Diurnal variation of NMR based blood metabolites in calves fed a high plane of milk replacer: a pilot study
Bibliographic record
Abstract
BACKGROUND: Blood profiles have been used to monitor herd health status, diagnose disorders, and predict the risk of diseases in cattle and calves. Characterizing plasma metabolites in dairy calves could provide further insight into daily metabolic variations and the mechanisms that lead to metabolic diseases. In addition, by understanding physiological ranges of plasma metabolites relative to meal and the time of feeding in healthy animals, veterinarians can accurately diagnose abnormalities with a blood test. For diagnostic purposes, nuclear magnetic resonance (NMR) spectroscopy shows promise as a new and reliable method to determine a large number of blood metabolites simultaneously. RESULTS: Results demonstrated that the concentration of specific metabolites in plasma (i.e., lysine, isoleucine, leucine, tyrosine, glutamine, creatine, and 1-methylhistidine) fluctuated around meal times, while others (i.e., glutamic acid, methanol, formic acid, and acetic acid) maintained a stable temporal concentration. In addition to temporal changes in concentration, results also characterized differences for overall plasma metabolite concentrations; for example, methionine had the lowest (38 μM) while glutamine had the highest concentration (239 μM) amongst plasma AA. This is the first report describing how the plasma metabolome changes during 24-h period in young calves fed an elevated plane of milk replacer twice daily. CONCLUSIONS: Data from this pilot study will help to establish reference standards for future metabolic diagnostics in dairy calves. In addition, this pilot study illustrated that feeding milk replacer may influence plasma metabolite concentrations. With the rapid implementation of blood metabolomics in monitoring animal health, it is then important to consider the time of feeding during the day when interpreting metabolomics analysis results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 teacher head, 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".