The Role of Fibroblast Growth Factor-18 in Follicular Atresia in Cattle1
Bibliographic record
Abstract
Although the various members of the fibroblast growth factor (FGF) family are generally mitotic, one member, FGF18, has been shown to increase the rate of apoptosis of ovarian granulosa cells. In the present study, we first determined whether granulosa cells express FGF18 and we then explored the mechanism through which FGF18 increases apoptosis in vitro. Under culture conditions that favored estradiol secretion and CYP19A1 expression, granulosa FGF18 mRNA levels were barely detectable; however, withdrawing gonadotropic support (follicle-stimulating hormone or insulin-like growth factor 1) reduced levels of CYP19A1 mRNA and increased abundance of mRNA encoding the death ligand FASLG and FGF18. Addition of FGF18, but not FGF2, FGF10, or EGF, increased the proportion of apoptotic cells and frequency of caspase 3 activation, and these effects were abrogated by coculture with estradiol. Addition of FGF18 decreased abundance of mRNA encoding the antiapoptotic proteins GADD45B and MDM2, and increased that encoding the proapoptotic protein BBC3; these effects were reversed by coculture with estradiol. The physiological relevance of FGF18 was determined using an in vivo model: injection of FGF18 directly into growing bovine dominant follicles caused cessation of follicle growth by 24 h after injection. Collectively, these data demonstrate that FGF18 is proapoptotic in vivo and may act through a mechanism involving the BBC3-MDM2 pathway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".