Qufengtongluo (QFTL) decoction is an effective treatment for diabetic nephropathy (DN). However, the underlying molecular mechanism is still unclear. In this study, we try to investigate whether QFTL decoction acts via inhibiting PI3K/Akt signaling pathway. Twenty-four GK rats were randomly divided into 3 groups: blank group, sham-operated group, and QFTL group. After model establishment, rats in QFTL group were given QFTL decoction by gavage, while the rest were given pure water. During the 8-week intervention, 24 hr urinal protein was measured every 2-3 weeks. After intervention, kidneys were removed for pathological smear, quantitative real-time PCR, and western blotting to detect expression levels of p-PI3K, p-Akt, PTEN, TGF-β, PI3K mRNA, Akt mRNA, PTEN mRNA, and TGF-β mRNA. QFTL group showed a slighter degree of renal fibrosis in Masson and PASM staining and a greater reduction of 24 hr urinal protein than blank group. Compared to blank group, expression levels of p-PI3K, p-Akt, PI3K mRNA, and Akt mRNA were lower in QFTL group, while expression levels of PTEN and PTEN mRNA were higher. Besides, TGF-β was downregulated by QFTL decoction. In conclusion, this study suggests that QFTL decoction might inhibit PI3K/Akt signaling pathway via activating PTEN and inhibiting TGF-β.
Learning discriminative and invariant feature representation is the key to visual image categorization. In this article, we propose a novel invariant deep compressible covariance pooling (IDCCP) to solve nuisance variations in aerial scene categorization. We consider transforming the input image according to a finite transformation group that consists of multiple confounding orthogonal matrices, such as the D4 group. Then, we adopt a Siamese-style network to transfer the group structure to the representation space, where we can derive a trivial representation that is invariant under the group action. The linear classifier trained with trivial representation will also be possessed with invariance. To further improve the discriminative power of representation, we extend the representation to the tensor space while imposing orthogonal constraints on the transformation matrix to effectively reduce feature dimensions. We conduct extensive experiments on the publicly released aerial scene image data sets and demonstrate the superiority of this method compared with state-of-the-art methods. In particular, with using ResNet architecture, our IDCCP model can reduce the dimension of the tensor representation by about 98% without sacrificing accuracy (i.e., <0.5%).
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