Correlation between biomarkers of exposure, effect and potential harm in the urine of electronic cigarette users.

OBJECTIVES: To determine if urinary biomarkers of effect and potential harm are elevated in electronic cigarette users compared with non-smokers and if elevation correlates with increased concentrations of metals in urine. STUDY DESIGN AND SETTING: This was a cross-sectional study of biomarkers of exposure, effect and potential harm in urine from non-smokers (n=20), electronic cigarette users (n=20) and cigarette smokers (n=13). Participant's screening and urine collection were performed at the Roswell Park Comprehensive Cancer Center, and biomarker analysis and metal analysis were performed at the University of California, Riverside. RESULTS: Metallothionein was significantly elevated in the electronic cigarette group (3761±3932 pg/mg) compared with the non-smokers (1129±1294 pg/mg, p=0.05). 8-OHdG (8-hydroxy-2'-deoxyguanosine) was significantly elevated in electronic cigarette users (442.8±300.7 ng/mg) versus non-smokers (221.6±157.8 ng/mg, p=0.01). 8-Isoprostane showed a significant increase in electronic cigarette users (750.8±433 pg/mg) versus non-smokers (411.2±287.4 pg/mg, p=0.03). Linear regression analysis in the electronic cigarette group showed a significant correlation between cotinine and total metal concentration; total metal concentration and metallothionein; cotinine and oxidative DNA damage; and total metal concentration and oxidative DNA damage. Zinc was significantly elevated in the electronic cigarette users (584.5±826.6 µg/g) compared with non-smokers (413.6±233.7 µg/g, p=0.03). Linear regression analysis showed a significant correlation between urinary zinc concentration and 8-OHdG in the electronic cigarette users. CONCLUSIONS: This study is the first to investigate biomarkers of potential harm and effect in electronic cigarette users and to show a linkage to metal exposure. The biomarker levels in electronic cigarette users were similar to (and not lower than) cigarette smokers. In electronic cigarette users, there was a link to elevated total metal exposure and oxidative DNA damage. Specifically, our results demonstrate that zinc concentration was correlated to oxidative DNA damage.

Deep Analysis of Mitochondria and Cell Health Using Machine Learning.

There is a critical need for better analytical methods to study mitochondria in normal and diseased states. Mitochondrial image analysis is typically done on still images using slow manual methods or automated methods of limited types of features. MitoMo integrated software overcomes these bottlenecks by automating rapid unbiased quantitative analysis of mitochondrial morphology, texture, motion, and morphogenesis and advances machine-learning classification to predict cell health by combining features. Our pixel-based approach for motion analysis evaluates the magnitude and direction of motion of: (1) molecules within mitochondria, (2) individual mitochondria, and (3) distinct morphological classes of mitochondria. MitoMo allows analysis of mitochondrial morphogenesis in time-lapse videos to study early progression of cellular stress. Biological applications are presented including: (1) establishing normal phenotypes of mitochondria in different cell types; (2) quantifying stress-induced mitochondrial hyperfusion in cells treated with an environmental toxicant, (3) tracking morphogenesis in mitochondria undergoing swelling, and (4) evaluating early changes in cell health when morphological abnormalities are not apparent. MitoMo unlocks new information on mitochondrial phenotypes and dynamics by enabling deep analysis of mitochondrial features in any cell type and can be applied to a broad spectrum of research problems in cell biology, drug testing, toxicology, and medicine.