<?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%">Nelson, Isabella M</style></author><author><style face="normal" font="default" size="100%">Vazquez, Joana Hernandez</style></author><author><style face="normal" font="default" size="100%">Poutasse, Carolyn M</style></author><author><style face="normal" font="default" size="100%">Adams, Kaley T</style></author><author><style face="normal" font="default" size="100%">O&#039;Connell, Steven G</style></author><author><style face="normal" font="default" size="100%">Smith, Brian W</style></author><author><style face="normal" font="default" size="100%">Herbstman, Julie B</style></author><author><style face="normal" font="default" size="100%">Raessler, Jana M</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%">Unraveling the environmental links to feline hyperthyroidism: Insights from silicone passive samplers.</style></title><secondary-title><style face="normal" font="default" size="100%">Environ Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Environ Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Cat Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Cats</style></keyword><keyword><style  face="normal" font="default" size="100%">Endocrine Disruptors</style></keyword><keyword><style  face="normal" font="default" size="100%">Environmental Exposure</style></keyword><keyword><style  face="normal" font="default" size="100%">Environmental Monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Environmental Pollutants</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Flame Retardants</style></keyword><keyword><style  face="normal" font="default" size="100%">Hyperthyroidism</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">New York</style></keyword><keyword><style  face="normal" font="default" size="100%">Silicones</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2025 Dec 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">286</style></volume><pages><style face="normal" font="default" size="100%">122885</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Feline hyperthyroidism (FH) is the most common endocrine disorder affecting cats and poses significant health challenges to domestic cats and veterinary professionals. This disease is caused by the effects of excess thyroid hormone production and causes a variety of symptoms including weight loss, increased urination, and increased appetite. Despite its prevalence, the underlying cause of this condition remains unclear. While many factors have been extensively studied, there isn&#039;t conclusive evidence linking hyperthyroidism to diet, litter, and indoor lifestyle. Recent research has suggested an association between FH and exposure to flame retardants in consumer products. Many consumer products also contain other endocrine-disrupting chemicals (EDCs) and potential endocrine-disrupting chemicals (pEDCs) in addition to flame retardants that could be linked to FH. To investigate this further, silicone passive sampling devices (PSDs) in the form of pet tags were used to measure the environmental chemical exposure of 78 cats, aged seven years and older, in Oregon and New York using a chemical screening method containing hundreds of EDCs/pEDCs. The objective of this study was to compare exposure frequencies and concentrations between hyperthyroid and non-hyperthyroid cats. While no statistically significant associations were identified, this study found higher concentrations of butyl benzyl phthalate (BBP), galaxolide, lilial, and tonalide in the tags worn by cats with FH compared to euthyroid cats. TCPP, b-ionone, lilial, cinnamal, benzyl salicylate, and tonalide have not been previously mentioned in past feline exposure studies. These chemicals are found in various personal care and consumer products such as vinyl tiles, fragrances, furniture, and cosmetics. Their presence in PSDs worn by cats that develop hyperthyroidism may indicate a potential role of these environmental chemicals in FH etiology.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">Pt 2</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%">Bramer, Lisa M</style></author><author><style face="normal" font="default" size="100%">Dixon, Holly M</style></author><author><style face="normal" font="default" size="100%">Degnan, David J</style></author><author><style face="normal" font="default" size="100%">Rohlman, Diana</style></author><author><style face="normal" font="default" size="100%">Herbstman, Julie B</style></author><author><style face="normal" font="default" size="100%">Kim A Anderson</style></author><author><style face="normal" font="default" size="100%">Waters, Katrina M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration.</style></title><secondary-title><style face="normal" font="default" size="100%">Pac Symp Biocomput</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Pac Symp Biocomput</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Environmental Monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Silicones</style></keyword><keyword><style  face="normal" font="default" size="100%">Wearable Electronic Devices</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">29</style></volume><pages><style face="normal" font="default" size="100%">170-186</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Wearable silicone wristbands are a rapidly growing exposure assessment technology that offer researchers the ability to study previously inaccessible cohorts and have the potential to provide a more comprehensive picture of chemical exposure within diverse communities. However, there are no established best practices for analyzing the data within a study or across multiple studies, thereby limiting impact and access of these data for larger meta-analyses. We utilize data from three studies, from over 600 wristbands worn by participants in New York City and Eugene, Oregon, to present a first-of-its-kind manuscript detailing wristband data properties. We further discuss and provide concrete examples of key areas and considerations in common statistical modeling methods where best practices must be established to enable meta-analyses and integration of data from multiple studies. Finally, we detail important and challenging aspects of machine learning, meta-analysis, and data integration that researchers will face in order to extend beyond the limited scope of individual studies focused on specific populations.&lt;/p&gt;
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