FGF10 inhibits dominant follicle growth and estradiol secretion <i>in vivo</i> in cattle
Bibliographic record
Abstract
Fibroblast growth factors (FGFs) are involved in paracrine control of follicle development. It was previously demonstrated that FGF10 decreases estradiol (E(2)) secretion in granulosa cell culture and that theca cell FGF10 mRNA expression is decreased in healthy follicles from abattoir ovaries. The main objectives of this study were to evaluate FGF10 and FGFR2b mRNA expression during follicular development in vivo, to evaluate the effect of FGF10 on follicle growth using Bos taurus taurus cows as a model, and to gain more insight into the mechanisms through which FGF10 inhibits steroidogenesis. Messenger RNA encoding both FGF10 and FGFR2b (main FGF10 receptor) was significantly more expressed in subordinate follicles (SFs) than in dominant follicles (DFs). The intrafollicular injection of FGF10 into the largest growing follicle at 7-8 mm in diameter interrupted the DF growth in a dose-dependent manner (11±0.4, 8.3±1 and 5.9±0.3 mm for 0, 0.1, and 1 μg/ml FGF10, respectively, at 72 h after treatment; P<0.05). In a third experiment, follicles were obtained 24 h after FGF10 (1 μg/ml) or PBS treatment through ovariectomy. In theca cells, FGF10 treatment did not affect mRNA encoding steroidogenic enzymes, LHCGR and IGFBPs, but significantly upregulated FGF10 mRNA expression. The expression of CYP19A1 mRNA in granulosa cells was downregulated by FGF10 treatment, which was accompanied by a 50-fold decrease in E(2) production, and decreased cyclin D2 mRNA. These results have shown that FGF10 and its receptor FGFR2b are more expressed in SFs and provide solid in vivo evidence that FGF10 acts as an important regulator of follicular growth in cattle.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".