Cumulus cell gene expression associated with pre-ovulatory acquisition of developmental competence in bovine oocytes
Bibliographic record
Abstract
The final days before ovulation impact significantly on follicular function and oocyte quality. This study investigated the cumulus cell (CC) transcriptomic changes during the oocyte developmental competence acquisition period. Six dairy cows were used for 24 oocyte collections and received FSH twice daily over 3 days, followed by FSH withdrawal for 20, 44, 68 and 92 h in four different oestrous cycles for each of the six cows. Half of the cumulus-oocyte complexes were subjected to in vitro maturation, fertilisation and culture to assess blastocyst rate. The other half of the CC underwent microarray analysis (n=3 cows, 12 oocyte collections) and qRT-PCR (n=3 other cows, 12 oocyte collections). According to blastocyst rates, 20 h of FSH withdrawal led to under-differentiated follicles (49%), 44 and 68 h to the most competent follicles (71% and 61%) and 92 h to over-differentiated ones (51%). Ten genes, from the gene lists corresponding to the three different follicular states, were subjected to qRT-PCR. Interestingly, CYP11A1 and NSDHL gene expression profiles reflected the blastocyst rate. However most genes were associated with the over-differentiated status: GATM, MAN1A1, VNN1 and NRP1. The early period of FSH withdrawal has a minimal effect on cumulus gene expression, whereas the longest period has a very significant one and indicates the beginning of the atresia process.
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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".