Smoking-induced microbial dysbiosis in health and disease
Bibliographic record
Abstract
Smoking is associated with an increased risk of cancer, pulmonary and cardiovascular diseases, but the precise mechanisms by which such risk is mediated remain poorly understood. Additionally, smoking can impact the oral, nasal, oropharyngeal, lung and gut microbiome composition, function, and secreted molecule repertoire. Microbiome changes induced by smoking can bear direct consequences on smoking-related illnesses. Moreover, smoking-associated dysbiosis may modulate weight gain development following smoking cessation. Here, we review the implications of cigarette smoking on microbiome community structure and function. In addition, we highlight the potential impacts of microbial dysbiosis on smoking-related diseases. We discuss challenges in studying host-microbiome interactions in the context of smoking, such as the correlations with smoking-related disease severity versus causation and mechanism. In all, understanding the microbiome's role in the pathophysiology of smoking-related diseases may promote the development of rational therapies for smoking- and smoking cessation-related disorders, as well as assist in smoking abstinence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".