Endoplasmic reticulum stress in the pathogenesis of alcoholic liver disease.

The endoplasmic reticulum (ER) plays a pivotal role in protein synthesis, folding, and modification. Under stress conditions such as oxidative stress and inflammation, the ER can become overwhelmed, leading to an accumulation of misfolded proteins and ensuing ER stress. This triggers the unfolded protein response (UPR) designed to restore ER homeostasis. Alcoholic liver disease (ALD), a spectrum disorder resulting from chronic alcohol consumption, encompasses conditions from fatty liver and alcoholic hepatitis to cirrhosis. Metabolites of alcohol can incite oxidative stress and inflammation in hepatic cells, instigating ER stress. Prolonged alcohol exposure further disrupts protein homeostasis, exacerbating ER stress which can lead to irreversible hepatocellular damage and ALD progression. Elucidating the contribution of ER stress to ALD pathogenesis may pave the way for innovative therapeutic interventions. This review delves into ER stress, its basic signaling pathways, and its role in the alcoholic liver injury.

Establishing a prediction model of severe acute mountain sickness using machine learning of support vector machine recursive feature elimination.

Severe acute mountain sickness (sAMS) can be life-threatening, but little is known about its genetic basis. The study was aimed to explore the genetic susceptibility of sAMS for the purpose of prediction, using microarray data from 112 peripheral blood mononuclear cell (PBMC) samples of 21 subjects, who were exposed to very high altitude (5260 m), low barometric pressure (406 mmHg), and hypobaric hypoxia (VLH) at various timepoints. We found that exposure to VLH activated gene expression in leukocytes, resulting in an inverted CD4/CD8 ratio that interacted with other phenotypic risk factors at the genetic level. A total of 2286 underlying risk genes were input into the support vector machine recursive feature elimination (SVM-RFE) system for machine learning, and a model with satisfactory predictive accuracy and clinical applicability was established for sAMS screening using ten featured genes with significant predictive power. Five featured genes (EPHB3, DIP2B, RHEBL1, GALNT13, and SLC8A2) were identified upstream of hypoxia- and/or inflammation-related pathways mediated by microRNAs as potential biomarkers for sAMS. The established prediction model of sAMS holds promise for clinical application as a genetic screening tool for sAMS.