A comparison of the uterine proteome of mares in oestrus and dioestrus
Bibliographic record
Abstract
Proteomic analysis of mare uterine flush fluid provides a minimally invasive technique for studying protein changes associated with the oestrous cycle. The aim of this study was to identify differentially abundant proteins in the uterine flush fluid of mares in oestrus and dioestrus. In this study, uterine flush fluid samples were collected from eight reproductively healthy mares in either oestrus (n = 5) or dioestrus (n = 3). Proteomic analysis was performed using liquid chromatography-tandem mass spectrometry. Of 172 proteins identified, six proteins (immunoglobulin lambda-like polypeptide 1, haemoglobin subunit alpha, alpha-1B-glycoprotein, serotransferrin, apolipoprotein A-1, and haemoglobin subunit beta) were significantly more abundant in oestrus. These proteins may contribute to the endometrial defence system through roles in inflammation, immunity or antimicrobial activity. In other species, some of these proteins have been described as immunoglobulins, negative acute phase proteins or defence agents against micro-organisms. During dioestrus, immunoglobulin alpha-1 chain C region-related, complement factor I, CD 109 antigen and uterocalin, were significantly more abundant. Research in other species suggests that these four proteins contribute to the immune response through proposed immunoregulatory characteristics, complement system involvement or roles in B cell-T cell interactions. In conclusion, ten differentially abundant proteins were identified in the uterine flush fluid of mares in oestrus and dioestrus. Targeted studies on these proteins could elucidate their role in uterine defence mechanisms during the oestrous cycle in the mare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".