Eicosapentaenoic acid and docosapentaenoic acid monoglycerides are more potent than docosahexaenoic acid monoglyceride to resolve inflammation in a rheumatoid arthritis model
Bibliographic record
Abstract
INTRODUCTION: Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease of the joints and bones. Omega-3 (ω3) fatty acid supplementation has been associated with a decreased production of inflammatory cytokines and eicosanoids involved in RA pathogenesis. The aim of this study was to determine the therapeutic potential of ω3 monoglyceride (MAG-ω3) compounds in an in vivo rat model of RA induced by Complete Freund's Adjuvant (CFA). METHOD: CFA rats were untreated or treated per os with three specific compounds, namely, MAG-docosahexaenoic acid (MAG-DHA), MAG-eicosapentaenoic acid (MAG-EPA) and MAG-docosapentaenoic acid (MAG-DPA). Morphological and histological analyses, as well as pro-inflammatory marker levels were determined following MAG-ω3 treatments. RESULTS: Morphological and histological analyses revealed that MAG-EPA and MAG-DPA exhibited strong activity in reducing the progression and severity of arthritic disease in CFA rats. Following MAG-EPA and MAG-DPA treatments, plasma levels of the pro-inflammatory cytokines; interleukin 17A (IL-17A), IL-1β, IL-6 and tumor necrosis factor α (TNFα) were markedly lower when compared to CFA-untreated rats. Results also revealed a decreased activation of p38 mitogen-activated protein kinases (p38 MAPK) and nuclear factor-kappa B (NFκB) pathways correlated with a reduced expression of TNFα, cyclooxygenase-2 (COX-2), matrix metalloproteinase-2 (MMP-2) and MMP-9 in paw homogenates derived from MAG-EPA and MAG-DPA-treated rats. Of interest, the combined treatment of MAG-EPA and vitamin E displayed an antagonistic effect on anti-inflammatory properties of MAG-EPA in CFA rats. CONCLUSION: Altogether, the present data suggest that MAG-EPA, without vitamin E, represents a new potential therapeutic strategy for resolving inflammation in arthritis.
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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 | 0.000 | 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) | 0.002 | 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".