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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Association between angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers use and the risk of infection and clinical outcome of COVID-19: a comprehensive systematic review and meta-analysis
This article has 11 authors:Reviewed by ScreenIT
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High SARS-CoV-2 load in the nasopharynx of patients with a mild form of COVID-19 is associated with clinical deterioration regardless of the hydroxychloroquine administration
This article has 8 authors:Reviewed by ScreenIT
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Comparison of Multimorbidity in COVID-19 infected and general population in Portugal
This article has 4 authors:Reviewed by ScreenIT
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Retrospective clinical evaluation of 4 lateral flow assays for the detection of SARS-CoV-2 IgG
This article has 7 authors:Reviewed by ScreenIT
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Indication for SARS-CoV-2 serology: first month follow-up
This article has 5 authors:Reviewed by ScreenIT
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SARS-CoV-2 genomic diversity and the implications for qRT-PCR diagnostics and transmission
This article has 20 authors:Reviewed by ScreenIT
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Using iCn3D and the World Wide Web for structure-based collaborative research: Analyzing molecular interactions at the root of COVID-19
This article has 4 authors:Reviewed by ScreenIT
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6-Thioguanine blocks SARS-CoV-2 replication by inhibition of PLpro
This article has 11 authors:Reviewed by ScreenIT
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Association between SARS-CoV-2 Neutralizing Antibodies and Commercial Serological Assays
This article has 9 authors:Reviewed by ScreenIT
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Alpha-1 antitrypsin inhibits SARS-CoV-2 infection
This article has 25 authors:Reviewed by ScreenIT