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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The Impact of Mass Exodus on the Resurgence of COVID-19 Cases: Case Study of Regions in Indonesia
This article has 4 authors:Reviewed by ScreenIT
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An Open One-Step RT-qPCR for SARS-CoV-2 detection
This article has 18 authors:Reviewed by ScreenIT
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Comprehensive serologic profile and specificity of maternal and neonatal cord blood SARS-CoV-2 antibodies
This article has 7 authors:Reviewed by ScreenIT
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Statistical Inferences and Analysis based on the COVID-19 Data from the United States
This article has 1 author:Reviewed by ScreenIT
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Omicron BA.1 and BA.2 variants increase the interactions of SARS-CoV-2 spike glycoprotein with ACE2
This article has 3 authors:Reviewed by ScreenIT
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Insertions in the SARS-CoV-2 Spike N-Terminal Domain May Aid COVID-19 Transmission
This article has 4 authors:Reviewed by ScreenIT
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Constructing a multiple-layer interactome for SARS-CoV-2 in the context of lung disease: Linking the virus with human genes and co-infecting microbes
This article has 3 authors:Reviewed by ScreenIT
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Post-Vaccination Symptoms after A Third Dose of mRNA SARS-CoV-2 Vaccination in Patients with Inflammatory Bowel Disease
This article has 6 authors:Reviewed by ScreenIT
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The Petasites hybridus CO2 Extract (Ze 339) Blocks SARS-CoV-2 Replication In Vitro
This article has 6 authors:Reviewed by ScreenIT
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Mutational cascade of SARS-CoV-2 leading to evolution and emergence of omicron variant
This article has 2 authors:Reviewed by ScreenIT