Comparative analysis of granulosa cell gene expression in association with oocyte competence in FSH-stimulated Holstein cows
Bibliographic record
Abstract
Ovarian stimulation with exogenous FSH followed by FSH withdrawal or 'coasting' is an effective means of increasing the number of oocytes obtainable for the in vitro production of cattle embryos. However, the quality of the oocytes thus obtained varies considerably from one cow to the next. The aim of the present study was to gain a better understanding of the follicular conditions associated with low oocyte developmental competence. Granulosa cells from 94 Holstein cows in a commercial embryo production facility were collected following ovarian stimulation and coasting. Microarray analysis showed 120 genes expressed with a differential of at least 1.5 when comparing donors of mostly competent with donors of mostly incompetent oocytes. Using ingenuity pathway analysis, we revealed the main biological functions and potential upstream regulators that distinguish donors of mostly incompetent oocytes. These are involved in cell proliferation, apoptosis, lipid metabolism, retinol availability and insulin signalling. In summary, we demonstrated that differences in follicle maturity at collection could explain differences in oocyte competence associated with individual animals. We also revealed deficiencies in lipid metabolism and retinol signalling in granulosa cells from donors of mostly incompetent oocytes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".