AIM:To investigate the improvement effect of blood flow-limited resistance training on renal fibro-sis in type 2 diabetes mellitus(T2DM)rats and its potential mechanism to attenuate renal fibrosis by inhibiting the trans-forming growth factor β1(TGF-β1)/Smad3 signaling pathway.METHODS:The T2DM model was prepared by combining a high-fat diet and streptozotocin(STZ),and after successful modeling,the rats were randomly divided into a T2DM con-trol group,a low-load resistance training group,a high-load resistance training group,a blood flow restriction group and a blood flow restriction combined with resistance training group for 8 weeks of exercise.The renal index,fasting blood glu-cose(FBG),serum creatinine(SCr),and blood urea nitrogen(BNU)were recorded in each group.The morphological changes of the kidneys were observed by hematoxylin and eosin(HE)and Masson's trichrome staining,and the collagen volume fraction was calculated.The mRNA expression levels of renal Klotho,TGF-β1,and α-smooth muscle actin(α-SMA)were detected by RT-qPCR.The protein expression levels of renal Klotho,TGF-β1,Smad3,phosphorylated Smad3(p-Smad3),α-SMA and connective tissue growth factor(CTGF)were detected using Western blot.RESULTS:Compared with the other groups,FBG,SCr,BNU,and renal collagen volume fraction were significantly decreased in the blood flow restriction combined with resistance training group of rats(P<0.05),Klotho expression was significantly in-creased(P<0.05),and the expression of TGF-β1,p-Smad3,CTGF and α-SMA was significantly decreased(P<0.05),and there was no significant change in the expression level of Smad3(P>0.05).CONCLUSION:Blood flow restriction combined with resistance training attenuates renal fibrosis in T2DM rats,the mechanism of which may be related to the up-regulation of Klotho expression,disruption of the TGF-β1/Smad3 signaling pathway,and inhibition of the deposition of epi-thelial-mesenchymal transformation.

Cardiac auscultation is the basic way for primary diagnosis and screening of congenital heart disease(CHD). A new classification algorithm of CHD based on convolution neural network was proposed for analysis and classification of CHD heart sounds in this work. The algorithm was based on the clinically collected diagnosed CHD heart sound signal. Firstly the heart sound signal preprocessing algorithm was used to extract and organize the Mel Cepstral Coefficient (MFSC) of the heart sound signal in the one-dimensional time domain and turn it into a two-dimensional feature sample. Secondly, 1 000 feature samples were used to train and optimize the convolutional neural network, and the training results with the accuracy of 0.896 and the loss value of 0.25 were obtained by using the Adam optimizer. Finally, 200 samples were tested with convolution neural network, and the results showed that the accuracy was up to 0.895, the sensitivity was 0.910, and the specificity was 0.880. Compared with other algorithms, the proposed algorithm has improved accuracy and specificity. It proves that the proposed method effectively improves the robustness and accuracy of heart sound classification and is expected to be applied to machine-assisted auscultation.
Algorithms ; Heart Defects, Congenital ; diagnosis ; Heart Sounds ; Humans ; Neural Networks (Computer) ; Sensitivity and Specificity