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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Clinical characteristics with inflammation profiling of long COVID and association with 1-year recovery following hospitalisation in the UK: a prospective observational study
This article has 1006 authors:Reviewed by ScreenIT
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Human serum from SARS-CoV-2-vaccinated and COVID-19 patients shows reduced binding to the RBD of SARS-CoV-2 Omicron variant
This article has 28 authors:Reviewed by ScreenIT
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Platform for isolation and characterization of SARS-CoV-2 variants enables rapid characterization of Omicron in Australia
This article has 42 authors:Reviewed by ScreenIT
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SARS-CoV-2 Omicron Variant Neutralization after mRNA-1273 Booster Vaccination
This article has 47 authors:Reviewed by ScreenIT
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Durability of mRNA-1273 against COVID-19 in the time of Delta: Interim results from an observational cohort study
This article has 12 authors:Reviewed by ScreenIT
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Development and Characterization of Recombinant Vesicular Stomatitis Virus (rVSV)-based Bivalent Vaccine Against COVID-19 Delta Variant and Influenza Virus
This article has 11 authors:Reviewed by ScreenIT
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Computational analysis of the effect of SARS-CoV-2 variant Omicron Spike protein mutations on dynamics, ACE2 binding and propensity for immune escape
This article has 4 authors:Reviewed by ScreenIT
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Considerable escape of SARS-CoV-2 Omicron to antibody neutralization
This article has 32 authors:Reviewed by ScreenIT
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Potential inhibitors for blocking the interaction of the coronavirus SARS-CoV-2 spike protein and its host cell receptor ACE2
This article has 24 authors:Reviewed by ScreenIT
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A comprehensive examination of ACE-2 receptor and prediction of spike glycoprotein and ACE-2 interaction based on in silico analysis of ACE-2 receptor
This article has 2 authors:Reviewed by ScreenIT