Nobiletin suppresses high-glucose-induced inflammation and ECM accumulation in human mesangial cells through STAT3/NF-κB pathway.

Diabetic nephropathy (DN) is a complication of chronic diabetes and the main cause of end-stage renal disease all over the world. Inflammation and extracellular matrix (ECM) accumulation play important roles in the pathogenesis of DN. Evidence suggested that nobiletin acts anti-inflammatory role and plays a critical role in diabetes; however, its role in DN remains unclear. In the current study, we promulgated the nobiletin involved in high-glucose-induced glomerular mesangial cell inflammation and ECM accumulation. Nobiletin treatment significantly abrogated high-glucose-induced glomerular mesangial cell proliferation. Nobiletin treatment markedly suppressed inflammation cytokine secretion, including interleukin (IL)-1ß, IL-6, tumor necrosis factor α, and monocyte chemoattractant protein 1 in high-glucose-induced glomerular mesangial cell. Also, exposed nobiletin to high-glucose-induced glomerular mesangial cell considerably reduced ECM accumulation through inhibited ECM-associated protein type 4 collagen and fibronectin expression. Furthermore, nobiletin treatment abolished nuclear factor κB (NF-κB) pathway activation through signal transducer and activator of transcription 3 (STAT3) inhibition. Overexpression STAT3 reversed the effects of nobiletin on high-glucose-induced glomerular mesangial cell proliferation, inflammation, ECM accumulation, and NF-κB pathway activation. Hence, our results suggest that nobiletin play roles in high-glucose-induced glomerular mesangial cells through inhibiting inflammation and ECM accumulation, and the STAT3/NF-κB pathway was involved in the function of nobiletin.

Predicting anion diffusion in bentonite using hybrid machine learning model and correlation of physical quantities.

Radionuclide diffusion will be influenced by numerous factors. Establishing a model that can elucidate the internal correlation between mesoscopic diffusion and the microscopic structure of bentonite can enhance the comprehension of radionuclide diffusion mechanisms. In this study, a light gradient boosting machine (LightGBM) was employed to predict the effective diffusion coefficients of HCrO4-, I-, and CoEDTA2- in bentonite. The model's hyperparameters were optimized using the particle swarm optimization (PSO) algorithm. Several correlated physical quantities, such as mesoscopic parameters (total porosity, rock capacity factor, and ion molar conductivity) and microscopic parameters (ionic radius and montmorillonite stacking number) were incorporated to develop a machine learning model that incorporated micro- and meso-scale features. The predictive performance of PSO-LightGBM was verified using diffusion experiments, which investigated the diffusion of HCrO4-, I-, and CoEDTA2- at compacted dry densities of 1200-1800 kg/m3 using a through-diffusion method. Spearman correlation and Shapley additive explanation analyses revealed that the compacted dry density, ionic diffusion coefficient in water, ionic radius, and total porosity were the top-four influencing factors among the 16 input features. Partial dependence plot analysis elucidated the relationship between the effective diffusion coefficient and each input feature. The analysis results were consistent with the experimental findings, demonstrating the reliability of machine learning. Due to the incorporation of multi-scale features, the PSO-LightGBM model demonstrated enhanced predictive accuracy, linking the microstructure of bentonite to radionuclide diffusion, and providing a comprehensive interpretation of the diffusion mechanism.