AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Aging

Showing 171 to 180 of 387 articles

Clear Filters

Using Convolutional Neural Networks to Measure the Physiological Age of Caenorhabditis elegans.

IEEE/ACM transactions on computational biology and bioinformatics
Caenorhabditis elegans (C. elegans) is a popular and excellent model for studies of aging due to its short lifespan. Methods for precisely measuring the physiological age of C. elegans are critically needed, especially for antiaging drug screening an...

BiLSTM-5mC: A Bidirectional Long Short-Term Memory-Based Approach for Predicting 5-Methylcytosine Sites in Genome-Wide DNA Promoters.

Molecules (Basel, Switzerland)
An important reason of cancer proliferation is the change in DNA methylation patterns, characterized by the localized hypermethylation of the promoters of tumor-suppressor genes together with an overall decrease in the level of 5-methylcytosine (5mC)...

Regulation of aged skeletal muscle regeneration by circulating extracellular vesicles.

Nature aging
Heterochronic blood exchange (HBE) has demonstrated that circulating factors restore youthful features to aged tissues. However, the systemic mediators of those rejuvenating effects remain poorly defined. We show here that the beneficial effect of yo...

Predicting physiological aging rates from a range of quantitative traits using machine learning.

Aging
It is widely thought that individuals age at different rates. A method that measures "physiological age" or physiological aging rate independent of chronological age could therefore help elucidate mechanisms of aging and inform an individual's risk o...

Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review.

Cells
Aging refers to progressive physiological changes in a cell, an organ, or the whole body of an individual, over time. Aging-related diseases are highly prevalent and could impact an individual's physical health. Recently, artificial intelligence (AI)...

Discrimination of vascular aging using the arterial pulse spectrum and machine-learning analysis.

Microvascular research
Aging contributes to the progression of vascular dysfunction and is a major nonreversible risk factor for cardiovascular disease. The aim of this study was to determine the effectiveness of using arterial pulse-wave measurements, frequency-domain pul...

A machine learning-based biological aging prediction and its associations with healthy lifestyles: the Dongfeng-Tongji cohort.

Annals of the New York Academy of Sciences
This study aims to establish a biological age (BA) predictor and to investigate the roles of lifestyles on biological aging. The 14,848 participants with the available information of multisystem measurements from the Dongfeng-Tongji cohort were used ...

Acceptability of Socially Assistive Robots Among Cognitively Intact Older Adults: An Integrative Review.

Journal of gerontological nursing
An aging population and technology are two rapidly evolving phenomena occurring simultaneously worldwide. To examine the literature on the acceptability of socially assistive robots (SAR) among cognitively intact older adults, an integrative review o...

Deep learning analysis and age prediction from shoeprints.

Forensic science international
Human gaits are the patterns of limb movements which involve both the upper and lower body parts. These patterns in terms of step rate, gait speed, stance widening, stride, and bipedal forces are influenced by different factors including environmenta...

Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing.

European journal of epidemiology
Deep Neural Networks (DNN) have been recently developed for the estimation of Biological Age (BA), the hypothetical underlying age of an organism, which can differ from its chronological age (CA). Although promising, these population-specific algorit...