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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The evolutionary making of SARS-CoV-2
This article has 6 authors:Reviewed by ScreenIT
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Fatigue Symptoms Associated With COVID-19 in Convalescent or Recovered COVID-19 Patients; a Systematic Review and Meta-Analysis
This article has 7 authors:Reviewed by ScreenIT
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SARS-CoV-2 host-shutoff impacts innate NK cell functions, but antibody-dependent NK activity is strongly activated through non-spike antibodies
This article has 21 authors:Reviewed by ScreenIT
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Growth Functions as a tool to model SARS-CoV-2 pandemic trajectory and related-deaths worldwide
This article has 5 authors:Reviewed by ScreenIT
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Increased mortality among individuals hospitalised with COVID-19 during the second wave in South Africa
This article has 15 authors:Reviewed by ScreenIT
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TMPRSS2 Inhibitor Discovery Facilitated through an In Silico and Biochemical Screening Platform
This article has 12 authors:Reviewed by ScreenIT
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Logistic Formula in Biology and Its Application to COVID-19 in Japan
This article has 2 authors:Reviewed by ScreenIT
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A proteome-wide genetic investigation identifies several SARS-CoV-2-exploited host targets of clinical relevance
This article has 18 authors:This article has been curated by 1 group: -
Characterizing altruistic motivation in potential volunteers for SARS-CoV-2 challenge trials
This article has 9 authors:Reviewed by ScreenIT
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Hydroxychloroquine-mediated inhibition of SARS-CoV-2 entry is attenuated by TMPRSS2
This article has 6 authors:Reviewed by ScreenIT