Metabolomic fingerprinting of milk fever cows: Pre- and postpartum metabolite alterations
Bibliographic record
Abstract
BACKGROUND: Milk fever (MF), a metabolic disorder in dairy cows characterized by low blood calcium concentrations postpartum, is well-recognized clinically. However, comprehensive data on the alteration of metabolites associated with this condition remains sparse. HYPOTHESIS: Delineate serum metabolite profiles and metabolic pathways preceding, coinciding with, and after the onset of MF. ANIMALS: Twenty-six cows, including 20 healthy cows and 6 cows initially affected by MF. Because of culling, the number of MF-affected cows decreased to 4 at MF week, +4 weeks, and +8 weeks postpartum. METHODS: A nested case-control longitudinal study was conducted, with blood samples collected at -8 and -4 weeks prepartum, MF week, and +4 and +8 weeks postpartum. Serum analysis utilized direct injection/liquid chromatography/tandem mass spectrometry (DI/LC/MS/MS) techniques. RESULTS: Key findings included the identification of diverse metabolites such as hexose, amino acids, phosphatidylcholines, lysophosphatidylcholines, and sphingomyelin, which varied between studied groups (P < .05). The most marked metabolic alterations were observed 4 weeks prepartum. In total, 42, 56, 38, 29, and 24 metabolites distinguished the MF group at the respective time points (P < .05). Additionally, 33 metabolic pathways, including amino acid, antioxidant metabolism, fatty acid degradation, and carbohydrate processing, were impacted (P < .05). CONCLUSIONS AND CLINICAL IMPORTANCE: Metabolic disruptions in dairy cows begin several weeks before the clinical manifestation of MF and persist up to 8 weeks postpartum. These findings emphasize the complexity of MF, extending beyond only hypocalcemia and indicate the necessity for preemptive monitoring in dairy herd management.
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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 | 0.000 | 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".