46 Sulfonation-Based Decontamination Alters Biotransformation and Excretion of Deoxynivalenol in Nursery Pigs
Bibliographic record
Abstract
Abstract Deoxynivalenol (DON) is a primary mycotoxin in cereal feed ingredients that negatively affects feed intake and immune function of pigs. Formation of DON sulfonate (DONS) with sulfites in feed has become an effective approach to mitigate the DON toxicities in practice, but the metabolic fate of DON under sulfonation-based decontamination was not well defined. In this study, 48 nursery pigs (35 d of age, 10.8 ± 1.3 kg) were stratified by weight and sex and grouped following a 2 x2 factorial design on the DON content in corn-soybean meal diets (1.2 vs. 4.1 ppm) and the inclusion of NoTox Ultimate D, a sulfite-based mitigant (0 vs. 0.25%). Four experimental diets, including low-DON feed with no mitigant treatment (LD-NT); low-DON feed with sulfonation treatment (LD-ST): high-DON feed with no mitigant treatment (HD-NT); high-DON feed with sulfonation treatment (HD-ST), were fed for 21 d for urine collection on day 0, 10, and 21 and fecal collection on day 21. Urine and fecal samples were analyzed by both targeted metabolite analysis and untargeted metabolomic modeling. DON itself was absent in all fecal and urine samples, indicating a complete biotransformation. As expected, sulfonation led to the formation of DONS in feces in correlation with the DON content in feed and the decrease of two DON glucuronides in urine (HD-ST < HD-NT). Interestingly, sulfonation did not decrease the concentrations of two deepoxy-deoxynivalenol (DOM) glucuronides in urine. Instead, it significantly increased DOM in feces (HD-ST > HD-NT). The results indicate that the formation of DONS by sulfite treatment alters the biotransformation and excretion of DON by decreasing the availability of free DON for absorption and simultaneously increasing its availability for microbial metabolism to form DOM, an inactivated metabolite, for fecal and urine excretion.
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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 | 0.000 | 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) | 0.001 | 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".