Cigarette smoke and platelet-activating factor receptor dependent adhesion of <i>Streptococcus pneumoniae</i> to lower airway cells
Bibliographic record
Abstract
BACKGROUND: Exposure to cigarette smoke (CS) is associated with increased risk of pneumococcal infection. The mechanism for this association is unknown. We recently reported that the particulate matter from urban air simulates platelet-activating factor receptor (PAFR)-dependent adhesion of pneumococci to airway cells. We therefore sought to determine whether CS stimulates pneumococcal adhesion to airway cells. METHODS: Human alveolar (A549), bronchial (BEAS2-B), and primary bronchial epithelial cells (HBEpC) were exposed to CS extract (CSE), and adhesion of Streptococcus pneumoniae determined. The role of PAFR in mediating adhesion was determined using a blocker (CV-3988). PAFR transcript level was assessed by quantitative real-time PCR, and PAFR expression by flow cytometry. Lung PAFR transcript level was assessed in mice exposed to CS, and bronchial epithelial PAFR expression assessed in active-smokers by immunostaining. RESULTS: In A549 cells, CSE 1% increased pneumococcal adhesion (p<0.05 vs control), PAFR transcript level (p<0.01), and PAFR expression (p<0.01). Pneumococcal adhesion to A549 cells was attenuated by CV-3988 (p<0.001). CSE 1% stimulated pneumococcal adhesion to BEAS2-B cells and HBEpC (p<0.01 vs control). CSE 1% increased PAFR expression in BEAS2-B (p<0.01), and in HBEpC (p<0.05). Lung PAFR transcript level was increased in mice exposed to CS in vivo (p<0.05 vs room air). Active smokers (n=16) had an increased percentage of bronchial epithelium with PAFR-positive cells (p<0.05 vs never smokers, n=11). CONCLUSION: CSE stimulates PAFR-dependent pneumococcal adhesion to lower airway epithelial cells. We found evidence that CS increases bronchial PAFR in vivo.
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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 | 0.000 |
| 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".