Cigarette Smoke Exposure Alters Bacterial-Host Interactions in the Respiratory Tract to Promote Disease
Bibliographic record
Abstract
Epidemiological studies clearly show an increased incidence of respiratory infections and community-acquired pneumonia (CAP) in smokers.Cigarette smoking is also a significant risk factor for invasive pneumococcal diseases (IPDs), leading to meningitis and sepsis.Moreover, the natural course of chronic obstructive pulmonary disease, a spectrum of lung disorders found predominantly in smokers, is punctuated by periods of disease exacerbation caused most frequently by microbial infections.Overall, these infectious episodes contribute to decreased quality of life and mortality, and place a large burden on healthcare systems.In this article, we review how cigarette smoke affects bacterial-host interactions in the upper and lower respiratory tract.We show that smoking affects multiple facets of bacterial-host interactions and postulate that these changes predispose to infection, as smokers fail to control colonizing bacteria in the upper respiratory tract (URT).Hence, we propose that targeting nasal bacterial colonization offers a novel therapeutic avenue to prevent subsequent disease pathogenesis.(BRN Rev. 2017;
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 | 0.001 |
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".