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Aging

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Is Artificial Intelligence ageist?

European geriatric medicine
INTRODUCTION: Generative Artificial Intelligence (AI) is a technological innovation with wide applicability in daily life, which could help elderly people. However, it raises potential conflicts, such as biases, omissions and errors.

Artificial neural network inference analysis identified novel genes and gene interactions associated with skeletal muscle aging.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: Sarcopenia is an age-related muscle disease that increases the risk of falls, disabilities, and death. It is associated with increased muscle protein degradation driven by molecular signalling pathways including Akt and FOXO1. This study ...

The revolution in high-throughput proteomics and AI.

Science (New York, N.Y.)
The recent capability to measure thousands of plasma proteins from a tiny blood sample has provided a new dimension of expansive data that can advance our understanding of human health. For example, the company SomaLogic has developed the means to me...

Identifying biological markers and sociodemographic factors that influence the gap between phenotypic and chronological ages.

Informatics for health & social care
INTRODUCTION: The world's population is aging rapidly, leading to increased public health and economic burdens due to age-related cardiovascular and neurodegenerative diseases. Early risk detection is essential for prevention and to improve the quali...

Development and validation of cardiometabolic risk predictive models based on LDL oxidation and candidate geromarkers from the MARK-AGE data.

Mechanisms of ageing and development
The predictive value of the susceptibility to oxidation of LDL particles (LDLox) in cardiometabolic risk assessment is incompletely understood. The main objective of the current study was to assess its relationship with other relevant biomarkers and ...

Aging-related biomarkers for the diagnosis of Parkinson's disease based on bioinformatics analysis and machine learning.

Aging
Parkinson's disease (PD) is a multifactorial disease that lacks reliable biomarkers for its diagnosis. It is now clear that aging is the greatest risk factor for developing PD. Therefore, it is necessary to identify novel biomarkers associated with a...

Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging.

The Journal of investigative dermatology
Hair quality is an important indicator of health in humans and other animals. Current approaches to assess hair quality are generally nonquantitative or are low throughput owing to technical limitations of splitting hairs. We developed a deep learnin...

Common and unique brain aging patterns between females and males quantified by large-scale deep learning.

Human brain mapping
There has been extensive evidence that aging affects human brain function. However, there is no complete picture of what brain functional changes are mostly related to normal aging and how aging affects brain function similarly and differently betwee...

Foundation model-driven distributed learning for enhanced retinal age prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However,...

EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.

Schizophrenia bulletin
BACKGROUND: Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG rec...