Cannabidiol Mitigates Deoxynivalenol-Induced Intestinal Toxicity by Regulating Inflammation, Oxidative Stress, and Barrier Integrity
Bibliographic record
Abstract
The deoxynivalenol (DON) mycotoxin poses serious health risks, especially to swine, which are highly susceptible to intestinal damage. Existing strategies to counteract DON toxicity remain insufficient. This study aimed to evaluate the protective effects of cannabidiol (CBD), a phytocannabinoid with anti-inflammatory properties, against DON-induced intestinal toxicity in porcine intestinal epithelial cells. Using differentiated and proliferating porcine intestinal epithelial cells (IPEC-J2), we evaluated CBD (2.5–5 μM) against DON (0.5–50 μM) through viability assays, apoptosis markers (Bax/Bcl-2 ratio), inflammatory mediators (NFκB, IL-6, COX-2), oxidative stress indicators (TXNIP, SOD1, CAT), tight junction gene expression (Claudin-1, Occludin), and barrier permeability. DON exhibited dose- and time-dependent cytotoxicity (IC50 = 2.60 μM at 24 h; 1.07 μM at 48 h). Pre-treatment with 5 μM CBD restored cell viability at low DON concentrations (0.5–2 μM) but failed at ≥8 μM. In differentiated cells, CBD suppressed apoptosis (reduced Bax/Bcl-2 ratio), oxidative stress (downregulated TXNIP; restored CAT expression), and inflammation (decreased IL-6 and COX-2) under high-dose DON (50 μM), while enhancing tight junction protein expression and barrier integrity at 5 μM DON. Conversely, in proliferating cells, CBD exacerbated apoptosis (elevated Bax/Bcl-2 ratio) and inflammatory responses (upregulated IL-6 and COX-2) at subtoxic levels of DON (2 μM). CBD alone induced cytotoxicity at ≥10 μM. Our findings demonstrate that CBD exhibits context-dependent efficacy, providing protection in differentiated epithelia under moderate DON exposure (≤5 μM) but exhibiting detrimental effects in proliferating cells. Its narrow therapeutic window and paradoxical actions necessitate cautious application. These findings position CBD as a potential adjunctive therapy for DON detoxification but highlight critical limitations for standalone use.
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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 | 0.001 |
| 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".