Neutrophils and IL-1α Regulate Surfactant Homeostasis during Cigarette Smoking
Bibliographic record
Abstract
Abstract Cigarette smoke exposure induces inflammation marked by rapid and sustained neutrophil infiltration, IL-1α, release and altered surfactant homeostasis. However, the extent to which neutrophils and IL-1α contribute to the maintenance of pulmonary surfactant homeostasis is not well understood. We sought to investigate whether neutrophils play a role in surfactant clearance as well as the effect of neutrophil depletion and IL-1α blockade on the response to cigarette smoke exposure. In vitro and in vivo administration of fluorescently labeled surfactant phosphatidylcholine was used to assess internalization of surfactant by lung neutrophils and macrophages during or following cigarette smoke exposure in mice. We also depleted neutrophils using anti–Ly-6G or anti–Gr-1 Abs, or we neutralized IL-1α using a blocking Ab to determine their respective roles in regulating surfactant homeostasis during cigarette smoke exposure. We observed that neutrophils actively internalize labeled surfactant both in vitro and in vivo and that IL-1α is required for smoke-induced elevation of surfactant protein (SP)–A and SP-D levels. Neutrophil depletion during cigarette smoke exposure led to a further increase in SP-A levels in the bronchoalveolar lavage and increased IL-1α, CCL2, GM-CSF, and G-CSF release. Finally, macrophage expression of Mmp12, a protease linked to emphysema, was increased in neutrophil-depleted groups and decreased following IL-1α blockade. Taken together, our results indicate that neutrophils and IL-1α signaling are actively involved in surfactant homeostasis and that the absence of neutrophils in the lungs during cigarette smoke exposure leads to an IL-1α–dependent exacerbation of the inflammatory response.
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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 | 0.000 |
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".