Ovarian extracellular matrix‐based hydrogel for human ovarian follicle survival in vivo: A pilot work
Bibliographic record
Abstract
To successfully assemble a bio-engineered ovary, we need to create a three-dimensional matrix able to accommodate isolated follicles and cells. The goal of this study was to develop an extracellular matrix hydrogel (oECM) derived from decellularized bovine ovaries able to support, in combination with alginate, human ovarian follicle survival and growth in vitro. Two different hydrogels (oECM1, oECM2) were produced and compared in terms of decellularization efficiency (dsDNA), ECM preservation (collagen and glycosaminoglycan levels), ultrastructure, rigidity, and cytotoxicity. oECM2 showed significantly less dsDNA, greater retention of glycosaminoglycans and better rigidity than oECM1. Isolated human ovarian follicles were then encapsulated in four selected hydrogel combinations: (1) 100% oECM2, (2) 90% oECM2 + 10% alginate, (3) 75% oECM2 + 25% alginate, and (4) 100% alginate. After 1 week of in vitro culture, follicle recovery rate, viability, and growth were analyzed. On day 7 of in vitro culture, follicle recovery rates were 0%, 23%, 65%, 82% in groups 1-4, respectively, rising proportionally with increased alginate content. However, there was no difference in follicle viability or growth between groups 2 and 3 and controls (group 4). In conclusion, since pure alginate cannot be used to graft preantral follicles due to its poor revascularization and degradation after grafting, oECM2 hydrogel combined with alginate may provide a new and promising alternative to graft isolated human follicles in a bio-engineered ovary.
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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".