[Linagliptin improves diabetic kidney disease in rats by promoting mitochondrial biogenesis through the AMPK/PGC-1α/TFAM pathway]

OBJECTIVE

To investigate whether linagliptin improves diabetic kidney disease (DKD) by promoting mitochondrial biosynthesis via activating adenosine monophosphate activated protein kinase/peroxisome proliferator-activated receptor gamma coactivator 1α/mitochondrial transcription factor A (AMPK/PGC-1α/TFAM) pathway.

METHODS

With 6 male SD rats feeding normal chow as the control group, 16 SD rat models of DKD induced by intraperitoneal injection of 45 mg/kg STZ and high-fat and high-glucose feeding for 4 weeks were randomized into DKD model group and linagliptin treatment group. The rats in the latter two groups were subjected to daily intragastric administration of vehicle or 5 mg/kg linagliptin (dissolved in 5 g/L sodium carboxymethylcellulose, final concentration of 2 mg/mL) for 12 weeks with further high-fat and high-glucose feeding. After the treatments, the rats were sacrificed and blood samples from the abdominal aorta and kidney tissues were collected for testing blood glucose, liver function and lipid metabolism; HE, PAS, Masson, Sirius red staining and electron microscopy were used to observe renal tissue damage. Renal expressions of transforming growth factor β1 (TGF-β1), fibronectin (FN) and collagen I (Col I) were detected by immunohistochemistry, and the changes in membrane potential (ΔψM) and ATP enzyme content were analyzed to assess mitochondrial damage; The expressions of AMPK/PGC-1α/TFAM pathway proteins were detected using Western blotting.

RESULTS

Compared with DKD model rats, the rats receiving linagliptin treatment showed significantly decreased blood glucose level (P < 0.01) and improved proteinuria (P < 0.05) with obviously alleviated renal ultrastructural damage and fibrosis, increased ATPase content and ΔψM (P < 0.0001), and enhanced renal expressions of P-AMPK/AMPK, PGC-1α and TFAM (P < 0.05).

CONCLUSIONS

Linagliptin improves proteinuria and renal fibrosis in rat models of DKD possibly by activating the AMPK/PGC-1α/TFAM pathway to promote mitochondrial biosynthesis.

[A lightweight convolutional neural network for myositis classification from muscle ultrasound images]

Existing classification methods for myositis ultrasound images have problems of poor classification performance or high computational cost. Motivated by this difficulty, a lightweight neural network based on a soft threshold attention mechanism is proposed to cater for a better IIMs classification. The proposed network was constructed by alternately using depthwise separable convolution (DSC) and conventional convolution (CConv). Moreover, a soft threshold attention mechanism was leveraged to enhance the extraction capabilities of key features. Compared with the current dual-branch feature fusion myositis classification network with the highest classification accuracy, the classification accuracy of the network proposed in this paper increased by 5.9%, reaching 96.1%, and its computational complexity was only 0.25% of the existing method. The obtained results support that the proposed method can provide physicians with more accurate classification results at a lower computational cost, thereby greatly assisting them in their clinical diagnosis.