Objective To investigate the effect of CKLF-like MARVEL transmembrane domain contai-ning member 5(CMTM 5)on EG-VEGF expression in prostate cancer cells , and to detect the potential mechanism of CMTM5 inhibiting prostate cancer .Methods The relative expression of CMTM 5 and EG-VEGF in prostate cells and prostate cancer cells was detected .Prostate cancer cells were given Plasmid transfection to overexpress CMTM5 and EG-VEGF expression was again detected .Then CMTM5 and EG-VEGF were compared between be-fore and after CMTM5 plasmid transfecting prostate cancer cells .Results Compared with the relative expresion of EG-VEGF and CMTM5 in prostate cells , prostate cancer cells showed high expression of EG-VEGF and low ex-pression of CMTM5, which were statistically significant , P<0.05.Compared with prostate cancer cells , the rela-tive expression of CMTM5 were obviously upregulated and EG-VEGF obviously decreased in prostate cancer cell after transfected by CMTM5 plasmid, which were statistically significant , P<0.05.Conclusions Prostate canc-er cells shows higher EG-VEGF expression and lower CMTM 5 campared with normal prostate cells .EG-VEGF is suppressed significantly when the prostate cancer cell is transfected by CMTM 5 plasmid and shows highlevel of CMTM5 expression , suggesting high expression of CMTM 5 may inhibit the development of prostate cancer by downregulating EG-VEGF expression .

Objective To establish a HPLC method for the determination of Shionone in Ziwan Zhisou granule. Methods Phenomenex C18 column (250? 4.6 mm) was used. The mobile phase was acetonitrile and the detection wave length was at 203 nm. Result A good linearity of Shionone was in the range of 0.025～ 0.250 ? g/? L, r=0.9996. The average recovery was 97.8 % , RSD was 2.37 % ( n=5) . Conclusion This method is sensitive, accurate and simple and with good reproducibility for the determination of Shionone in Ziwan Zhisou granule. 

Objective To study the role of glucagon-like peptide-1 (GLP-1) analogue liraglutide played in the proliferation of CD4+CD25 T cells in normal people and newly-onset type 1 diabetic patients,and to evaluate the possible immune regulatory role of liraglutide in the therapy of type 1 diabetes.Methods CD4+ CD25-T cells of 10 normal people and 10 newly-onset type 1 diabetic patients were separated from peripheral blood by MACS immunomagnetic beads and stimulated by Human T-Activator CD3/CD28 Dynabeads to proliferate.CFSE labeling technique was used to evaluate the proliferation of CD4+ CD25-T cells by flow cytometry.Liraglutide of different concentrations(0,25,50,and 100 nmol/ml) was added to the proliferation system,then the proliferation of CD4+CD25-T cell was measured.Results (1) Liraglutide suppressed the proliferation of CD4+ CD25-T cells from either normal people or type 1 diahetic patients with dose-dependent manner (P ＜ 0.05).(2) Under the different concentrationsofliraglutide,the proliferation ofCD4+CD25 T cells from diabetic patients was mueh more robust than that of normal people (P＜0.01).(3) The inhibitory effects of liraglutide on CD4+ CD25-T cells proliferation in normal people and diabetic patients were similar (P＞0.05).Conclusion The proliferation of CD4+ CD25 T cells in type 1 diabetic patients was more robust than normal people,which indicated cellular immune dysfunction in type 1diabetes.Liraglutide inhibits the proliferation of CD4+ CD25-T cells of type 1 diabetic patients in vitro.The immunosuppression effect of liraglutide may have potential value in the treatment of type 1 diabetes.

Logistic regression is a kind of multiple regression method to analyze the relationship between a binary outcome or categorical outcome and multiple influencing factors, including multiple logistic regression, conditional logistic regression, polytomous logistic regression, ordinal logistic regression and adjacent categorical logistic regression. This paper illustrates the basic principle, independent variable selection and assignment, applied condition, model evaluation and diagnosis for multiple logistic regression model. Moreover, the principle and application for polytomous logistic regression and ordinal logistic regression models were also introduced. By providing SAS codes and detailed explanations of the result for an example of obesity, readers could be able to better understand logistic regression model, and apply this method correctly to their research and daily work, so as to improve their capacity of the data analysis.

Logistic regression is a kind of multiple regression method to analyze the relationship between a binary outcome or categorical outcome and multiple influencing factors, including multiple logistic regression, conditional logistic regression, polytomous logistic regression, ordinal logistic regression and adjacent categorical logistic regression. This paper illustrates the basic principle, independent variable selection and assignment, applied condition, model evaluation and diagnosis for multiple logistic regression model. Moreover, the principle and application for polytomous logistic regression and ordinal logistic regression models were also introduced. By providing SAS codes and detailed explanations of the result for an example of obesity, readers could be able to better understand logistic regression model, and apply this method correctly to their research and daily work, so as to improve their capacity of the data analysis.

Analyses of the multivariate regression model are ued very widely in the medical research. Analytical methods of the mutivariate regression model including multiple linear regression, logistic regression, Poisson regression and Cox proportional hazard model were introduced in this article. The contents of the article covered the application conditions of regression models, analytical procedures, strategies of selecting independent variables, extended discussions of regression models and application notes. It is expected that authors could understand the principle of the mutivariate regression model, accurately use these analytical methods in their research, improve the efficiency of data utilization, and enhance the level of statistical analyses.

Multiple Linear Regression (MLR) is a generalization of simple linear regression and is one of the commonly used models in multivariate statistical analysis. This article introduces the MLR model from the perspective of practical application. Four parts, including basic principle, application examples, the application condition and diagnosis, and the extension of the model, are sequentially illustrated in this article. Particularly, in the last part, alternative methods of the model are introduced when the application condition of the model is not met. We sincerely hope that this article could make our audiences have a better understanding of the MLR model in order to improve the efficiency of data utilization and statistical analysis by correctly performing this model in their research.

Logistic regression is a kind of multiple regression method to analyze the relationship between a binary outcome or categorical outcome and multiple influencing factors, including multiple logistic regression, conditional logistic regression, polytomous logistic regression, ordinal logistic regression and adjacent categorical logistic regression. This paper illustrates the basic principle, independent variable selection and assignment, applied condition, model evaluation and diagnosis for multiple logistic regression model. Moreover, the principle and application for polytomous logistic regression and ordinal logistic regression models were also introduced. By providing SAS codes and detailed explanations of the result for an example of obesity, readers could be able to better understand logistic regression model, and apply this method correctly to their research and daily work, so as to improve their capacity of the data analysis.

Analyses of the multivariate regression model are ued very widely in the medical research. Analytical methods of the mutivariate regression model including multiple linear regression, logistic regression, Poisson regression and Cox proportional hazard model were introduced in this article. The contents of the article covered the application conditions of regression models, analytical procedures, strategies of selecting independent variables, extended discussions of regression models and application notes. It is expected that authors could understand the principle of the mutivariate regression model, accurately use these analytical methods in their research, improve the efficiency of data utilization, and enhance the level of statistical analyses.

Multiple Linear Regression (MLR) is a generalization of simple linear regression and is one of the commonly used models in multivariate statistical analysis. This article introduces the MLR model from the perspective of practical application. Four parts, including basic principle, application examples, the application condition and diagnosis, and the extension of the model, are sequentially illustrated in this article. Particularly, in the last part, alternative methods of the model are introduced when the application condition of the model is not met. We sincerely hope that this article could make our audiences have a better understanding of the MLR model in order to improve the efficiency of data utilization and statistical analysis by correctly performing this model in their research.