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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Effectiveness of Covid-19 Vaccines against the B.1.617.2 (Delta) Variant
This article has 19 authors:Reviewed by ScreenIT
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Severe Acute Respiratory Syndrome Coronavirus-2 genome sequence variations relate to morbidity and mortality in Coronavirus Disease-19
This article has 14 authors:Reviewed by ScreenIT
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Evaluation of Panbio rapid antigen test for SARS‐CoV‐2 in symptomatic patients and their contacts: a multicenter study
This article has 19 authors:Reviewed by ScreenIT
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ACTIVATE-2: A Double-Blind Randomized Trial of BCG Vaccination Against COVID-19 in Individuals at Risk
This article has 32 authors:Reviewed by ScreenIT
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Isolation of SARS-CoV-2 B.1.1.28.2 (P2) variant and pathogenicity comparison with D614G variant in hamster model
This article has 13 authors:Reviewed by ScreenIT
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Persistent clotting protein pathology in Long COVID/Post-Acute Sequelae of COVID-19 (PASC) is accompanied by increased levels of antiplasmin
This article has 7 authors:Reviewed by ScreenIT
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Receptor binding, immune escape, and protein stability direct the natural selection of SARS-CoV-2 variants
This article has 5 authors:Reviewed by ScreenIT
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COVID-19 in the context of pregnancy, infancy and parenting (CoCoPIP) study: protocol for a longitudinal study of parental mental health, social interactions, physical growth and cognitive development of infants during the pandemic
This article has 7 authors:Reviewed by ScreenIT
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Evidence for Deleterious Antigenic Imprinting in SARS-CoV-2 Immune Response
This article has 9 authors:Reviewed by ScreenIT
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Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning
This article has 12 authors:Reviewed by ScreenIT