Research report (Health Effects Institute)最新文献

{"title":"Part 3. Modeling of Multipollutant Profiles and Spatially Varying Health Effects with Applications to Indicators of Adverse Birth Outcomes.","authors":"John Molitor,&nbsp;Eric Coker,&nbsp;Michael Jerrett,&nbsp;Beate Ritz,&nbsp;Arthur Li","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The highly intercorrelated nature of air pollutants makes it difficult to examine their combined effects on health. As such, epidemiological studies have traditionally focused on single-pollutant models that use regression-based techniques to examine the marginal association between a pollutant and a health outcome. These relatively simple, additive models are useful for discerning the effect of a single pollutant on a health outcome with all other pollutants held to fixed values. However, pollutants occur in complex mixtures consisting of highly correlated combinations of individual exposures. For example, evidence for synergy among pollutants in causing health effects has been recently reviewed by Mauderly and Samet (2009). Also, studies cited in the Ozone Criteria Document (U.S. Environmental Protection Agency [U.S. EPA*] 2006) confirmed that synergisms between ozone and other pollutants have been demonstrated in laboratory studies involving humans and animals. Thus, the highly correlated nature of air pollution exposures makes marginal, single-pollutant models inadequate. This issue was raised in a report by the National Research Council (NRC 2004), which called for a multipollutant approach to air quality management. Here we present and apply a series of statistical approaches that treat patterns of covariates as a whole unit, stochastically grouping pollutant patterns into clusters and then using these cluster assignments as random effects in a regression model. Using this approach, the effect of a multipollutant pattern, or profile, is determined in a manner that takes into account the uncertainty in the clustering process. The models are set in a Bayesian framework, and in general, Markov chain Monte Carlo (MCMC) techniques (Gilks et al. 1998). For interpretation purposes, a best clustering is derived, and the uncertainty related to this best clustering is determined by utilizing model averaging techniques, in a manner such that consistent clustering obtained by the estimation process generally yields smaller standard errors while inconsistent clustering is generally associated with larger errors. These multivariate methods are applied to a range of different problems related to air pollution exposures, namely an association of multipollutant profiles with indicators of poverty and to an assessment of the association between measures of various air pollutants, patterns of socioeconomic status (SES), and birth outcomes. All of these studies involve an examination of regional-level exposures, at the census tract (CT) and census block group (CBG) levels, and individual-level outcomes throughout Los Angeles (LA) County. Results indicate that effects of pollutants vary spatially and vary in a complex interconnected manner that cannot be discerned using standard additive line ar models. Results obtaine d from these studies can be used to efficiently use limited resources to inform policies in targeting are as where air pollution reduction","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 183 Pt 3","pages":"3-47"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34595528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adverse Reproductive Health Outcomes and Exposure to Gaseous and Particulate-Matter Air Pollution in Pregnant Women.","authors":"Jun Wu, Olivier Laurent, Lianfa Li, Jianlin Hu, Michael Kleeman","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>There is growing epidemiologic evidence of associations\u0000between maternal exposure to ambient air\u0000pollution and adverse birth outcomes, such as\u0000preterm birth (PTB). Recently, a few studies have\u0000also reported that exposure to ambient air pollution\u0000may also increase the risk of some common\u0000pregnancy complications, such as preeclampsia\u0000and gestational diabetes mellitus (GDM). Research\u0000findings, however, have been mixed. These inconsistent\u0000results could reflect genuine differences in\u0000the study populations, the study locations, the specific\u0000pollutants considered, the designs of the study,\u0000its methods of analysis, or random variation.\u0000Dr. Jun Wu of the University of California–\u0000Irvine, a recipient of HEI’s Walter A. Rosenblith\u0000New Investigator Award, and colleagues have\u0000examined the association between air pollution\u0000and adverse birth and pregnancy outcomes in\u0000California women. In addition, they examined the\u0000effect modification by socioeconomic status (SES)\u0000and other factors.</p><p><strong>Approach: </strong>A retrospective nested case–control study was\u0000conducted using birth certificate data from about\u00004.4 million birth records in California from 2001 to\u00002008. Wu and colleagues analyzed data on low\u0000birth weight (LBW) at term (infants born between\u000037 and 43 weeks of gestation and weighing less\u0000than 2500 g), PTB (infants born before 37 weeks of\u0000gestation), and preeclampsia (including eclampsia)\u0000of the mother during the pregnancy. In addition,\u0000they obtained data on GDM for the years 2006–\u00002008. In the analyses, all outcomes were included\u0000as binary variables.\u0000Maternal residential addresses at the time of\u0000delivery were geocoded, and a large suite of air\u0000pollution exposure metrics was considered, such\u0000as (1) regulatory monitoring data on concentrations\u0000of criteria pollutants NO2, PM2.5 (particulate\u0000matter ≤ 2.5 μm in aerodynamic diameter), and\u0000ozone (O3) estimated by empirical Bayesian kriging;\u0000(2) concentrations of primary and secondary\u0000PM2.5 and PM0.1 components and sources estimated\u0000by the University of California–Davis\u0000Chemical Transport Model; (3) traffic-related ultrafine\u0000particles and concentrations of carbon\u0000monoxide (CO) and nitrogen oxides (NOx) estimated\u0000by a modified CALINE4 air pollution dispersion\u0000model; and (4) proximity to busy roads, road\u0000length, and traffic density calculated for different\u0000buffer sizes using geographic information system\u0000tools. In total, 50 different exposure metrics were\u0000available for the analyses. The exposure of primary\u0000interest was the mean of the entire pregnancy\u0000period for each mother.\u0000For the health analyses, controls were randomly\u0000selected from the source population. PTB controls\u0000were matched on conception year. Term LBW, preeclampsia,\u0000and GDM were analyzed using generalized\u0000additive mixed models with inclusion of a\u0000random effect per hospital. PTB analyses were conducted\u0000using conditional logistic regression, with\u0000no adjustment for hospital. The main results—\u0000adjusted for race and edu","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":"2016 188","pages":"1-58"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266373/pdf/hei-2016-188.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36015350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Part 1. Statistical Learning Methods for the Effects of Multiple Air Pollution Constituents.","authors":"Brent A Coull,&nbsp;Jennifer F Bobb,&nbsp;Gregory A Wellenius,&nbsp;Marianthi-Anna Kioumourtzoglou,&nbsp;Murray A Mittleman,&nbsp;Petros Koutrakis,&nbsp;John J Godleski","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>The United States Environmental Protection Agency (U.S. EPA*) currently regulates individual air pollutants on a pollutant-by-pollutant basis, adjusted for other pollutants and potential confounders. However, the National Academies of Science concluded that a multipollutant regulatory approach that takes into account the joint effects of multiple constituents is likely to be more protective of human health. Unfortunately, the large majority of existing research had focused on health effects of air pollution for one pollutant or for one pollutant with control for the independent effects of a small number of copollutants. Limitations in existing statistical methods are at least partially responsible for this lack of information on joint effects. The goal of this project was to fill this gap by developing flexible statistical methods to estimate the joint effects of multiple pollutants, while allowing for potential nonlinear or nonadditive associations between a given pollutant and the health outcome of interest.</p><p><strong>Methods: </strong>We proposed Bayesian kernel machine regression (BKMR) methods as a way to simultaneously achieve the multifaceted goals of variable selection, flexible estimation of the exposure-response relationship, and inference on the strength of the association between individual pollutants and health outcomes in a health effects analysis of mixtures. We first developed a BKMR variable-selection approach, which we call component-wise variable selection, to make estimating such a potentially complex exposure-response function possible by effectively using two types of penalization (or regularization) of the multivariate exposure-response surface. Next we developed an extension of this first variable-selection approach that incorporates knowledge about how pollutants might group together, such as multiple constituents of particulate matter that might represent a common pollution source category. This second grouped, or hierarchical, variable-selection procedure is applicable when groups of highly correlated pollutants are being studied. To investigate the properties of the proposed methods, we conducted three simulation studies designed to evaluate the ability of BKMR to estimate environmental mixtures responsible for health effects under potentially complex but plausible exposure-response relationships. An attractive feature of our simulation studies is that we used actual exposure data rather than simulated values. This real-data simulation approach allowed us to evaluate the performance of BKMR and several other models under realistic joint distributions of multipollutant exposure. The simulation studies compared the two proposed variable-selection approaches (component-wise and hierarchical variable selection) with each other and with existing frequentist treatments of kernel machine regression (KMR). After the simulation studies, we applied the newly developed methods to an epidemiol","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 183 Pt 1-2","pages":"5-50"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34039337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Part 2. Development of Enhanced Statistical Methods for Assessing Health Effects Associated with an Unknown Number of Major Sources of Multiple Air Pollutants.","authors":"Eun Sug Park,&nbsp;Elaine Symanski,&nbsp;Daikwon Han,&nbsp;Clifford Spiegelman","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>A major difficulty with assessing source-specific health effects is that source-specific exposures cannot be measured directly; rather, they need to be estimated by a source-apportionment method such as multivariate receptor modeling. The uncertainty in source apportionment (uncertainty in source-specific exposure estimates and model uncertainty due to the unknown number of sources and identifiability conditions) has been largely ignored in previous studies. Also, spatial dependence of multipollutant data collected from multiple monitoring sites has not yet been incorporated into multivariate receptor modeling. The objectives of this project are (1) to develop a multipollutant approach that incorporates both sources of uncertainty in source-apportionment into the assessment of source-specific health effects and (2) to develop enhanced multivariate receptor models that can account for spatial correlations in the multipollutant data collected from multiple sites. We employed a Bayesian hierarchical modeling framework consisting of multivariate receptor models, health-effects models, and a hierarchical model on latent source contributions. For the health model, we focused on the time-series design in this project. Each combination of number of sources and identifiability conditions (additional constraints on model parameters) defines a different model. We built a set of plausible models with extensive exploratory data analyses and with information from previous studies, and then computed posterior model probability to estimate model uncertainty. Parameter estimation and model uncertainty estimation were implemented simultaneously by Markov chain Monte Carlo (MCMC*) methods. We validated the methods using simulated data. We illustrated the methods using PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter) speciation data and mortality data from Phoenix, Arizona, and Houston, Texas. The Phoenix data included counts of cardiovascular deaths and daily PM2.5 speciation data from 1995-1997. The Houston data included respiratory mortality data and 24-hour PM2.5 speciation data sampled every six days from a region near the Houston Ship Channel in years 2002-2005. We also developed a Bayesian spatial multivariate receptor modeling approach that, while simultaneously dealing with the unknown number of sources and identifiability conditions, incorporated spatial correlations in the multipollutant data collected from multiple sites into the estimation of source profiles and contributions based on the discrete process convolution model for multivariate spatial processes. This new modeling approach was applied to 24-hour ambient air concentrations of 17 volatile organic compounds (VOCs) measured at nine monitoring sites in Harris County, Texas, during years 2000 to 2005. Simulation results indicated that our methods were accurate in identifying the true model and estimated parameters were close to the true values. The results from our methods agreed ","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 183 Pt 1-2","pages":"51-113"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34039338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Part 3. Assessment of genotoxicity and oxidative damage in rats after chronic exposure to new-technology diesel exhaust in the ACES bioassay.","authors":"Lance M Hallberg,&nbsp;Jonathan B Ward,&nbsp;Caterina Hernandez,&nbsp;Bill T Ameredes,&nbsp;Jeffrey K Wickliffe","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>In 2001, the U.S. Environmental Protection Agency (EPA*) and the California Air Resources Board (CARB) adopted new standards for diesel fuel and emissions from heavy-duty diesel engines. By 2007, diesel engines were required to meet these new standards for particulate matter (PM), with other standards to follow. Through a combination of advanced compression-ignition engine technology, development of exhaust aftertreatment systems, and reformulated fuels, stringent standards were introduced. Before the 2007 standards were put in place by the EPA, human health effects linked to diesel exhaust (DE) exposure had been associated with diesel-fuel solvent and combustion components. In earlier research, diesel engine exhaust components were, in turn, linked to increased mutagenicity in cultures of Salmonella typhimurium and mammalian cells (Tokiwa and Ohnishi 1986). In addition, DE was shown to increase both the incidence of tumors and the induction of 8-hydroxy-deoxyguanosine (8-OHdG) adducts in rodents (Ichinose et al. 1997) and total DNA adducts in rats (Bond et al. 1990). Furthermore, DE is composed of a complex mixture of polycyclic aromatic hydrocarbons (PAHs) and particulates. One such PAH, 3-nitrobenzanthrone (3-NBA), is also found in urban air. 3-NBA has been observed to induce micronucleus formation in the DNA of human hepatoma cells (Lamy et al. 2004). The current study is part of the Advanced Collaborative Emissions Study (ACES), a multidisciplinary program carried out by the Health Effects Institute and the Coordinating Research Council. Its purpose was to determine whether recent improvements in the engineering of heavy-duty diesel engines reduce the toxicity associated with exposure to DE components. To this end, we evaluated potential genotoxicity and induction of oxidative stress in bioassays of serum and tissues from Wistar Han rats chronically exposed--for up to 24 months--to DE from a 2007-compliant diesel engine (new-technology diesel exhaust, or NTDE). Genotoxicity was measured as DNA strand breaks in lung tissue, using an alkaline-modified comet assay. As a correlate of possible DNA damage evaluated in the comet assay, concentrations of the free DNA adduct 8-OHdG were evaluated in serum by a competitive enzyme-linked immunosorbent assay (ELISA). The 8-OHdG fragment found in the serum is a specific biomarker for the repair of oxidative DNA damage. In addition, an assay for thiobarbituric acid reactive substances (TBARS) was used to assess oxidative stress and damage in the form of lipid peroxidation in the hippocampus region of the brains of the DE-exposed animals. These endpoints were evaluated at 1, 3, 12, and 24 months of exposure to DE or to a control atmosphere (filtered air). At the concentrations of DE evaluated, there were no significant effects of exposure in male or female rats after 1, 3, 12, or 24 months in any measure of DNA damage in the comet assay (%DNA in tail, tail length, tail moment, or olive moment). The c","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 184","pages":"87-105; discussion 141-71"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33188633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Part 1. Assessment of carcinogenicity and biologic responses in rats after lifetime inhalation of new-technology diesel exhaust in the ACES bioassay.","authors":"Jacob D McDonald,&nbsp;Melanie Doyle-Eisele,&nbsp;JeanClare Seagrave,&nbsp;Andrew P Gigliotti,&nbsp;Judith Chow,&nbsp;Barbara Zielinska,&nbsp;Joe L Mauderly,&nbsp;Steven K Seilkop,&nbsp;Rodney A Miller","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The Health Effects Institute and its partners conceived and funded a program to characterize the emissions from heavy-duty diesel engines compliant with the 2007 and 2010 on-road emissions standards in the United States and to evaluate indicators of lung toxicity in rats and mice exposed repeatedly to 2007-compliant new-technology diesel exhaust (NTDE*). The a priori hypothesis of this Advanced Collaborative Emissions Study (ACES) was that 2007-compliant on-road diesel emissions \"... will not cause an increase in tumor formation or substantial toxic effects in rats and mice at the highest concentration of exhaust that can be used ... although some biological effects may occur.\" This hypothesis was tested at the Lovelace Respiratory Research Institute (LRRI) by exposing rats by chronic inhalation as a carcinogenicity bioassay. Indicators of pulmonary toxicity in rats were measured after 1, 3, 12, 24, and 28-30 months of exposure. Similar indicators of pulmonary toxicity were measured in mice, as an interspecies comparison of the effects of subchronic exposure, after 1 and 3 months of exposure. A previous HEI report (Mauderly and McDonald 2012) described the operation of the engine and exposure systems and the characteristics of the exposure atmospheres during system commissioning. Another HEI report described the biologic responses in mice and rats after subchronic exposure to NTDE (McDonald et al. 2012). The primary motivation for the present chronic study was to evaluate the effects of NTDE in rats in the context of previous studies that had shown neoplastic lung lesions in rats exposed chronically to traditional technology diesel exhaust (TDE) (i.e., exhaust from diesel engines built before the 2007 U.S. requirements went into effect). The hypothesis was largely based on the marked reduction of diesel particulate matter (DPM) in NTDE compared with emissions from older diesel engine and fuel technologies, although other emissions were also reduced. The DPM component of TDE was considered the primary driver of lung tumorigenesis in rats exposed chronically to historical diesel emissions. Emissions from a 2007-compliant, 500-horsepower-class engine and after treatment system operated on a variable-duty cycle were used to generate the animal inhalation test atmospheres. Four groups were exposed to one of three concentrations (dilutions) of exhaust combined with crankcase emissions, or to clean air as a negative control. Dilutions of exhaust were set to yield average integrated concentrations of 4.2, 0.8, and 0.1 ppm nitrogen dioxide (NO2). Exposure atmospheres were analyzed by daily measurements of key effects of NTDE in the present study were generally consistent with those observed previously in rats exposed chronically to NO2 alone. This suggests that NO2 may have been the primary driver of the biologic responses to NTDE in the present study. There was little evidence of effects characteristic of rats exposed chronically to high concentrat","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 184","pages":"9-44; discussion 141-71"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33188631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Part 4. Assessment of plasma markers and cardiovascular responses in rats after chronic exposure to new-technology diesel exhaust in the ACES bioassay.","authors":"Daniel J Conklin,&nbsp;Maiying Kong","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Although epidemiologic and experimental studies suggest that chronic exposure to diesel exhaust (DE*) emissions causes adverse cardiovascular effects, neither the specific components of DE nor the mechanisms by which DE exposure could induce cardiovascular dysfunction and exacerbate cardiovascular disease (CVD) are known. Because advances in new technologies have resulted in cleaner fuels and decreased engine emissions, uncertainty about the relationship between DE exposure and human cardiovascular health effects has increased. To address this ever-changing baseline of DE emissions, as part of the larger Advanced Collaborative Emissions Study (ACES) bioassay studying the health effects of 2007-compliant diesel engine emissions (new-technology diesel exhaust), we examined whether plasma markers of vascular inflammation, thrombosis, cardiovascular aging, cardiac fibrosis, and aorta morphometry were changed over 24 months in an exposure-level-, sex-, or exposure-duration-dependent manner. Many plasma markers--several recognized as human CVD risk factors--were measured in the plasma of rats exposed for up to 24 months to filtered air (the control) or DE. Few changes in plasma markers resulted from 12 months of DE exposure, but significant exposure-level-dependent increases in soluble intercellular adhesion molecule 1 (sICAM-1) and interleukin-6 (IL-6) levels, as well as decreases in total and non-high-density-lipoprotein cholesterol (non-HDL) levels in plasma, were observed in female rats after 24 months of DE exposure. These effects were not observed in male rats, and no changes in cardiac fibrosis or aorta morphometry resulting from DE exposure were observed in either sex. Collectively, the significant changes may reflect an enhanced sensitivity of the female cardiovascular system to chronic DE exposure; however, this conclusion should be interpreted within both the context and limitations of the current study.</p>","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 184","pages":"111-39; discussion 141-71"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33189142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Part 2. Assessment of micronucleus formation in rats after chronic exposure to new-technology diesel exhaust in the ACES bioassay.","authors":"Jeffrey C Bemis,&nbsp;Dorothea K Torous,&nbsp;Stephen D Dertinger","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The formation of micronuclei (MN*) is a well-established endpoint in genetic toxicology; studies designed to examine MN formation in vivo have been conducted for decades. Conditions that cause double-strand breaks or disrupt the proper segregation of chromosomes during division result in increases in MN formation frequency. This endpoint is therefore commonly used in preclinical studies designed to assess the potential risks to humans of exposure to a myriad of chemical and physical agents, including inhaled diesel exhaust (DE). As part of the Advanced Collaborative Emissions Study (ACES) Phase 3B, which examined numerous additional toxicity endpoints associated with lifetime exposure to DE in a rodent model, this ancillary 24-month investigation examined the potential of inhaled DE to induce chromosome damage in chronically exposed rodents. The ACES design included exposure of both mice and rats to DE derived from heavy-duty engines that met U.S. Environmental Protection Agency (EPA) 2007 standards for diesel-exhaust emissions (new-technology diesel exhaust). The exposure conditions consisted of air (the control) and three dilutions of DE, resulting in four levels of exposure. At specific times, blood samples were collected, fixed, and shipped by the bioassay staff at Lovelace Respiratory Research Institute (LRRI) to Litron Laboratories (Rochester, NY) for further processing and analysis. In recent years, significant improvements have been made to MN scoring by using objective, automated methods such as flow cytometry, which allows the detection of micronucleated reticulocytes (MN-RET), micronucleated normochromatic erythrocytes (MN-NCE), and reticulocytes (RET) in peripheral blood samples from mice and rats. By using a simple staining procedure coupled with rapid and efficient analysis, many more cells can be examined in less time than was possible using traditional, microscopy-based MN assays. Thus, for each sample in the current study, 20,000 RET were scored for the presence of MN. In the chronic-exposure (12 and 24 months) bioassay, blood samples were obtained from separate groups of exposed animals at specific time points throughout the course of the study. The automated method using flow cytometry has found widespread use in safety assessment and is supported by regulatory guidelines, including International Conference on Harmonisation (ICH) S2(R1) (2011). Statistical analyses included the use of analysis of variance (ANOVA) to compare the effects of sex, exposure condition, and duration, as well asthe interactions between them. Analyses of blood samples from rats combined data from our earlier 1- and 3-month exposure studies (Bemis et al. 2012) with data from our current 12- and 24-month exposure studies. Consistent with findings from the preliminary studies, no sex-based differences in MN frequency were observed in the rats. An initial examination of mean frequencies across the treatment groups and durations of exposure showed no e","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 184","pages":"69-82; discussion 141-71"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33188632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic effects of particulate matter and substrate stiffness on epithelial-to-mesenchymal transition.","authors":"Thomas H Barker,&nbsp;Marilyn M Dysart,&nbsp;Ashley C Brown,&nbsp;Alison M Douglas,&nbsp;Vincent F Fiore,&nbsp;Armistead G Russell","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Dysfunctional pulmonary homeostasis and repair, including diseases such as pulmonary fibrosis, chronic obstructive pulmonary disease (COPD*), and tumorigenesis, have been increasing steadily over the past decade, a fact that heavily implicates environmental influences. Several investigations have suggested that the lung \"precursor cell\"--the alveolar type II (ATII) epithelial cell--is central in the initiation and progression of pulmonary fibrosis. Specifically, ATII cells have been shown (Iwano et al. 2002) to be capable of undergoing an epithelial-to-mesenchymal transition (EMT). EMT, the de-differentiation of an epithelial cell into a mesenchymal cell, has been theorized to increase the number of extracellular matrix (ECM)-secreting mesenchymal cells, perpetuating fibrotic conditions and resulting in increased lung tissue stiffness. In addition, increased exposure to pollution and inhalation of particulate matter (PM) have been shown to be highly correlated with an increased incidence of pulmonary fibrosis. Although both of these events are involved in the progression of pulmonary fibrosis, the relationship between tissue stiffness, exposure to PM, and the initiation and course of EMT remains unclear. The hypothesis of this study was twofold: 1. That alveolar epithelial cells cultured on increasingly stiff substrates become increasingly contractile, leading to enhanced transforming growth factor beta (TGF-β) activation and EMT; and 2. That exposure of alveolar epithelial cells to PM with an aerodynamic diameter ≤ 2.5 μm (PM2.5; also known as fine PM) results in enhanced cell contractility and EMT. Our study focused on the relationship between the micromechanical environment and external environmental stimuli on the phenotype of alveolar epithelial cells. This relationship was explored by first determining how increased tissue stiffness affects the regulation of fibronectin (Fn)-mediated EMT in ATII cells in vitro. We cultured ATII cells on substrates of increasing stiffness and evaluated changes in cell contractility and EMT. We found that stiff, but not soft, Fn substrates were able to induce EMT and that this event depended on a contractile phenotype of the cell and the subsequent activation of TGF-β. In addition, we were able to show that activation or suppression of cell contractility by way of exogenous factors was sufficient to overcome the effect of substrate stiffness. Pulse-chase experiments indicated that the effect on cell contractility is dose- and time-dependent. In response to low levels of TGF-β on soft surfaces, either added exogenously or produced through contraction induced by the stiffness agonist thrombin, cells initiate EMT; on removal of the TGF-β, they revert to an epithelial phenotype. Overall, the results from this first part of our study identified matrix stiffness or cell contractility as critical targets for the control of EMT in fibrotic diseases. For the second part of our study, we wanted to investigate whe","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 182","pages":"3-41"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33044331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and application of an aerosol screening model for size-resolved urban aerosols.","authors":"Charles O Stanier,&nbsp;Sang-Rin Lee","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Predictive models of vehicular ultrafine particles less than 0.1 microm in diameter (UFPs*) and other urban pollutants with high spatial and temporal variation are useful and important in applications such as (1) decision support for infrastructure projects, emissions controls, and transportation-mode shifts; (2) the interpretation and enhancement of observations (e.g., source apportionment, extrapolation, interpolation, and gap-filling in space and time); and (3) the generation of spatially and temporally resolved exposure estimates where monitoring is unfeasible. The objective of the current study was to develop, test, and apply the Aerosol Screening Model (ASM), a new physically based vehicular UFP model for use in near-road environments. The ASM simulates hourly average outdoor concentrations of roadway-derived aerosols and gases. Its distinguishing features include user-specified spatial resolution; use of the Weather Research and Forecasting (WRF) meteorologic model for winds estimates; use of a database of more than 100,000 road segments in the Los Angeles, California, region, including freeway ramps and local streets; and extensive testing against more than 9000 hours of observed particle concentrations at 11 sites. After initialization of air parcels at an upwind boundary, the model solves for vehicle emissions, dispersion, coagulation, and deposition using a Lagrangian modeling framework. The Lagrangian parcel of air is subdivided vertically (into 11 levels) and in the crosswind direction (into 3 parcels). It has overall dimensions of 10 m (downwind), 300 m (vertically), and 2.1 km (crosswind). The simulation is typically started 4 km upwind from the receptor, that is, the location at which the exposure is to be estimated. As parcels approach the receptor, depending on the user-specified resolution, step size is decreased, and crosswind resolution is enhanced through subdivision of parcels in the crosswind direction. Hourly concentrations and size distributions of aerosols were simulated for 11 sites in the Los Angeles area with large variations in proximal traffic and particle number concentrations (ranging from 6000 to 41,000/cm3). Observed data were from the 2005-2007 Harbor Community Monitoring Study (HCMS; Moore et al. 2009), in Long Beach, California, and the Coronary Health and Air Pollution Study (CHAPS; Delfino et al. 2008), in the Los Angeles area. Meteorologic fields were extracted from 1-km-resolution meteorologic simulations, and observed wind direction and speed were incorporated. Using on-road and tunnel measurements, size-resolved emission factors ranging from 1.4 x 10(15) to 16 x 10(15) particles/kg fuel were developed specifically for the ASM. Four separate size-resolved emissions were used. Traffic and emission factors were separately estimated for heavy-duty diesel and light-duty vehicles (LDV), and both cruise and acceleration emission factors were used. The light-duty cruise size-resolved number emission fact","PeriodicalId":74687,"journal":{"name":"Research report (Health Effects Institute)","volume":" 179","pages":"3-79"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32603857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}