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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Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium
This article has 21 authors: -
Development and simulation of fully glycosylated molecular models of ACE2-Fc fusion proteins and their interaction with the SARS-CoV-2 spike protein binding domain
This article has 7 authors:Reviewed by ScreenIT
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Coronavirus activates a stem cell-mediated defense mechanism that reactivates dormant tuberculosis: implications in COVID-19 pandemic
This article has 9 authors:Reviewed by ScreenIT
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Identification of novel mutations in RNA-dependent RNA polymerases of SARS-CoV-2 and their implications on its protein structure
This article has 3 authors: -
Applying Lexical Link Analysis to Discover Insights from Public Information on COVID-19
This article has 2 authors:Reviewed by ScreenIT
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Bacteremia and Blood Culture Utilization during COVID-19 Surge in New York City
This article has 14 authors:Reviewed by ScreenIT
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Psychophysical Olfactory Findings of Mild-to-moderate COVID-19 Patients: Preliminary Report
This article has 7 authors:Reviewed by ScreenIT
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Inhibition of the replication of SARS-CoV-2 in human cells by the FDA-approved drug chlorpromazine
This article has 14 authors:Reviewed by ScreenIT
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Repurposing Low-Molecular-Weight Drugs against the Main Protease of Severe Acute Respiratory Syndrome Coronavirus 2
This article has 12 authors:Reviewed by ScreenIT
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Neutralization of SARS-CoV-2 by Destruction of the Prefusion Spike
This article has 25 authors:Reviewed by ScreenIT