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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Global Analysis of an SEIRS Model for COVID-19 Capturing Saturated Incidence with Treatment Response
This article has 3 authors:Reviewed by ScreenIT
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COVID-19: Effects of Environmental Conditions on the Propagation of Respiratory Droplets
This article has 5 authors:Reviewed by ScreenIT
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Survival Analysis of Patients With COVID-19 in India by Demographic Factors: Quantitative Study
This article has 3 authors:Reviewed by ScreenIT
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Nature and Dimensions of Systemic Hyperinflammation and its Attenuation by Convalescent Plasma in Severe COVID-19
This article has 25 authors:Reviewed by ScreenIT
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Genomic epidemiology reveals multiple introductions of SARS-CoV-2 from mainland Europe into Scotland
This article has 53 authors:Reviewed by ScreenIT
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Targeting androgen regulation of TMPRSS2 and ACE2 as a therapeutic strategy to combat COVID-19
This article has 5 authors:Reviewed by ScreenIT
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Elucidation of Cryptic and Allosteric Pockets within the SARS-CoV-2 Main Protease
This article has 3 authors:Reviewed by ScreenIT
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The Safety of Contemporary Planned Cancer Surgery During the COVID-19 Pandemic
This article has 14 authors:Reviewed by ScreenIT
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Sequential ER stress and inflammatory responses are induced by SARS-CoV-2 ORF3 through ERphagy
This article has 8 authors:Reviewed by ScreenIT
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Analysis of the intestinal microbiota in COVID-19 patients and its correlation with the inflammatory factor IL-18
This article has 16 authors:Reviewed by ScreenIT