Bibliographic record
Abstract
Over 1.1 billion people smoke worldwide despite the association of smoking with numerous diseases including chronic obstructive pulmonary disease (COPD). The decline in lung function observed in COPD patients is thought to be related to smoke-induced inflammation. COPD patients are also at increased risk of acquiring lung bacterial infections that are associated with exacerbations, characterized by worsened disease symptoms and inflammation. The focus of this thesis is on how cigarette smoke impacts bacterial-host interactions and bacterial community interactions to promote infection and disease. In chapter 3.1, we sought to understand how cigarette smoke primed the lungs towards an amplified inflammatory response to bacterial infection reflective of COPD exacerbations that accelerate disease progression. We present a novel finding that exacerbated neutrophilia elicited by nontypeable Haemophilus influenzae (NTHi) lung challenge in smoke-exposed mice occurred dependent on IL-1α. Smokers and patients with COPD are additionally at increased risk of acquiring bacterial infection that may be related to impaired containment of nasally colonizing pathogens. In chapter 3.2, we found that cigarette smoke predisposed mice to invasive pneumococcal disease (IPD) following nasal pneumococcal colonization associated with attenuated nasal inflammatory responses. To our knowledge, this is the first study to describe the progression from asymptomatic nasal pneumococcal colonization to the development of IPD in the context of cigarette smoking. It has been suggested that smokers have higher rates of pathogen colonization as a consequence of cigarette smoke-induced nasal microbiome dysbiosis. The last study in chapter 3.3 advanced knowledge in the field by testing this hypothesis. We observed that cigarette smoke alone did not alter the mouse nasal microbiome and concluded that microbiome dysbiosis observed in smokers likely occur as a consequence of nasal pathogen colonization. Overall, work presented in this thesis advanced our understanding of how cigarette smoking alters bacterial-host interactions to promote infection and disease.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".