Evidence that fibroblast growth factor 10 plays a role in follicle selection in cattle
Bibliographic record
Abstract
There is evidence that regulation of follicle selection in cattle involves locally produced growth factors. In the present study, we investigated the expression of members of the fibroblast growth factor (FGF) 7 family during follicle deviation. The largest and second largest follicles were recovered during the second day of a synchronised follicle wave and the future dominant and future subordinate follicles were identified based on diameter and cytochrome P450, family 19, subfamily A, polypeptide 1 (CYP19A1) mRNA levels in granulosa cells. Theca cells of the future dominant follicle contained less mRNA encoding FGF7 and FGF10 compared with those from the future subordinate follicle 2.5 days after ovulation, before a significant difference between the diameters of the future dominant and future subordinate follicles could be observed, but FGF22 mRNA levels did not change. Levels of mRNA encoding FGF receptors FGFR1B and FGFR2B in theca and granulosa cells, respectively, were lower in the future dominant follicle compared with the future subordinate follicle. Addition of FGF10 to granulosa cells in vitro significantly decreased oestradiol secretion, as well as CYP19A1, FSH receptor (FSHR) and insulin-like growth factor 1 receptor (IGF1R) mRNA abundance, whereas FGF22 had no effect. We conclude that FGF10 and FGFR2B expression is increased in the future subordinate follicle before morphological deviation, which may contribute to follicle selection.
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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) | 0.000 | 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".