AIMC Topic: Aging

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Gut microbiota dynamics in SAMP8 mice: insights from machine learning and longitudinal analysis.

Microbiology spectrum
UNLABELLED: The gut microbiota plays a crucial role in maintaining host health, and its composition is significantly influenced by aging. The SAMP8 mouse model, known for its accelerated aging process, is widely used to study age-related changes. How...

Biological Age Estimation From the Age Gap Using Deep Learning Integrating Morbidity and Mortality: Model Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Biological age (BA) is increasingly recognized as a valuable alternative to chronological age (CA) for assessing an individual's health and aging status. However, existing models are based on limited clinical parameters and have not thoro...

Identifying key brain pathology in bipolar and unipolar depression using a region-specific brain aging trajectories approach: Insights from the Taiwan Aging and Mental Illness Cohort.

Psychological medicine
BACKGROUND: Identifying key areas of brain dysfunction in mental illness is critical for developing precision diagnosis and treatment. This study aimed to develop region-specific brain aging trajectory prediction models using multimodal magnetic reso...

Aging associated immunosenescence in rheumatoid arthritis identified by machine learning and single cell profiling.

Scientific reports
Rheumatoid arthritis (RA) is increasingly prevalent among older adults, who often experience more severe symptoms and face significant treatment challenges. This study aims to identify specific genes associated with aging in RA and to analyze their i...

Altered brain structure age gap estimation in major depressive disorder patients with and without anhedonia: a machine learning-based study.

Translational psychiatry
Previous studies have found that major depressive disorder (MDD) may accelerate overall structural brain aging. Nevertheless, it still remains unknown whether anhedonia, a critical negative prognostic indicator in MDD, further leads to advanced brain...

Unsupervised clustering of biochemical markers reveals health profiles associated with function and survival in active aging.

Scientific reports
This study explores the relationships between biochemical phenotypes identified using machine learning, and key health outcomes, including body composition, physical function, and mortality risk. Data were collected from 536 physically active Spanish...

Unraveling Microstructural and Macrostructural Brain Age Dynamics in Multiple Sclerosis.

Neurology(R) neuroimmunology & neuroinflammation
BACKGROUND AND OBJECTIVES: In multiple sclerosis (MS), neurodegeneration results from the interplay between disease-specific pathology and normal aging. Conventional MRI captures morphologic changes in neurodegeneration, while quantitative MRI (qMRI)...

Detection of aging-induced vascular remodeling based on Raman imaging and deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Vascular aging-related remodeling is a common pathological basis for many chronic diseases, so early detection of physical arterial aging is important for their prevention and control. Existing staining methods can only analyze a limited number of ti...

Telomere-targeted medicine: Bridging molecular mechanisms and clinical applications in age-related diseases.

Life sciences
Telomeres, the nucleoprotein structures at the ends of chromosomes, have emerged as critical regulators of cellular aging and key contributors to the pathogenesis of age-related diseases. This comprehensive review examines the evolution of telomere b...

Advancements in the investigation of the mechanisms underlying cognitive aging.

Biogerontology
Cognitive aging, a pivotal domain at the intersection of neuroscience and psychology, exhibits a strong association with neurodegenerative disorders; however, its comprehensive underlying mechanisms remain incompletely elucidated. This review aims to...