The two-step process of ovarian follicular growth and maturation in mammals can be compared to a fruit ripening where quality depends on the second step
Bibliographic record
Abstract
In human in vitro fertilization, the main uncertainty factor impacting on success is oocyte quality, which largely depends on the follicular status at the time of collection. Decades of debate ensued to find the perfect stimulation protocol demonstrated the complexity of the ovarian response to exogenous gonadotropins and the dynamic nature of late folliculogenesis. Although several follicular markers, proteins, RNA from granulosa cells or microRNA, and follicular fluid metabolites have been associated with outcome, the possibility to influence them during stimulation remains elusive. The heterogeneity of the follicle's maturity following control ovarian stimulation is also an important factor to explain average poor oocyte quality still observed today. In this review, the analogy between the apple ripening on the tree and follicular development is presented to focus the attention on a biphasic process: growth and differentiation. The molecular analysis of the progressive follicular differentiation indicates two competing phenomena: growth and differentiation, where a delicate balance must operate from one to the other to ensure proper maturity at ovulation. As long as follicle-stimulating hormone (FSH) stimulates growth, follicles remain green, and it is only when FSH is replaced by luteinizing hormone that the ripening process begins, and "apples" become red. Both fruits, follicles and apples, depend on a perfect timing of events to generate offspring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".