ScreenIT
The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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A prenylated dsRNA sensor protects against severe COVID-19
This article has 49 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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Covid-19 and the South Asian Countries: factors ruling the pandemic
This article has 7 authors:Reviewed by ScreenIT
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A hybrid PDE–ABM model for viral dynamics with application to SARS-CoV-2 and influenza
This article has 4 authors:Reviewed by ScreenIT
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Secretory IgA and T cells targeting SARS-CoV-2 spike protein are transferred to the breastmilk upon mRNA vaccination
This article has 10 authors:Reviewed by ScreenIT
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Prevalence use of nonsteroidal anti-inflammatory drugs in the general population with COVID-19 and associated COVID-19 risk, hospitalization, severity, death, and safety outcomes: A systematic review and meta-analysis
This article has 10 authors:Reviewed by ScreenIT
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Distinct Patterns of Blood Cytokines Beyond a Cytokine Storm Predict Mortality in COVID-19
This article has 32 authors:Reviewed by ScreenIT
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Extracorporeal membrane oxygenation outcomes in COVID‐19 patients: Case series from the Brazilian COVID‐19 Registry
This article has 15 authors:Reviewed by ScreenIT
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Population behavioural dynamics can mediate the persistence of emerging infectious diseases
This article has 4 authors:Reviewed by ScreenIT
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Japan’s Covid mitigation strategy and its epidemic prediction
This article has 2 authors:Reviewed by ScreenIT
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Interventions to Disrupt Coronavirus Disease Transmission at a University, Wisconsin, USA, August–October 2020
This article has 29 authors:Reviewed by ScreenIT