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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Teletherapy for children with developmental disorders during the COVID‐19 pandemic in the Philippines: A mixed‐methods evaluation from the perspectives of parents and therapists
This article has 2 authors:Reviewed by ScreenIT
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A novel highly potent inhibitor of TMPRSS2-like proteases blocks SARS-CoV-2 variants of concern and is broadly protective against infection and mortality in mice
This article has 23 authors:Reviewed by ScreenIT
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In Vivo protection from SARS-CoV-2 infection by ATN-161 in k18-hACE2 transgenic mice
This article has 8 authors:Reviewed by ScreenIT
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Subtle differences in the pathogenicity of SARS-CoV-2 variants of concern B.1.1.7 and B.1.351 in rhesus macaques
This article has 20 authors:Reviewed by ScreenIT
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Multicentric evaluation of a novel point of care electrochemical ELISA platform for SARS-CoV-2 specific IgG and IgM antibody assay
This article has 14 authors:Reviewed by ScreenIT
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Social inequalities in COVID-19 vaccine acceptance and uptake for children and adolescents in Montreal, Canada
This article has 5 authors:Reviewed by ScreenIT
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RT-qPCR-based tests for SARS-CoV-2 detection in pooled saliva samples for massive population screening to monitor epidemics
This article has 14 authors:Reviewed by ScreenIT
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In-solution buffer-free digestion for the analysis of SARS-CoV-2 RBD proteins allows a full sequence coverage and detection of post-translational modifications in a single ESI-MS spectrum
This article has 34 authors:Reviewed by ScreenIT
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Cost-effectiveness analysis of statins for the treatment of hospitalized COVID-19 patients
This article has 4 authors:Reviewed by ScreenIT
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Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic
This article has 16 authors:Reviewed by ScreenIT