Lipoxin A <sub>4</sub> and aspirin-triggered 15-epi-lipoxin A <sub>4</sub> inhibit peroxynitrite formation, NF-κB and AP-1 activation, and IL-8 gene expression in human leukocytes
Bibliographic record
Abstract
Lipoxin A(4) (LXA(4)) and aspirin-triggered 15-epi-LXA(4) (ATL) are emerging as endogenous braking signals for neutrophil-mediated tissue injury. Recent studies indicate that peroxynitrite (ONOO(-)) may function as an intracellular signal for the production of IL-8, a potent proinflammatory cytokine in human leukocytes. In this study, we evaluated the impact of the metabolically stable analogues of LXA(4)/ATL on lipopolysaccharide (LPS)-induced ONOO(-) formation and ONOO(-)-mediated IL-8 gene expression in human leukocytes. At nanomolar concentrations, LXA(4) analogues markedly reduced LPS-stimulated superoxide formation, evoked increases in intracellular diamino-fluorescein fluorescence (an indicator of NO formation), and consequently reduced ONOO(-) formation in isolated neutrophils, as well as in neutrophils, monocytes, and lymphocytes, in whole blood. LXA(4)/ATL analogues attenuated nuclear accumulation of activator protein-1 and nuclear factor-kappaB in both polymorphonuclear and mononuclear leukocytes and inhibited IL-8 mRNA expression and IL-8 release by 50-65% in response to LPS. The LXA(4) inhibitory responses were concentration dependent and were not shared by 15-deoxy-LXA(4). None of the LXA(4) analogues studied affected neutrophil survival, nor reversed the apoptosis delaying action of LPS in neutrophils. In addition, LXA(4) analogues had no significant effect on exogenous ONOO(-)-induced IL-8 gene and protein expression. These findings suggest that by attenuating ONOO(-) formation, LXA(4) and ATL can oppose ONOO(-) signaling in leukocytes and provide a rationale for using stable synthetic analogues as antiinflammatory compounds in vivo.
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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) | 0.000 | 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 | 0.000 |
| 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".