A 10-year examination of people with psychosis in American television programing: are representations improving over time?

Background: Media portrayals inform understandings of mental illness; yet little research has investigated representations of characters with psychosis in fictional television programming.

Aims: This study examined the valence and trends regarding representations of people with psychosis in popular fictional television programing in the United States, one of the most influential markets in the world.

Methods: A content analysis was conducted of the 50 most-watched American primetime fictional television shows from 2011 to 2021.

Insights of SEDRIC, the Surveillance and Epidemiology of Drug-Resistant Infections Consortium.

The increasing threat from infection with drug-resistant pathogens is among the most serious public health challenges of our time. Formed by Wellcome in 2018, the Surveillance and Epidemiology of Drug-Resistant Infections Consortium (SEDRIC) is an international think tank whose aim is to inform policy and change the way countries track, share, and analyse data relating to drug-resistant infections, by defining knowledge gaps and identifying barriers to the delivery of global surveillance. SEDRIC delivers its aims through discussions and analyses by world-leading scientists that result in recommendations and advocacy to Wellcome and others.

Optimizing nanopore adaptive sampling for pneumococcal serotype surveillance in complex samples using the graph-based GNASTy algorithm.

Serotype surveillance of (the pneumococcus) is critical for understanding the effectiveness of current vaccination strategies. However, existing methods for serotyping are limited in their ability to identify the co-carriage of multiple pneumococci and detect novel serotypes. To develop a scalable and portable serotyping method that overcomes these challenges, we employed Nanopore Adaptive Sampling (NAS), an on-sequencer enrichment method that selects for target DNA in real-time, for direct detection of in complex samples.

Ribosome phenotypes for rapid classification of antibiotic-susceptible and resistant strains of Escherichia coli.

Rapid antibiotic susceptibility tests (ASTs) are an increasingly important part of clinical care as antimicrobial resistance (AMR) becomes more common in bacterial infections. Here, we use the spatial distribution of fluorescently labelled ribosomes to detect intracellular changes associated with antibiotic susceptibility in E. coli cells using a convolutional neural network (CNN).