Streptococcus pneumoniae Colonization Is Required To Alter the Nasal Microbiota in Cigarette Smoke-Exposed Mice
Bibliographic record
Abstract
ABSTRACT Smokers have nasal microbiota dysbiosis, with an increased frequency of colonizing bacterial pathogens. It is possible that cigarette smoke increases pathogen acquisition by perturbing the microbiota and decreasing colonization resistance. However, it is difficult to disentangle microbiota dysbiosis due to cigarette smoke exposure from microbiota changes caused by increased pathogen acquisition in human smokers. Using an experimental mouse model, we investigated the impact of cigarette smoke on the nasal microbiota in the absence and presence of nasal pneumococcal colonization. We observed that cigarette smoke exposure alone did not alter the nasal microbiota composition. The microbiota composition was also unchanged at 12 h following low-dose nasal pneumococcal inoculation, suggesting that the ability of the microbiota to resist initial nasal pneumococcal acquisition was not impaired in smoke-exposed mice. However, nasal microbiota dysbiosis occurred as a consequence of established high-dose nasal pneumococcal colonization at day 3 in smoke-exposed mice. Similar to clinical reports on human smokers, an enrichment of potentially pathogenic bacterial genera such as Fusobacterium , Gemella , and Neisseria was observed. Our findings suggest that cigarette smoke exposure predisposes to pneumococcal colonization independent of changes to the nasal microbiota and that microbiota dysbiosis observed in smokers may occur as a consequence of established pathogen colonization.
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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) | 0.000 | 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 | 0.001 |
| 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".