Human β-defensin 2 (hBD-2) improves neutrophilia and bacterial clearance during cigarette smoke exposure
Bibliographic record
Abstract
Abstract Rationale Treatment of cigarette smoke-associated lung diseases has largely focused on broad-spectrum anti-inflammatory therapies, such as inhaled corticosteroids. However, these therapies have recently been shown to enhance patient susceptibility to pneumonia. Objective Assess whether the anti-microbial peptide, human β-defensin 2 (hBD-2), can simultaneously reduce pulmonary inflammation in cigarette smoke-exposed mice while maintaining immune competence during bacterial exacerbation. Methods Mice were exposed to cigarette smoke acutely (4 days) or chronically (5 days/week for 7 weeks) and administered hBD-2 intranasally or by gavage. In a separate model of acute exacerbation, chronically exposed mice treated with hBD-2 were infected with non-typeable Haemophilus influenzae prior to sacrifice. Results In the acute exposure model, cigarette smoke-associated pulmonary neutrophilia was significantly blunted by both local and systemic hBD-2 administration. Similarly, chronically exposed mice administered hBD-2 therapeutically exhibited reduced pulmonary neutrophil infiltration and downregulated pro-inflammatory signaling in the lungs compared to vehicle-treated mice, as assessed via microarray. Finally, in a model of bacterial exacerbation, hBD-2 administration effectively limited neutrophil infiltration in the lungs while markedly reducing pulmonary bacterial load. Conclusions This study shows that hBD-2 treatment can significantly attenuate lung neutrophilia induced by cigarette smoke exposure while preserving immune competence and promoting an appropriate host-defense response to bacterial stimuli. Supported by CIHR
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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".