Objective To investigate medical students' cognitive status and attitude toward urban-rural integration and to find the influencing factors in an aim to provide information for the process of urban and rural integration.Methods Sampling survey was conducted among the junior students who were major in eight-year clinical medicine,five-year clinical medicine and nursing (undergraduate) with self-made questionnaire.The data entry was done by 19.0 SPSS software and descriptive statistical analysis and ONE-WAY ANOVA were used to do statistical analysis.Results Students who didn't know urban-rural integration accounted for 53.2％,while 80.7％ students supported urban-rural integration.Students' major and residence were two influencing factors of working in the countryside.Conclusion Measures should be taken to raise students' awareness of urban-rural integration based.Targeted measures should be adopted based on students' majors and residences.

Objective To observe the lipid regulation role of electroacupuncture intervention in the rats with non-alcoholic fatty liver disease(NAFLD)induced with high fat and cholesteol forage,and to clarify the regulation mechanism of serum retinol binding protein 4 (RBP4 )level and liver X receptor a (LXR-α)and sterol regulatory element binding protein-1c(SREBP-1c)expressions.Methods 44 female SD rats were fed for 7 d to adapt the environment and were randomly divided into normal group, model group, Dongbaogantai group, electroacupuncture group;11 rats in each group.The rats in normal group got routine feeding,and the others were fed with high fat and high cholesterol forage. After 8 weeks, the models were established, the rats in electroacupuncture group were treated with electroacupuncture method (1.5-2.0 Hz, D.-D.wave, 9V, 1-3 mA)in“Ganshu”,“Pishu”,“Geshu”for 15 min,once a day,lasted for 28 d.The changes of fasting blood-glucose(FBG),serum free fatty acids(FFA)and liver tissue homogenate triglyceride(TG)and total cholesterol (TC)levels of the rats in various groups were tested, and enzyme-linked immunosorbent (ELISA)was used to determine the serum RBP4 levels, and Western blotting method was used to detect the LXR-αand SREBP-1 c protein expression levels in rat liver tissue.Results Compared with normal group,the FBG,serum FFA,TG and TC levels in liver tissue homogenate and serum RBP4 level of the rats in model group were increased (P<0.01);the LXR-αand SREBP-1c protein expression levels were also increased (P<0.01).Compared with model group, the FBG,serum FFA,the TG and TC levels in liver tissue homogenate and serum RBP4 levels of the rats in electroacupuncture group and Dongbaogantai group were decreased (P<0.01);the LXR-αand SREBP-1c protein expression levels were also decreased (P< 0.05 or P< 0.01 ). Conclusion Electroacupuncture method in“Fenglong”,“Zusanli”,“Sanyinjiao”can reduce the serum RBP4 level, regulate the lipid metabolism, and improve the lipid deposition of the NAFLD rats;they have obvious therapeutic effect on NAFLD, and its mechanism may be related to inhibiting the increasing of LXR-αand SREBP-1 c protein expressions in liver tissue.

Objective:To establish a radiomics-based biomarker for predicting pathological response after preoperative neoadjuvant chemoradiotherapy (nCRT) in locally advanced esophageal cancer.Methods:From 2008 to 2018, 112 patients with locally advanced esophageal cancer who received nCRT were enrolled. All patients were treated with preoperative nCRT combined with surgery. Enhanced CT images and clinical information before nCRT were collected. A lesion volume of interest was manually delineated. In total, 670 radiomics features (including tumor intensity, shape and size, texture and wavelet characteristics) were extracted using the pyradiomics package in PYTHON. The stepwise regression combined with the best subset were employed to select the features, and finally the Logistic regression model was adopted to establish the prediction model. The performance of the classifier was evaluated by the area under the ROC curve (AUC). Results:The pathological complete remission (pCR) rate was 58.0%(65/112). 10 radiomics features were included in the final model, The most relevant radiomics feature was the gray feature (the texture information of the image), followed by the shape and voxel intensity-related features. In the training set, the AUC was 0.750 with a sensitivity of 0.711 and a specificity of 0.778, the corresponding values in the testing set were 0.870, 0.757 and 0.900, respectively.Conclusions:Models based on radiomics features from CT images can be utilized to predict the pathological response to nCRT in esophageal cancer. As it is efficient, non-invasive and economic model, it could serve as a promising tool for individualized treatment when validated by further prospective trials in the future.