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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Substitutions in Spike and Nucleocapsid proteins of SARS-CoV-2 circulating in South America
This article has 15 authors:Reviewed by ScreenIT
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Long-term SARS-CoV-2 RNA shedding and its temporal association to IgG seropositivity
This article has 17 authors:Reviewed by ScreenIT
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Increased Serum Levels of sCD14 and sCD163 Indicate a Preponderant Role for Monocytes in COVID-19 Immunopathology
This article has 13 authors:Reviewed by ScreenIT
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A prediction model based on machine learning for diagnosing the early COVID-19 patients
This article has 7 authors:Reviewed by ScreenIT
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Genetic polymorphisms mediating behavioural and immune response to pathogens may moderate the impact of the COVID-19 pandemic: a pilot study
This article has 1 author:Reviewed by ScreenIT
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Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods
This article has 4 authors:Reviewed by ScreenIT
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An exploration of the SARS-CoV-2 spike receptor binding domain (RBD) – a complex palette of evolutionary and structural features
This article has 4 authors:Reviewed by ScreenIT
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The inhibitory effect of a coronavirus spike protein fragment with ACE2
This article has 2 authors:Reviewed by ScreenIT
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Neighborhood-level disparities and subway utilization during the COVID-19 pandemic in New York City
This article has 7 authors:Reviewed by ScreenIT
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Multi-parametric disease dynamics study and analysis of the COVID-19 epidemic and implementation of population-wide intrusions: The Indian perspective
This article has 6 authors:Reviewed by ScreenIT