978 resultados para particulate fraction
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We present results from the international field campaign DAURE (Detn. of the sources of atm. Aerosols in Urban and Rural Environments in the Western Mediterranean), with the objective of apportioning the sources of fine carbonaceous aerosols. Submicron fine particulate matter (PM1) samples were collected during Feb.-March 2009 and July 2009 at an urban background site in Barcelona (BCN) and at a forested regional background site in Montseny (MSY). We present radiocarbon (14C) anal. for elemental and org. carbon (EC and OC) and source apportionment for these data. We combine the results with those from component anal. of aerosol mass spectrometer (AMS) measurements, and compare to levoglucosan-based ests. of biomass burning OC, source apportionment of filter data with inorg. compn. + EC + OC, submicron bulk potassium (K) concns., and gaseous acetonitrile concns. At BCN, 87 % and 91 % of the EC on av., in winter and summer, resp., had a fossil origin, whereas at MSY these fractions were 66 % and 79 %. The contribution of fossil sources to org. carbon (OC) at BCN was 40 % and 48 %, in winter and summer, resp., and 31 % and 25 % at MSY. The combination of results obtained using the 14C technique, AMS data, and the correlations between fossil OC and fossil EC imply that the fossil OC at Barcelona is ∼47 % primary whereas at MSY the fossil OC is mainly secondary (∼85 %). Day-to-day variation in total carbonaceous aerosol loading and the relative contributions of different sources predominantly depended on the meteorol. transport conditions. The estd. biogenic secondary OC at MSY only increased by ∼40 % compared to the order-of-magnitude increase obsd. for biogenic volatile org. compds. (VOCs) between winter and summer, which highlights the uncertainties in the estn. of that component. Biomass burning contributions estd. using the 14C technique ranged from similar to slightly higher than when estd. using other techniques, and the different estns. were highly or moderately correlated. Differences can be explained by the contribution of secondary org. matter (not included in the primary biomass burning source ests.), and/or by an over-estn. of the biomass burning OC contribution by the 14C technique if the estd. biomass burning EC/OC ratio used for the calcns. is too high for this region. Acetonitrile concns. correlate well with the biomass burning EC detd. by 14C. K is a noisy tracer for biomass burning. [on SciFinder(R)]
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The aim of this study was to evaluate whether a change of left ventricular ejection fraction (LVEF) depending on percentage of right ventricular pacing is found in a real-life setting of a pacemaker clinic.
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This study investigated the changes in somatic cell counts (SCC) in different fractions of milk, with special emphasis on the foremilk and cisternal milk fractions. Therefore, in Experiment 1, quarter milk samples were defined as strict foremilk (F), cisternal milk (C), first 400 g of alveolar milk (A1), and the remaining alveolar milk (A2). Experiment 2 included 6 foremilk fractions (F1 to F6), consisting of one hand-stripped milk jet each, and the remaining cisternal milk plus the entire alveolar milk (RM). In Experiment 1, changes during milking indicated the importance of the sampled milk fraction for measuring SCC because the decrease in the first 3 fractions (F, C, and A1) was enormous in milk with high total quarter SCC. The decline in SCC from F to C was 50% and was 80% from C to A1. Total quarter SCC presented a value of approximately 20% of SCC in F or 35% of SCC in C. Changes in milk with low or very low SCC were marginal during milking. Fractions F and C showed significant differences in SCC among different total SCC concentrations. These differences disappeared with the alveolar fractions A1 and A2. In Experiment 2, a more detailed investigation of foremilk fractions supported the findings of Experiment 1. A significant decline in the foremilk fractions even of F1 to F6 was observed in high-SCC milk at concentrations >350 x 10(3) cells/mL. Although one of these foremilk fractions presented only 0.1 to 0.2% of the total milk, the SCC was 2- to 3-fold greater than the total quarter milk SCC. Because the trait of interest (SCC) was measured directly by using the DeLaval cell counter (DCC), the quality of measurement was tested. Statistically interesting factors (repeatability, recovery rate, and potential matrix effects of milk) proved that the DCC is a useful tool for identifying the SCC of milk samples, and thus of grading udder health status. Generally, the DCC provides reliable results, but one must consider that SCC even in strict foremilk can differ dramatically from SCC in the total cisternal fraction, and thus also from SCC in the alveolar fraction.
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As a part of the respiratory tissue barrier, lung epithelial cells play an important role against the penetration of the body by inhaled particulate foreign materials. In most cell culture models, which are designed to study particle-cell interactions, the cells are immersed in medium. This does not reflect the physiological condition of lung epithelial cells which are exposed to air, separated from it only by a very thin liquid lining layer with a surfactant film at the air-liquid interface. In this study, A549 epithelial cells were grown on microporous membranes in a two chamber system. After the formation of a confluent monolayer the cells were exposed to air. The morphology of the cells and the expression of tight junction proteins were studied with confocal laser scanning and transmission electron microscopy. Air-exposed cells maintained monolayer structure for 2 days, expressed tight junctions and developed transepithelial electrical resistance. Surfactant was produced and released at the apical side of the air-exposed epithelial cells. In order to study particle-cell interactions fluorescent 1 microm polystyrene particles were sprayed over the epithelial surface. After 4 h, 8.8% of particles were found inside the epithelium. This fraction increased to 38% after 24 h. During all observations, particles were always found in the cells but never between them. In this study, we present an in vitro model of the respiratory tract wall consisting of air-exposed lung epithelial cells covered by a liquid lining layer with a surfactant film to study particle-cell interactions.
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BACKGROUND: Particulate matter <10 mum (PM(10)) from fossil fuel combustion is associated with an increased prevalence of respiratory symptoms in children and adolescents. However, the effect of PM(10) on respiratory symptoms in young children is unclear. METHODS: The association between primary PM(10) (particles directly emitted from local sources) and the prevalence and incidence of respiratory symptoms was studied in a random sample cohort of 4400 Leicestershire children aged 1-5 years surveyed in 1998 and again in 2001. Annual exposure to primary PM(10) was calculated for the home address using the Airviro dispersion model and adjusted odds ratios (ORS) and 95% confidence intervals were calculated for each microg/m(3) increase. RESULTS: Exposure to primary PM(10) was associated with the prevalence of cough without a cold in both 1998 and 2001, with adjusted ORs of 1.21 (1.07 to 1.38) and 1.56 (1.32 to 1.84) respectively. For night time cough the ORs were 1.06 (0.94 to 1.19) and 1.25 (1.06 to 1.47), and for current wheeze 0.99 (0.88 to 1.12) and 1.28 (1.04 to 1.58), respectively. There was also an association between primary PM(10) and new onset symptoms. The ORs for incident symptoms were 1.62 (1.31 to 2.00) for cough without a cold and 1.42 (1.02 to 1.97) for wheeze. CONCLUSION: In young children there was a consistent association between locally generated primary PM(10) and the prevalence and incidence of cough without a cold and the incidence of wheeze which was independent of potential confounders.
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Recent research highlights the promise of remotely-sensed aerosol optical depth (AOD) as a proxy for ground-level PM2.5. Particular interest lies in the information on spatial heterogeneity potentially provided by AOD, with important application to estimating and monitoring pollution exposure for public health purposes. Given the temporal and spatio-temporal correlations reported between AOD and PM2.5 , it is tempting to interpret the spatial patterns in AOD as reflecting patterns in PM2.5 . Here we find only limited spatial associations of AOD from three satellite retrievals with PM2.5 over the eastern U.S. at the daily and yearly levels in 2004. We then use statistical modeling to show that the patterns in monthly average AOD poorly reflect patterns in PM2.5 because of systematic, spatially-correlated error in AOD as a proxy for PM2.5 . Furthermore, when we include AOD as a predictor of monthly PM2.5 in a statistical prediction model, AOD provides little additional information to improve predictions of PM2.5 when included in a model that already accounts for land use, emission sources, meteorology and regional variability. These results suggest caution in using spatial variation in AOD to stand in for spatial variation in ground-level PM2.5 in epidemiological analyses and indicate that when PM2.5 monitoring is available, careful statistical modeling outperforms the use of AOD.
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The last two decades have seen intense scientific and regulatory interest in the health effects of particulate matter (PM). Influential epidemiological studies that characterize chronic exposure of individuals rely on monitoring data that are sparse in space and time, so they often assign the same exposure to participants in large geographic areas and across time. We estimate monthly PM during 1988-2002 in a large spatial domain for use in studying health effects in the Nurses' Health Study. We develop a conceptually simple spatio-temporal model that uses a rich set of covariates. The model is used to estimate concentrations of PM10 for the full time period and PM2.5 for a subset of the period. For the earlier part of the period, 1988-1998, few PM2.5 monitors were operating, so we develop a simple extension to the model that represents PM2.5 conditionally on PM10 model predictions. In the epidemiological analysis, model predictions of PM10 are more strongly associated with health effects than when using simpler approaches to estimate exposure. Our modeling approach supports the application in estimating both fine-scale and large-scale spatial heterogeneity and capturing space-time interaction through the use of monthly-varying spatial surfaces. At the same time, the model is computationally feasible, implementable with standard software, and readily understandable to the scientific audience. Despite simplifying assumptions, the model has good predictive performance and uncertainty characterization.
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We propose a method for diagnosing confounding bias under a model which links a spatially and temporally varying exposure and health outcome. We decompose the association into orthogonal components, corresponding to distinct spatial and temporal scales of variation. If the model fully controls for confounding, the exposure effect estimates should be equal at the different temporal and spatial scales. We show that the overall exposure effect estimate is a weighted average of the scale-specific exposure effect estimates. We use this approach to estimate the association between monthly averages of fine particles (PM2.5) over the preceding 12 months and monthly mortality rates in 113 U.S. counties from 2000-2002. We decompose the association between PM2.5 and mortality into two components: 1) the association between “national trends” in PM2.5 and mortality; and 2) the association between “local trends,” defined as county-specificdeviations from national trends. This second component provides evidence as to whether counties having steeper declines in PM2.5 also have steeper declines in mortality relative to their national trends. We find that the exposure effect estimates are different at these two spatio-temporalscales, which raises concerns about confounding bias. We believe that the association between trends in PM2.5 and mortality at the national scale is more likely to be confounded than is the association between trends in PM2.5 and mortality at the local scale. If the association at the national scale is set aside, there is little evidence of an association between 12-month exposure to PM2.5 and mortality.
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Prospective cohort studies have provided evidence on longer-term mortality risks of fine particulate matter (PM2.5), but due to their complexity and costs, only a few have been conducted. By linking monitoring data to the U.S. Medicare system by county of residence, we developed a retrospective cohort study, the Medicare Air Pollution Cohort Study (MCAPS), comprising over 20 million enrollees in the 250 largest counties during 2000-2002. We estimated log-linear regression models having as outcome the age-specific mortality rate for each county and as the main predictor, the average level for the study period 2000. Area-level covariates were used to adjust for socio-economic status and smoking. We reported results under several degrees of adjustment for spatial confounding and with stratification into by eastern, central and western counties. We estimated that a 10 µg/m3 increase in PM25 is associated with a 7.6% increase in mortality (95% CI: 4.4 to 10.8%). We found a stronger association in the eastern counties than nationally, with no evidence of an association in western counties. When adjusted for spatial confounding, the estimated log-relative risks drop by 50%. We demonstrated the feasibility of using Medicare data to establish cohorts for follow-up for effects of air pollution. Particulate matter (PM) air pollution is a global public health problem (1). In developing countries, levels of airborne particles still reach concentrations at which serious health consequences are well-documented; in developed countries, recent epidemiologic evidence shows continued adverse effects, even though particle levels have declined in the last two decades (2-6). Increased mortality associated with higher levels of PM air pollution has been of particular concern, giving an imperative for stronger protective regulations (7). Evidence on PM and health comes from studies of acute and chronic adverse effects (6). The London Fog of 1952 provides dramatic evidence of the unacceptable short-term risk of extremely high levels of PM air pollution (8-10); multi-site time-series studies of daily mortality show that far lower levels of particles are still associated with short-term risk (5)(11-13). Cohort studies provide complementary evidence on the longer-term risks of PM air pollution, indicating the extent to which exposure reduces life expectancy. The design of these studies involves follow-up of cohorts for mortality over periods of years to decades and an assessment of mortality risk in association with estimated long-term exposure to air pollution (2-4;14-17). Because of the complexity and costs of such studies, only a small number have been conducted. The most rigorously executed, including the Harvard Six Cities Study and the American Cancer Society’s (ACS) Cancer Prevention Study II, have provided generally consistent evidence for an association of long- term exposure to particulate matter air pollution with increased all-cause and cardio-respiratory mortality (2,4,14,15). Results from these studies have been used in risk assessments conducted for setting the U.S. National Ambient Air Quality Standard (NAAQS) for PM and for estimating the global burden of disease attributable to air pollution (18,19). Additional prospective cohort studies are necessary, however, to confirm associations between long-term exposure to PM and mortality, to broaden the populations studied, and to refine estimates by regions across which particle composition varies. Toward this end, we have used data from the U.S. Medicare system, which covers nearly all persons 65 years of age and older in the United States. We linked Medicare mortality data to (particulate matter less than 2.5 µm in aerodynamic diameter) air pollution monitoring data to create a new retrospective cohort study, the Medicare Air Pollution Cohort Study (MCAPS), consisting of 20 million persons from 250 counties and representing about 50% of the US population of elderly living in urban settings. In this paper, we report on the relationship between longer-term exposure to PM2.5 and mortality risk over the period 2000 to 2002 in the MCAPS.
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Terminal sialic acid residues on surface-associated glycoconjugates mediate host cell interactions of many pathogens. Addition of sialic acid-rich fetuin enhanced, and the presence of the sialidiase inhibitor 2-deoxy-2,3-dehydro-N-acetylneuraminic acid reduced, the physical interaction of Neospora caninum tachyzoites and bradyzoites with Vero cell monolayers. Thus, Neospora extracts were subjected to fetuin-agarose affinity chromatography in order to isolate components potentially interacting with sialic acid residues. SDS-PAGE and silver staining of the fetuin binding fraction revealed the presence of a single protein band of approximately 65 kDa, subsequently named NcFBP (Neospora caninum fetuin-binding protein), which was localized at the apical tip of the tachyzoites and was continuously released into the surrounding medium in a temperature-independent manner. NcFBP readily interacted with Vero cells and bound to chondroitin sulfate A and C, and anti-NcFBP antibodies interfered in tachyzoite adhesion to host cell monolayers. In additon, analysis of the fetuin binding fraction by gelatin substrate zymography was performed, and demonstrated the presence of two bands of 96 and 140 kDa exhibiting metalloprotease-activity. The metalloprotease activity readily degraded glycosylated proteins such as fetuin and bovine immunoglobulin G heavy chain, whereas non-glycosylated proteins such as bovine serum albumin and immunoglobulin G light chain were not affected. These findings suggest that the fetuin-binding fraction of Neospora caninum tachyzoites contains components that could be potentially involved in host-parasite interactions.