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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Expression of ACE2, the SARS-CoV-2 Receptor, and TMPRSS2 in Prostate Epithelial Cells
This article has 4 authors:Reviewed by ScreenIT
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Experience With Hydroxychloroquine and Azithromycin in the Coronavirus Disease 2019 Pandemic: Implications for QT Interval Monitoring
This article has 11 authors:Reviewed by ScreenIT
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Elevated expression of ACE2 in tumor‐adjacent normal tissues of cancer patients
This article has 2 authors:Reviewed by ScreenIT
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Insight towards the effect of the multibasic cleavage site of SARS-CoV-2 spike protein on cellular proteases
This article has 4 authors:Reviewed by ScreenIT
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Estimating COVID-19-Related Infections, Deaths, and Hospitalizations in Iran Under Different Physical Distancing and Isolation Scenarios
This article has 13 authors:Reviewed by ScreenIT
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Rapid identification of SARS-CoV-2-infected patients at the emergency department using routine testing
This article has 10 authors:Reviewed by ScreenIT
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The Association of Lymphocyte count, CRP, D-Dimer, and LDH with Severe Coronavirus Disease 2019 (COVID-19): A Meta-Analysis
This article has 10 authors:Reviewed by ScreenIT
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A phased approach to unlocking during the COVID‐19 pandemic—Lessons from trend analysis
This article has 6 authors:Reviewed by ScreenIT
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COVID-19, An early investigation from exposure to treatment outcomes in Tehran, Iran
This article has 17 authors:Reviewed by ScreenIT
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Prevalence of SARS-CoV-2 infection in previously undiagnosed health care workers in New Jersey, at the onset of the U.S. COVID-19 pandemic
This article has 13 authors:Reviewed by ScreenIT