Overexpression of sirtuin 1 attenuates calcium oxalate-induced kidney injury by promoting macrophage polarization.

Sirtuin 1 (SIRT1) protein is involved in macrophage differentiation, while NOTCH signaling affects inflammation and macrophage polarization. Inflammation and macrophage infiltration are typical processes that accompany kidney stone formation. However, the role and mechanism of SIRT1 in renal tubular epithelial cell injury caused by calcium oxalate (CaOx) deposition and the relationship between SIRT1 and the NOTCH signaling pathway in this urological disorder are unclear. This study investigated whether SIRT1 promotes macrophage polarization to inhibit CaOx crystal deposition and reduce renal tubular epithelial cell injury. Public single-cell sequencing data, RT-qPCR, immunostaining approaches, and Western blotting showed decreased SIRT1 expression in macrophages treated with CaOx or exposed to kidney stones. Macrophages overexpressing SIRT1 differentiated towards the anti-inflammatory M2 phenotype, significantly inhibiting apoptosis and alleviating injury in the kidneys of mice with hyperoxaluria. Conversely, decreased SIRT1 expression in CaOx-treated macrophages triggered Notch signaling pathway activation, promoting macrophage polarization towards the pro-inflammatory M1 phenotype. Our results suggest that SIRT1 promotes macrophage polarization towards the M2 phenotype by repressing the NOTCH signaling pathway, which reduces CaOx crystal deposition, apoptosis, and damage in the kidney. Therefore, we propose SIRT1 as a potential target for preventing disease progression in patients with kidney stones.

Neural network-based fault detection for nonlinear networked systems with uncertain medium access constraint: Application to motor systems.

The fault detection for a class of continuous-time nonlinear networked control systems with medium access constraint is concerned in this paper, where the occurring probability of transition from one sensor to another is allowed to be partially unknown and uncertain. First of all, a Markovian system approach is adopted to describe the access process of sensors, in which only one sensor is allowed to access the communication channel. A robust filter based residual generator is proposed to generate the residual signal such that it can be used to indicate whether the fault has occurred or not. The nonlinear term is approximated by a neural network, and the Lyapunov-Krasovskii functional is introduced to analyze the fault detection system, three sufficient conditions for the stochastic stability of fault detection error system are given, and the fault detection filter gains are calculated via solving some matrix inequalities. In the simulation, a second-order DC motor system is used to validate of the main results, which shows the effectiveness of the fault detector design.