Expression of LH receptor mRNA splice variants in bovine granulosa cells: changes with follicle size and regulation by FSH in vitro
Bibliographic record
Abstract
In cattle, most evidence suggests that granulosa cells express LH receptors (LHR) after (or as) the follicle becomes dominant, however there is some suggestion that granulosa cells from smaller pre-dominant follicles may express several LHR mRNA splice variants. The objective of this study was to measure LHR expression in bovine follicles of defined size and steroidogenic ability, and in granulosa cells from small follicles (<6 mm diameter) undergoing differentiation in vitro. Semiquantitative RT-PCR demonstrated that LHR mRNA was undetectable in granulosa cells of follicles <7 mm diameter (nondominant follicles), and increased with follicle diameter in follicles >7 mm diameter. Splice variants with deletions of exon 10 and part of exon 11 were detected as previously described, and we detected a novel splice variant with a deletion of exon 3. Cultured granulosa cells contained LHR mRNA, but with significantly greater amounts of variants with deletions of exon 10 and/or exon 11 compared with cells from dominant follicles. FSH increased the abundance of some but not all LHR mRNA splice variants in cultured granulosa cells. The addition of LH to cultured cells did not increase progesterone secretion, despite the presence of LHR mRNA. Collectively, these data suggest that granulosa cells do not acquire functional LHR until follicle dominance occurs.
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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".