PSIII-21 Efficacy of deoxynivalenol detoxification by sodium metabisulfite (SMBS) containing nanofibrous mats using an in vitro intestinal epithelial cell IPEC-J2
Bibliographic record
Abstract
Abstract Deoxynivalenol (DON) occurs in many commonly used cereal grains. Pigs feed with DON concentrations as low as 0.6–2.0 mg/kg can result in reduced feed intake and growth rate, damage to intestinal epithelial cells, and increase susceptibility to enteric pathogens. Sodium metabisulfite (SMBS) can efficiently detoxify DON by converting it 8-DONS or 10-DONS in vitro. However, if SMBS is added directly to the feed, SMBS rapidly degrades under acidic aqueous conditions (e.g. pig stomach) and little SMBS is delivered to the intestinal absorption site where it can effectively detoxify DON. Thus, the objective of this study was to encapsulate SMBS into Eudragit L100-55 nanofibrous mats to deliver intact SMBS to the small intestine and evaluate the efficacy of DON detoxification in the simulated intestine fluid (SIF) using an in vitro intestinal epithelial cell (IPEC-J2) model. Nanofibrous mats were produced by coaxial electrospinning, with peak loading capacity and loading efficiency of SMBS reaching 32.00% and 80.01%. DON-induced cytotoxicity was not observed during in vitro analysis consisting of incubation of DON in the presence of SMBS-containing nanofibers (0.5% w/w) in simulated gastric fluid (SGF) for 2 h followed by incubation in a mixture of SGF and SIF (1:1) for 20 min. Meanwhile, compared to the DON treatment, incubation of DON in the presence of SMBS-containing nanofiber (0.5% w/w) in SGF for 2 h and SIF for 20 min decreased the gene expression of inflammatory cytokines in the IPEC-J2 cells and maintained the cell integrity. To conclude, SMBS released from Eudragit L100-55 nanofibrous mats in the SIF effectively decreased the adverse effects induced by DON in the IPEC-J2 cells. Nanofibrous mats can release a large amount of SMBS in a short time in SIF to achieve the effect of detoxifying DON.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".