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
-
Engineered ACE2 counteracts vaccine-evading SARS-CoV-2 Omicron variant
This article has 18 authors:Reviewed by ScreenIT
-
COVID-19: Salient Aspects of Coronavirus Infection, Vaccines and Vaccination Testing and their Implications
This article has 2 authors:Reviewed by ScreenIT
-
Human 14-3-3 Proteins Site-selectively Bind the Mutational Hotspot Region of SARS-CoV-2 Nucleoprotein Modulating its Phosphoregulation
This article has 8 authors:Reviewed by ScreenIT
-
Machine Learning Guided Design of High-Affinity ACE2 Decoys for SARS-CoV-2 Neutralization
This article has 4 authors:Reviewed by ScreenIT
-
Establishment of a stable SARS-CoV-2 replicon system for application in high-throughput screening
This article has 7 authors:Reviewed by ScreenIT
-
Two doses of mRNA vaccine elicit cross-neutralizing memory B-cells against SARS-CoV-2 Omicron variant
This article has 19 authors:Reviewed by ScreenIT
-
Waning immune responses against SARS-CoV-2 among vaccinees in Hong Kong
This article has 13 authors:Reviewed by ScreenIT
-
Interferon-induced transmembrane protein 3 (IFITM3) limits lethality of SARS-CoV-2 in mice
This article has 15 authors:Reviewed by ScreenIT
-
Rapid longitudinal SARS-CoV-2 intra-host emergence of novel haplotypes regardless of immune deficiencies
This article has 21 authors:Reviewed by ScreenIT
-
Troponin and short-term mortality in hospitalised patients with COVID-19 infection: a retrospective study in an inner-city London hospital
This article has 6 authors:Reviewed by ScreenIT