Differential inflammatory potential of particulate matter (PM) size fractions from Imperial Valley, CA.

Particulate matter (PM) in Imperial Valley originates from a variety of sources such as agriculture, traffic at the border crossing, emissions from the cross-border city of Mexicali, and the drying lakebed of the Salton Sea. Dust storms in Imperial Valley, California regularly lead to exceedances of the federal air quality standards for PM10 (diameter less than 10 microns). To determine if there are differences in the composition and biological response to Imperial County PM by size, ambient PM samples were collected from a sampling unit stationed in the northern-most part of the valley, South of the Salton Sea. Ultrafine, fine, and coarse PM samples were collected and extracted separately. Chemical composition of each size fraction was obtained after extraction by using several analytical techniques, and biological response was measured by exposing a cell line of macrophages to particles and quantifying subsequent gene expression. Biological measurements demonstrated coarse PM induced an inflammatory response in macrophages measured in increases of inflammatory markers IL-1ß, IL-6, IL-8 and CXCL2 expression, whereas ultrafine and fine PM only demonstrated significant increases in expression of CYP1a1. These differential responses were due not only to particle size, but to the distinct chemical profiles of each size faction as well. Community groups in Imperial Valley have already completed several projects to learn more about local air quality, giving residents access to data that provides real-time levels of PM2.5 and PM10 as well as recommendations on health-based practices dependent on the current AQI (air quality index). However, to date there is no information on the composition or toxicity of ambient PM from the region. The data presented here could provide more definitive information on the toxicity of PM by size, and further inform the community on local air quality.

Health status and return to work in trauma patients at 3 and 6 months post-discharge: an Australian major trauma centre study.

INTRODUCTION: The aim of this study was to describe post-discharge outcomes, and determine predictors of 3 and 6 months health status outcomes in a population of trauma patients at an inner city major trauma centre. METHODS: This was a prospective cohort study of adult trauma patients admitted to this hospital with 3 and 6 months post-discharge outcomes assessment. Outcome measures were the Physical Component Scores (PCS) and Mental Component Scores (MCS) of the Short Form 12, EQ-5D, and return to work (in any capacity) if working prior to injury. Repeated measures mixed models and generalised estimating equation models were used to determine predictors of outcomes at 3 and 6 months. RESULTS: One hundred and seventy-nine patients were followed up. Patients with lower limb injuries reported lower mean PCS scores between 3 and 6 months (coefficient -4.21, 95 % CI -7.58, -0.85) than those without lower limb injuries. Patients involved in pedestrian incidents or assaults and those with pre-existing mental health diagnoses reported lower mean MCS scores. In adjusted models upper limb injuries were associated with reduced odds of return to work at 3 and 6 months (OR 0.20, 95 % CI 0.07, 0.57) compared to those without upper limb injuries. DISCUSSION: Predictors of poorer physical health status were lower limb injuries and predictors of mental health were related to the mechanism of injury and past mental health. Increasing injury severity score and upper limb injuries were the only predictors of reduced return to work. The results provide insights into the feasibility of routine post-discharge follow-up at a trauma service level.

Refining the trauma triage algorithm at an Australian major trauma centre: derivation and internal validation of a triage risk score.

PURPOSE: To derive and internally validate a clinical prediction rule for trauma triage. METHODS: Ambulance presentations requiring trauma team activation between 2007 and 2011 at a single inner city major trauma centre were analysed. The primary outcome was major trauma, defined as Injury Severity Score >15, intensive care unit admission or in-hospital death. Demographic details, vital signs on arrival at hospital, mechanism of injury and injured body regions were used in the modelling process. Multivariable logistic regression was used on a randomly selected derivation sample. Receiver operating characteristic (ROC) analysis and Hosmer-Lemeshow tests were used to assess the discrimination and calibration of the derived model. The model was further tested using bootstrapping cross-validation. RESULTS: A total of 3027 patients were identified. Predictors selected for the prediction model were age ≥65 years (OR 1.58, 95 %CI 1.08-2.32, p = 0.02), abnormal vital signs (OR 3.72, 95 %CI 2.64-5.25), Glasgow Coma Scale score ≤13 (OR 14, 95 %CI 9.23-23.34 p < 0.001), penetrating injury (OR 5.13, 95 %CI 2.76-9.54, p < 0.001), multiregion injury (OR 4.72 95 %CI 3.45-6.46, p < 0.001), falls (OR 1.51 95 %CI 1.06-2.15, p = 0.02) and motor vehicle crashes (OR 0.56, 95 %CI 0.35-0.90, p = 0.02). The ROC area under the curve (AUC) for the final model was 0.85 (95 %CI 0.83-0.87) with a Hosmer-Lemeshow test statistic p = 0.83. Bootstrapping cross-validation demonstrated an identical AUC. CONCLUSION: We have derived and internally validated a trauma risk prediction rule using trauma registry data. This may assist with the formulation of revised local and regional trauma triage protocols. External validation is required before implementation.

Conditional sampling for source-oriented toxicological studies using a single particle mass spectrometer.

Current particulate matter regulations control the mass concentration of particles in the atmosphere regardless of composition, but some primary and/or secondary particulate matter components are no doubt more or less toxic than others. Testing direct emissions of pollutants from different sources neglects atmospheric transformations that may increase or decrease their toxicity. This work describes a system that conditionally samples particles from the atmosphere depending on the sources or source combinations that predominate at the sampling site at a given time. A single particle mass spectrometer (RSMS-II), operating in the 70-150 nm particle diameter range, continuously provides the chemical composition of individual particles. The mass spectra indicate which sources are currently affecting the site. Ten ChemVol samplers are each assigned one source or source combination, and the RSMS-II controls which one operates depending on the sources or source combinations observed. By running this system for weeks at a time, sufficient sample is collected by the ChemVols for comparative toxicological studies. This paper describes the instrument and algorithmic design, implementation, and first results from operating this system in Fresno, CA, during summer 2008 and winter 2009.