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
-
Changes of Small Non-coding RNAs by Severe Acute Respiratory Syndrome Coronavirus 2 Infection
This article has 15 authors:Reviewed by ScreenIT
-
Temporal associations of B and T cell immunity with robust vaccine responsiveness in a 16-week interval BNT162b2 regimen
This article has 33 authors:Reviewed by ScreenIT
-
Reduced sera neutralization to Omicron SARS-CoV-2 by both inactivated and protein subunit vaccines and the convalescents
This article has 12 authors:Reviewed by ScreenIT
-
Possible Cross-Reactivity of Feline and White-Tailed Deer Antibodies against the SARS-CoV-2 Receptor Binding Domain
This article has 12 authors:Reviewed by ScreenIT
-
SARS-COV-2 Omicron variant predicted to exhibit higher affinity to ACE-2 receptor and lower affinity to a large range of neutralizing antibodies, using a rapid computational platform
This article has 8 authors:Reviewed by ScreenIT
-
A Computational Dissection of Spike protein of SARS-CoV-2 Omicron Variant
This article has 6 authors:Reviewed by ScreenIT
-
Modeling and predicting the overlap of B- and T-cell receptor repertoires in healthy and SARS-CoV-2 infected individuals
This article has 4 authors:This article has been curated by 1 group: -
Key benefits of dexamethasone and antibody treatment in COVID-19 hamster models revealed by single-cell transcriptomics
This article has 25 authors:Reviewed by ScreenIT
-
Pre-clinical evaluation of antiviral activity of nitazoxanide against SARS-CoV-2
This article has 21 authors:Reviewed by ScreenIT
-
Modeling the dynamics of within-host viral infection and evolution predicts quasispecies distributions and phase boundaries separating distinct classes of infections
This article has 3 authors:Reviewed by ScreenIT