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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CryoEM and AI reveal a structure of SARS-CoV-2 Nsp2, a multifunctional protein involved in key host processes
This article has 81 authors:Reviewed by ScreenIT
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Comparison of mental health outcomes in seropositive and seronegative adolescents during the COVID19 pandemic
This article has 10 authors:Reviewed by ScreenIT
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Antibody Response to COVID-19 Vaccination in Patients Receiving Dialysis
This article has 13 authors:Reviewed by ScreenIT
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COVID-19 Pandemic and Academic Speculation of Medical Students of Bangladesh: A Cross-sectional, Comparative Study
This article has 7 authors:Reviewed by ScreenIT
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Clinicopathological Features and Outcome of COVID-19 – Early Experiences from Three COVID Hospitals, Chittagong, Bangladesh
This article has 8 authors:Reviewed by ScreenIT
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The majority of SARS-CoV-2-specific antibodies in COVID-19 patients with obesity are autoimmune and not neutralizing
This article has 7 authors:Reviewed by ScreenIT
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Antibody responses after a single dose of ChAdOx1 nCoV-19 vaccine in healthcare workers previously infected with SARS-CoV-2
This article has 16 authors:Reviewed by ScreenIT
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Structural basis for cell-type specific evolution of viral fitness by SARS-CoV-2
This article has 17 authors:Reviewed by ScreenIT
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Sars-Cov-2 Variant Identification Using a Genome Tiling Array and Genotyping Probes
This article has 8 authors:Reviewed by ScreenIT
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Mechanism of molnupiravir-induced SARS-CoV-2 mutagenesis
This article has 8 authors:Reviewed by ScreenIT