Aspirin‐triggered lipoxins enhance resolution of myeloperoxidase‐mediated lung inflammation by promoting neutrophil apoptosis
Bibliographic record
Abstract
We have reported that myeloperoxidase (MPO) prolongs carrageenan‐induced lung injury parallel with suppression of neutrophil apoptosis in a mouse model of inflammation. Since aspirin‐triggered 15‐epi‐lipoxin A 4 (15‐epi‐LXA 4 ) overrides MPO signaling in neutrophils in vitro, we investigated the impact of 15‐epi‐LXA 4 on the resolution of lung inflammation. Treatment of mice with 15‐epi‐LXA 4 accelerated the resolution of established inflammation when administered intravenously at the peak of inflammation evoked by carrageenan plus MPO. 15‐epi‐LXA 4 produced decreases in bronchoalveolar neutrophil accumulation and increased the number of monocytes/macrophages. 15‐epi‐LXA 4 reduced lung injury, tissue MPO content, attenuated edema formation and IL‐6 release. 15‐epi‐LXA 4 augmented DNA fragmentation and caspase‐3 activity in bronchoalveolar neutrophils. Decreases in neutrophil number occurred parallel with increases in the percentage of apoptotic neutrophils and the percentage of macrophages containing apoptotic bodies. Co‐treatement with the pan‐caspase inhibitor zVAD‐fmk fully abolished the beneficial actions of 15‐epi‐LXA 4. Our results indicate that aspirin‐triggered 15‐epi‐LXA 4 overrides the anti‐apoptosis signal of MPO in neutrophils, thereby demonstrating a hitherto unrecognized mechanism by which aspirin could promote resolution of inflammation. (Support: CIHR MOP‐64283).
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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) | 0.003 | 0.001 |
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".