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 Hotspots for Binding of Novel Coronavirus to Angiotensin Converter Enzyme 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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Dynamics of 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 for decentralized and confidential clinical machine learning
This article has 181 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