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Aging

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An interactive AI-driven platform for fish age reading.

PloS one
Fish age is an important biological variable required as part of routine stock assessment and analysis of fish population dynamics. Age estimates are traditionally obtained by human experts from the count of ring-like patterns along calcified structu...

Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and normal aging.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: To apply machine learning approaches on EEG event-related oscillations (ERO) to discriminate preclinical Alzheimer's disease (AD) from age- and sex-matched controls.

Evaluating the reproducibility of a deep learning algorithm for the prediction of retinal age.

GeroScience
Recently, a deep learning algorithm (DLA) has been developed to predict the chronological age from retinal images. The Retinal Age Gap (RAG), a deviation between predicted age from retinal images (Retinal Age, RA) and chronological age, correlates wi...

Social Robots and Sensors for Enhanced Aging at Home: Mixed Methods Study With a Focus on Mobility and Socioeconomic Factors.

JMIR aging
BACKGROUND: Population aging affects society, with a profound impact on daily activities for those of a low socioeconomic status and with motor impairments. Social assistive robots (SARs) and monitoring technologies can improve older adults' well-bei...

Insights to aging prediction with AI based epigenetic clocks.

Epigenomics
Over the past century, human lifespan has increased remarkably, yet the inevitability of aging persists. The disparity between biological age, which reflects pathological deterioration and disease, and chronological age, indicative of normal aging, h...

Blood Biomarker Signatures for Slow Gait Speed in Older Adults: An Explainable Machine Learning Approach.

Brain, behavior, and immunity
Maintaining physical function is crucial for independent living in older adults, with gait speed being a key predictor of health outcomes. Blood biomarkers may potentially monitor older adults' mobility, yet their association with slow gait speed sti...

AcidAGE: a biological age determination neural network based on urine organic acids.

Biogerontology
Organic acids reflect the course of all important metabolic processes and the effects of diet, nutrient deficiency, lifestyle, and microbiota composition. In present work, we focused on identifying age-related changes in organic acids in urine, and c...

Validation requirements for AI-based intervention-evaluation in aging and longevity research and practice.

Ageing research reviews
The field of aging and longevity research is overwhelmed by vast amounts of data, calling for the use of Artificial Intelligence (AI), including Large Language Models (LLMs), for the evaluation of geroprotective interventions. Such evaluations should...

Discovering geroprotectors through the explainable artificial intelligence-based platform AgeXtend.

Nature aging
Aging involves metabolic changes that lead to reduced cellular fitness, yet the role of many metabolites in aging is unclear. Understanding the mechanisms of known geroprotective molecules reveals insights into metabolic networks regulating aging and...

Artificial intelligence-derived electrocardiographic aging and risk of atrial fibrillation: a multi-national study.

European heart journal
BACKGROUND AND AIMS: Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronolog...