Expression and Function of Fibroblast Growth Factor 10 and Its Receptor, Fibroblast Growth Factor Receptor 2B, in Bovine Follicles1
Bibliographic record
Abstract
Some fibroblast growth factors (FGFs) affect ovarian follicle cell growth and/or differentiation. Whereas many FGFs activate several FGF receptors, FGF7 and FGF10 primarily activate only one, FGFR2B. As FGF7 is produced by bovine theca cells and acts on granulosa cells, we tested the hypothesis that FGF10 may also play a role in folliculogenesis in cattle. Reverse transcription-polymerase chain reaction demonstrated the presence of FGF10 mRNA in the oocytes and theca cells of the antral follicles, as well as in the preantral follicles. FGF10 protein was detected by immunohistochemistry in the oocytes of the preantral and antral follicles, and in the granulosa and theca cells of the antral follicles. FGF10 expression in theca cells changed during follicle development; mRNA abundance decreased with increasing follicular estradiol concentration in healthy follicles, and was lowest in highly atretic follicles. Culturing of granulosa cells in serum-free medium revealed FSH regulation of FGF10 receptor expression. The addition of FGF10 to cultured granulosa cells decreased the level of estradiol but did not alter cell proliferation. These data support a role for FGF10 in signaling to granulosa cells from theca cells and/or the oocyte.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".