A Comprehensive Review: Current Strategies for Detoxification of Deoxynivalenol in Feedstuffs for Pigs
Bibliographic record
Abstract
, primarily contaminating wheat, corn, and their derivatives. Although less toxic than some other mycotoxins, DON is significant due to its abundance, particularly in wheat, and its resistance to in vitro detoxification. Compared to other livestock, pigs are the most susceptible animals to DON's presence due to its interaction with wheat-heavy diets. The best way to attenuate the effects of DON is to prevent its presence on cultivated land; however, given that it is difficult to prevent the occurrence of DON in freshly harvested cereals completely, other strategies must be applied. For pigs, chronic consumption of feed with DON concentrations below even the regulated limits can still negatively impact growth. Thus, DON-detoxifying agents including antidotes, DON binding agents, and exogenous in vivo enzyme additives, have been added to feed in an attempt to detoxify DON. This comprehensive review highlights the most recently developed DON-detoxification techniques and assesses each treatment's viability and efficacy, with an emphasis on chemical and biochemical techniques and the rapid development of artificial intelligence (AI) technologies. Herein we explore the unmet needs and future directions of current technologies while discussing promising strategies that can advance the DON-detoxification field.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".