Objective To observe the alteration of capillary density and apelin/APJ expression in soleus muscles of high-fat diet rats.Methods Male 5-week-old Sprague-Dawley rats were randomly divided into a control group and a high-fat diet group.After 12 weeks of high-fat diet,16 rats were se lected and randomly divided into a sedentary group and a treadmill running group.The exercise rats underwent 60-minute treadmill running at 26 m/min 5 days a week for 10 weeks.The body weight,body fat and blood lipid level were measured for all rats.The protein expression of Soleus CD31 and apelin was determined using immunohistochemical staining,soleus apelin content was determined using the radioimmunoassay,and the mRNA expression of apelin/APJ was detected using real-time PCR.Results Compared with the control group,significant increase was observed in the body weight,body fat and the level of total triglyceride,total cholesterol and low density lipoprotein cholesterin,but significant decrease was found in the high density lipoprotein cholesterin in the high-fat diet group.There were no significant differences in the capillary density and mRNA levels of apelin/APJ between the two groups.Compared with the sedentary high-fat diet group,significant improvement was observed in the body weight and blood lipid level of the treadmill running group.Moreover,significant increase was also observed in the capillary density,the expression of apelin/APJ mRNA,as well as that of apelin protein in the treadmill running group (P＜0.05).Conclusion The treadmill running can significantly increase capillary density of obese rats,as it may activate the expression of Apelin/APJ.

Objective To develop and validate a model based on deep learning for automatic diagnosis of early gastric cancer ( EGC) to improve detection and diagnosis of EGC. Methods A total of 5159 images ( including 1000 images of EGC and 4159 images of other benign lesions or normal patients) obtained from May 2014 to December 2016 were collected from endoscopic database in changhai Hospital. Then 4449 images were selected randomly for a deep convolutional neural network ( CNN ) training, of which 768 were diagnosed as EGC and 3681 diagnosed as other benign lesions or normal. The remaining 710 images were used to test the model by comparing with diagnostic results of four endoscopists. Results The deep learning model showed accuracy of 89. 4％ ( 635/710 ) , sensitivity of 88. 8％ ( 206/232 ) and specificity of 89. 7％ ( 429/478) for EGC. The mean time required for diagnosis was 0. 30 ± 0. 02 s. The performance of the model was superior to that of four endoscopists. Conclusion The model based on deep learning has high accuracy,sensitivity and specificity for detecting EGC,which could assist endoscopists in real-time diagnosis.