Fibroblast growth factor 18 regulates steroidogenesis in fetal bovine ovarian tissue in vitro
Bibliographic record
Abstract
In cattle and other species, the fetal ovary is steroidogenically active before follicular development commences, and there is evidence that estradiol and progesterone inhibit follicle formation and activation. Estradiol levels decline sharply around the time of follicle formation. In the present study, we hypothesized that FGF10 and FGF18, which inhibit estradiol secretion from granulosa cells of antral follicles, also regulate fetal ovarian steroid production. Fetuses were collected at local abattoirs, and age determined by crown-rump length measurements. Real-time polymerase chain reaction assays with RNA extracted from whole ovaries revealed that the abundance of CYP19A1 messenger RNA (mRNA) decreased from 60 to 90 days of gestation, which is consistent with the decline in estradiol secretion previously observed. Immunohistochemistry revealed the presence of FGF18 in ovigerous cords in early gestation and in oocytes later in fetal age (≥150 days). The abundance of FGF18 mRNA increased after Day 90 gestation. Addition of recombinant FGF18 to fetal ovarian pieces inhibited estradiol and progesterone secretion in vitro, whereas FGF10 was without effect. Consistent with these results, FGF18 decreased levels of mRNA for CYP19A1 and CYP11A1 in ovarian pieces in vitro. These data suggest that FGF18 may be an intraovarian factor that regulates steroidogenesis in fetal ovaries.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".