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Biological aging manifests as complex, systemic deterioration across tissues, yet conventional molecular biomarkers lack universality. Biophysical phenotyping offers an alternative, capturing the structural consequences of cellular senescence and matrix remodeling as functional readouts. This review explores advanced nanomechanical tools and the integrated mechanome framework. By leveraging deep learning to decode these multi-parametric physical signatures, we outline a translational roadmap for tracking physiological decline and evaluating therapeutic interventions.
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Yoon et al. (2026) studied this question.
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