Docosapentaenoic acid (DPA, 22:5n-3) ameliorates inflammation in an ulcerative colitis model
Bibliographic record
Abstract
The anti-inflammatory profile of DPA was investigated via a dextran sulphate sodium (DSS)-induced colitis model, and was also compared with those of EPA and DHA. The results showed that DPA could significantly reduce (stronger than EPA and DHA) the disease activity index score, macroscopic appearance score, colon shortening, histological assessment, and myeloperoxidase accumulation in the colon. In addition, DPA also inhibited the abnormal production and mRNA expression of pro-inflammatory cytokines, namely tumor necrosis factor (TNF)-α, interleukin (IL)-1β and IL-6 and improved the production and expression of an anti-inflammatory cytokine, IL-10. Furthermore, the molecular mechanisms underlying these effects were also explored through the synthesis pathway of eicosanoids. DPA could inhibit the synthesis of leukotriene B4 (LTB4) and prostaglandin E2 (PGE2) more greatly while differences of cyclooxygenase (COX) and 5-lipoxidase (LOX) contents in these three groups were not significant. We ascribed these effects to the easier incorporation of DPA into inflammatory cells leading to the decrease in the substrate for the synthesis of pro-inflammatory eicosanoids (PGE2 and LTB4). Besides, DPA-derived mediators might also be involved.
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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 | 0.000 |
| 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) | 0.001 | 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".