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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Anticancer pan-ErbB inhibitors reduce inflammation and tissue injury and exert broad-spectrum antiviral effects
This article has 34 authors:Reviewed by ScreenIT
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KL-MOB: automated COVID-19 recognition using a novel approach based on image enhancement and a modified MobileNet CNN
This article has 6 authors:Reviewed by ScreenIT
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Immunomodulatory LncRNA on antisense strand of ICAM-1 augments SARS-CoV-2 infection-associated airway mucoinflammatory phenotype
This article has 12 authors:Reviewed by ScreenIT
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Risk of COVID-19 variant importation – How useful are travel control measures?
This article has 4 authors:Reviewed by ScreenIT
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Long-term Symptoms After SARS-CoV-2 Infection in Children and Adolescents
This article has 4 authors:Reviewed by ScreenIT
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Broad neutralization against SARS-CoV-2 variants induced by a modified B.1.351 protein-based COVID-19 vaccine candidate
This article has 21 authors:Reviewed by ScreenIT
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SARS-CoV-2 Vaccine-Induced Antibody Response and Reinfection in Persons with Past Natural Infection
This article has 6 authors:Reviewed by ScreenIT
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Predictors of Social Distancing and Mask-Wearing Behavior: Panel Survey in Seven U.S. States
This article has 4 authors:Reviewed by ScreenIT
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Distinct immune responses in patients infected with influenza or SARS-CoV-2, and in COVID-19 survivors, characterised by transcriptomic and cellular abundance differences in blood
This article has 20 authors:Reviewed by ScreenIT
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Computational modelling of COVID-19: A study of compliance and superspreaders
This article has 3 authors:Reviewed by ScreenIT