Cigarette Smoke Attenuates the Nasal Host Response to Streptococcus pneumoniae and Predisposes to Invasive Pneumococcal Disease in Mice
Bibliographic record
Abstract
Streptococcus pneumoniae is a leading cause of invasive bacterial infections, with nasal colonization an important first step in disease. While cigarette smoking is a strong risk factor for invasive pneumococcal disease, the underlying mechanisms remain unknown. This is partly due to a lack of clinically relevant animal models investigating nasal pneumococcal colonization in the context of cigarette smoke exposure. We present a model of nasal pneumococcal colonization in cigarette smoke-exposed mice and document, for the first time, that cigarette smoke predisposes to invasive pneumococcal infection and mortality in an animal model. Cigarette smoke increased the risk of bacteremia and meningitis without prior lung infection. Mechanistically, deficiency in interleukin 1α (IL-1α) or platelet-activating factor receptor (PAFR), an important host receptor thought to bind and facilitate pneumococcal invasiveness, did not rescue cigarette smoke-exposed mice from invasive pneumococcal disease. Importantly, we observed cigarette smoke to attenuate nasal inflammatory mediator expression, particularly that of neutrophil-recruiting chemokines, normally elicited by pneumococcal colonization. Smoking cessation during nasal pneumococcal colonization rescued nasal neutrophil recruitment and prevented invasive disease in mice. We propose that cigarette smoke predisposes to invasive pneumococcal disease by suppressing inflammatory processes of the upper respiratory tract. Given that smoking prevalence remains high worldwide, these findings are relevant to the continued efforts to reduce the invasive pneumococcal disease burden.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".