Sirt3 Pharmacologically Promotes Insulin Sensitivity through PI3/AKT/mTOR and Their Downstream Pathway in Adipocytes.

Sirtuin-3 (Sirt3) is a major mitochondrial deacetylase enzyme that regulates multiple metabolic pathways, and its expression is decreased in diabetes type 1 and type 2 diabetes. This study aimed to elucidate Sirt3's molecular mechanism in regulating insulin sensitivity in adipocytes that can contribute to the effort of targeting Sirt3 for the treatment of obesity and type 2 diabetes. We found that the Sirt3 activator honokiol (HNK) induced adipogenesis compared to the control, in contrast to Sirt3 inhibitor, 3-TYP. Accordingly, HNK increased expression of adipocyte gene markers, gene-involved lipolysis and glucose transport (GLUT4), while 3-TYP reduced expression of those genes. Interestingly, 3-TYP caused an increase in gene expression of adipocyte-specific cytokines including IL6, resistin, and TNF-α. However, changes in adipocyte-specific cytokines in HNK treated cells were not significant. In addition, HNK stimulated insulin pathway by promoting insulin receptor beta (IRß) and PI3K/AKT/mTOR pathways, resulting in an increase in phosphorylation of the forkhead family FoxO1/FoxO3a/FoxO4 and glycogen synthase kinase-3 (GSK-3ß), opposing 3-TYP. In line with these findings, HNK increased free fatty acid and glucose uptake, contrary to 3-TYP. In conclusion, Sirt3 activator-HNK induced adipogenesis and lipolysis reduced adipocytes specific cytokines. Intriguingly, HNK activated insulin signaling pathway and increased free fatty acid as well as glucose uptake and transport, in sharp contrast to 3-TYP. These results indicate that, via insulin signaling regulation, Sirt3 activation by HNK improves insulin resistance, while Sirt3 inhibition by 3-TYP might precipitate insulin resistance.

On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils.

The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity of pavement subgrade materials. In this research, random subspace optimization-based hybrid computing models were trained and developed for the prediction of the CBR of soil. Three models were developed, namely reduced error pruning trees (REPTs), random subsurface-based REPT (RSS-REPT), and RSS-based extra tree (RSS-ET). An experimental database was compiled from a total of 214 soil samples, which were classified according to AASHTO M 145, and included 26 samples of A-2-6 (clayey gravel and sand soil), 3 samples of A-4 (silty soil), 89 samples of A-6 (clayey soil), and 96 samples of A-7-6 (clayey soil). All CBR tests were performed in soaked conditions. The input parameters of the models included the particle size distribution, gravel content (G), coarse sand content (CS), fine sand content (FS), silt clay content (SC), organic content (O), liquid limit (LL), plastic limit (PL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). The accuracy of the developed models was assessed using numerous performance indexes, such as the coefficient of determination, relative error, MAE, and RMSE. The results show that the highest prediction accuracy was obtained using the RSS-based extra tree optimization technique.