BACKGROUND:Current studies have shown that electronic moxibustion can improve memory function in amnestic mild cognitive impairment;however,its mechanism of action needs to be further investigated.The atrophy of hippocampal volume and impairment of functional connectivity are important imaging markers of amnestic mild cognitive impairment.Whether electronic moxibustion can regulate the volume of hippocampal subregion of partients with amnestic mild cognitive impairment is worth studying. OBJECTIVE:To observe the effect of electronic moxibustion on the volume of hippocampal subregions in patients with amnestic mild cognitive impairment. METHODS:Forty patients with amnestic mild cognitive impairment were recruited from April 1,2018 to January 31,2019 at the community service centers around the Second Affiliated Hospital of Shenzhen University(Baoan Hospital of Southern Medical University),Shenzhen,China.They were randomly divided into treatment group(n=20)and control group(n=20).The treatment group was treated with electronic moxibustion of regulating the mind and benefiting the intelligence,while the control group was treated with placebo moxibustion.Moxibustion was given at 45 oC,20 minutes each time,once a day,5 times a week,for 8 weeks in total.Memory evaluation using Rivermead behavioral memory test and magnetic resonance imaging scanning for detecting the hippocampal subregion volume were performed for each patient before and after treatment,and cognitive function of each patient was assessed using Montreal cognitive assessment and mini-mental state examination.Correlation of hippocampal subregion volumes with scores on each scale was analyzed. RESULTS AND CONCLUSION:After treatment,the volumes of the left parasubiculum and the left hippocampal-amygdala migrating area increased in the treatment group but decreased in the control group,and there was a significant difference between the two groups(P＜0.05).Compared with the pre-treatment data,the Rivermead behavioral memory test,Montreal cognitive assessment,and mini-mental state examination scores were significantly higher in the treatment group after treatment(P＜0.05),while there was no significant change in the three scale scores in the control group after treatment(P＞0.05).The three scale scores were higher in the treatment group than in the control group after treatment(P＜0.05).Pearson correlation analysis showed that the changes in the volume of the left parasubiculum was significantly and positively correlated with the Rivermead behavioral memory test scale score in the treatment group(r=0.418,P=0.014).To conclude,electronic moxibustion can improve memory in patients with amnestic mild cognitive impairment,and the mechanism may be the regulation of structural plasticity in hippocampal subregions.

ObjectiveTo explore the clinical efficacy and safety of Qifu Yixin prescription in treating chronic heart failure in the patients with the syndrome of heart Qi deficiency， so as to provide clinical evidence for the treatment of chronic heart failure with this prescription and promote the clinical application and transformation of this prescription. MethodA total of 106 chronic heart failure patients with the syndrome of heart Qi deficiency who met the criteria in Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine （TCM） from September 2022 to May 2023 were selected and randomized into an observation group （53 cases） and a control group （53 cases）. Both groups received routine Western medicine treatment. In addition， the observation group received Qifu Yixin prescription， while the control group received placebo. The treatment course for both groups was 12 weeks. The New York heart association （NYHA） cardiac function grading， N-terminal pro-B-type natriuretic peptide （NT-proBNP）， soluble growth-stimulating expression gene 2 （sST2）， left ventricular ejection fraction （LVEF）， the ratio of early diastolic maximum mitral flow velocity （E） to early diastolic mitral annular motion velocity （e'） （E/e'）， left ventricular end diastolic diameter （LVEDD）， TCM syndrome scores， 6-minute walking test （6MWT）， Minnesota Living with Heart Failure Questionnaire （MLHFQ）， and Kansas City Cardiomyopathy Questionnaire （KCCQ） were determined before and after treatment. ResultFinally， 102 patients were included for analysis， including 51 patients in the observation group and 51 patients in the control group. After treatment， 42 patients in the observation group showed improved cardiac function grading， with a total response rate of 82.35%， and 32 patients in the control group showed improved cardiac function grading， with a total response rate of 62.75%. The total response rate regarding the cardiac function in the observation group was higher than that in the control group （χ²=4.923， P<0.05）. The observation group outperformed the control group in lowering the NT-proBNP level， elevating LVEF， decreasing the E/e' ratio （P<0.05）， reducing LVEDD and sST2 levels， and recovering TCM syndrome score， 6MWT score， MLHFQ score， and KCCQ score （P<0.05）. None of the safety indexes in the two groups showed abnormal values before and after treatment， and no serious adverse reaction was observed. ConclusionQifu Yixin prescription can improve the heart function， exercise tolerance， and quality of life and alleviate the TCM syndrome of the chronic heart failure patients with the syndrome of heart Qi deficiency by inhibiting myocardial fibrosis.

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.

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.

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.

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.

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.

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.