Up-Regulation of microRNA-424 Causes an Imbalance in AKT Phosphorylation and Impairs Enteric Neural Crest Cell Migration in Hirschsprung Disease.

Insights into the role of microRNAs (miRNAs) in disease pathogenesis have made them attractive therapeutic targets, and numerous miRNAs have been functionally linked to Hirschsprung disease (HSCR), a life-threatening genetic disorder due to defective migration, proliferation, and colonization of enteric neural crest cells (ENCCs) in the gut. Recent studies have demonstrated that miR-424 strongly inhibits migration in a variety of cell types and its potential target RICTOR is essential for neural crest cell development. We therefore sought to interrogate how miR-424 and RICTOR contribute to the pathogenesis of HSCR. We utilized HSCR cases and human neural cells to evaluate the miR-424-mediated regulation of RICTOR and the downstream AKT phosphorylation. We further developed an ex vivo model to assess the effects of miR-424 on ENCC migration and proliferation. Then, single-cell atlases of gene expression in both human and mouse fetal intestines were used to determine the characteristics of RICTOR and AKT expression in the developing gut. Our findings demonstrate that miR-424 levels are markedly increased in the colonic tissues of patients with HSCR and that it regulates human neural cell migration by directly targeting RICTOR. Up-regulation of miR-424 leads to decreased AKT phosphorylation levels in a RICTOR-dependent manner, and this, in turn, impairs ENCC proliferation and migration in the developing gut. Interestingly, we further identified prominent RICTOR and AKT expressions in the enteric neurons and other types of enteric neural cells in human and mouse fetal intestines. Our present study reveals the role of the miR-424/RICTOR axis in HSCR pathogenesis and indicates that miR-424 is a promising candidate for the development of targeted therapies against HSCR.

Prediction of the Tribological Properties of Polytetrafluoroethylene Composites Based on Experiments and Machine Learning.

Because of the complex nonlinear relationship between working conditions, the prediction of tribological properties has become a difficult problem in the field of tribology. In this study, we employed three distinct machine learning (ML) models, namely random forest regression (RFR), gradient boosting regression (GBR), and extreme gradient boosting (XGBoost), to predict the tribological properties of polytetrafluoroethylene (PTFE) composites under high-speed and high-temperature conditions. Firstly, PTFE composites were successfully prepared, and tribological properties under different temperature, speed, and load conditions were studied in order to explore wear mechanisms. Then, the investigation focused on establishing correlations between the friction and wear of PTFE composites by testing these parameters through the prediction of the friction coefficient and wear rate. Importantly, the correlation results illustrated that the friction coefficient and wear rate gradually decreased with the increase in speed, which was also proven by the correlation coefficient. In addition, the GBR model could effectively predict the tribological properties of the PTFE composites. Furthermore, an analysis of relative importance revealed that both load and speed exerted a greater influence on the prediction of the friction coefficient and wear rate.