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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Bayesian Inference of Dependent Population Dynamics in Coalescent Models
This article has 3 authors:Reviewed by ScreenIT
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Reconstitution of the SARS-CoV-2 ribonucleosome provides insights into genomic RNA packaging and regulation by phosphorylation
This article has 7 authors:Reviewed by ScreenIT
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RAGE engagement by SARS-CoV-2 enables monocyte infection and underlies COVID-19 severity
This article has 29 authors:Reviewed by ScreenIT
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Incidence of new onset glomerulonephritis after SARS-CoV-2 mRNA vaccination is not increased
This article has 15 authors:Reviewed by ScreenIT
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Host-directed therapy with 2-deoxy-D-glucose inhibits human rhinoviruses, endemic coronaviruses, and SARS-CoV-2
This article has 13 authors:Reviewed by ScreenIT
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Further antibody escape by Omicron BA.4 and BA.5 from vaccine and BA.1 serum
This article has 35 authors:Reviewed by ScreenIT
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Using a reverse genetics system to generate recombinant SARS-CoV-2 expressing robust levels of reporter genes
This article has 2 authors:Reviewed by ScreenIT
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Anti-chemokine antibodies after SARS-CoV-2 infection correlate with favorable disease course
This article has 46 authors:Reviewed by ScreenIT
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Intranasal pediatric parainfluenza virus-vectored SARS-CoV-2 vaccine is protective in monkeys
This article has 22 authors:Reviewed by ScreenIT
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Reduced Neutralization of SARS-CoV-2 Omicron Variant in Sera from SARS-CoV-1 Survivors after 3-dose of Vaccination
This article has 12 authors:Reviewed by ScreenIT