<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mitra Geier</style></author><author><style face="normal" font="default" size="100%">D James Minick</style></author><author><style face="normal" font="default" size="100%">Truong, Lisa</style></author><author><style face="normal" font="default" size="100%">Susan C Tilton</style></author><author><style face="normal" font="default" size="100%">Pande, Paritosh</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author><author><style face="normal" font="default" size="100%">JG Teeguarden</style></author><author><style face="normal" font="default" size="100%">Robyn L Tanguay</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Systematic developmental neurotoxicity assessment of a representative PAH Superfund mixture using zebrafish.</style></title><secondary-title><style face="normal" font="default" size="100%">Toxicol Appl Pharmacol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Toxicol. Appl. Pharmacol.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Apr 06</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Superfund sites often consist of complex mixtures of polycyclic aromatic hydrocarbons (PAHs). It is widely recognized that PAHs pose risks to human and environmental health, but the risks posed by exposure to PAH mixtures are unclear. We constructed an environmentally relevant PAH mixture with the top 10 most prevalent PAHs (SM10) from a Superfund site derived from environmental passive sampling data. Using the zebrafish model, we measured body burden at 48 hours post fertilization (hpf) and evaluated the developmental and neurotoxicity of SM10 and the 10 individual constituents at 24 hours post fertilization (hpf) and 5 days post fertilization (dpf). Zebrafish embryos were exposed from 6 to 120 hpf to (1) the SM10 mixture, (2) a variety of individual PAHs: pyrene, fluoranthene, retene, benzo[a]anthracene, chrysene, naphthalene, acenaphthene, phenanthrene, fluorene, and 2-methylnaphthalene. We demonstrated that SM10 and only 3 of the individual PAHs were developmentally toxic. Subsequently, we constructed and exposed developing zebrafish to two sub-mixtures: SM3 (comprised of 3 of the developmentally toxicity PAHs) and SM7 (7 non-developmentally toxic PAHs). We found that the SM3 toxicity profile was similar to SM10, and SM7 unexpectedly elicited developmental toxicity unlike that seen with its individual components. The results demonstrated that the overall developmental toxicity in the mixtures could be explained using the general concentration addition model. To determine if exposures activated the AHR pathway, spatial expression of CYP1A was evaluated in the 10 individual PAHs and the 3 mixtures at 5 dpf. Results showed activation of AHR in the liver and vasculature for the mixtures and some individual PAHs. Embryos exposed to SM10 during development and raised in chemical-free water into adulthood exhibited decreased learning and responses to startle stimulus indicating that developmental SM10 exposures affect neurobehavior. Collectively, these results exemplify the utility of zebrafish to investigate the developmental and neurotoxicity of complex mixtures.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">JG Teeguarden</style></author><author><style face="normal" font="default" size="100%">Tan, Yu-Mei</style></author><author><style face="normal" font="default" size="100%">Edwards, Stephen W</style></author><author><style face="normal" font="default" size="100%">Leonard, Jeremy A</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author><author><style face="normal" font="default" size="100%">Corley, Richard A</style></author><author><style face="normal" font="default" size="100%">Molly Kile</style></author><author><style face="normal" font="default" size="100%">Staci M Simonich</style></author><author><style face="normal" font="default" size="100%">Stone, David</style></author><author><style face="normal" font="default" size="100%">Robyn L Tanguay</style></author><author><style face="normal" font="default" size="100%">Katrina M Waters</style></author><author><style face="normal" font="default" size="100%">Harper, Stacey L</style></author><author><style face="normal" font="default" size="100%">Williams, David E</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework.</style></title><secondary-title><style face="normal" font="default" size="100%">Environ Sci Technol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Environ. Sci. Technol.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2016</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">50</style></volume><pages><style face="normal" font="default" size="100%">4579-86</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the &quot;systems approaches&quot; used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences. Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more meaningful integration of exposure assessment and hazard identification. Together, the two frameworks form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">LB Paulik</style></author><author><style face="normal" font="default" size="100%">Brian W Smith</style></author><author><style face="normal" font="default" size="100%">Alan J Bergmann</style></author><author><style face="normal" font="default" size="100%">Gregory J Sower</style></author><author><style face="normal" font="default" size="100%">Norman D Forsberg</style></author><author><style face="normal" font="default" size="100%">JG Teeguarden</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Passive samplers accurately predict PAH levels in resident crayfish.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Total Environ</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci. Total Environ.</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2016</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">544</style></volume><pages><style face="normal" font="default" size="100%">782-791</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Contamination of resident aquatic organisms is a major concern for environmental risk assessors. However, collecting organisms to estimate risk is often prohibitively time and resource-intensive. Passive sampling accurately estimates resident organism contamination, and it saves time and resources. This study used low density polyethylene (LDPE) passive water samplers to predict polycyclic aromatic hydrocarbon (PAH) levels in signal crayfish, Pacifastacus leniusculus. Resident crayfish were collected at 5 sites within and outside of the Portland Harbor Superfund Megasite (PHSM) in the Willamette River in Portland, Oregon. LDPE deployment was spatially and temporally paired with crayfish collection. Crayfish visceral and tail tissue, as well as water-deployed LDPE, were extracted and analyzed for 62 PAHs using GC-MS/MS. Freely-dissolved concentrations (Cfree) of PAHs in water were calculated from concentrations in LDPE. Carcinogenic risks were estimated for all crayfish tissues, using benzo[a]pyrene equivalent concentrations (BaPeq). ∑PAH were 5-20 times higher in viscera than in tails, and ∑BaPeq were 6-70 times higher in viscera than in tails. Eating only tail tissue of crayfish would therefore significantly reduce carcinogenic risk compared to also eating viscera. Additionally, PAH levels in crayfish were compared to levels in crayfish collected 10years earlier. PAH levels in crayfish were higher upriver of the PHSM and unchanged within the PHSM after the 10-year period. Finally, a linear regression model predicted levels of 34 PAHs in crayfish viscera with an associated R-squared value of 0.52 (and a correlation coefficient of 0.72), using only the Cfree PAHs in water. On average, the model predicted PAH concentrations in crayfish tissue within a factor of 2.4±1.8 of measured concentrations. This affirms that passive water sampling accurately estimates PAH contamination in crayfish. Furthermore, the strong predictive ability of this simple model suggests that it could be easily adapted to predict contamination in other shellfish of concern.&lt;/p&gt;
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