The delivery of therapeutics to the central nervous system (CNS) remains a major challenge in part due to the presence of the blood-brain barrier (BBB). Recently, cell-derived vesicles, particularly exosomes, have emerged as an attractive vehicle for targeting drugs to the brain, but whether or how they cross the BBB remains unclear. Here, we investigated the interactions between exosomes and brain microvascular endothelial cells (BMECs) in vitro under conditions that mimic the healthy and inflamed BBB in vivo. Transwell assays revealed that luciferase-carrying exosomes can cross a BMEC monolayer under stroke-like, inflamed conditions (TNF-α activated) but not under normal conditions. Confocal microscopy showed that exosomes are internalized by BMECs through endocytosis, co-localize with endosomes, in effect primarily utilizing the transcellular route of crossing. Together, these results indicate that cell-derived exosomes can cross the BBB model under stroke-like conditions in vitro. This study encourages further development of engineered exosomes as drug delivery vehicles or tracking tools for treating or monitoring neurological diseases.

Figures S1-S16 and Table S1 and S2, which depict additional results (PDF) The authors declare the following competing financial interest(s): W.Z. is a co-founder of Velox Biosystems Inc., Baylx Inc., and Amberstone Biosciences Inc. J.L. has equity in Codiak BioSciences and holds the rights to multiple extracellular vesicle diagnostics and therapeutics patents.

Objective The purpose of this study was to develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease. Design Retrospective cohort. Setting Tertiary care medical center. Patients Patients admitted to the hospital for ≥48 hours from 1-1-2003 through 12-31-2003. Methods Data were collected electronically from the hospital’s Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients’ risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic (ROC) curve was calculated to evaluate potential risk cut-offs. Results 35,350 admissions with 329 CDI cases were included. Variables in the risk prediction model were age, CDI pressure, admissions in previous 60 days, modified Acute Physiology Score, days on high risk antibiotics, low albumin, admission to an ICU, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index=0.88; Brier score 0.009). Conclusions The CDI risk prediction model performed well. Further study is needed to determine if it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs.

Healthcare-associated cSSSIs are common and are likely to be caused by gram-negative organisms. Mixed infections carry a >2-fold greater risk of inappropriate treatment. Healthcare-associated cSSSIs are associated with increased mortality and prolonged length of hospital stay, compared with community-acquired cSSSIs.