Oocyte-granulosa cell interactions during mouse follicular development: regulation of kit ligand expression and its role in oocyte growth
Bibliographic record
Abstract
Ovarian folliculogenesis is regulated by both endocrine and intraovarian mechanisms that coordinate the processes of oocyte growth and somatic cell proliferation and differentiation. Within the follicle, paracrine interactions between the oocyte and surrounding granulosa cells are critical for normal cell development and function. This review focuses on the role of paracrine interactions during early oocyte and follicular development that ensure proper coordination of oocyte and somatic cell function. Particular emphasis is given to granulosa cell-derived Kit Ligand (KitL), whose functional importance for oocyte growth has been demonstrated by a wide range of in vivo and in vitro studies. Reported interactions between KitL and oocyte-derived growth differentiation factor-9 (GDF9) and bone morphogenetic protein-15 (BMP15) suggest the molecular basis of oocyte-granulosa cell interactions, but also hint at the complexity of these communications. These paracrine interactions and the structure of the oocyte-granulosa cell interface are follicle stage-specific and regulated by FSH. Elucidation of the molecular mechanisms that promote the development of healthy oocytes with good developmental competence has potential applications for improving fertility and for in vitro growth systems for oocytes from domestic animals and humans.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".