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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Risk Interactions of Coronavirus Infection across Age Groups after the Peak of COVID-19 Epidemic
This article has 1 author:Reviewed by ScreenIT
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Human IgG neutralizing monoclonal antibodies block SARS-CoV-2 infection
This article has 17 authors:Reviewed by ScreenIT
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Multi-Antigenic Virus-like Particle of SARS CoV-2 produced in Saccharomyces cerevisiae as a vaccine candidate
This article has 18 authors:Reviewed by ScreenIT
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Clinical evaluation of IFN beta1b in COVID-19 pneumonia: a retrospective study
This article has 20 authors:Reviewed by ScreenIT
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Upper airway gene expression reveals suppressed immune responses to SARS-CoV-2 compared with other respiratory viruses
This article has 23 authors:Reviewed by ScreenIT
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Antiviral activity of Glucosylceramide synthase inhibitors against SARS-CoV-2 and other RNA virus infections
This article has 12 authors:Reviewed by ScreenIT
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The impact of super-spreaders in COVID-19: mapping genome variation worldwide
This article has 5 authors:Reviewed by ScreenIT
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High speed large scale automated isolation of SARS-CoV-2 from clinical samples using miniaturized co-culture coupled with high content screening
This article has 7 authors:Reviewed by ScreenIT
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Risk factors for hospital admissions related to COVID-19 in patients with autoimmune inflammatory rheumatic diseases
This article has 10 authors:Reviewed by ScreenIT
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Transcriptional Profiling of Immune and Inflammatory Responses in the Context of SARS-CoV-2 Fungal Superinfection in a Human Airway Epithelial Model
This article has 13 authors:Reviewed by ScreenIT