首页|Academy of Mathematics and Systems Science Reports Findings in Machine Learning (Biologically informed machine learning modeling of immune cells to reveal physi ological and pathological aging process)
Academy of Mathematics and Systems Science Reports Findings in Machine Learning (Biologically informed machine learning modeling of immune cells to reveal physi ological and pathological aging process)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Machine Learning is th e subject of a report. According to newsreporting out of Beijing, People's Repu blic of China, by NewsRx editors, research stated, "The immunesystem undergoes progressive functional remodeling from neonatal stages to old age. Therefore, un derstandinghow aging shapes immune cell function is vital for precise treatment of patients at different lifestages."Our news journalists obtained a quote from the research from the Academy of Math ematics andSystems Science, "Here, we constructed the first transcriptomic atla s of immune cells encompassing humanlifespan, ranging from newborns to supercen tenarians, and comprehensively examined gene expressionsignatures involving cel l signaling, metabolism, differentiation, and functions in all cell types to inv estigateimmune aging changes. By comparing immune cell composition among differ ent age groups, HLA highlyexpressing NK cells and CD83 positive B cells were id entified with high percentages exclusively in theteenager (Tg) group, whereas u nknown_T cells were exclusively enriched in the supercentenarian (S c)group. Notably, we found that the biological age (BA) of pediatric COVID-19 p atients with multisysteminflammatory syndrome accelerated aging according to th eir chronological age (CA). Besides, we provedthat inflammatory shift- myeloid abundance and signature correlate with the progression of complications inKawas aki disease (KD). The shift- myeloid signature was also found to be associated w ith KD treatmentresistance, and effective therapies improve treatment outcomes by reducing this signaling. Finally, basedon those age-related immune cell comp ositions, we developed a novel BA prediction model PHARE, which can apply to both scRNA-seq and bulk RNA-seq data. Using thismodel, w e found patients with coronary artery disease (CAD) also exhibit accelerated agi ng compared tohealthy individuals."
BeijingPeople's Republic of ChinaAsi aCOVID-19 ModelCyborgsDisease ModelEmerging TechnologiesEpidemiologyImmunologyMachine LearningRisk and Prevention
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