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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Evaluation of maternal-infant dyad inflammatory cytokines in pregnancies affected by maternal SARS-CoV-2 infection in early and late gestation
This article has 14 authors:Reviewed by ScreenIT
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Expanded ACE2 dependencies of diverse SARS-like coronavirus receptor binding domains
This article has 5 authors:Reviewed by ScreenIT
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Structural basis for potent antibody neutralization of SARS-CoV-2 variants including B.1.1.529
This article has 25 authors:Reviewed by ScreenIT
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Super-immunity by broadly protective nanobodies to sarbecoviruses
This article has 12 authors:Reviewed by ScreenIT
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mRNA-1273 vaccination protects against SARS-CoV-2–elicited lung inflammation in nonhuman primates
This article has 10 authors:Reviewed by ScreenIT
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SARS-CoV-2 vaccination induces immunological memory able to cross-recognize variants from Alpha to Omicron
This article has 17 authors:Reviewed by ScreenIT
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The SARS-CoV-2 nucleocapsid protein preferentially binds long and structured RNAs
This article has 11 authors:Reviewed by ScreenIT
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Clinical grade ACE2 effectively inhibits SARS-CoV-2 Omicron infections
This article has 19 authors:Reviewed by ScreenIT
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Cryo-EM structures and binding of mouse and human ACE2 to SARS-CoV-2 variants of concern indicate that mutations enabling immune escape could expand host range
This article has 10 authors:Reviewed by ScreenIT
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Vaccine‐breakthrough infection by the SARS‐CoV‐2 omicron variant elicits broadly cross‐reactive immune responses
This article has 14 authors:Reviewed by ScreenIT