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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Intra-Host Evolution Provides for the Continuous Emergence of SARS-CoV-2 Variants
This article has 14 authors:Reviewed by ScreenIT
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Infants are more susceptible to COVID-19 than children
This article has 1 author:Reviewed by ScreenIT
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SARS-CoV-2 B.1.617.2 Delta variant replication, sensitivity to neutralising antibodies and vaccine breakthrough
This article has 55 authors:Reviewed by ScreenIT
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Utilizing wearable sensors for continuous and highly-sensitive monitoring of reactions to the BNT162b2 mRNA COVID-19 vaccine
This article has 9 authors:Reviewed by ScreenIT
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A Hemagglutination-Based Semiquantitative Test for Point-of-Care Determination of SARS-CoV-2 Antibody Levels
This article has 11 authors:Reviewed by ScreenIT
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Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning
This article has 28 authors:Reviewed by ScreenIT
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Point of care testing for detection of coronaviruses including SARS-CoV-2 from saliva without treating RNA in advance
This article has 4 authors:Reviewed by ScreenIT
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The method utilized to purify the SARS-CoV-2 N protein can affect its molecular properties
This article has 6 authors:Reviewed by ScreenIT
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India’s pragmatic vaccination strategy against COVID-19: a mathematical modelling-based analysis
This article has 4 authors:Reviewed by ScreenIT
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Human organoid systems reveal in vitro correlates of fitness for SARS-CoV-2 B.1.1.7
This article has 19 authors:Reviewed by ScreenIT