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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Varenicline Prevents SARS-CoV-2 Infection In Vitro and in Rhesus Macaques
This article has 6 authors:Reviewed by ScreenIT
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Pathology and Immunity After SARS-CoV-2 Infection in Male Ferrets Is Affected by Age and Inoculation Route
This article has 11 authors:Reviewed by ScreenIT
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Classifying COVID-19 variants based on genetic sequences using deep learning models
This article has 2 authors:Reviewed by ScreenIT
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Models of COVID-19 vaccine prioritisation: a systematic literature search and narrative review
This article has 9 authors:Reviewed by ScreenIT
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Widespread contamination of SARS‐CoV ‐2 on highly touched surfaces in Brazil during the second wave of the COVID ‐19 pandemic
This article has 11 authors:Reviewed by ScreenIT
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Selectively expressing SARS-CoV-2 Spike protein S1 subunit in cardiomyocytes induces cardiac hypertrophy in mice
This article has 5 authors:Reviewed by ScreenIT
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Dysphagic disorder in a cohort of COVID-19 patients: Evaluation and evolution
This article has 8 authors:Reviewed by ScreenIT
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Data from The Polarity and Specificity of Antiviral T Lymphocyte Responses Determine Susceptibility to SARS-CoV-2 Infection in Patients with Cancer and Healthy Individuals
This article has 67 authors:Reviewed by ScreenIT
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Thermodynamically coupled biosensors for detecting neutralizing antibodies against SARS-CoV-2 variants
This article has 24 authors:Reviewed by ScreenIT
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Mask wearing in community settings reduces SARS-CoV-2 transmission
This article has 14 authors:Reviewed by ScreenIT