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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Impact of long-COVID on health-related quality of life in Japanese COVID-19 patients
This article has 6 authors:Reviewed by ScreenIT
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Modulation of immunosuppressant drug treatment to improve SARS-CoV-2 vaccine efficacy in mice
This article has 5 authors:Reviewed by ScreenIT
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Protection of Hamsters Challenged with SARS-CoV-2 Variants of Concern by Two Doses of MVC-COV1901 Vaccine Followed by a Single Dose of Beta Variant Version of MVC-COV1901
This article has 12 authors:Reviewed by ScreenIT
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Monoclonal Antibodies against SARS-CoV-2: Potential Game-Changer Still Underused
This article has 7 authors:Reviewed by ScreenIT
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Pyronaridine Protects Against SARS-CoV-2 in Mouse
This article has 20 authors:Reviewed by ScreenIT
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Structural insights of a highly potent pan-neutralizing SARS-CoV-2 human monoclonal antibody
This article has 23 authors:Reviewed by ScreenIT
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Ad26.COV2.S Prevents SARS-CoV-2 Induced Pathways of Inflammation and Thrombosis in Hamsters
This article has 13 authors:Reviewed by ScreenIT
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Metabolic Snapshot of Plasma Samples Reveals New Pathways Implicated in SARS-CoV-2 Pathogenesis
This article has 11 authors:Reviewed by ScreenIT
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Full Genome Nobecovirus Sequences From Malagasy Fruit Bats Define a Unique Evolutionary History for This Coronavirus Clade
This article has 15 authors:Reviewed by ScreenIT
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Data-driven approaches for genetic characterization of SARS-CoV-2 lineages
This article has 17 authors:Reviewed by ScreenIT