GC–MS Metabolomics Identifies Metabolite Alterations That Precede Subclinical Mastitis in the Blood of Transition Dairy Cows
Bibliographic record
Abstract
The objectives of this study were to determine alterations in the serum metabolites related to amino acid (AA), carbohydrate, and lipid metabolism in transition dairy cows before diagnosis of subclinical mastitis (SCM), during, and after diagnosis of disease. A subclinical mastitis case was determined as a cow having somatic cell count (SCC) > 200 000/mL of milk for two or more consecutive reports. Blood samples were collected from 100 Holstein dairy cows at five time points at -8 and -4 weeks before parturition, at the week of SCM diagnosis, and +4 and +8 weeks after parturition. Twenty healthy control cows (CON) and six cows that were diagnosed with SCM were selected for serum analysis with GC-MS. At -8 weeks a total of 13 metabolites were significantly altered in SCM cows. In addition, at the week of SCM diagnosis 17 metabolites were altered in these cows. Four weeks after parturition 10 metabolites were altered in SCM cows and at +8 weeks 11 metabolites were found to be different between the two groups. Valine (Val), serine (Ser), tyrosine (Tyr), and phenylalanine (Phe) had very good predictive abilities for SCM and could be used at -8 weeks and -4 weeks before calving. Combination of Val, isoleucine (Ile), Ser, and proline (Pro) can be used as diagnostic biomarkers of SCM during early stages of lactation at +4 to +8 weeks after parturition. In conclusion, SCM is preceded and followed by alteration in AA metabolism.
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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) | 0.000 | 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".