Sentence examples for multiple exposure biomarkers from inspiring English sources
Developing suitable analytical methods for the concurrent quantification of multiple exposure biomarkers is challenging because optimal conditions for the hydrolysis of such conjugates (e.g., O-glucuronides, N-glucuronides, sulfates) may differ depending on the biomarker.
These results highlight the relevance of method validation procedures that include optimizing the hydrolysis of phase II urinary conjugates (e.g., enzyme type and amount used, reaction time, temperature) to quantify accurately and concurrently multiple exposure biomarkers for biomonitoring purposes.
The strengths of this study include the use of multiple exposure biomarkers in a large population of women living in an agricultural community.
The use of multiple Pb exposure biomarkers allowed us to finely dissect this relationship with respect to different windows of exposure.
On the other hand, if sources of OPFR exposure are multiple and varied, exposure biomarkers such as urinary metabolites that account for all routes of exposure may be the best approach.
Finally, for exposure biomarkers with multiple significant predictors, to explore confounding we constructed multivariable models where these predictors were included simultaneously.
Exposures are typically at low concentrations, measurements in water are frequently insufficient, chemicals are present in mixtures, exposure periods are usually long, multiple exposure routes may be involved, and valid biomarkers reflecting the relevant exposure period are scarce.
Some exposure biomarkers are common metabolites of multiple parent compounds.
The first, generally called the exposome, is based on high throughput technological measures of exogenous exposure biomarkers at a single point (or multiple points) in time.
We used multiple regression analysis to compare the predictive validity of individual exposure biomarkers and the latent exposure variable on individual and latent outcomes.
Regression coefficients of these exposure biomarkers except bisphenol A remained significantly in the multiple regression models after controlling for age, sex, weight, smoking, and exercise for at least one of the two oxidative stress biomarkers (P < 0.05).
