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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Associations and prognostic accuracy of electrolyte imbalances in predicting poor COVID-19 outcome: a systematic review and meta-analysis
This article has 7 authors:Reviewed by ScreenIT
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A live attenuated virus-based intranasal COVID-19 vaccine provides rapid, prolonged, and broad protection against SARS-CoV-2
This article has 36 authors:Reviewed by ScreenIT
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Excessive inflammatory and metabolic responses to acute SARS-CoV-2 infection are associated with a distinct gut microbiota composition
This article has 21 authors:Reviewed by ScreenIT
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COVID-19 first and delta waves in relation to ACEI, ARB, Influenza vaccination, and comorbidity in a North Metropolitan Barcelona Health Consortium
This article has 11 authors:Reviewed by ScreenIT
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Glycosylated extracellular mucin domains protect against SARS-CoV-2 infection at the respiratory surface
This article has 11 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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An interactome landscape of SARS-CoV-2 virus-human protein-protein interactions by protein sequence-based multi-label classifiers
This article has 1 author:Reviewed by ScreenIT
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A Booster Dose of CoronaVac Increases Neutralizing Antibodies and T Cells that Recognize Delta and Omicron Variants of Concern
This article has 39 authors:Reviewed by ScreenIT
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Susceptibility of sheep to experimental co-infection with the ancestral lineage of SARS-CoV-2 and its alpha variant
This article has 19 authors:Reviewed by ScreenIT
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Adaptive trends of sequence compositional complexity over pandemic time in the SARS-CoV-2 coronavirus
This article has 8 authors:Reviewed by ScreenIT
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Care Models for Long COVID : A Rapid Systematic Review
This article has 11 authors:Reviewed by ScreenIT