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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Evolutionary arms race between virus and host drives genetic diversity in bat SARS related coronavirus spike genes
This article has 7 authors:Reviewed by ScreenIT
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Novel ACE2-Independent Carbohydrate-Binding of SARS-CoV-2 Spike Protein to Host Lectins and Lung Microbiota
This article has 11 authors:Reviewed by ScreenIT
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A rapid, point of care red blood cell agglutination assay for detecting antibodies against SARS-CoV-2
This article has 8 authors:Reviewed by ScreenIT
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Tracing two causative SNPs reveals SARS-CoV-2 transmission in North America population
This article has 19 authors:Reviewed by ScreenIT
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Online COVID-19 diagnosis with chest CT images: Lesion-attention deep neural networks
This article has 5 authors:Reviewed by ScreenIT
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Characterization of SARS-CoV-2 viral diversity within and across hosts
This article has 4 authors:Reviewed by ScreenIT
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Origin of Novel Coronavirus (COVID-19): A Computational Biology Study using Artificial Intelligence
This article has 10 authors:Reviewed by ScreenIT
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Evaluation of the EUROIMMUN Anti-SARS-CoV-2 ELISA Assay for detection of IgA and IgG antibodies
This article has 8 authors:Reviewed by ScreenIT
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In Vitro Inhibition of SARS-CoV-2 Infection by Bovine Lactoferrin
This article has 17 authors:Reviewed by ScreenIT
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Evaluation Of SYBR Green Real Time PCR For Detecting SARS-CoV-2 From Clinical Samples
This article has 8 authors:Reviewed by ScreenIT