here's a few opportunities straight quoted from the study - "ovary as a systemic regulator of female health and aging"

When and how do different tissue physical structures deteriorate during aging (structural aging)? What molecular changes occur in tissues during periods of major changes? Are there tissue-specific periods of accelerated structural aging? Which organs age early vs. late? Is there cross-organ coordination in structural aging? And, finally, how lifestyle, diseases and genetics impact these organ-specific trajectories? While important, these questions haven’t been answered yet because we lack structural and molecular data from normal aging tissues at scale. We present a framework taking the first stab at scale at these questions using high resolution histology images and omics (+more) from 25,000 post-mortem tissues (public data: GTex). We reason that structure determines function and learning how tissue structure changes with age can help us understand the process of aging in different tissues - a central question with yet little understanding. An example is to visualize these two ovaries histology: young vs. old ovary- young ovaries cortex is intact, with follicles, no fibrosis - basis of its function (partially). First, we extract tissue structure from these organs using their high-res histology images using a pre-trained digpath foundation model (UNI) and asked how much they change with age (Structural Aging Rate)? As an example, the ovary has two peaks around the late 30s and then around 55. First aligns with accelerated follicle loss and second is just after post-menopause. This shows that change in morphology of the ovary captures its functional milestones during aging with no training. Bonus Puzzle: Does anyone know how these two functional milestones of ovary were originally found in the last century? What if we repeat the same analysis on bulk-omics, transcriptomics and methylation, from the same samples? Can they capture this bimodal functional decline? No. (read the paper of our explanation) Can molecular clocks trained on chronological age track this? No. They assume aging is linear. Note: Unlike molecular clocks trained on chronological age, PathStAR learns with no age labels. We simply ask: When and how does tissue morphology change most rapidly during life?