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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CAR Macrophages for SARS-CoV-2 Immunotherapy
This article has 7 authors:Reviewed by ScreenIT
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Prediction of severe adverse events, modes of action and drug treatments for COVID-19’s complications
This article has 4 authors:Reviewed by ScreenIT
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Applying the unified models of ecology to forecast epidemics, with application to Covid-19
This article has 2 authors:Reviewed by ScreenIT
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Identification of drugs associated with reduced severity of COVID-19 – a case-control study in a large population
This article has 10 authors:Reviewed by ScreenIT
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Countries should aim to lower the reproduction number ℛ close to 1.0 for the short-term mitigation of COVID-19 outbreaks
This article has 1 author:Reviewed by ScreenIT
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Validation of Saliva and Self-Administered Nasal Swabs for COVID-19 Testing
This article has 11 authors:Reviewed by ScreenIT
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Robust neutralizing antibodies to SARS-CoV-2 infection persist for months
This article has 18 authors:Reviewed by ScreenIT
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Betulonic Acid Derivatives Interfering with Human Coronavirus 229E Replication via the nsp15 Endoribonuclease
This article has 12 authors:Reviewed by ScreenIT
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Critical Interactions Between the SARS-CoV-2 Spike Glycoprotein and the Human ACE2 Receptor
This article has 7 authors:Reviewed by ScreenIT
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Insufficient fibrinolysis in COVID-19: a systematic review of thrombolysis based on meta-analysis and meta-regression
This article has 8 authors:Reviewed by ScreenIT