Short-term effect of FSH on gene expression in bovine granulosa cells in vitro
Bibliographic record
Abstract
In reproduction, FSH is one of the most important hormones, especially in females, because it controls the number of follicles and the rate of follicular growth. Although several studies have examined the follicular response at the transcriptome level, it is difficult to obtain a clear and complete picture of the genes responding to an increase in FSH in an in vivo context because follicles undergo rapid morphological and physical changes during their growth. To help define the transcriptome downstream response to FSH, an in vitro model was used in the present study to observe the short-term (4h) cellular response. Gene expression analysis highlighted a set of novel transcripts that had not been reported previously as being part of the FSH response. Moreover, the results of the present study indicate that the epithelial to mesenchymal transition pathway is inhibited by short-term FSH stimuli, maintaining follicles in a growth phase and preventing differentiation. Modulating gene expression in vitro has physiological limitations, but it can help assess the potential downstream response and begin the mapping of the granulosa cell transcriptome in relation to FSH. This information is a key feature to help discriminate between the effects of FSH and LH, or to elucidate the overlapping of insulin-like growth factor 1 and FSH in the granulosa mitogenic response.
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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 | 0.000 | 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) | 0.001 | 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".