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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SARS-CoV-2 in sewer systems and connected facilities
This article has 6 authors:Reviewed by ScreenIT
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Broad and strong memory CD4 + and CD8 + T cells induced by SARS-CoV-2 in UK convalescent COVID-19 patients
This article has 61 authors:Reviewed by ScreenIT
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SARS-CoV-2-specific T cells exhibit phenotypic features reflecting robust helper function, lack of terminal differentiation, and high proliferative potential
This article has 15 authors:Reviewed by ScreenIT
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SARS-CoV-2 Whole Genome Amplification and Sequencing for Effective Population-Based Surveillance and Control of Viral Transmission
This article has 11 authors:Reviewed by ScreenIT
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Cross-Sectional Evaluation of Humoral Responses against SARS-CoV-2 Spike
This article has 35 authors:Reviewed by ScreenIT
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Effects of Renin-Angiotensin Inhibition on ACE2 and TMPRSS2 Expression: Insights into COVID-19
This article has 7 authors:Reviewed by ScreenIT
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Evolutionary and structural analyses of SARS-CoV-2 D614G spike protein mutation now documented worldwide
This article has 12 authors:Reviewed by ScreenIT
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Convolutional Neural Network Model to Detect COVID-19 Patients Utilizing Chest X-ray Images
This article has 8 authors:Reviewed by ScreenIT
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Relationship between blood group and risk of infection and death in COVID-19: a live meta-analysis
This article has 6 authors:Reviewed by ScreenIT
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Dynamics of the SARS-CoV-2 epidemic in the earliest-affected areas in Italy: Mass screening for SARS-CoV-2 serological positivity (SARS-2-SCREEN)
This article has 11 authors:Reviewed by ScreenIT