Glycyrrhinic acid and probiotics alleviate deoxynivalenol-induced cytotoxicity in intestinal epithelial cells
Bibliographic record
Abstract
Abstract Deoxynivalenol (DON) is one of the most prevalent mycotoxin contaminants, which posing a serious health threat to animals and humans. Previous studies have found that individually supplemented probiotic or glycyrrhinic acid (GA) could degrade DON and alleviate DON-induced cytotoxicity. The present study investigated the effect of combining GA with Saccharomyces cerevisiae ( S. cerevisiae ) and Enterococcus faecalis ( E. faecalis ) using orthogonal design on alleviating IPEC-J2 cell damage induced by DON. The results showed that the optimal counts of S. cerevisiae and E. faecalis significantly promoted cell viability. The optimal combination for increasing cell viability was 400 µg/mL GA, 1 × 10 6 CFU/mL S. cerevisiae and 1 × 10 6 CFU/mL E. faecalis to make GAP, which not only significantly alleviated the DON toxicity but also achieved the highest degradation rate of DON (34.7%). Moreover, DON exposure significantly increased IL-8, Caspase3 and NF-κB contents, and upregulated the mRNA expressions of Bax, Caspase 3, NF-κB and the protein expressions of Bax, TNF-α and COX-2. However, GAP addition significantly reduced aforementioned genes and proteins. Furthermore, GAP addition significantly increased the mRNA expressions of Claudin-1, Occludin, GLUT2 and ASCT2, and the protein expressions of ZO-1, Claudin-1 and PePT1. It was inferred that the combination of GA, S. cerevisiae , and E. faecalis had the synergistic effect on enhancing cell viability and DON degradation, which could protect cells from DON-induced damage by reducing DON cytotoxicity, alleviating cell apoptosis and inflammation via inhibiting NF-κB signaling pathway, improving intestinal barrier function, and regulating nutrient absorption and transport. These findings suggest that GAP may have potential as a dietary supplement for livestock or humans exposed to DON-contaminated food or feed.
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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) | 0.001 | 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".