Skeletal muscle adaptations and post-exertional malaise in long COVID.

When acute SARS-CoV-2 infections cause symptoms that persist longer than 3 months, this condition is termed long COVID. Symptoms experienced by patients often include myalgia, fatigue, brain fog, cognitive impairments, and post-exertional malaise (PEM), which is the worsening of symptoms following mental or physical exertion. There is little consensus on the pathophysiology of exercise-induced PEM and skeletal-muscle-related symptoms.

Skeletal muscle mitochondrial fragmentation predicts age-associated decline in physical capacity.

Ageing substantially impairs skeletal muscle metabolic and physical function. Skeletal muscle mitochondrial health is also impaired with ageing, but the role of skeletal muscle mitochondrial fragmentation in age-related functional decline remains imprecisely characterized. Here, using a cross-sectional study design, we performed a detailed comparison of skeletal muscle mitochondrial characteristics in relation to in vivo markers of exercise capacity between young and middle-aged individuals.

Association of adiponectin gene single nucleotide polymorphisms with environmental risk factors in type 2 diabetes mellitus: An updated evidence of haplotype-based analysis study.

Background And Aim: Adiponectin (ADIPOQ) gene is considered to be one of the promising players in deciphering the genetic bases of type 2 diabetes. This study investigated the associations between haplotype combinations of three single nucleotide polymorphisms (SNPs) of the ADIPOQ gene and two SNPs of the adiponectin receptor 1 (AdipoR1) and 2 (AdipoR2) genes with environmental risk factors for the prediction of T2DM disorder susceptibility in the Iranian population.

Methods: This case-control and cross-sectional study was conducted on 182 patients with T2DM and 155 healthy controls.

Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features.

Purpose: Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent performance of each test, it is necessary to adapt the power increase of the test protocol to the physical characteristics of each individual. This study aimed to use machine learning models to determine individualized ramp protocols based on non-exercise features.