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The miR-17-92 cluster (miR-17, miR-18a, miR-19a, miR-20a, miR-19b-1 and miR-92a) contributes to the occurrence and development of various diseases by inhibiting multiple target genes. Here, we explored the effects of miR-18a on insulin sensitivity. Quantitative real-time PCR indicated that serum miR-18a levels were lower in type 2 diabetes mellitus patients than in healthy controls, suggesting that miR-18a may influence blood glucose levels. Global overexpression of miR-18a in transgenic mice increased their glucose tolerance and insulin sensitivity, while it reduced expression of the phosphatase and tensin homolog deleted on chromosome ten (PTEN) in their skeletal muscle and adipose tissue. Western blotting indicated that overexpressing miR-18a in 3T3-L1 and C2C12 cells enhanced insulin-stimulated AKT phosphorylation and suppressed PTEN expression, while inhibiting miR-18a had the opposite effects. These results suggest that miR-18a improves insulin sensitivity by downregulating PTEN. This makes miR-18a a potentially useful target for the treatment of diabetes mellitus in the future.

As a fine-grained classification problem, food image classification faces many difficulties in the specific implementation. Different countries and regions have different eating habits. In particular, Asian food images have a complicated structure, and the related classification methods are still very scarce. There is an urgent need to develop a feature extraction and fusion scheme based on the characteristics of Asian food images. To solve the above problems, we proposed an image classification model SLGC (SURF-Local and Global Color) that combines image segmentation and feature fusion. By studying the unique structure of Asian foods, the color features of the images are merged into the representation vectors in the local and global dimensions, respectively, thereby further enhancing the effect of feature extraction. The experimental results show that the SLGC model can express the intrinsic characteristics of Asian food images more comprehensively and improve classification accuracy.