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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Clinical observation of high-flow nasal cannula with non-rebreather mask use on severe or critically ill COVID-19 diabetic patients
This article has 12 authors:Reviewed by ScreenIT
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Back to the medical classes-Part I-Strategy for return to the presential practices during COVID-19 pandemics in a Brazilian Medical School
This article has 12 authors:Reviewed by ScreenIT
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Immunogenicity and Safety of a Third SARS-CoV-2 Vaccine Dose in Patients With Multiple Sclerosis and Weak Immune Response After COVID-19 Vaccination
This article has 7 authors:Reviewed by ScreenIT
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Factors shaping the mental health and well-being of people experiencing persistent COVID-19 symptoms or ‘long COVID’: qualitative study
This article has 4 authors:Reviewed by ScreenIT
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Hymecromone: a clinical prescription hyaluronan inhibitor for efficiently blocking COVID-19 progression
This article has 23 authors:Reviewed by ScreenIT
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A study on willingness to take the COVID-19 vaccine at a tertiary institution community in Johannesburg, South Africa
This article has 3 authors:Reviewed by ScreenIT
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Impact of adaptive natural killer cells, KLRC2 genotype and cytomegalovirus reactivation on late mortality in patients with severe COVID‐19 lung disease
This article has 8 authors:Reviewed by ScreenIT
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Variation at Spike position 142 in SARS-CoV-2 Delta genomes is a technical artifact caused by dropout of a sequencing amplicon
This article has 2 authors:Reviewed by ScreenIT
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Transmission of SARS-CoV-2 in educational settings in 2020: a review
This article has 9 authors:Reviewed by ScreenIT
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Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus
This article has 4 authors:Reviewed by ScreenIT