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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".