Analysis of the uterine flush fluid proteome of healthy mares and mares with endometritis or fibrotic endometrial degeneration
Bibliographic record
Abstract
The objective of this study was to evaluate the differences in the uterine flush fluid proteome between healthy mares and mares with endometritis or fibrotic endometrial degeneration (FED). Uterine flush fluid samples were collected from healthy mares (n=8; oestrus n=5 and dioestrus n=3) and mares with endometritis (n=23; oestrus n=14 and dioestrus n=9) or FED (n=7; oestrus n=6 and dioestrus n=1). Proteomic analysis was performed using label-free liquid chromatography-tandem mass spectrometry. Of 216 proteins identified during oestrus, 127 were common to all three groups, one protein was exclusively detected in healthy mares, 47 proteins were exclusively detected in mares with endometritis and four proteins were exclusively detected in mares with FED. Of 188 proteins identified during dioestrus, 113 proteins were common between healthy mares and mares with endometritis, eight proteins were exclusively detected in healthy mares and 67 proteins were exclusively detected in mares with endometritis. Quantitative analysis revealed a subset of proteins differing in abundance between the three groups during oestrus and between healthy mares and mares with endometritis during dioestrus. These results provide a springboard for evaluation of specific proteins as biomarkers of uterine health and disease and for investigation of their roles in the establishment and maintenance of pregnancy.
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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) | 0.000 | 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".