8 research outputs found
Induction of Inflammation in Vascular Endothelial Cells by Metal Oxide Nanoparticles: Effect of Particle Composition
BACKGROUND: The mechanisms governing the correlation between exposure to ultrafine particles and the increased incidence of cardiovascular disease remain unknown. Ultrafine particles appear to cross the pulmonary epithelial barrier into the bloodstream, raising the possibility of direct contact with the vascular endothelium. OBJECTIVES: Because endothelial inflammation is critical for the development of cardiovascular pathology, we hypothesized that direct exposure of human aortic endothelial cells (HAECs) to ultrafine particles induces an inflammatory response and that this response depends on particle composition. METHODS: To test the hypothesis, we incubated HAECs for 1–8 hr with different concentrations (0.001–50 μg/mL) of iron oxide (Fe(2)O(3)), yttrium oxide (Y(2)O(3)), and zinc oxide (ZnO) nanoparticles and subsequently measured mRNA and protein levels of the three inflammatory markers intra-cellular cell adhesion molecule-1, interleukin-8, and monocyte chemotactic protein-1. We also determined nanoparticle interactions with HAECs using inductively coupled plasma mass spectrometry and transmission electron microscopy. RESULTS: Our data indicate that nanoparticle delivery to the HAEC surface and uptake within the cells correlate directly with particle concentration in the cell culture medium. All three types of nanoparticles are internalized into HAECs and are often found within intracellular vesicles. Fe(2)O(3) nanoparticles fail to provoke an inflammatory response in HAECs at any of the concentrations tested; however, Y(2)O(3) and ZnO nanoparticles elicit a pronounced inflammatory response above a threshold concentration of 10 μg/mL. At the highest concentration, ZnO nanoparticles are cytotoxic and lead to considerable cell death. CONCLUSIONS: These results demonstrate that inflammation in HAECs following acute exposure to metal oxide nanoparticles depends on particle composition
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Induction of inflammation in vascular endothelial cells by metal oxide nanoparticles: effect of particle composition.
BackgroundThe mechanisms governing the correlation between exposure to ultrafine particles and the increased incidence of cardiovascular disease remain unknown. Ultrafine particles appear to cross the pulmonary epithelial barrier into the bloodstream, raising the possibility of direct contact with the vascular endothelium.ObjectivesBecause endothelial inflammation is critical for the development of cardiovascular pathology, we hypothesized that direct exposure of human aortic endothelial cells (HAECs) to ultrafine particles induces an inflammatory response and that this response depends on particle composition.MethodsTo test the hypothesis, we incubated HAECs for 1-8 hr with different concentrations (0.001-50 mug/mL) of iron oxide (Fe(2)O(3)), yttrium oxide (Y(2)O(3)), and zinc oxide (ZnO) nanoparticles and subsequently measured mRNA and protein levels of the three inflammatory markers intra-cellular cell adhesion molecule-1, interleukin-8, and monocyte chemotactic protein-1. We also determined nanoparticle interactions with HAECs using inductively coupled plasma mass spectrometry and transmission electron microscopy.ResultsOur data indicate that nanoparticle delivery to the HAEC surface and uptake within the cells correlate directly with particle concentration in the cell culture medium. All three types of nanoparticles are internalized into HAECs and are often found within intracellular vesicles. Fe(2)O(3) nanoparticles fail to provoke an inflammatory response in HAECs at any of the concentrations tested; however, Y(2)O(3) and ZnO nanoparticles elicit a pronounced inflammatory response above a threshold concentration of 10 mug/mL. At the highest concentration, ZnO nanoparticles are cytotoxic and lead to considerable cell death.ConclusionsThese results demonstrate that inflammation in HAECs following acute exposure to metal oxide nanoparticles depends on particle composition
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Repurposing Clinical Decision Support System Data to Measure Dosing Errors and Clinician-Level Quality of Care.
We aimed to develop and validate an instrument to detect hospital medication prescribing errors using repurposed clinical decision support system data. Despite significant efforts to eliminate medication prescribing errors, these events remain common in hospitals. Data from clinical decision support systems have not been used to identify prescribing errors as an instrument for physician-level performance. We evaluated medication order alerts generated by a knowledge-based electronic prescribing system occurring in one large academic medical center's acute care facilities for patient encounters between 2009 and 2012. We developed and validated an instrument to detect medication prescribing errors through a clinical expert panel consensus process to assess physician quality of care. Six medication prescribing alert categories were evaluated for inclusion, one of which - dose - was included in the algorithm to detect prescribing errors. The instrument was 93% sensitive (recall), 51% specific, 40% precise, 62% accurate, with an F1 score of 55%, positive predictive value of 96%, and a negative predictive value of 32%. Using repurposed electronic prescribing system data, dose alert overrides can be used to systematically detect medication prescribing errors occurring in an inpatient setting with high sensitivity