Prostaglandin F<sub>2α</sub> induced luteolysis is delayed but not prevented by acute administration of luteotropic drugs in lactating nonpregnant Holstein cows
Bibliographic record
Abstract
Inadvertent use of prostaglandin F2α (PGF2α) in pregnant cattle could result in luteolysis and pregnancy loss. Using a nonpregnant cow model, we determined if luteotropic agents could counteract luteolytic effects of PGF2α. Ovarian status of 20 lactating nonpregnant Holstein cows was synchronized using an Ovsynch protocol and ovulation confirmed by transrectal ultrasonography 48 hours after the second gonadotropin releasing hormone (GnRH). Eight days after ovulation, 25 mg of dinoprost (native PGF2α) was administered IM to induce luteolysis. Five minutes after PGF2α, cows were treated IM with gonadorelin (GnRH, 100 μg; n = 5), human chorionic gonadotropin (hCG, 1,000 IU; n = 6), porcine pituitary luteinizing hormone (pLH, 25 mg; n = 5), or saline (control, 2 ml; n = 4). Blood samples were collected before PGF2α (0 hour) and at 1, 6, 12, 18, 24, 30, 36, 42, 48, 60, 72, and 84 hours after PGF2α to monitor subsequent luteal activity by measuring plasma progesterone concentrations. Although none of the treatments counteracted the luteolytic action of PGF2α, the rate of decline in plasma progesterone within 1 hour after PGF2α was greater (p = 0.04) in control than in GnRH, hCG, and pLH (2.2 versus 0.2, 0.3, and 0.1 ng/ml/hour, respectively). In addition, pLH-treated cows tended (p = 0.07) to have greater overall mean plasma progesterone for up to 84 hours (1.6 ± 0.2 ng/ml) than other treatments combined (1.1 ± 0.1 ng/ml). In the present study, giving GnRH, hCG or pLH 5 minutes after PGF2α administration was not effective in countering luteolytic effects of PGF2α. Nevertheless, pLH treatment tended to delay PGF2α induced luteolysis, which warrants further investigation.
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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 | 0.001 |
| 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".