Qualitative analysis and functional classification of the uterine proteome of mares in oestrus and dioestrus
Bibliographic record
Abstract
Quantitative analysis of the uterine flush fluid proteome of mares in oestrus and dioestrus has been previously reported. The objectives of this study were to: a) evaluate qualitative differences in the uterine flush fluid proteome between mares in oestrus and mares in dioestrus and b) perform a functional classification of proteins either unique to each stage or common between the two stages. Uterine flush fluid samples were collected from 8 light breed mares in either oestrus (n = 5) or dioestrus (n = 3). Proteomic analysis of the samples was conducted using liquid chromatography-tandem mass spectrometry. Proteins exclusively detected in oestrus or dioestrus and those common to both stages were identified using the Scaffold software (version 4.4.8, Proteome Software Inc., Portland, OR). The identified proteins were classified into gene ontology (GO) categories (cellular component [CC], molecular function [MF] and biological process [BP]) using the PANTHER (www.pantherdb.org) classification system version 14.0. Of 172 proteins identified, 51 and 28 were exclusively detected in mares in oestrus and dioestrus, respectively, and 93 proteins were common to both stages. The most represented terms in various GO categories were similar among the three subsets of proteins. The most represented CC terms were extracellular region and cell, the most represented MF terms were catalytic activity and binding, and the most represented BP terms were metabolic process and cellular process. In conclusion, proteomic analysis of the uterine flush fluid enabled the identification of subsets of proteins unique to oestrus or dioestrus, or common to both stages. The results of this study can serve as a baseline for future research focused on finding stage-specific protein markers or evaluating differences in the uterine flush fluid proteome between normal mares and those with uterine disease.
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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 | 0.001 |
| 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".