046 Serum and urine metallotyping of preketotic and ketotic dairy cows reveals major alterations in multiple mineral elements
Bibliographic record
Abstract
Ketotic cows have elevated concentrations of ketone bodies or ketoacids (i.e., β-hydroxybutyric acid [BHBA], acetoacetic acid [AcAc], and acetone) in 3 body fluids including blood, urine, and milk. Both BHBA and AcAc are strong acids that cause ketoacidosis and affect physiological functions of various tissues. The objectives of the current study were to 1) investigate mineral alterations in both serum and urine of preketotic, ketotic, and postketotic cows and 2) identify potential predictive and diagnostic mineral biomarkers for ketosis in serum and urine. Metallotyping was performed in the serum and urine of 6 cases of ketosis and 20 control cows using inductively coupled plasma mass spectrometry at −8 and −4 wk, disease diagnosis week, and +4 and +8 wk relative to parturition. Univariate analysis of data was performed using the Wilcoxon–Mann–Whitney (rank sum) test provided by R (statistical significance: P < 0.05). Multivariate data analysis was processed by the MetaboAnalyst software. Results showed disturbances in concentrations of metals in the serum and urine of cows with ketosis at all 5 time points tested. The most important finding of this study was that 4 trace elements including Al, Fe, Mn, and As were persistently elevated in the serum of preketotic, ketotic, and postketotic cows. Moreover, 3 minerals (i.e., B, Al, and Rb) were increased in the urine of preketotic (8 and 4 wk prepartum) cows. It is interesting to point out that Al was the most elevated metal in the serum of preketotic cows at 8 and 4 wk prior to parturition at 91.4-fold (120.6 vs. 1.32 μM) and 78.12-fold (111.34 vs. 1.43 μM), respectively (P < 0.001). Similar alterations for Al were also detected in the urine samples of preketotic (i.e., 0.39 vs. 0.05 μM/mM creatinine at −8 wk [P < 0.001] and 0.16 vs. 0.05 μM/mM creatinine at −4 wk [P < 0.001]) cows in comparison with control cows. Because preketotic and ketotic cows were on a state of chronic metabolic acidosis, altered mineral elements in both serum and urine are thought to be related to the effects of acidosis on bone metabolism and urine excretion of metals. Findings from the current study might encourage development of early diagnostic biomarkers for risk of ketosis as well as new preventative intervention to lower the risk of ketosis in transition dairy cows.
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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) | 0.001 | 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".