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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Comparative infection and pathogenesis of SARS-CoV-2 Omicron and Delta variants in aged and young Syrian hamsters
This article has 4 authors:Reviewed by ScreenIT
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Viral cultures, Polymerase Chain Reaction Cycle Threshold Values and Viral Load Estimation for SARS-CoV-2 Infectious Potential Assessment in Hematopoietic Stem Cell and Solid Organ Transplant Patients: A Systematic Review
This article has 10 authors:Reviewed by ScreenIT
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Severe Neuro-COVID is associated with peripheral immune signatures, autoimmunity and neurodegeneration: a prospective cross-sectional study
This article has 32 authors:Reviewed by ScreenIT
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Comparison of Rapid Antigen Tests' Performance Between Delta and Omicron Variants of SARS-CoV-2
This article has 34 authors:Reviewed by ScreenIT
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Variability in SARS-Cov-2 IgG Antibody Affinity To Omicron and Delta Variants in Convalescent and Community mRNA Vaccinated Individuals
This article has 4 authors:Reviewed by ScreenIT
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Assessment of potential risk factors for COVID-19 among health care workers in a health care setting in Delhi, India -a cohort study
This article has 12 authors:Reviewed by ScreenIT
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Cross-cultural adaptation and validation of the “COVID Stress Scales” in Greek
This article has 7 authors:Reviewed by ScreenIT
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Unquantifiably low aldosterone concentrations are prevalent in hospitalised COVID-19 patients but may not be revealed by chemiluminescent immunoassay
This article has 7 authors:Reviewed by ScreenIT
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A structural dynamic explanation for observed escape of SARS-CoV-2 BA.2 variant mutation S371L/F
This article has 4 authors:Reviewed by ScreenIT
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Stable nebulization and muco‐trapping properties of regdanvimab/ IN ‐006 support its development as a potent, dose‐saving inhaled therapy for COVID ‐19
This article has 16 authors:Reviewed by ScreenIT