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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Analysis of changes occurring in Codon Positions due to mutations through the cellular automata transition rules
This article has 3 authors:Reviewed by ScreenIT
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Co-expression analysis to identify key modules and hub genes associated with COVID19 in Platelets
This article has 5 authors:Reviewed by ScreenIT
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Early post-infection treatment of SARS-CoV-2 infected macaques with human convalescent plasma with high neutralizing activity reduces lung inflammation
This article has 31 authors:Reviewed by ScreenIT
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Severe COVID-19 is characterised by inflammation and immature myeloid cells early in disease progression
This article has 29 authors:Reviewed by ScreenIT
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How varying intervention, vaccination, mutation and ethnic conditions affect COVID-19 resurgence
This article has 2 authors:Reviewed by ScreenIT
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A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater
This article has 15 authors:Reviewed by ScreenIT
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Characterization of SARS-CoV-2 variants B.1.617.1 (Kappa), B.1.617.2 (Delta) and B.1.618 on cell entry, host range, and sensitivity to convalescent plasma and ACE2 decoy receptor
This article has 13 authors:Reviewed by ScreenIT
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The coronavirus disease 2019 (COVID-19) vaccination psychological antecedent assessment using the Arabic 5c validated tool: An online survey in 13 Arab countries
This article has 10 authors:Reviewed by ScreenIT
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Longitudinal Analysis of SARS-CoV-2 Vaccine Breakthrough Infections Reveals Limited Infectious Virus Shedding and Restricted Tissue Distribution
This article has 43 authors:Reviewed by ScreenIT
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Fine Analysis of Lymphocyte Subpopulations in SARS-CoV-2 Infected Patients: Differential Profiling of Patients With Severe Outcome
This article has 16 authors:Reviewed by ScreenIT