Mixtures modeling identifies chemical inducers versus repressors of toxicity associated with wildfire smoke.

ABSTRACT: Exposure to wildfire smoke continues to be a growing threat to public health, yet the chemical components in wildfire smoke that primarily drive toxicity and associated disease are largely unknown. This study utilized a suite of computational approaches to identify groups of chemicals induced by variable biomass burn conditions that were associated with biological responses in the mouse lung, including pulmonary immune response and injury markers. Smoke condensate samples were collected and characterized, resulting in chemical distribution information for 86 constituents across ten different exposures. Mixtures-relevant statistical methods included (i) a chemical clustering and data-reduction method, weighted chemical co-expression network analysis (WCCNA), (ii) a quantile g-computation ap

SUBMITTER: Rager JE 

PROVIDER: S-EPMC8243846 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature