Differential gene expression of granulosa cells after ovarian superstimulation in beef cattle
Bibliographic record
Abstract
Microarray analysis was used to compare the gene expression of granulosa cells from dominant follicles with that of those after superstimulatory treatment. Cows were allocated randomly to two groups (superstimulation and control, n=6/group). A new follicular wave was induced by ablation of follicles ≥5 mm in diameter, and a progesterone-releasing device controlled internal drug release (CIDR) was placed in the vagina. The superstimulation group was given eight doses of 25 mg FSH at 12-h intervals starting from the day of wave emergence (day 0), whereas the control group was not given FSH treatment. Both groups were given prostaglandin F2α twice, 12 h apart, on day 3 and the CIDR was removed at the second injection; 25 mg porcine luteinizing hormone (pLH) was given 24 h after CIDR removal, and cows were ovariectomized 24 h later. Granulosa cells were collected for RNA extraction, amplification, and microarray hybridization. A total of 190 genes were downregulated and 280 genes were upregulated. To validate the microarray results, five genes were selected for real-time PCR (NTS, FOS, THBS1, FN1, and IGF2). Expression of four genes increased significantly in the three different animals tested (NTS, FOS, THBS1, and FN1). The upregulated genes are related to matrix remodeling (i.e. tissue proliferation), disturbance of angiogenesis, apoptosis, and oxidative stress response. We conclude that superstimulation treatment i) results in granulosa cells that lag behind in maturation and differentiation (most of the upregulated genes are markers of the follicular growth stage), ii) activates genes involved with the NFE2L2 oxidative stress response and endoplasmic reticulum stress response, and iii) disturbs angiogenesis.
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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".