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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Mutations of SARS-CoV-2 variants of concern escaping Spike-specific T cells
This article has 15 authors:Reviewed by ScreenIT
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Design of immunogens for eliciting antibody responses that may protect against SARS-CoV-2 variants
This article has 2 authors:Reviewed by ScreenIT
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Dynamics of infection-elicited SARS-CoV-2 antibodies in children over time
This article has 17 authors:Reviewed by ScreenIT
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Binding of human ACE2 and RBD of Omicron enhanced by unique interaction patterns among SARS‐CoV ‐2 variants of concern
This article has 7 authors:Reviewed by ScreenIT
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Glycogen Synthase Kinase-3 Interaction Domain Enhances Phosphorylation of SARS-CoV-2 Nucleocapsid Protein
This article has 12 authors:Reviewed by ScreenIT
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Structural insight into antibody evasion of SARS-CoV-2 omicron variant
This article has 2 authors:Reviewed by ScreenIT
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Structural Ramifications of Spike Protein D614G Mutation in SARS-CoV-2
This article has 2 authors:Reviewed by ScreenIT
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The Existence of at Least Three Genomic Signature Patterns and at Least Seven Subtypes of COVID-19 and the End of the Disease
This article has 1 author:Reviewed by ScreenIT
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Substantial immune response in Omicron infected breakthrough and unvaccinated individuals against SARS-CoV-2 variants of concern
This article has 12 authors:Reviewed by ScreenIT
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Structural changes in the SARS-CoV-2 spike E406W mutant escaping a clinical monoclonal antibody cocktail
This article has 12 authors:Reviewed by ScreenIT