Investigating T cell-derived extracellular vesicles as biomarkers of disease activity, axonal injury, and disability in multiple sclerosis.

Introduction: Multiple sclerosis (MS) is a chronic immune-mediated demyelinating disease of the CNS, whereby clinical disease activity is primarily monitored by magnetic resonance imaging (MRI).

Methods: Given the limitations associated with implementing and acquiring novel and emerging imaging biomarkers in routine clinical practice, the discovery of biofluid biomarkers may offer a more simple and cost-effective measure that would improve accessibility, standardization, and patient care. Extracellular vesicles (EVs) are nanoparticles secreted from cells under both homeostatic and pathological states, and have been recently investigated as biomarkers in MS.

What are the experiences of Aboriginal and/or Torres Strait Islander midwifery students and midwives? A scoping review.

Background: Aboriginal and/or Torres Strait Islander midwives are underrepresented within the midwifery workforce and is likely compounded by lower graduate rates. This review is a part of the Midwifery Futures Project. It explores the experiences of Aboriginal and/or Torres Strait Islander midwives and midwifery students to illuminate issues impacting work and study and uncover successful strategies towards addressing current disparities.

Association between insurance type, clinical characteristics, and healthcare use in adults with congenital heart disease.

Introduction: Adults with congenital heart disease (CHD) represent a heterogeneous and growing population with high healthcare utilization. We sought to understand the association between insurance type, healthcare use, and outcomes among adults with CHD in Oregon.

Methods: The Oregon All Payers All Claims database from 2010 to 2017 was queried for adults aged 18-65 in 2014 with ICD-9 or 10 codes consistent with CHD; patient demographics, comorbidities, healthcare use, and disease severity were identified.

Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion.

Quantitative pupillometry has been proposed as an objective means to diagnose acute sports-related concussion (SRC). To assess the diagnostic accuracy of a smartphone-based quantitative pupillometer in the acute diagnosis of SRC. Division I college football players had baseline pupillometry including pupillary light reflex (PLR) parameters of maximum resting diameter, minimum diameter after light stimulus, percent change in pupil diameter, latency of pupil constriction onset, mean constriction velocity, maximum constriction velocity, and mean dilation velocity using a smartphone-based app.