Efficacy of combined therapy with fish oil and phytocannabinoids in murine intestinal inflammation
Bibliographic record
Abstract
Fish oil (FO) and phytocannabinoids have received considerable attention for their intestinal anti-inflammatory effects. We investigated whether the combination of FO with cannabigerol (CBG) and cannabidiol (CBD) or a combination of all three treatments results in a more pronounced intestinal antiinflammatory action compared to the effects achieved separately. Colitis was induced in mice by 2,4-dinitrobenzenesulfonic acid (DNBS). CBD and CBG levels were detected and quantified by liquid chromatography coupled with time of flight mass spectrometry and ion trap mass spectrometry (LC-MS-IT-TOF). Endocannabinoids and related mediators were assessed by LC-MS. DNBS increased colon weight/colon length ratio, myeloperoxidase activity, interleukin-1β, and intestinal permeability. CBG, but not CBD, given by oral gavage, ameliorated DNBS-induced colonic inflammation. FO pretreatment (at the inactive dose) increased the antiinflammatory action of CBG and rendered oral CBD effective while reducing endocannabinoid levels. Furthermore, the combination of FO, CBD, and a per se inactive dose of CBG resulted in intestinal anti-inflammatory effects. Finally, FO did not alter phytocannabinoid levels in the serum and in the colon. By highlighting the apparent additivity between phytocannabinoids and FO, our preclinical data support a novel strategy of combining these substances for the potential development of a treatment of inflammatory bowel disease.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".