235 Milk metabotyping by DI/LC-MS/MS demonstrated major alterations in metabolites related to lipid and amino acid metabolism in dairy cows affected by subclinical mastitis.
Bibliographic record
Abstract
The objective of this study was to determine metabolite alterations in the milk of Holstein dairy cows affected by subclinical mastitis (SCM) immediately after calving and identify potential biomarkers for diagnosis of SCM. Eight Holstein dairy cows with milk somatic cell count (SCC) >200,000 cells /mL (SCM) and 20 healthy controls with SCC <200,000 cells/mL (CON) were sampled at 2 wks postpartum and DI-LC-MS/MS-based metabolomics was used to screen milk for concentration of 20 acylcarnitines (AC), 7 lysophosphatidylcholines (LPC), 58 phosphatidylcholines (PCs), hexose (multiple sugars), 20 amino acids, and 13 metabolites related to amino acid metabolic pathways. The kit used for analyses was AbsoluteIDQ 180 (BIOCRATES, Austria). Univariate analysis was performed using R (version 3.0.3; 2008). Metabolomic data were analyzed by MetaboAnalyst. Statistical significance was declared at P < 0.05. Results showed that concentrations of 19 metabolites were significantly altered in the milk of SCM cows with Arg and 7 PCs elevated (P<0.05) in SCM compared with CON cows. PLS-DA analysis showed clear separation of clusters for the two groups of cows on the basis of measured milk metabolites. Pathway analysis demonstrated that the most important metabolic pathways affected were those of biotin and tyrosine metabolism, catecholamine biosynthesis, taurine and hypotaurine metabolism, urea cycle, and lysine degradation. Biomarker analysis indicated that AUCs for the ROC curve based on the top 8 metabolites with the greatest VIP values was greater than 0.98, which indicates that biomarkers identified have very high diagnostic abilities for SCM in postpartum dairy cows. Overall, metabolite changes observed might be related to mounting of an efficient immune response to bacterial infections. Moreover, more research is warranted to better understand the pathomechanism of SCM and the host response to bacterial infection.
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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".