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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Vitamin D and COVID-19 infection and mortality in UK Biobank
This article has 3 authors:Reviewed by ScreenIT
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Critical Sequence Hot-spots for Binding of nCOV-2019 to ACE2 as Evaluated by Molecular Simulations
This article has 3 authors:Reviewed by ScreenIT
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Structures and distributions of SARS-CoV-2 spike proteins on intact virions
This article has 17 authors:Reviewed by ScreenIT
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Binding Ability Prediction between Spike Protein and Human ACE2 Reveals the Adaptive Strategy of SARS-CoV-2 in Humans
This article has 18 authors:Reviewed by ScreenIT
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Saliva sampling is an excellent option to increase the number of SARS CoV2 diagnostic tests in settings with supply shortages
This article has 11 authors:Reviewed by ScreenIT
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Modified Vaccinia Ankara Based SARS-CoV-2 Vaccine Expressing Full-Length Spike Induces Strong Neutralizing Antibody Response
This article has 14 authors:Reviewed by ScreenIT
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Swarm Learning as a privacy-preserving machine learning approach for disease classification
This article has 33 authors:Reviewed by ScreenIT
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Nanotrap ® particles improve detection of SARS-CoV-2 for pooled sample methods, extraction-free saliva methods, and extraction-free transport medium methods
This article has 24 authors:Reviewed by ScreenIT
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Tipiracil binds to uridine site and inhibits Nsp15 endoribonuclease NendoU from SARS-CoV-2
This article has 11 authors:Reviewed by ScreenIT
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Evaluation of K18- hACE2 mice as a model of SARS-CoV-2 infection
This article has 6 authors:Reviewed by ScreenIT