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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Ischemic stroke in COVID-19: An urgent need for early identification and management
This article has 8 authors:Reviewed by ScreenIT
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Preliminary analysis of scRNA sequencing of children’s lung tissues excludes the expression of SARS-CoV-2 entry related genes as the key reason for the milder syndromes of COVID-19 in children
This article has 15 authors:Reviewed by ScreenIT
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Organizing pneumonia of COVID-19: Time-dependent evolution and outcome in CT findings
This article has 17 authors:Reviewed by ScreenIT
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The fate of SARS-COV-2 in WWTPS points out the sludge line as a suitable spot for detection of COVID-19
This article has 7 authors:Reviewed by ScreenIT
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Simeprevir potently suppresses SARS-CoV-2 replication and synergizes with remdesivir
This article has 33 authors:Reviewed by ScreenIT
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Structure, function and variants analysis of the androgen-regulated TMPRSS2 , a drug target candidate for COVID-19 infection
This article has 5 authors:Reviewed by ScreenIT
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Study of cell to cell transmission of SARS CoV 2 virus particle using gene network from microarray data
This article has 3 authors:Reviewed by ScreenIT
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An Immunoinformatics Study to Predict Epitopes in the Envelope Protein of SARS-COV-2
This article has 2 authors:Reviewed by ScreenIT
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T-cell hyperactivation and paralysis in severe COVID-19 infection revealed by single-cell analysis
This article has 5 authors:Reviewed by ScreenIT
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Human H-ferritin presenting RBM of spike glycoprotein as potential vaccine of SARS-CoV-2
This article has 14 authors:Reviewed by ScreenIT