Current perspective on primordial follicle cryopreservation and culture for reproductive medicine
Bibliographic record
Abstract
Mature oocytes are rare and precious cells. A technology which generates larger numbers would be very welcome in clinical practice, animal production technology and research. Since de-novo formation of female germ cells has ceased by the time of birth, the most attractive strategy, in theory, is to harvest and culture primordial follicles, the most abundant stage in the ovary at all ages. So far, there has been more success with cryopreservation of primordial follicles than with culture, and frozen-thawed ovarian tissue grafts have restored fertility to a number of species after oophorectomy. However, in-vitro development of isolated follicles is not sustained beyond the primary follicle stage. To meet their requirements for growth, metabolism and differentiation, a multistage protocol will probably be required for the prolonged period of development to maturity. The mouse is the only model, to date, in which a live offspring has ever been produced after growing follicles completely in vitro. A triple-stage process was required, involving culture of ovarian explants followed by isolation of granulosa-oocyte complexes and, finally, suitable conditions for completing meiotic maturation. Achievement of this goal for the larger and more slowly developing follicles from human and farm animal ovaries is still a remote possibility.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".