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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A virus‐derived microRNA targets immune response genes during SARS‐CoV‐2 infection
This article has 15 authors: -
Dynamics, outcomes and prerequisites of the first SARS-CoV-2 superspreading event in Germany in February 2020: a cross-sectional epidemiological study
This article has 12 authors:Reviewed by ScreenIT
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Entrectinib—A SARS-CoV-2 Inhibitor in Human Lung Tissue (HLT) Cells
This article has 11 authors:Reviewed by ScreenIT
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SARS-CoV-2 originated from SARS-CoV-1-related Bat-CoVs through Pan-CoVs rather than from SARS-CoV-2-related Bat-CoVs
This article has 2 authors:Reviewed by ScreenIT
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Neutralization of the SARS-CoV-2 Mu Variant by Convalescent and Vaccine Serum
This article has 8 authors:Reviewed by ScreenIT
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Development and Validation of an Enzyme Immunoassay for Detection and Quantification of SARS-CoV-2 Salivary IgA and IgG
This article has 15 authors:Reviewed by ScreenIT
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Surveillance, contact tracing and characteristics of SARS-CoV-2 transmission in educational settings in Northern Italy, September 2020 to April 2021
This article has 14 authors:Reviewed by ScreenIT
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The early impact of vaccination against SARS-CoV-2 in Region Stockholm, Sweden
This article has 5 authors:Reviewed by ScreenIT
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Potentially effective drugs for the treatment of COVID-19 or MIS-C in children: a systematic review
This article has 16 authors:Reviewed by ScreenIT
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SARS-CoV-2 infection results in immune responses in the respiratory tract and peripheral blood that suggest mechanisms of disease severity
This article has 51 authors:Reviewed by ScreenIT