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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Applying Benford’s law to COVID-19 data: the case of the European Union
This article has 1 author:Reviewed by ScreenIT
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COVIDFast™: A high-throughput and RNA extraction-free method for SARS-CoV-2 detection in swab (SwabFAST™) or saliva (SalivaFAST™)
This article has 14 authors:Reviewed by ScreenIT
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Omicron variant escapes therapeutic mAbs contrary to eight prior main VOC
This article has 5 authors:Reviewed by ScreenIT
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A dual-receptor mechanism between integrins and ACE2 widens SARS-CoV-2 tissue tropism
This article has 6 authors:Reviewed by ScreenIT
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Head-to-head comparison of nasal and nasopharyngeal sampling using SARS-CoV-2 rapid antigen testing in Lesotho
This article has 14 authors:Reviewed by ScreenIT
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A Global Study on the Correlates of Gross Domestic Product (GDP) and COVID-19 Vaccine Distribution
This article has 8 authors:Reviewed by ScreenIT
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Endothelin-1 is increased in the plasma of patients hospitalised with Covid-19
This article has 10 authors:Reviewed by ScreenIT
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An in vitro and in vivo approach for the isolation of Omicron variant from human clinical specimens
This article has 17 authors:Reviewed by ScreenIT
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Convalescent plasma improves overall survival in patients with B-cell lymphoid malignancy and COVID-19: a longitudinal cohort and propensity score analysis
This article has 21 authors:Reviewed by ScreenIT
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Vaccination-infection interval determines cross-neutralization potency to SARS-CoV-2 Omicron after breakthrough infection by other variants
This article has 36 authors:Reviewed by ScreenIT