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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Neuroinvasion of SARS-CoV-2 in human and mouse brain
This article has 39 authors:Reviewed by ScreenIT
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ALG-097111, a potent and selective SARS-CoV-2 3-chymotrypsin-like cysteine protease inhibitor exhibits in vivo efficacy in a Syrian Hamster model
This article has 24 authors:Reviewed by ScreenIT
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Clinical course and outcomes of critically ill COVID-19 patients in two successive pandemic waves
This article has 16 authors:Reviewed by ScreenIT
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Recombination and low-diversity confound homoplasy-based methods to detect the effect of SARS-CoV-2 mutations on viral transmissibility
This article has 6 authors:Reviewed by ScreenIT
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Identifying SARS-CoV-2 antiviral compounds by screening for small molecule inhibitors of nsp13 helicase
This article has 19 authors:Reviewed by ScreenIT
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Inpatient Administration of Alpha-1-Adrenergic Receptor Blocking Agents Reduces Mortality in Male COVID-19 Patients
This article has 15 authors:Reviewed by ScreenIT
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SARS-CoV-2 mutations among minks show reduced lethality and infectivity to humans
This article has 1 author:Reviewed by ScreenIT
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The SARS-CoV-2 nucleocapsid phosphoprotein forms mutually exclusive condensates with RNA and the membrane-associated M protein
This article has 9 authors:Reviewed by ScreenIT
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Evidence for the utility of cfDNA plasma concentrations to predict disease severity in COVID-19
This article has 10 authors:Reviewed by ScreenIT
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Rapid and low‐cost sampling for detection of airborne SARS‐CoV‐2 in dehumidifier condensate
This article has 13 authors:Reviewed by ScreenIT