy and open-loop calibration curve data of each positioner are obtained in this paper through the photographic measurement method.Based on a systematic analysis of the operational characteristics of the faulty positioner,the fault causes are classified.After training a deep learning model based on long short-term memory,the positioner fault causes can be quickly located to effectively improve the efficiency of positioner fault cause analysis.The relevant data can also provide valuable information for annual routine maintenance methods and positioner designs in the future.The method of using a deep learning model to analyze positioner operation failures introduced in this paper is also of general significance for the maintenance and design optimization of fiber positioners using a similar double-turn gear transmission system.展开更多
基金Project supported by National Training Program of Innovation and Entrepreneurship for Undergraduates(202310163020,S202310163079).
文摘The objective of this work was to investigate the mechanism of action of Balanophora involucrata polyphenolic compounds in the treatment of myocardial injury.In the present study,Balanophora involucrata was extracted by refluxing 75%of ethanol.The obtained extract was extracted with petroleum ether,ethyl acetate and n-butanol respectively.And the ethyl acetate layer was separated.The extract was prepared by silica gel column chromatography,sephadex LH-20 elution and thin layer chromatography.After that,the Swiss target prediction database was utilized to obtain the targets of Balanophora involucrata,and the Genecards,OMIM and TTD databases were used to predict and screen the targets of Balanophora involucrata for the treatment of myocardial injury.The active ingredient-target network was constructed using Cytoscape software,and the PPI network was mapped using String database and Cytoscape software.GO bioprocess enrichment analysis and KEGG pathway enrichment analysis were performed by Metascape software to predict the mechanism of action.Molecular docking was performed in Discovery Studio 2016 client software to verify the binding of Balanophora involucrata polyphenols to key targets.In this study,six polyphenolic compounds were isolated from Balanophora involucrata.By GO enrichment analysis,1614 biological processes(BP),127 cellular compositions(CC),and 215 molecular functions(MF)were obtained;a total of 155 cross-targets were involved in the KEGG enrichment analysis.The PPI network showed that quercetin was the main active component of polyphenolic compounds against myocardial injury and that AKT1,EGFR,STAT3,SRC,ESR1,MMP9,HSP90AA1 and other related signals were associated with myocardial injury treatment.Finally,the multi-component-multi-target-multi-pathway action of Balanophora involucrata was concluded,which provided new ideas and methods for further research on the mechanism of action of Balanophora involucrata in myocardial injury.
基金Funding for the research was provided by Cui Xiangqun’s Academician StudioFunding for the project has been provided by the National Development and Reform Commission。
文摘The Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST)has been in normal operation for more than 10 yr,and routine maintenance is performed on the fiber positioner every summer.The positioning accuracy of the fiber positioner directly affects the observation performance of LAMOST,and incorrect fiber positioner positioning accuracy will not only increase the interference probability of adjacent fiber positioners but also reduces the observation efficiency of LAMOST.At present,during the manual maintenance process of the positioner,the fault cause of the positioner is determined and analyzed when the positioning accuracy does not meet the preset requirements.This causes maintenance to take a long time,and the efficiency is low.To quickly locate the fault cause of the positioner,the repeated positioning accuracy and open-loop calibration curve data of each positioner are obtained in this paper through the photographic measurement method.Based on a systematic analysis of the operational characteristics of the faulty positioner,the fault causes are classified.After training a deep learning model based on long short-term memory,the positioner fault causes can be quickly located to effectively improve the efficiency of positioner fault cause analysis.The relevant data can also provide valuable information for annual routine maintenance methods and positioner designs in the future.The method of using a deep learning model to analyze positioner operation failures introduced in this paper is also of general significance for the maintenance and design optimization of fiber positioners using a similar double-turn gear transmission system.
