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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Sequence-assignment validation in cryo-EM models with checkMySequence
This article has 1 author:Reviewed by ScreenIT
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SARS-CoV-2 Infection of Microglia Elicits Pro-inflammatory Activation and Apoptotic Cell Death
This article has 11 authors:Reviewed by ScreenIT
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Genomic Perspectives on the Emerging SARS-CoV-2 Omicron Variant
This article has 10 authors:Reviewed by ScreenIT
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Autophagy and evasion of immune system by SARS-CoV-2. Structural features of the Non-structural protein 6 from Wild Type and Omicron viral strains interacting with a model lipid bilayer. †
This article has 4 authors:Reviewed by ScreenIT
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Towards an optimal monoclonal antibody with higher binding affinity to the receptor-binding domain of SARS-CoV-2 spike proteins from different variants
This article has 4 authors:Reviewed by ScreenIT
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Structure-Based Identification of Naphthoquinones and Derivatives as Novel Inhibitors of Main Protease M pro and Papain-like Protease PL pro of SARS-CoV-2
This article has 21 authors:Reviewed by ScreenIT
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SIRT5 is a proviral factor that interacts with SARS-CoV-2 Nsp14 protein
This article has 16 authors:Reviewed by ScreenIT
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The Runaway Evolution of SARS-CoV-2 Leading to the Highly Evolved Delta Strain
This article has 8 authors:Reviewed by ScreenIT
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Structural basis of Omicron neutralization by affinity-matured public antibodies
This article has 12 authors:Reviewed by ScreenIT
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Host kinase CSNK2 is a target for inhibition of pathogenic β-coronaviruses including SARS-CoV-2
This article has 17 authors:Reviewed by ScreenIT