044 Serum metabolomics fingerprinting during the dry off period identifies metabolite signatures that can predict the risk of metritis
Bibliographic record
Abstract
The objective of this study was to screen transition dairy cows during the dry off period for identification of metabolite signatures in the serum that can be used for prediction of risk of metritis and give insights into the pathobiology of the disease. Blood samples were collected from coccygeal vein at 8 and 4 wk prepartum, disease diagnosis week, and 4 and 8 wk postpartum. Gas chromatography mass spectrometry was used to identify and quantify 29 metabolites in the serum of 20 healthy control cows (CON) and 6 cows that were diagnosed with metritis. Data were analyzed using univariate and multivariate analysis. Results showed that 16, 12, 14, 15, and 10 metabolites were significantly altered at −8 and −4 wk, at disease diagnosis, and at +4 and +8 wk around calving in cows diagnosed with metritis versus healthy CON. The multivariate analyses indicated consistent disease-dependent clustering with fairly similar set of metabolites distinguishing premetritis cows from healthy controls at −8 and −4 wk. The utility of these metabolites as biomarkers of risk of disease was assessed by the area under the curve (AUC), and AUC values of 1.0 and 0.969 were observed at −8 and −4 wk, respectively. Overall, results of this study indicated that selected metabolites can be used to early predict the risk of metritis in transition dairy cows. Results indicated significant (P < 0.05) metabolite alterations at −8 and −4 wk and at disease diagnosis in premitritis cows and those that developed metritis compared with healthy CON. The multivariate analyses also demonstrated consistent disease-dependent clustering with fairly similar set of metabolites distinguishing premetritis cows from healthy controls at −8 and −4 wk. Among the metabolites that distinguished premetritic cows from healthy CON at −8 wk, oxalate, ornithine, pyroglutamic acid, glutamic acid, and d-mannose were ranked as the top 5 in variable importance in the projection. A similar set of metabolites (except oxalate substituted by phosphoric acid) were ranked as the top 5 at −4 wk, indicating that those top 5 metabolites can be used as predictive biomarkers at −8 and −4 wk before the incidence of postpartum metritis. Intriguingly, multiple metabolites, such as galactose, phosphoric acid, oleic acid, urea, and oxalate, were identified to be different (P < 0.5) even at 4 and 8 wk after parturition. The significant alterations of serum metabolite concentrations and disease-dependent clustering around parturition indicate the potential of these metabolites to track the progression and development of metritis in dairy cattle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".