Impact of Aging on the Ovarian Extracellular Matrix and Derived 3D Scaffolds
Bibliographic record
Abstract
Advances in medical care, improvements in sanitation, and rising living standards contribute to increased life expectancy. Although this reflects positive human development, it also poses new challenges. Among these, reproductive aging is gradually becoming a key health issue because the age of menopause has remained constant at ~50 years, leading women to live longer in suboptimal endocrine conditions. An adequate understanding of ovarian senescence mechanisms is essential to prevent age-related diseases and to promote wellbeing, health, and longevity in women. We here analyze the impact of aging on the ovarian extracellular matrix (ECM), and we demonstrate significant changes in its composition and organization with collagen, glycosaminoglycans, and laminins significantly incremented, and elastin, as well as fibronectin, decreased. This is accompanied by a dynamic response in gene expression levels of the main ECM- and protease-related genes, indicating a direct impact of aging on the transcription machinery. Furthermore, in order to study the mechanisms driving aging and identify possible strategies to counteract ovarian tissue degeneration, we here described the successful production of a 3D ECM-based biological scaffold that preserves the structural modifications taking place in vivo and that represents a powerful high predictive in vitro model for reproductive aging and its prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".