340 Metabolomics-based profiling identifies serum signatures that predict the risk of metritis in transition dairy cows
Bibliographic record
Abstract
The objectives of this study were to identify metabolite signatures in the blood of dairy cows before, during, and after diagnosis of metritis that could be used to predict the risk of metritis in transition dairy cows. Direct-injection liquid chromatography–tandem mass spectroscopy was used to analyze serum samples collected from both 20 healthy (CON) and 6 metritic cows during −8 and −4 wk, at disease diagnosis, and +4 and +8 wk relative to parturition. Univariate (Wilcoxon–Mann–Whitney test by R; statistical significance: P < 0.05) and multivariate data (i.e., principal component analysis and partial least squares discriminant analysis [PLS-DA]; permutation test for the PLS-DA model, P < 0.05) analyses were conducted to examine alterations of serum metabolites throughout the progress of the disease. Results from univariate analysis indicated that cows with metritis experienced altered concentrations of multiple serum AA, glycerophospholipids, sphingolipids, acylcarnitines, and hexose during the entire experimental period. Principal component analysis and PLS-DA analyses showed clearly separated clusters for the 2 groups on the basis of measured serum metabolites during 5 time points. It is interesting to note that throughout the 17 wk of the study, several serum metabolites (e.g., PC aa C30:0, PC ae C30:1, SM [OH] C24:1, and SM C24:0) appeared to play a consistent role in distinguishing between the CON and metritic cows. For example, concentrations of PC ae C30:1 were consistently 2-fold greater in premetritic (i.e., mean of 3.10 μM [SEM 0.46] vs. mean of 1.32 μM [SEM 0.18; P = 0.002] at −8 wk prepartum and mean of 1.91 μM [SEM 0.22] vs. mean of 0.83 μM [SEM 0.10; P = 0.001] at −4 wk prepartum) and metritic (1.89 ± 0.23 vs. 0.90 ± 0.11; P = 0.001 at the disease week) cows compared with CON cows. Furthermore, 5 metabolic pathways (i.e., Lys degradation, biotin metabolism, Trp metabolism, Val–Leu–Ile degradation, and protein biosynthesis) were altered in both premetritic and metritic cows. These new findings give insights into the pathomechanism of metritis in dairy cows. Moreover, the area under the curve for 5 ROC curves was 0.995 (95% CI 0.945 to 1) at −8 wk, 0.992 (95% CI 0.938 to 1) at −4 wk, 0.988 (95% CI 0.913 to 1) at disease week, 1 (95% CI 1 to 1) at +4 wk, and 0.99 (95% CI 1 to 1) at 8 wk, respectively, which suggests that serum biomarkers identified have pretty accurate predictive, diagnostic, and prognostic abilities for metritis in transition dairy cows.
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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 | 0.001 | 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".