Interferon-β regulates proresolving lipids to promote the resolution of acute airway inflammation
Bibliographic record
Abstract
Aberrant immune responses, including hyperresponsiveness to Toll-like receptor (TLR) ligands, underlie acute respiratory distress syndrome (ARDS). Type I interferons confer antiviral activities and could also regulate the inflammatory response, whereas little is known about their actions to resolve aberrant inflammation. Here we report that interferon-β (IFN-β) exerts partially overlapping, but also cooperative actions with aspirin-triggered 15-epi-lipoxin A 4 (15-epi-LXA 4 ) and 17-epi-resolvin D1 to counter TLR9-generated cues to regulate neutrophil apoptosis and phagocytosis in human neutrophils. In mice, TLR9 activation impairs bacterial clearance, prolongs Escherichia coli –evoked lung injury, and suppresses production of IFN-β and the proresolving lipid mediators 15-epi-LXA 4 and resolvin D1 (RvD1) in the lung. Neutralization of endogenous IFN-β delays pulmonary clearance of E. coli and aggravates mucosal injury. Conversely, treatment of mice with IFN-β accelerates clearance of bacteria, restores neutrophil phagocytosis, promotes neutrophil apoptosis and efferocytosis, and accelerates resolution of airway inflammation with concomitant increases in 15-epi-LXA 4 and RvD1 production in the lungs. Pharmacological blockade of the lipoxin receptor ALX/FPR2 partially prevents IFN-β–mediated resolution. These findings point to a pivotal role of IFN-β in orchestrating timely resolution of neutrophil and TLR9 activation–driven airway inflammation and uncover an IFN-β–initiated resolution program, activation of an ALX/FPR2-centered, proresolving lipids-mediated circuit, for ARDS.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".