PSXVII-34 Targeted metabolomics profiling for identification of novel serum biomarkers in early prediction of subclinical mastitis in transition dairy cows.
Bibliographic record
Abstract
The objectives of this study were to targetedly investigate metabolic profiles of the serum in dairy cows (both healthy controls (CON) and cows that developed subclinical mastitis (SCM) at early lactation) up to -8 wks prepartum, and evaluate the performance of new biomarkers for both early onset and progression of the disease. In this research, DI/LC-MS/MS based metabolomics was employed to identify and quantify biochemical signatures in the serum of dairy cows at -8 wks, -4 wks, disease diagnosis, +4 wks, and +8 wks relative to parturition. One hundred and twenty-eight metabolites including amino acids (21), acylcarnitines (7), biogenic amines (8), glycerophospholipids (77), sphingolipids (14), and hexose (1) were quantified in all sera. Univariate analysis showed that several metabolites (e.g., phosphatidylcholine (PC aa C30:2), hydroxysphingomyelin (SM (OH) C22:2), isoleucine, leucine, and lysine; P < 0.01) were consistently elevated in the serum of cows with SCM at five tested time points. In the supervised multivariate analysis (i.e., PLS-DA; permutation test: P < 0.05), a variable importance in projection (VIP) plot was used to rank the most significant discriminators between SCM and CON cows. Two predictive biomarker models and one diagnostic biomarker model for SCM were developed by combination of amino acids, sphingomyelin, phosphatidylcholine, and kynurenine. AUC values of three ROC curve of biomarker models were all greater than 0.995. The predictive and diagnostic models for SCM based on metabolites in serum are practical biomarker alternatives as compared to the current milk somatic cell count (SCC) assays. The data demonstrate that metabolomics may be effective for screening cows during the dry off for susceptibility to SCM and evaluating the health status of udder in dairy cows in the future. Results also help in better understanding the metabolic status and pathomechanisms 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 | 0.000 | 0.000 |
| 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".