A sensitive chemiluminescence(CL) method was developed for determining melamine in urine and plasma samples based on the fact that melamine can remarkably enhance the chemiluminescence of Luminol-K3 Fe(CN)6 system in alkaline medium.The determination conditions were optimized.Under optimum conditions,the chemiluminescence intensity had a good linear relationship with melamine in the range of 9.0 × 10 97.0 × 10 6 g/mL with a correlation coefficient of 0.9992.The detection limits (3σ) were 3.54 ng/mL for urine sample and 6.58 ng/mL for plasma sample.The average recoveries of melamine were 102.6％ for urine sample and 95.1％ for plasma sample.Melamine in samples was extracted with liquid-liquid extraction procedures and the assay results coincided very well with that determined with flow injection chemiluminescence method.The method provides a reproducible and stable approach for sensitive detection and quantification of melamine in urine and plasma samples.

A sensitive chemiluminescence（CL） method was developed for determining melamine in urine and plasma samples based on the fact that melamine can remarkably enhance the chemiluminescence of Luminol-K3 Fe（CN）6 system in alkaline medium. The determination conditions were optimized. Under optimum conditions, the chemiluminescence intensity had a good linear relationship with melamine in the range of 9.0 × 10^-9 - 7.0 × 10^-6 g/mL with a correlation coefficient of 0. 9992. The detection limits （3o） were 3.54 ng/mL for urine sample and 6.58 ng/mL for plasma sample. The average recoveries of melamine were 102.6% for urine sample and 95.1% for plasma sample. Melamine in samples was extracted with liquid-liquid extraction procedures and the assay results coincided very well with that determined with flow injection chemiluminescence method. The method provides a reproducible and stable approach for sensitive detection and quantification of melamine in urine and plasma samples.

Spasmolytic polypeptide expressing metaplasia(SPEM)is a kind of metaplasia of gastric mucosa.There are three hypotheses of its origin,including chief cell transdifferentiation,stem cell differentiation,and pre-SPEM hypotheses.Currently,animal models are commonly used for the mechanism research.According to results of the researches,SPEM plays a role in the repair of gastric mucosal damage,Helicobacter pylori infection and the development of gastric cancer.This paper provides an overview of the above issues and the multiple ways of modeling the SPEM.

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.

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.

Interrupted time series (ITS) is a statistical method for the quasi?experimental design specific to the outcome of time series, in which the effectiveness of an intervening measure is evaluated by examining change in slope and immediate change in level. The key feature of ITS is that the secular trend of time series prior to the intervention can be effectively controlled so as to accurately estimate the intervention effect. The design principle and statistical method for ITS were illustrated by an example of evaluating halving policy for the expert registration fee in the general hospital of a city. The segmented linear regression was used to fit the above time series data and the results were explained in detail. Meanwhile, the study design and model fitting along with explanations of the results with respect to the effects of two types of successive interventions and on different time?points of an intervention were illustrated as well in this paper. The existed upward or downward trend should be taken into account in order to accurately estimate the intervention effect as it exists in most of the public health surveillance data. Two parameters, known as change in slope and immediate change in level, were employed to evaluate the effect of the intervention. The ITS analysis can be widely applied to the program evaluation as it could enrich methods of the evaluation compared to the traditional model of the program evaluation.

In general, the application conditions of linear regression models could be met after the natural logarithmic transformation of data. From the practical perspective, this paper introduced the linear regression models with natural logarithmic transformation of independent variable, dependent variable, and both independent and dependent variables in detail. The paper illustrated why the equation and coefficients could not be directly explained after the natural logarithmic transformation of data. The percentage changes of X and/or Y were applied to elaborate the principle and method for the explanation of the equation and coefficients. Three examples were used to fit simple linear models with natural logarithmic transformation of independent, dependent, and both independent and dependent variables and the results of theses models were explained in detail.

Interrupted time series (ITS) is a statistical method for the quasi?experimental design specific to the outcome of time series, in which the effectiveness of an intervening measure is evaluated by examining change in slope and immediate change in level. The key feature of ITS is that the secular trend of time series prior to the intervention can be effectively controlled so as to accurately estimate the intervention effect. The design principle and statistical method for ITS were illustrated by an example of evaluating halving policy for the expert registration fee in the general hospital of a city. The segmented linear regression was used to fit the above time series data and the results were explained in detail. Meanwhile, the study design and model fitting along with explanations of the results with respect to the effects of two types of successive interventions and on different time?points of an intervention were illustrated as well in this paper. The existed upward or downward trend should be taken into account in order to accurately estimate the intervention effect as it exists in most of the public health surveillance data. Two parameters, known as change in slope and immediate change in level, were employed to evaluate the effect of the intervention. The ITS analysis can be widely applied to the program evaluation as it could enrich methods of the evaluation compared to the traditional model of the program evaluation.

In general, the application conditions of linear regression models could be met after the natural logarithmic transformation of data. From the practical perspective, this paper introduced the linear regression models with natural logarithmic transformation of independent variable, dependent variable, and both independent and dependent variables in detail. The paper illustrated why the equation and coefficients could not be directly explained after the natural logarithmic transformation of data. The percentage changes of X and/or Y were applied to elaborate the principle and method for the explanation of the equation and coefficients. Three examples were used to fit simple linear models with natural logarithmic transformation of independent, dependent, and both independent and dependent variables and the results of theses models were explained in detail.