PSV-4 Determination of Deoxynivalenol (DON) Content in Biological Samples as an Indicator of DON Intake in Grower-finisher Pigs
Bibliographic record
Abstract
Abstract Due to difficulties in obtaining consistent and/or reliable measures of deoxynivalenol (DON) in complete swine diets, we investigated whether measuring DON in biological samples could be used as an indicator of DON ingestion in pigs. In this study, graded levels of DON (1, 3, or 5 ppm) were fed to grower-finisher pigs for a period of 77-d. On d 35 and 77 of the study, urine samples were quantitatively collected over a 24-h period and blood samples were collected between 3 – 4 h after the morning meal on each of those days for serum DON analysis. For direct quantification of DON in urine, high-performance liquid chromatography with tandem mass spectrometry was performed. For serum samples, indirect quantification of DON was performed via enzymatic hydrolysis. We observed that DON content in urine increased linearly as intake of DON increased (Fig.1A; P < 0.05). Analysis of DON in serum follow a similar trend, where serum DON content was increased as DON intake increased (Fig.1B; P < 0.05). An average of 30% of DON ingested was recovered as DON in urine over a 24-h period. In summary, there was a linear relationship between DON intake and DON content in both urine and blood serum, therefore, analyzing DON concentration in serum and urine could be used as a tool to estimate for DON exposure in pigs under controlled conditions.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 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".