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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Rapid detection of novel coronavirus/Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) by reverse transcription-loop-mediated isothermal amplification
This article has 4 authors:Reviewed by ScreenIT
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Rapid Detection of COVID-19 Coronavirus Using a Reverse Transcriptional Loop-Mediated Isothermal Amplification (RT-LAMP) Diagnostic Platform
This article has 8 authors:Reviewed by ScreenIT
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Cryo-EM structures of HKU2 and SADS-CoV spike glycoproteins and insights into coronavirus evolution
This article has 4 authors:Reviewed by ScreenIT
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Comparative study of the lymphocyte change between COVID-19 and non-COVID-19 pneumonia cases suggesting uncontrolled inflammation might not be the main reason of tissue injury
This article has 18 authors:Reviewed by ScreenIT
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Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study
This article has 24 authors:Reviewed by ScreenIT
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Clinical characteristics of 51 patients discharged from hospital with COVID-19 in Chongqing,China
This article has 14 authors:Reviewed by ScreenIT
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COVID-19 in Wuhan: Sociodemographic characteristics and hospital support measures associated with the immediate psychological impact on healthcare workers
This article has 16 authors:Reviewed by ScreenIT
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Profiling ACE2 expression in colon tissue of healthy adults and colorectal cancer patients by single-cell transcriptome analysis
This article has 16 authors:Reviewed by ScreenIT
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Public Exposure to Live Animals, Behavioural Change, and Support in Containment Measures in response to COVID-19 Outbreak: a population-based cross sectional survey in China
This article has 8 authors:Reviewed by ScreenIT
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A Note on COVID-19 Diagnosis Number Prediction Model in China
This article has 6 authors:Reviewed by ScreenIT