Sentence examples for multiple microbial biomarkers from inspiring English sources

We evaluated home characteristics as predictors of multiple microbial biomarkers, with a focus on common and unique determinants and with attention to the extent of their explanatory ability.

To build multiple regression models for bed and family room microbial biomarkers, we first constructed reduced models containing a priori home characteristics (those identified from the literature) and those statistically significant (p < 0.05) in univariate models.

Despite their associations with multiple microbial flora, home characteristics only partially explain the variability in microbial biomarker levels and cannot substitute for specific microbial measurements in studies concerned with distinguishing effects of specific classes of microbes.

Multiple microbial factors promote development of TH2-inflammation, including the influence of certain microbial adjuvants during antigen presenting cell activation, and the nature of the protein itself.

Microbial biomarkers showed right-skewed distributions.

We expect microbial biomarkers to be a rapid and sensitive measure of exposure to bioavailable contaminants, as microbes are ubiquitous in the environment, have no migratory behavior, and integrate responses to multiple stressors.

We used log-transformed data for analyses of microbial biomarkers as continuous outcomes.

Characteristics related to dampness were the most consistent predictors of microbial biomarkers.

Family room dust samples were analyzed first for allergen levels and then for microbial biomarkers.

The proportion of variance in microbial biomarkers explained by home characteristics ranged from 4.2%to19.0%0%.

Our initial approach to developing microbial biomarkers of exposure to bioavailable contaminants focused on examination of microbial molecular diversity along pollution gradients.