PSXVII-33 Identification of urine metabolite signatures for monitoring dairy cows for susceptibility to metritis by DI/LC-MS/MS-based metabolomics.
Bibliographic record
Abstract
Metritis and infertility are the number one reason for culling of cows in dairy herds. Early identification of cows susceptible to metritis might help selective preventive treatment. Urine can be collected non-invasively and can be used to monitor cows for susceptibility to metritis. The objective of this study was to identify urine metabolite signatures that characterize pre-metritic dairy cows using DI/LC-MS/MS based metabolomics. Urine samples were collected from hundred transition dairy cows from which 20 healthy (CON), and six cows with metritis were considered for analysis. Samples were collected before parturition (at –8 and -4wks prepartum). One hundred and twenty-eight urine metabolites were quantitatively profiled in CON and metritis cows using a targeted metabolomics approach (DI/LC-MS/MS) at two time points (-8 and -4wk). Concentrations of metabolites at each time point were analyzed using univariate and multivariate analyses. Results indicated significant (P ≤ 0.05) urine metabolites concentration alterations at -8 and -4wks in cows that developed metritis as compared to healthy controls. Results from the univariate analysis showed that the concentration of 44 and 32 urine metabolites were significantly altered at -8 and -4wks, respectively, before parturition. These metabolites belong to the groups of glycerphospholipids, sphingolipids, amino acids, and acyl carnitines. The multivariate analysis (PLS-DA) also showed a clear separation between pre-metritic and CON groups of cows at -8 and -4wks before parturition. The top 5 metabolites that contributed to the separation of the two groups at -8 and -4 wks were C5-M-DC, PC ae 42:2, C3-OH, Tyr, C12 and PC ae C32:2, C16, C5-M-DC, C3-OH, C16:2, respectively, in variable importance in the projection. Taken together, the results indicate that urine metabolite profiles were altered in pre-metritic cows (-8 and -4wks) and these biomarkers can be used for early diagnosis of the 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".