Metabolomics of Four Biofluids from Dairy Cows: Potential Biomarkers for Milk Production and Quality
Bibliographic record
Abstract
The fundamental understanding of the mechanisms regulating milk protein synthesis is limited. This study aimed to elucidate the metabolic mechanisms of milk production affected by forage quality through studying metabolites from four biofluids (rumen fluid, milk, serum, and urine) collected from 16 lactating cows fed alfalfa hay (AH, high-quality, n = 8) and corn stover (CS, low-quality, n = 8) using gas chromatography-time-of-flight/mass spectrometry. The cows fed AH exhibited higher milk yield (P < 0.01), milk protein yield (P = 0.04), and milk efficiency (P < 0.01) than those fed CS. A total of 165, 195, 218, and 156 metabolites were identified in the rumen fluid, milk, serum, and urine, respectively, while 29 metabolites were found in all four biofluids. In addition 55, 8, 28, and 31 metabolites in each biofluid were significantly different (VIP > 1 and P < 0.05) between the AH- and CS-fed animals. These metabolites were involved in glycine, serine, and threonine metabolism; tyrosine metabolism; and phenylalanine metabolism. Further integrated key metabolic pathway analysis showed that the AH-fed cows may have more comprehensive amino acid metabolisms, suggesting that these metabolite-associated pathways may serve as biomarkers for higher milk yield and better milk protein quality.
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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".