Abnormal cannabidiol attenuates experimental colitis in mice, promotes wound healing and inhibits neutrophil recruitment
Bibliographic record
Abstract
BACKGROUND: Non-psychotropic atypical cannabinoids have therapeutic potential in a variety of inflammatory conditions including those of the gastrointestinal tract. Here we examined the effects of the atypical cannabinoid abnormal cannabidiol (Abn-CBD) on wound healing, inflammatory cell recruitment and colitis in mice. METHODS: Colitis was induced in CD1 mice by a single intrarectal administration of trinitrobenzene sulfonic acid (TNBS, 4 mg/100 μl in 30 % ethanol) and Abn-CBD and/or the antagonists O-1918 (Abd-CBD), AM251 (CB1 receptor) and AM630 (CB2 receptor), were administered intraperitoneally (all 5 mg/kg, twice daily for 3 days). The degree of colitis was assessed macro- and microscopically and tissue myeloperoxidase activity was determined. The effects of Abn-CBD on wound healing of endothelial and epithelial cells (LoVo) were assessed in a scratch injury assay. Human neutrophils were employed in Transwell assays or perfused over human umbilical vein endothelial cells (HUVEC) to study the effect of Abn-CBD on neutrophil accumulation and transmigration. RESULTS: TNBS-induced colitis was attenuated by treatment with Abn-CBD. Histological, macroscopic colitis scores and tissue myeloperoxidase activity were significantly reduced. These effects were inhibited by O-1918, but not by AM630, and only in part by AM251. Wound healing of both HUVEC and LoVo cells was enhanced by Abn-CBD. Abn-CBD inhibited neutrophil migration towards IL-8, and dose-dependently inhibited accumulation of neutrophils on HUVEC. CONCLUSIONS: Abn-CBD is protective against TNBS-induced colitis, promotes wound healing of endothelial and epithelial cells and inhibits neutrophil accumulation on HUVEC monolayers. Thus, the atypical cannabinoid Abn-CBD represents a novel potential therapeutic in the treatment of intestinal inflammatory diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".