Eating habits and lifestyle changes during COVID-19 lockdown: an Italian survey

Journal of Translational Medicine, Journal Year: 2020, Volume and Issue: 18(1)

Published: June 8, 2020

On December 12th 2019, a new coronavirus (SARS-Cov2) emerged in Wuhan, China, sparking pandemic of acute respiratory syndrome humans (COVID-19). the 24th April 2020, number COVID-19 deaths world, according to COVID-Case Tracker by Johns Hopkins University, was 195,313, and confirmed cases 2,783,512. The represents massive impact on human health, causing sudden lifestyle changes, through social distancing isolation at home, with economic consequences. Optimizing public health during this requires not only knowledge from medical biological sciences, but also all sciences related lifestyle, behavioural studies, including dietary habits lifestyle.Our study aimed investigate immediate eating changes among Italian population aged ≥ 12 years. comprised structured questionnaire packet that inquired demographic information (age, gender, place residence, current employment); anthropometric data (reported weight height); (adherence Mediterranean diet, daily intake certain foods, food frequency, meals/day); (grocery shopping, habit smoking, sleep quality physical activity). survey conducted 5th 2020.A total 3533 respondents have been included study, between 86 years (76.1% females). perception gain observed 48.6% population; 3.3% smokers decided quit smoking; slight increased activity has reported, especially for bodyweight training, 38.3% respondents; group 18-30 resulted having higher adherence diet when compared younger elderly (p < 0.001; p 0.001, respectively); 15% turned farmers or organic, purchasing fruits vegetables, North Center Italy, where BMI values were lower.In we provided first time Diet pattern lockdown. However, as is ongoing, our need be investigated future more extensive studies.

Language: Английский

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Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy

Nature Medicine, Journal Year: 2020, Volume and Issue: 26(6), P. 855 - 860

Published: April 22, 2020

Language: Английский

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Coronavirus Disease 2019–COVID-19

Clinical Microbiology Reviews, Journal Year: 2020, Volume and Issue: 33(4)

Published: June 23, 2020

In recent decades, several new diseases have emerged in different geographical areas, with pathogens including Ebola virus, Zika Nipah and coronaviruses (CoVs). Recently, a type of viral infection Wuhan City, China, initial genomic sequencing data this virus do not match previously sequenced CoVs, suggesting novel CoV strain (2019-nCoV), which has now been termed severe acute respiratory syndrome CoV-2 (SARS-CoV-2). Although coronavirus disease 2019 (COVID-19) is suspected to originate from an animal host (zoonotic origin) followed by human-to-human transmission, the possibility other routes should be ruled out.

Language: Английский

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SARS-CoV-2 infection: The role of cytokines in COVID-19 disease

Cytokine & Growth Factor Reviews, Journal Year: 2020, Volume and Issue: 54, P. 62 - 75

Published: June 2, 2020

Language: Английский

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Insights into SARS-CoV-2 genome, structure, evolution, pathogenesis and therapies: Structural genomics approach

Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, Journal Year: 2020, Volume and Issue: 1866(10), P. 165878 - 165878

Published: June 13, 2020

Language: Английский

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A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)

European Radiology, Journal Year: 2021, Volume and Issue: 31(8), P. 6096 - 6104

Published: Feb. 24, 2021

Abstract Objective The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases Corona virus disease (COVID-19) in the world so far. To control spread disease, screening large numbers suspected for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically gold standard, but it bears burden significant false negativity, adding to urgent need alternative diagnostic methods combat disease. Based on COVID-19 radiographic changes CT images, this study hypothesized that artificial intelligence might be able extract specific graphical features provide clinical diagnosis ahead pathogenic test, thus saving critical time control. Methods We collected 1065 images pathogen-confirmed along with those previously diagnosed typical viral pneumonia. modified inception transfer-learning model establish algorithm, followed by internal external validation. Results validation achieved total accuracy 89.5% specificity 0.88 sensitivity 0.87. dataset showed 79.3% 0.83 0.67. In addition, 54 first two nucleic acid test results were negative, 46 predicted as positive an 85.2%. Conclusion These demonstrate proof-of-principle using radiological timely accurate diagnosis. Key Points • evaluated performance deep learning algorithm screen during influenza season. As method, our relatively high image datasets. was used distinguish between other pneumonia, both which have quite similar radiologic characteristics.

Language: Английский

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A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2020, Volume and Issue: unknown

Published: Feb. 17, 2020

Abstract Background The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 2.5 million cases Corona Virus Disease (COVID-19) in the world so far, with that number continuing to grow. To control spread disease, screening large numbers suspected for appropriate quarantine and treatment is a priority. Pathogenic laboratory testing gold standard but time-consuming significant false negative results. Therefore, alternative diagnostic methods are urgently needed combat disease. Based on COVID-19 radiographical changes CT images, we hypothesized Artificial Intelligence’s deep learning might be able extract COVID-19’s specific graphical features provide clinical diagnosis ahead pathogenic test, thus saving critical time disease control. Methods Findings We collected 1,065 images pathogen-confirmed (325 images) along those previously diagnosed typical viral pneumonia (740 images). modified Inception transfer-learning model establish algorithm, followed by internal external validation. validation achieved total accuracy 89.5% specificity 0.88 sensitivity 0.87. dataset showed 79.3% 0.83 0.67. In addition, 54 first two nucleic acid test results were negative, 46 predicted as positive 85.2%. Conclusion These demonstrate proof-of-principle using artificial intelligence radiological timely accurate diagnosis. Author summary COVID-19, measures time. pneumonia. algorithm. Our study represents apply effectively COVID-19.

Language: Английский

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Environmental perspective of COVID-19

The Science of The Total Environment, Journal Year: 2020, Volume and Issue: 728, P. 138870 - 138870

Published: April 22, 2020

Language: Английский

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COVID-19: what has been learned and to be learned about the novel coronavirus disease

International Journal of Biological Sciences, Journal Year: 2020, Volume and Issue: 16(10), P. 1753 - 1766

Published: Jan. 1, 2020

The outbreak of Coronavirus disease 2019 , caused by severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2), has thus far killed over 3,000 people and infected 80,000 in China elsewhere the world, resulting catastrophe for humans.Similar to its homologous virus, SARS-CoV, which SARS thousands 2003, SARS-CoV-2 might also be transmitted from bats causes similar symptoms through a mechanism.However, COVID-19 lower severity mortality than but is much more transmissive affects elderly individuals youth men women.In response rapidly increasing number publications on emerging disease, this article attempts provide timely comprehensive review swiftly developing research subject.We will cover basics about epidemiology, etiology, virology, diagnosis, treatment, prognosis, prevention disease.Although many questions still require answers, we hope that helps understanding eradication threatening disease.

Language: Английский

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Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study

Journal of Medical Internet Research, Journal Year: 2020, Volume and Issue: 22(4), P. e19016 - e19016

Published: April 9, 2020

The recent coronavirus disease (COVID-19) pandemic is taking a toll on the world's health care infrastructure as well social, economic, and psychological well-being of humanity. Individuals, organizations, governments are using social media to communicate with each other number issues relating COVID-19 pandemic. Not much known about topics being shared platforms COVID-19. Analyzing such information can help policy makers organizations assess needs their stakeholders address them appropriately.This study aims identify main posted by Twitter users related pandemic.Leveraging set tools (Twitter's search application programming interface (API), Tweepy Python library, PostgreSQL database) predefined terms ("corona," "2019-nCov," "COVID-19"), we extracted text metadata (number likes retweets, user profile including followers) public English language tweets from February 2, 2020, March 15, 2020. We analyzed collected word frequencies single (unigrams) double words (bigrams). leveraged latent Dirichlet allocation for topic modeling discussed in tweets. also performed sentiment analysis mean likes, followers calculated interaction rate per topic.Out approximately 2.8 million included, 167,073 unique 160,829 met inclusion criteria. Our identified 12 topics, which were grouped into four themes: origin virus; its sources; impact people, countries, economy; ways mitigating risk infection. was positive 10 negative 2 (deaths caused increased racism). tweet account ranged 2722 (increased racism) 13,413 (economic losses). highest 15.4 loss), while lowest 3.94 (travel bans warnings).Public crisis response activities ground online becoming increasingly simultaneous intertwined. Social provides an opportunity directly public. Health systems should work building national international detection surveillance through monitoring media. There need more proactive agile presence combat spread fake news.

Language: Английский

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