Alveolar macrophages are early targets of mumps virus.

Formerly a common childhood pathogen, mumps virus (MuV) remains active worldwide, despite relatively high vaccine coverage. MuV is thought to infect the upper respiratory tract before disseminating to other organs; however, the early cellular targets of MuV in vivo are unknown. To address this, we generated a green fluorescent protein (GFP)-tagged vaccine strain (JL5) of MuV to infect leukocytic cell lines and found that replication was greatest in monocytes.

Impact of Anti-CD4 Autoantibodies on Immune Reconstitution in People With Advanced Human Immunodeficiency Virus.

Background: Despite suppressive antiretroviral therapy (ART), 15%-30% of people with human immunodeficiency virus (HIV) experience a limited recovery of CD4 T cells. Although autoantibodies against the CD4 receptor have previously been identified in people with HIV (PWH), little is known about their longitudinal impact on CD4 T-cell reconstitution.

Methods: Anti-CD4 autoantibodies were evaluated by the fluid-phase luciferase immunoprecipitation systems immunoassay in ART-naive people with advanced HIV (CD4 count ≤100 cells/µL), PWH with CD4 count >200 cells/µL, long-term nonprogressors, people with idiopathic CD4 lymphopenia, people with autoimmune lymphoproliferative syndrome, and healthy volunteers without HIV.

Factors influencing confidence in tracheal intubation among neonatal trainees: A questionnaire-based study.

Aim: Tracheal intubation (TI) is pivotal in managing critically ill neonates. This study aims to investigate the disparities in exposure and training techniques that affect self-perceived confidence in neonatal fellows concerning TI.

Methods: A comprehensive, structured questionnaire-based survey was conducted among neonatal trainees from October to November 2022.

ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports.

Accurately interpreting medical images and writing radiology reports is a critical but challenging task in healthcare. Both human-written and AI-generated reports can contain errors, ranging from clinical inaccuracies to linguistic mistakes. To address this, we introduce ReXErr, a methodology that leverages Large Language Models to generate representative errors within chest X-ray reports.