Pregnancy and Bovine Somatotropin in Nonlactating Dairy Cows: II. Endometrial Gene Expression Related to Maintenance of Pregnancy
Bibliographic record
Abstract
The objective was to evaluate the effects of pregnancy and bovine somatotropin (bST) on endometrial gene and protein expression related to maintenance of pregnancy in nonlactating dairy cows at d 17. In endometrial tissues, treatment with bST increased the steady state concentration of oxytocin receptor (OTR) mRNA; bST-treated cyclic (bST-C) cows had greater OTR mRNA than bST-treated pregnant (bST-P) cows. Estradiol receptor alpha (ERalpha) mRNA was reduced in bST-P cows compared with control P and C (no bST) cows. Western blotting revealed that pregnancy decreased the abundance of ERalpha protein, and bST stimulated an increase in ERalpha protein in C and P cows. Treatment with bST increased steady state concentrations of progesterone receptor (PR) mRNA. No differences were detected in steady state mRNA concentrations of prostaglandin H synthase-2 (PGHS-2), prostaglandin E synthase, and prostaglandin F synthase due to pregnancy or bST treatment. However, PGHS-2 protein was increased in response to pregnancy and bST treatment. Immunostaining indicated that P decreased ERalpha protein in luminal epithelium and increased PR protein in epithelial cells of the uterine glands. The PR protein response in the glands was less in bST-P cows than in P cows. In the stromal layer of the endometrium, bST decreased PR protein abundance in C and P cows. The PGHS-2 protein was localized exclusively in the luminal epithelium cells of endometrium and was increased in P cows. In conclusion, distinctly different mRNA and protein responses were detected between C and P cows related to prostaglandin biosynthesis, and bST-induced changes may potentially impact mechanisms associated with maintenance of pregnancy in nonlactating cows.
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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".