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Tobacco smoking and COVID-19 pandemic: old and new issues. A summary of the evidence from the scientific literature.

ABSTRACT: INTRODUCTION:COVID-19 pandemic burst onto the international scene as a new disease not affecting patients uniformly; hence it has risen many questions yet to be clarified. The aim of this study was to outline the main issues that led tobacco smoking being discussed as a potential risk factor associated with COVID-19. METHODS:articles from MEDLINE and pre-prints published from January to April 2020 were identified. RESULTS:data from China showed that men had more severe outcomes of COVID-19 than women. Since smoking prevalence is very high among Chinese men in comparison to women, it was hypothesized that smoking could be a risk factor for poor prognosis. This was also supported by the higher prevalence of comorbidities, many of which are tobacco-related diseases, in patients with severe COVID-19, who were also more likely to have a smoking history. A meta-analysis confirmed these results reporting an OR=2.25 (95% CI: 1.49-3.39) for developing severe Covid-19 among patients with a smoking history. Some authors, noticing that reported smoking prevalence among hospitalized patients was substantially below smoking prevalence in the corresponding populations, speculated a protective role of nicotine. However, it is likely that low prevalence among hospitalized patients are partially due to many smokers misclassified as nonsmokers. Tobacco smoking seems to cause a dose-dependent upregulation of the angiotensin-converting- enzyme-2 (ACE2), the virus cellular entry receptor, which could explain the higher risk of severe COVID-19 in smokers. CONCLUSIONS:There is need for further independent studies to clarify the role of smoking on COVID-19 incidence, progression and mortality.

SUBMITTER: Cattaruzza MS

PROVIDER: S-EPMC7569632 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Tobacco smoking and COVID-19 pandemic: old and new issues. A summary of the evidence from the scientific literature.

Cattaruzza Maria Sofia MS Zagà Vincenzo V Gallus Silvano S D'Argenio Paolo P Gorini Giuseppe G

Acta bio-medica : Atenei Parmensis 20200511 2

<h4>Introduction</h4>COVID-19 pandemic burst onto the international scene as a new disease not affecting patients uniformly; hence it has risen many questions yet to be clarified. The aim of this study was to outline the main issues that led tobacco smoking being discussed as a potential risk factor associated with COVID-19.<h4>Methods</h4>articles from MEDLINE and pre-prints published from January to April 2020 were identified.<h4>Results</h4>data from China showed that men had more severe outc ...[more]

PMID: 32420934

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Project description:BACKGROUND:Text mining (TM) methods have been used extensively to extract relations and events from the literature. In addition, TM techniques have been used to extract various types or dimensions of interpretative information, known as Meta-Knowledge (MK), from the context of relations and events, e.g. negation, speculation, certainty and knowledge type. However, most existing methods have focussed on the extraction of individual dimensions of MK, without investigating how they can be combined to obtain even richer contextual information. In this paper, we describe a novel, supervised method to extract new MK dimensions that encode Research Hypotheses (an author's intended knowledge gain) and New Knowledge (an author's findings). The method incorporates various features, including a combination of simple MK dimensions. METHODS:We identify previously explored dimensions and then use a random forest to combine these with linguistic features into a classification model. To facilitate evaluation of the model, we have enriched two existing corpora annotated with relations and events, i.e., a subset of the GENIA-MK corpus and the EU-ADR corpus, by adding attributes to encode whether each relation or event corresponds to Research Hypothesis or New Knowledge. In the GENIA-MK corpus, these new attributes complement simpler MK dimensions that had previously been annotated. RESULTS:We show that our approach is able to assign different types of MK dimensions to relations and events with a high degree of accuracy. Firstly, our method is able to improve upon the previously reported state of the art performance for an existing dimension, i.e., Knowledge Type. Secondly, we also demonstrate high F1-score in predicting the new dimensions of Research Hypothesis (GENIA: 0.914, EU-ADR 0.802) and New Knowledge (GENIA: 0.829, EU-ADR 0.836). CONCLUSION:We have presented a novel approach for predicting New Knowledge and Research Hypothesis, which combines simple MK dimensions to achieve high F1-scores. The extraction of such information is valuable for a number of practical TM applications.

Dataset Information

Tobacco smoking and COVID-19 pandemic: old and new issues. A summary of the evidence from the scientific literature.

Publications

Tobacco smoking and COVID-19 pandemic: old and new issues. A summary of the evidence from the scientific literature.

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