Objectives:
Diabetic nephropathy (DN) is the most common microvascular complication of diabetes mellitus. This study investigated the mechanism of triptolide (TP) in podocyte injury in DN.
Methods:
DN mouse models were established by feeding with a high-fat diet and injecting with streptozocin and MPC5 podocyte injury models were induced by high-glucose (HG), followed by TP treatment. Fasting blood glucose and renal function indicators, such as 24 h urine albumin (UAlb), serum creatinine (SCr), blood urea nitrogen (BUN), and kidney/body weight ratio of mice were examined. H&E and TUNEL staining were performed for evaluating pathological changes and apoptosis in renal tissue. The podocyte markers, reactive oxygen species (ROS), oxidative stress (OS), serum inflammatory cytokines, nuclear factor-erythroid 2-related factor 2 (Nrf2) pathway-related proteins, and pyroptosis were detected by Western blotting and corresponding kits. MPC5 cell viability and pyroptosis were evaluated by MTT and Hoechst 33342/PI double-fluorescence staining. Nrf2 inhibitor ML385 was used to verify the regulation of TP on Nrf2.
Results:
TP improved renal function and histopathological injury of DN mice, alleviated podocytes injury, reduced OS and ROS by activating the Nrf2/heme oxygenase-1 (HO-1) pathway, and weakened pyroptosis by inhibiting the nod-like receptor (NLR) family pyrin domain containing 3 (NLRP3) inflammasome pathway.
In vitro
experiments further verified the inhibition of TP on OS and pyroptosis by mediating the Nrf2/HO-1 and NLRP3 inflammasome pathways. Inhibition of Nrf2 reversed the protective effect of TP on MPC5 cells.
Conclusions:
Overall, TP alleviated podocyte injury in DN by inhibiting OS and pyroptosis
via
Nrf2/ROS/NLRP3 axis.
Based on Fully Convolutional Networks, recent salient object detection (SOD) methods achieve impressive results. Some studies improve the classical SOD frameworks by utilizing auxiliary information like fixation points, salient numbers and salient edges. These works adopt auxiliary information by embedding sub-networks into the main network. However, how to incorporate auxiliary information regardless of the specific structure with less coupling is unexplored in SOD. In this paper, we present DANet, a new Dynamic network to leverage arbitrary Auxiliary information for SOD. The proposed framework consists of 1) a Dynamic Weight Generator (DWG) which converts arbitrary auxiliary features into dynamic weights, 2) a Dynamic Bridge Block (DBB) which uses dynamic weight convolution to incorporate auxiliary information from DWG and then refines the fused features, and 3) a two-step training strategy to alleviate the side effect caused by drastic changes among different input images. Extensive experiments demonstrate the effect of different auxiliary information and the proposed framework is a universal method to improve SOD with auxiliary information. Comparison experiments show that DANet achieves state-of-the-art performance without any pre-processing and post-processing.
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