Health effects associated with particulate matter (PM) show seasonal variations. We hypothesized that these heterogeneous effects may be attributed partly to the differences in the elemental composition of PM. Normal human bronchial epithelial (NHBE) cells and alveolar macrophages (AMs) were exposed to equal mass of coarse [PM with aerodynamic diameter of 2.5-10 µm (PM 2.5-10 )], fine (PM 2.5 ), and ultrafine (PM < 0.1 ) ambient PM from Chapel Hill, North Carolina, during October 2001 (fall) and January (winter), April (spring), and July (summer) 2002. Production of interleukin (IL)-8, IL-6, and reactive oxygen species (ROS) was measured. Coarse PM was more potent in inducing cytokines, but not ROSs, than was fine or ultrafine PM. In AMs, the October coarse PM was the most potent stimulator for IL-6 release, whereas the July PM consistently stimulated the highest ROS production measured by dichlorofluorescein acetate and dihydrorhodamine 123 (DHR). In NHBE cells, the January and the October PM were consistently the strongest stimulators for IL-8 and ROS, respectively. The July PM increased only ROS measured by DHR. PM had minimal effects on chemiluminescence. Principal-component analysis on elemental constituents of PM of all size fractions identified two factors, Cr/Al/Si/Ti/Fe/Cu and Zn/As/V/Ni/Pb/Se, with only the first factor correlating with IL-6/IL-8 release. Among the elements in the first factor, Fe and Si correlated with IL-6 release, whereas Cr correlated with IL-8 release. These positive correlations were confirmed in additional experiments with PM from all 12 months. These results indicate that elemental constituents of PM may in part account for the seasonal variations in PM-induced adverse health effects related to lung inflammation. Key words: air pollutant, interleukin-6, interleukin-8, reactive oxygen species.

BACKGROUND:The purpose of this analysis is to provide evidence-based and consensusderived guidance for clinicians to improve individual diagnostic decision-making for hypersensitivity pneumonitis (HP) and decrease diagnostic practice variability.STUDY DESIGN AND METHODS: Approved panelists developed key questions regarding the diagnosis of HP using the PICO (Population, Intervention, Comparator, Outcome) format. MEDLINE (via PubMed) and the Cochrane Library were systematically searched for relevant literature, which was supplemented by manual searches. References were screened for inclusion, and vetted evaluation tools were used to assess the quality of included studies, to extract data, and to grade the level of evidence supporting each recommendation or statement. The quality of the evidence was assessed using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach. Graded recommendations and ungraded consensus-based statements were drafted and voted on using a modified Delphi technique to achieve consensus. A diagnostic algorithm is provided, using supporting data from the recommendations where possible, along with expert consensus to help physicians gauge the probability of HP. RESULTS:The systematic review of the literature based on 14 PICO questions resulted in 14 key action statements: 12 evidence-based, graded recommendations and 2 ungraded consensusbased statements. All evidence was of very low quality.INTERPRETATION: Diagnosis of HP should employ a patient-centered approach and include a multidisciplinary assessment that incorporates the environmental and occupational exposure history and CT pattern to establish diagnostic confidence prior to considering BAL and/or lung biopsy. Criteria are presented to facilitate diagnosis of HP. Additional research is needed on the performance characteristics and generalizability of exposure assessment tools and traditional and new diagnostic tests in modifying clinical decision-making for HP, particularly among those with a provisional diagnosis.

Background-Ventilation heterogeneity is impossible to detect with spirometry. Alternatively, pulmonary ventilation can be imaged 3-dimensionally using inhaled 129 Xe MRI. To date such images have been quantified primarily based on ventilation defects. Here, we introduce a robust means to transform 129 Xe MRI scans such that the underlying ventilation distribution and its heterogeneity can be quantified.