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Aging

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AI, ageing and brain-work productivity: Technological change in professional Japanese chess.

PloS one
Using Japanese professional chess (Shogi) players' records in the setting where various external factors are controlled in deterministic and finite games, this paper examines how and the extent to which the emergence of technological changes influenc...

A machine-learning method isolating changes in wrist kinematics that identify age-related changes in arm movement.

Scientific reports
Normal aging often results in an increase in physiological tremors and slowing of the movement of the hands, which can impair daily activities and quality of life. This study, using lightweight wearable non-invasive sensors, aimed to detect and ident...

Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET-derived features.

GeroScience
To better assess the pathology of neurodegenerative disorders and the efficacy of neuroprotective interventions, it is necessary to develop biomarkers that can accurately capture age-related biological changes in the human brain. Brain serotonin 2A r...

Prospective prediction of anxiety onset in the Canadian longitudinal study on aging (CLSA): A machine learning study.

Journal of affective disorders
BACKGROUND: Anxiety disorders are among the most common mental health disorders in the middle aged and older population. Because older individuals are more likely to have multiple comorbidities or increased frailty, the impact of anxiety disorders on...

Collagen and elastic fibers assessment of the human heart valves for age estimation in Thais using image analysis.

Forensic science, medicine, and pathology
The study investigated the relationship between the histological compositions of the tricuspid, pulmonary, mitral, and aortic valves, and age. All 85 fresh human hearts were obtained with an age range between 20 and 90 years. The central area of the ...

An orchestra of machine learning methods reveals landmarks in single-cell data exemplified with aging fibroblasts.

PloS one
In this work, a Python framework for characteristic feature extraction is developed and applied to gene expression data of human fibroblasts. Unlabeled feature selection objectively determines groups and minimal gene sets separating groups. ML explai...

Identification and validation of aging-related genes in heart failure based on multiple machine learning algorithms.

Frontiers in immunology
BACKGROUND: In the face of continued growth in the elderly population, the need to understand and combat age-related cardiac decline becomes even more urgent, requiring us to uncover new pathological and cardioprotective pathways.

Higher blood biochemistry-based biological age developed by advanced deep learning techniques is associated with frailty in geriatric rehabilitation inpatients: RESORT.

Experimental gerontology
BACKGROUND: Accelerated biological ageing is a major underlying mechanism of frailty development. This study aimed to investigate if the biological age measured by a blood biochemistry-based ageing clock is associated with frailty in geriatric rehabi...

Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators.

Cell metabolism
Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses challenges for studying and targeting senescent cells. We present here a machine learning program senescent cell identification (SenCID), which accurately ident...