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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Relative Abundance of SARS-CoV-2 Entry Genes in the Enterocytes of the Lower Gastrointestinal Tract
This article has 6 authors:Reviewed by ScreenIT
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Structural basis for the inhibition of SARS-CoV-2 main protease by antineoplastic drug carmofur
This article has 21 authors:Reviewed by ScreenIT
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Room-temperature-storable PCR Mixes for SARS-CoV-2 Detection
This article has 13 authors:Reviewed by ScreenIT
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Structural basis for inhibition of the RNA-dependent RNA polymerase from SARS-CoV-2 by remdesivir
This article has 22 authors:Reviewed by ScreenIT
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Analysis of the SARS-CoV-2 spike protein glycan shield: implications for immune recognition
This article has 4 authors:Reviewed by ScreenIT
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Computational search of hybrid human/SARS-CoV-2 dsRNA reveals unique viral sequences that diverge from those of other coronavirus strains
This article has 2 authors:Reviewed by ScreenIT
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Prediction of SARS-CoV Interaction with Host Proteins during Lung Aging Reveals a Potential Role for TRIB3 in COVID-19
This article has 7 authors:Reviewed by ScreenIT
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Decoding the lethal effect of SARS-CoV-2 (novel coronavirus) strains from global perspective: molecular pathogenesis and evolutionary divergence
This article has 4 authors:Reviewed by ScreenIT
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Analysis of Infected Host Gene Expression Reveals Repurposed Drug Candidates and Time-Dependent Host Response Dynamics for COVID-19
This article has 10 authors:Reviewed by ScreenIT
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Immunoglobulin fragment F(ab’)2 against RBD potently neutralizes SARS-CoV-2 in vitro
This article has 15 authors:Reviewed by ScreenIT