SARS-CoV-2 and Smoker’s Paradox: Mediation by Ciliary Beat Frequency and Mucociliary Clearance?
Bibliographic record
Abstract
A COVID-19 smoker’s paradox was identified during the early days of the COVID-19 pandemic—many studies reported that smokers appeared to be protected against SARS-CoV-2 infections. Conversely, other studies added to the smoker’s paradox controversy with findings of increased risk for COVID-19 in heavy smokers. Ciliary beat frequency (CBF) within the ciliated epithelium of the nasal tract can be stimulated to a higher frequency and provide increased protection against transient exposure to airway irritants. Smokers as well as non-smokers exposed to secondhand tobacco smoke were found to have higher CBFs. However, with extended exposure to irritants, persistent upregulated CBF can damage and remodel the epithelial layer with fewer protective cilia. Additionally, mucociliary clearance (MCC), the innate defense mechanism of the respiratory system, traps particles and pathogens within the mucous layer of the epithelium and propels them out of the airways through ciliary activity. However, this mechanism becomes defective as disease progresses, increasing susceptibility to viral respiratory infections. This paper proposes that a smoker’s paradox associated with SARS-CoV-2 infection in COVID-19 patients may be mediated by upregulated ciliary beating frequency and mucociliary clearance with transient exposure to tobacco smoke, and downregulated CBF and MCC with extended exposure to tobacco smoke.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".