Cigarette smokers have exaggerated alveolar barrier disruption in response to lipopolysaccharide inhalation
Bibliographic record
Abstract
RATIONALE: Cigarette smoke exposure is associated with an increased risk of the acute respiratory distress syndrome (ARDS); however, the mechanisms underlying this relationship remain largely unknown. OBJECTIVE: To assess pathways of lung injury and inflammation in smokers and non-smokers with and without lipopolysaccharide (LPS) inhalation using established biomarkers. METHODS: We measured plasma and bronchoalveolar lavage (BAL) biomarkers of inflammation and lung injury in smokers and non-smokers in two distinct cohorts of healthy volunteers, one unstimulated (n=20) and one undergoing 50 μg LPS inhalation (n=30). MEASUREMENTS AND MAIN RESULTS: After LPS inhalation, cigarette smokers had increased alveolar-capillary membrane permeability as measured by BAL total protein, compared with non-smokers (median 274 vs 208 μg/mL, p=0.04). Smokers had exaggerated inflammation compared with non-smokers, with increased BAL interleukin-1β (p=0.002), neutrophils (p=0.02), plasma interleukin-8 (p=0.003), and plasma matrix metalloproteinase-8 (p=0.006). Alveolar epithelial injury after LPS was more severe in smokers than non-smokers, with increased plasma (p=0.04) and decreased BAL (p=0.02) surfactant protein D. Finally, smokers had decreased BAL vascular endothelial growth factor (VEGF) (p<0.0001) with increased soluble VEGF receptor-1 (p=0.0001). CONCLUSIONS: Cigarette smoke exposure may predispose to ARDS through an abnormal response to a 'second hit,' with increased alveolar-capillary membrane permeability, exaggerated inflammation, increased epithelial injury and endothelial dysfunction. LPS inhalation may serve as a useful experimental model for evaluation of the acute pulmonary effects of existing and new tobacco products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".