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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Protective Efficacy of Gastrointestinal SARS-CoV-2 Delivery against Intranasal and Intratracheal SARS-CoV-2 Challenge in Rhesus Macaques
This article has 31 authors:Reviewed by ScreenIT
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CHARACTERIZING AND MANAGING AN EPIDEMIC: A FIRST PRINCIPLES MODEL AND A CLOSED FORM SOLUTION TO THE KERMACK AND MCKENDRICK EQUATIONS
This article has 2 authors:Reviewed by ScreenIT
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Implicit, Intrinsic, Extrinsic (or Environmental), and Host Factors Attributing the Covid-19 Pandemic. Part 2-Implicit Factor Pesticide Use: A Systematic Analysis
This article has 4 authors:Reviewed by ScreenIT
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BNT162b2-Elicited Neutralization of Delta Plus, Lambda, and Other Variants
This article has 15 authors:Reviewed by ScreenIT
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Development of a novel, pan-variant aerosol intervention for COVID-19
This article has 25 authors:Reviewed by ScreenIT
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A strategy to assess spillover risk of bat SARS-related coronaviruses in Southeast Asia
This article has 9 authors:Reviewed by ScreenIT
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The Evolutionary Landscape of SARS-CoV-2 Variant B.1.1.519 and Its Clinical Impact in Mexico City
This article has 25 authors:Reviewed by ScreenIT
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Y380Q novel mutation in receptor-binding domain of SARS-CoV-2 spike protein together with C379W interfere in the neutralizing antibodies interaction
This article has 22 authors:Reviewed by ScreenIT
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COVID‐19 bimodal clinical and pathological phenotypes
This article has 33 authors:Reviewed by ScreenIT
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Mitoxantrone dihydrochloride, an FDA approved drug, binds with SARS-CoV-2 NSP1 C-terminal
This article has 3 authors:Reviewed by ScreenIT