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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Evaluation of Vaccination Strategies for the metropolitan area of Madrid
This article has 9 authors:Reviewed by ScreenIT
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Heteologous saRNA-Prime, DNA Dual-Antigen-Boost SARS-CoV-2 Vaccination Elicits Robust Cellular Immunogenicity and Cross-Variant Neutralizing Antibodies
This article has 17 authors:Reviewed by ScreenIT
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Protection against SARS-CoV-2 after Covid-19 Vaccination and Previous Infection
This article has 34 authors:Reviewed by ScreenIT
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Google Trends as a Predictive Tool for COVID-19 Vaccinations in Italy: Retrospective Infodemiological Analysis
This article has 1 author:Reviewed by ScreenIT
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Th1, Th2 and Th17 inflammatory pathways synergistically correlate with cardiometabolic processes. A case study in COVID-19
This article has 5 authors:Reviewed by ScreenIT
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AImmune: a new blood-based machine learning approach to improving immune profiling analysis on COVID-19 patients
This article has 2 authors:Reviewed by ScreenIT
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Peptide–Antibody Fusions Engineered by Phage Display Exhibit an Ultrapotent and Broad Neutralization of SARS-CoV-2 Variants
This article has 15 authors:Reviewed by ScreenIT
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Trends and associated factors for Covid-19 hospitalisation and fatality risk in 2.3 million adults in England
This article has 11 authors:Reviewed by ScreenIT
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Normalisation of SARS-CoV-2 concentrations in wastewater: The use of flow, electrical conductivity and crAssphage
This article has 10 authors:Reviewed by ScreenIT
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In silico study on the effects of disulfide bonds in ORF8 of SARS-CoV-2
This article has 2 authors:Reviewed by ScreenIT