Urinary and Serum Concentration of Deoxynivalenol (DON) and DON Metabolites as an Indicator of DON Contamination in Swine Diets
Bibliographic record
Abstract
Pig health is impaired and growth performance is reduced when exposed to deoxynivalenol (DON). The measurement of DON in individual feedstuffs and complete swine diets is variable because of the inconsistent distribution of mycotoxins in feed and the difficulties in obtaining representative samples. We investigated whether measuring DON and its metabolites in biological samples could be used as a predictor of DON ingestion by pigs. Blood samples were collected between 3 and 4 h after the morning meal and urine samples were quantitatively collected over a 24 h period on d 40 and 82 of the study to evaluate serum and urinary content of DON and DON metabolites (iso-deoxynivalenol, DON-3-glucuronide, DON-15-glcurunide, deepoxy-deoxynivalenol, iso-deepoxy-deoxynivalenol, deepoxy-deoxynivalenol-3-glucuronide, and deepoxy-deoxynivalenol-15-glucuronide). The intake of DON was positively correlated with urinary DON output. Similarly, there was an increase in serum DON level with increasing DON intake. Overall, it was found that DON intake correlated with DON concentration in urine and blood serum when samples were collected under controlled conditions. Analyzing DON levels in urine and blood serum could be used to predict a pig's DON intake.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".