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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SARS-CoV-2 Spike Protein Mutations and Escape from Antibodies: A Computational Model of Epitope Loss in Variants of Concern
This article has 10 authors:Reviewed by ScreenIT
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Genetic regulation of OAS1 nonsense-mediated decay underlies association with COVID-19 hospitalization in patients of European and African ancestries
This article has 70 authors:Reviewed by ScreenIT
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COVID-19 and Herpes Simplex Virus Infection: A Cross-Sectional Study
This article has 2 authors:Reviewed by ScreenIT
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Viral infection and transmission in a large, well-traced outbreak caused by the SARS-CoV-2 Delta variant
This article has 46 authors: -
Risk of Severe COVID–19 Outcomes Among Patients with Rheumatoid Arthritis in the United States
This article has 5 authors:Reviewed by ScreenIT
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Impact of COVID-19 Pandemic on Weight and BMI among UK Adults: A Longitudinal Analysis of Data from the HEBECO Study
This article has 7 authors:Reviewed by ScreenIT
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Analyzing the U.S. Post-marketing safety surveillance of COVID-19 vaccines
This article has 5 authors:Reviewed by ScreenIT
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High Rate of Mutational Events in SARS-CoV-2 Genomes across Brazilian Geographical Regions, February 2020 to June 2021
This article has 7 authors:Reviewed by ScreenIT
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Daily longitudinal sampling of SARS-CoV-2 infection reveals substantial heterogeneity in infectiousness
This article has 39 authors:Reviewed by ScreenIT
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Endothelial dysfunction contributes to severe COVID-19 in combination with dysregulated lymphocyte responses and cytokine networks
This article has 17 authors:Reviewed by ScreenIT