AIMC Topic: Aging

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Aging as an active player in Alzheimer's disease classification: Insights from feature selection in BrainAge models.

NeuroImage
BACKGROUND: BrainAge models estimate the biological age of the brain using neuroimaging or clinical features, making them promising tools for studying neurodegenerative diseases like Alzheimer's disease. However, the reliance of BrainAge models on ne...

Microenvironment-driven satellite cell regeneration and repair in aging-related sarcopenia: mechanisms and therapeutic frontiers.

Stem cell research & therapy
Sarcopenia, a progressive age-related decline in skeletal muscle mass and function, is closely linked to impaired regenerative capacity of satellite cells (SCs), also known as satellite cells. Age-dependent SCs dysfunction, driven by intrinsic senesc...

Anston attentional network for structured data based stroke risk prediction in smart aging.

Scientific reports
To reduce the pressure on public health services caused by the aging population, nursing homes need to predict disease risks for the elderly periodically. To improve the disease risks predicting ability of nursing homes, we designed Anston (An Attent...

AI-driven chemotoxicity prediction in colorectal cancer: impact of race, SDOH, and biological aging.

BMC cancer
BACKGROUND: Patients with colorectal cancer (CRC) often experience chemotoxicity that impacts treatment adherence, survival, and quality of life. Early screening for chemotoxicity risk is vital, yet comprehensive predictive models are lacking. The ob...

A deep learning pipeline for age prediction from vocalisations of the domestic feline.

Scientific reports
Accurate age estimation is essential for advancing interspecies communication but remains a challenge across non-human species. This study presents the first dataset of domestic feline vocalisations specifically designed for age prediction and introd...

Creative experiences and brain clocks.

Nature communications
Creative experiences may enhance brain health, yet metrics and mechanisms remain elusive. We characterized brain health using brain clocks, which capture deviations from chronological age (i.e., accelerated or delayed brain aging). We combined M/EEG ...

Activities of Daily Living Detection through Energy Consumption Data and Machine Learning to Support Independent Aging.

Journal of medical systems
The aging population presents significant challenges for healthcare and social services, emphasizing the need for innovative solutions that support independent living. This study explores the feasibility of identifying Instrumental Activities of Dail...

Steady-state neuron-predominant LINE-1 encoded ORF1p protein and LINE-1 RNA increase with aging in the mouse and human brain.

eLife
Recent studies have established a reciprocal causal link between aging and the activation of transposable elements, characterized in particular by a de-repression of LINE-1 retrotransposons. These LINE-1 elements represent 21% of the human genome, bu...

Machine learning and data-driven inverse modeling of metabolomics unveil key processes of active aging.

NPJ systems biology and applications
Physical inactivity and low fitness have become global health concerns. Metabolomics, as an integrative approach, may link fitness to molecular changes. In this study, we analyzed blood metabolomes from elderly individuals under different treatments....

Prediction of biological age using machine learning.

PloS one
In response to Taiwan's rapidly aging population and the rising demand for personalized health care, accurately assessing individual physiological aging has become an essential area of study. This research utilizes health examination data to propose ...