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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Neutralization of SARS-CoV-2 Variants by mRNA and Adenoviral Vector Vaccine-Elicited Antibodies
This article has 8 authors:Reviewed by ScreenIT
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Perceived discrimination as a modifier of health, disease, and medicine: empirical data from the COVID-19 pandemic
This article has 4 authors:Reviewed by ScreenIT
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Low seropositivity and suboptimal neutralisation rates in patients fully vaccinated against COVID‐19 with B‐cell malignancies
This article has 20 authors:Reviewed by ScreenIT
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Macrophages govern antiviral responses in human lung tissues protected from SARS-CoV-2 infection
This article has 33 authors:Reviewed by ScreenIT
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Molecular evolution and structural analyses of the spike glycoprotein from Brazilian SARS-CoV-2 genomes: the impact of selected mutations
This article has 6 authors:Reviewed by ScreenIT
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SARS-CoV-2 Spike Pseudoviruses: A Useful Tool to Study Virus Entry and Address Emerging Neutralization Escape Phenotypes
This article has 6 authors:Reviewed by ScreenIT
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Susceptibilities of Human ACE2 Genetic Variants in Coronavirus Infection
This article has 18 authors:Reviewed by ScreenIT
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Airborne SARS-CoV-2 in home and hospital environments investigated with a high-powered air sampler
This article has 8 authors:Reviewed by ScreenIT
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COVID-19 convalescent plasma donors: impact of vaccination on antibody levels, breakthrough infections and reinfection rate
This article has 13 authors:Reviewed by ScreenIT
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COVID-19 Chest X-Ray Image Classification Using Deep Learning
This article has 4 authors:Reviewed by ScreenIT