Deciphering the relationship between sarcopenia and aging: A combined text mining and bioinformatics approach

Geriatrics and gerontology international/Geriatrics & gerontology international, Journal Year: 2025, Volume and Issue: unknown

Published: April 21, 2025

Aim Sarcopenia is recognized as an age‐related muscle disease, but there has been no comprehensive analysis of what different between normal aging and sarcopenia, with awareness the worldwide research to date. Therefore, in this study, we used text mining PubMed articles on sarcopenia focused our bioinformatics items that have identified. Methods This study compared gene‐level changes identify sarcopenia‐specific gene using high‐throughput sequencing data. In particular, was pathways mechanisms interest research, focus more these mechanisms. Results We identified common aging. Interleukin‐7 were associated both conditions. Although phagosome‐related suggested sarcopenia‐specific, significant phagosome formation, lysosome‐related mitophagy‐related groups However, genes nicotinamide adenine dinucleotide phosphate oxidase catalytic subunit family shown be possibly altered, suggesting involvement oxidative stress regulatory pathways. Conclusions A analysis, complemented by extant literature, might not characterized a failure autophagy whole, rather, disruption regulation, particularly subunit‐related at subsequent stage after phagosome‐lysosome fusion. Geriatr Gerontol Int 2025; ••: ••–•• .

Language: Английский

Evaluation of Perioperative Cardiovascular Event Risk in Gastrointestinal Surgery ― Predictive Modeling and Risk Stratification Using Machine Learning ―

Circulation Journal, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Preoperative risk assessment is very important to ensure surgical safety and predict postoperative complications. However, no large-scale studies have evaluated the of perioperative cardiovascular events in Japan. This study using real-world data. In addition, applicability machine learning stratification was examined develop a predictive model for events. an observational cohort Japan Medical Data Center database, which includes claim health examination data Japan, between January 2005 April 2021. all, 133,634 gastrointestinal surgeries were included analysis. The primary outcome 30-day major adverse (MACE). MACE incidence rate following surgery 3.8%. Machine used perform binary classification task occurrence within 30 days after surgery. A clustering algorithm developed based on Shapley additive explanation values obtained from training data, generalizability test Of variables, age, history ischemic heart disease or failure, stroke, diabetes, hypertension, atrial fibrillation, cases malignancy, pancreatic biliary identified as factors associated with occurrence. built basic clinical information, comorbidities, information demonstrated capacity stratify patients undergoing

Language: Английский