Characterization of serum and tissue oxytocinase and tissue oxytocin in the pregnant and non-pregnant mare
Bibliographic record
Abstract
Oxytocin is a hormone with functions in: reproduction, maternal bonding, milk ejection, and feeding/social behavior, and is reported to be present in a variety of tissues. Our goal is to characterize oxytocin and leucyl and cystinyl aminopeptidase (LNPEP/oxytocinase), a key regulator of oxytocin in mares. We measured serum and tissue LNPEP by ELISA from ovulation (D0) until D21-22 in non-pregnant (n = 5) and pregnant mares (n = 6); and in periparturient and postpartum mares (n = 18). Placenta (n = 7) and homogenized tissue of diestrus mares (n = 6) were evaluated using protein determinations and LNPEP ELISAs. Identification of LNPEP and OXT protein in tissues was also performed via western blot, immunohistochemistry and liquid chromatography-mass spectrometry (LC-MS/MS). Furthermore, in situ hybridization was performed for LNPEP and OXT on endometrium, myometrium, pituitary and corpus luteum (CL). Serum LNPEP concentration were similar. Placental LNPEP U/mg protein was highest in the body and pregnant horn. The highest to lowest LNPEP U/mg protein by tissue were: myometrium > follicle wall > endometrium > kidney > CL > liver. Oxytocin was identified in the equine pituitary, CL and placenta and is likely to act in autocrine or paracrine manner, while LNPEP may act systemically and locally to regulate the availability of OXT.
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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".