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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Impact of control strategies on COVID-19 pandemic and the SIR model based forecasting in Bangladesh.
This article has 6 authors:Reviewed by ScreenIT
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Structural and biochemical characterization of nsp12-nsp7-nsp8 core polymerase complex from COVID-19 virus
This article has 12 authors:Reviewed by ScreenIT
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Functional and Genetic Analysis of Viral Receptor ACE2 Orthologs Reveals a Broad Potential Host Range of SARS-CoV-2
This article has 17 authors:Reviewed by ScreenIT
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Recombinant SARS-CoV-2 spike S1-Fc fusion protein induced high levels of neutralizing responses in nonhuman primates
This article has 24 authors:Reviewed by ScreenIT
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Coronavirus surveillance of wildlife in the Lao People’s Democratic Republic detects viral RNA in rodents
This article has 19 authors:Reviewed by ScreenIT
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Targeted proteomics as a tool to detect SARS-CoV-2 proteins in clinical specimens
This article has 8 authors: -
TMPRSS2 and TMPRSS4 mediate SARS-CoV-2 infection of human small intestinal enterocytes
This article has 14 authors: -
Epidemiological Impact of SARS-CoV-2 Vaccination: Mathematical Modeling Analyses
This article has 7 authors:Reviewed by ScreenIT
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Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients
This article has 30 authors:Reviewed by ScreenIT
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Repositioned chloroquine and hydroxychloroquine as antiviral prophylaxis for COVID-19: A protocol for rapid systematic review of randomized controlled trials
This article has 2 authors:Reviewed by ScreenIT