The controversial effect of smoking and nicotine in SARS-CoV-2 infection
Bibliographic record
Abstract
The effects of nicotine and cigarette smoke in many diseases, notably COVID-19 infection, are being debated more frequently. The current basic data for COVID-19 is increasing and indicating the higher risk of COVID-19 infections in smokers due to the overexpression of corresponding host receptors to viral entry. However, current multi-national epidemiological reports indicate a lower incidence of COVID-19 disease in smokers. Current data indicates that smokers are more susceptible to some diseases and more protective of some other. Interestingly, nicotine is also reported to play a dual role, being both inflammatory and anti-inflammatory. In the present study, we tried to investigate the effect of pure nicotine on various cells involved in COVID-19 infection. We followed an organ-based systematic approach to decipher the effect of nicotine in damaged organs corresponding to COVID-19 pathogenesis (12 related diseases). Considering that the effects of nicotine and cigarette smoke are different from each other, it is necessary to be careful in generalizing the effects of nicotine and cigarette to each other in the conducted researches. The generalization and the undifferentiation of nicotine from smoke is a significant bias. Moreover, different doses of nicotine stimulate different effects (dose-dependent response). In addition to further assessing the role of nicotine in COVID-19 infection and any other cases, a clever assessment of underlying diseases should also be considered to achieve a guideline for health providers and a personalized approach to treatment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".