Transcriptional effect of the LH surge in bovine granulosa cells during the peri-ovulation period
Bibliographic record
Abstract
The LH surge induces a multitude of events that are essential for ovulation and corpus luteum formation. The transcriptional responses to the LH surge of preovulatory granulosa cells (GCs) are complex and still poorly understood. In this study, a genome-wide bovine oligo array was used to determine how the gene expression profile of GCs is modulated by the LH surge. GCs from three different stages were used to assess the short- and long-term effects of this hormone on follicle differentiation: 1) 2 h before induction of the LH surge, 2) 6 h and 3) 22 h after the LH surge. The results obtained were a list of differentially expressed transcripts for each GC group. To provide a comprehensive understanding of the processes at play, biological annotations were used to reveal the different functions of transcripts, confirming that the LH surge acts in a temporal manner. The pre-LH group is involved in typical tasks such as cell division, development, and proliferation, while the early response to the LH surge included features such as response to stimulus, vascularization, and lipid synthesis, which are indicative of cells preparing for ovulation. The late response of GCs revealed terms associated with protein localization and intracellular transport, corresponding to the future secretion task that will be required for the transformation of GCs into corpus luteum. Overall, results described in this study provide new insights into the different transcriptional steps that GCs go through during ovulation and before luteinization.
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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".