Mechanisms of fibroblast growth factor signaling in the ovarian follicle
Bibliographic record
Abstract
Fibroblast growth factors (FGFs) have been shown to alter growth and differentiation of reproductive tissues in a variety of species. Within the female reproductive tract, the effects of FGFs have been focused on the ovary, and the most studied one is FGF2, which stimulates granulosa cell proliferation and decreases differentiation (decreased steroidogenesis). Other FGFs have also been implicated in ovarian function, and this review summarizes the effects of members of two subfamilies on ovarian function; the FGF7 subfamily that also contains FGF10, and the FGF8 subfamily that also contains FGF18. There are data to suggest that FGF8 and FGF18 have distinct actions on granulosa cells, despite their apparent similar receptor binding properties. Studies of non-reproductive developmental biology also indicate that FGF8 is distinct from FGF18, and that FGF7 is also distinct from FGF10 despite similar receptor binding properties. In this review, the potential mechanisms of differential action of FGF7/FGF10 and FGF8/FGF18 during organogenesis will be reviewed and placed in the context of follicle development. A model is proposed in which FGF8 and FGF18 differentially activate receptors depending on the properties of the extracellular matrix in the follicle.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".