Study on medication rules of traditional Chinese medicine for Meniere's disease based on data analysis

Objective:Study on medication rules of traditional Chinese medicine in treating Meniere's disease based on data mining technology.Methods:Computer retrieval with since establishment of the“CNKI”,“WF”,“VIP”,there have been literatures on the treatment of Meniere syndrome with Traditional Chinses medicine.Memory preprocessing in accordance with inclusion criteria.Then,EXCLE 2010,SPSS Statistics(ver.25)and SPSS Modeler(ver.18.0)were adopted respectively for frequency analysis,cluster analysis and association rules analysis.Result:A total of 133 references were included in this study,146 prescriptions,192 kinds of drugs,and the total frequency of drugs was 1702.The high frequency Meniere syndrome types were spleen deficiency and phlegm dampness syndrome,wind phlegm syndrome,gallbladder depression and phlegm disturbance syndrome,hyperactivity of liver yang syndrome,phlegm and blood stasis syndrome.The efficacy of drug frequency≥25 were summarized as follows,antiasthmatic drugs for relieving phlegm and relieving cough,diuresis-removing dampness,Tonic drugs,etc.High frequency meridian of drugs are:lung,liver,spleen,etc.High frequency drug properties are:temperature,cold,flat etc.High frequency drug taste:bitter,sweet,pungent,etc.Core drugs:Pinellia,Atractylodes,Poria,Gastrodia,etc.The main prescriptions werealisma soup,Banxia Baizhu Tianma Decoction,Zhengan Xifeng Decoction and Xiaochaihu Decoction.Conclusion:In this study,data mining was used to sort out the treatment of Meniere's disease by traditional Chinese medicine.It was found that the treatment of Meniere's disease was mainly to calm the liver and strengthen the spleen,supplemented by regulating qi and activating blood,clearing heat and opening orifices,tonifying deficiency and tranquilizing mind.

Bioinformatics screening of breast cancer-related genes and potential drug research

Objective:To search the the differentially expressed genes between breast cell carcinoma tissues and normal tissues by using bioinformatics technology,and the potential therapeutic drugs for breast cancer were identified,which can provide reference for clinical immune targeted therapy and drug therapy of breast cancer in the future.Methods:"Breast cancer"was searched by using Gene Expression Omnibus(GEO),and GSE79586 chip data was downloaded.The differentially expressed genes in the control group and the breast cancer model group were screened by using bio-communication technology and subjected to GO function analysis,KEGG pathway analysis,differential gene characteristic expression analysis and protein-protein interaction network(PPI)analysis,and the analysis results were further visualized.Prognosis analysis,related function prediction and immune infiltration analysis were performed using the GEPIA,GeneMANIA,and Timer2.0 databases,respectively.Finally,the compounds with potential therapeutic effects on breast cancer are identified through Connectivity Map(CMap).Western blotting and real-time PCR(RT-PCR)were used to verify the core genes and potential therapeutic agents with the highest correlation in vitro.Results:A total of 3916 differentially expressed genes including 1786 up-regulated genes and 2130 down-regulated genes were screened.GO analysis showed that the differential genes were mainly involved in the positive regulation of phosphorylation,secretory vesicles,racemase and epimerase activities.KEGG analysis showed that differential genes were involved in systemic lupus erythematosus,alcoholism,sticky spots,amoebic dysentery Ras signal pathways and other disease pathways.The characteristic expression analysis of differential genes showed that MEK inhibitors,HSP90 inhibitors and signal transduction pathway kinase inhibitors were drugs similar to the differential genes.PPI results showed that H2AFJ,TFF1,GATA3,FOXA1,and CDH1 were core genes related to breast cancer.Two core genes of H2AFJ and TFF1 with the highest correlation were further selected for GEPIA analysis.The results of the analysis showed that the mRNA expression levels of H2AFJ and TFF1 in breast cancer cells were significantly higher than those in normal tissues,and there was a significant correlation with the pathological staging,overall survival rate and disease-free survival rate of breast cancer patients.H2AFJ and TFF1 may be potential prognostic biomarkers for survival of breast cancer patients.The functions of differentially expressed H2AFJ and TFF1 are mainly related to hormone receptor binding,epithelial structure maintenance and epigenetic negative regulation of genes,chromatin tissue involved in negative regulation of transcription,etc.The results of immune infiltration showed that the expressions of H2AFJ and TFF1 had a significant correlation with the infiltration of macrophages,neutrophils,monocytes,CD4+T,CD8+T,B lymphocytes and other immune cells.CMap results showed that compounds such as Gefitinib,Alpelisib,Sorafenib,and Sunitinib had potential therapeutic effects on breast cancer.Western blot and RT-PCR results showed that H2AFJ and TFF1 were significantly overexpressed in breast cancer cells.Gefitinib significantly inhibited the expression of H2AFJ and TFF1 in breast cancer cells(P<0.05,P<0.01).Conclusion:In this study,differentially expressed genes between breast cell carcinoma tissues and normal tissues were screened out by bioinformatics means to further identify key genes and compounds with potential therapeutic effects in the onset process of breast cancer and to further verify the effectiveness of the screened drugs on breast cancer through experiments.It will provide reference for clinical research and development of new drugs against breast cancer in the future in order to develop more effective treatment options.

Optimization of extraction technology of safflower polysaccharide based on central composite design-response surface methodology

Objective:To explore the best extraction technology of safflower polysaccharide by central composite design response surface methodology and evaluate its quality.Methods:Taking the extraction rate of Carthamus tinctorius polysaccharide as the index,taking the ratio of solid to liquid,extraction time,extraction temperature and extraction times as the investigation factors,based on the single factor experiment,the central composite design-response surface methodology was used to optimize the optimum extraction process of Carthamus tinctorius polysaccharide and verify it.Results:The response surface model was established with the extraction rate of Carthamus tinctorius polysaccharide as dependent variable Y,the ratio of solid to liquid,extraction time and extraction temperature as independent variables X,P<0.0001.The optimum extraction process was as follows:the ratio of solid to liquid was 1:16.69,the extraction temperature was 91.39℃,and the extraction working time was 89.78min.Under these conditions,the extraction rate of safflower polysaccharide can reach 7.45%,The experimental results show that RSD is 1.05%,and the model can well predict the experimental results.Conclusion:Central Composite Design-Response Surface Methodology has the advantages of high extraction rate,simple,effective and reasonable process operation,high stability and high precision,which can be fully applied to the resource management and utilization of safflower polysaccharide.