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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SARS‐CoV ‐2 antibody persistence in COVID ‐19 convalescent plasma donors: Dependency on assay format and applicability to serosurveillance
This article has 18 authors:Reviewed by ScreenIT
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Ethnicity and COVID-19 outcomes among healthcare workers in the UK: UK-REACH ethico-legal research, qualitative research on healthcare workers’ experiences and stakeholder engagement protocol
This article has 10 authors:Reviewed by ScreenIT
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Infection and transmission of SARS‐CoV‐2 depend on heparan sulfate proteoglycans
This article has 15 authors:Reviewed by ScreenIT
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Pre-pandemic SARS-CoV-2 potential natural immunity among population of the Democratic Republic of Congo
This article has 10 authors:Reviewed by ScreenIT
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Systematic analysis of SARS-CoV-2 infection of an ACE2-negative human airway cell
This article has 31 authors:Reviewed by ScreenIT
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A bispecific monomeric nanobody induces spike trimer dimers and neutralizes SARS-CoV-2 in vivo
This article has 18 authors:Reviewed by ScreenIT
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SARS-CoV-2 spike protein induces inflammation via TLR2-dependent activation of the NF-κB pathway
This article has 6 authors:This article has been curated by 1 group: -
Antibodies against type-I Interferon: detection and association with severe clinical outcome in COVID-19 patients
This article has 13 authors:Reviewed by ScreenIT
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Impact of COVID-19 restrictions on diabetes health checks and prescribing for people with type 2 diabetes: a UK-wide cohort study involving 618 161 people in primary care
This article has 8 authors:Reviewed by ScreenIT
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Structural and functional ramifications of antigenic drift in recent SARS-CoV-2 variants
This article has 17 authors:Reviewed by ScreenIT