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A quantitative tissue architecture framework to understand human aging and disease

CORDIS · observation · Publication date unknown

ce of baselines defining healthy aging, hindering the accurate interpretation of alterations. To address these challenges, the proposed project leverages large-scale human tissue imaging datasets and machine learning techniques. Specifically, Aim 1 employs unsupervised learning to systemically quantify and categorize microanatomical structures across 40 human tissues. In a parallel stream, Aim 2 aims to detect and characterize the manifestation of age-associated pathologies in tissue, pinpointing the molecular changes associated with them, across spatial scales. Aim 3 integrates the insights from previous aims, seeking to identify how they contribute to the process of aging and onset of age-associated diseases. Ultimately, this will unearth early predictors of pathology, providing a transformative approach to disease detection and management. This project will not only offer a novel, comprehensive view of human tissue complexity in the context of aging but also challenge the traditional notion that age-associated pathology is merely an aggregation of cellular dysfunctions. By quantif

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recordType

award

status

SIGNED

region

EU

value

1499046

unit

EUR