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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A systematic review and meta-analysis of Long COVID symptoms
This article has 11 authors:Reviewed by ScreenIT
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13 cis retinoic acid improved the outcomes of COVID-19 patients. A randomized clinical trial
This article has 4 authors:Reviewed by ScreenIT
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SARS-CoV-2 remodels the Golgi apparatus to facilitate viral assembly and secretion
This article has 13 authors:Reviewed by Review Commons, ScreenIT
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Defining Factors that Influence vaccine-induced, cross-variant neutralizing antibodies for SARS-CoV-2 in Asians
This article has 21 authors:Reviewed by ScreenIT
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The relative impact of vaccination momentum on COVID-19 rates of death in the USA in 2020/2021. The forgotten role of population wellness
This article has 1 author:Reviewed by ScreenIT
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Extensive neutralization against SARS-CoV-2 variants elicited by Omicron-specific subunit vaccine booster
This article has 15 authors:Reviewed by ScreenIT
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High frequency of anti-DSG 2 antibodies in post COVID-19 serum samples
This article has 7 authors:Reviewed by ScreenIT
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Diagnostic accuracy of non-invasive detection of SARS-CoV-2 infection by canine olfaction
This article has 15 authors:Reviewed by ScreenIT
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Three Separate Spike Antigen Exposures by COVID-19 Vaccination or SARS-CoV-2 Infection Elicit Strong Humoral Immune Responses in Healthcare Workers
This article has 13 authors:Reviewed by ScreenIT
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Hetero-bivalent nanobodies provide broad-spectrum protection against SARS-CoV-2 variants of concern including Omicron
This article has 15 authors:Reviewed by ScreenIT