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Record W4213327659 · doi:10.3390/biomed2010009

SARS-CoV-2 and Smoker’s Paradox: Mediation by Ciliary Beat Frequency and Mucociliary Clearance?

2022· article· en· W4213327659 on OpenAlexaff
Ronald B. Brown

Bibliographic record

VenueBioMed · 2022
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMucociliary clearanceCiliumRespiratory tractRespiratory epitheliumMedicineRespiratory systemTobacco smokeImmunologyBiologyLungInternal medicineCell biologyEnvironmental health

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.282
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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