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 genetic variations associated with COVID-19 pathogenicity
This article has 5 authors:Reviewed by ScreenIT
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An open resource for T cell phenotype changes in COVID-19 identifies IL-10-producing regulatory T cells as characteristic of severe cases
This article has 15 authors:Reviewed by ScreenIT
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CRISPR-Cas3-based diagnostics for SARS-CoV-2 and influenza virus
This article has 9 authors:Reviewed by ScreenIT
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High seroprevalence for SARS-CoV-2 among household members of essential workers detected using a dried blood spot assay
This article has 11 authors:Reviewed by ScreenIT
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Low levels of the prognostic biomarker suPAR are predictive of mild outcome in patients with symptoms of COVID-19 - a prospective cohort study
This article has 12 authors:Reviewed by ScreenIT
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The SARS-CoV-2 pandemic course in Saudi Arabia: A dynamic epidemiological model
This article has 4 authors:Reviewed by ScreenIT
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Increased expression of ACE2, the SARS-CoV-2 entry receptor, in alveolar and bronchial epithelium of smokers and COPD subjects
This article has 7 authors:Reviewed by ScreenIT
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Immunochromatographic assays for COVID-19 epidemiological screening: our experience
This article has 6 authors:Reviewed by ScreenIT
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Epidemiological Characteristics of COVID-19 in Mexico and the Potential Impact of Lifting Confinement Across Regions
This article has 2 authors:Reviewed by ScreenIT
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G6PD distribution in sub-Saharan Africa and potential risks of using chloroquine/hydroxychloroquine based treatments for COVID-19
This article has 15 authors:Reviewed by ScreenIT