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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Use of a humanized anti-CD6 monoclonal antibody (itolizumab) in elderly patients with moderate COVID-19
This article has 21 authors:Reviewed by ScreenIT
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Prediction of COVID-19 Pandemic of Top Ten Countries in the World Establishing a Hybrid AARNN LTM Model
This article has 4 authors:Reviewed by ScreenIT
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The proportion testing positive for SARS-COV-2 among the tested population in the U.S.: Benefits of the positive test ratio under scaled testing scenarios
This article has 2 authors:Reviewed by ScreenIT
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High rate of major drug–drug interactions of lopinavir–ritonavir for COVID-19 treatment
This article has 12 authors:Reviewed by ScreenIT
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Direct on-the-spot detection of SARS-CoV-2 in patients
This article has 15 authors:Reviewed by ScreenIT
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Modeling the progression of SARS-CoV-2 infection in patients with COVID-19 risk factors through predictive analysis
This article has 2 authors:Reviewed by ScreenIT
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Severity of COVID-19 at elevated exposure to perfluorinated alkylates
This article has 8 authors:Reviewed by ScreenIT
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Insights into the molecular mechanism of anticancer drug ruxolitinib repurposable in COVID-19 therapy
This article has 2 authors:Reviewed by ScreenIT
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The N-glycosylation sites and Glycan-binding ability of S-protein in SARS-CoV-2 Coronavirus
This article has 7 authors:Reviewed by ScreenIT
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Modelling the COVID-19 Fatality Rate in England and its Regions
This article has 1 author:Reviewed by ScreenIT