Sentence examples for models of arsenic from inspiring English sources
Assigned concentrations for 32,225 EYs (17%) were derived from regression models of arsenic concentration in private wells in New England (IQR 0.5 8.8 µg/L).
The TK model can be incorporated into larger-scale physiologically based toxicokinetic (PBTK) models of arsenic for improving the estimates of PBTK model parameters.
A challenge to elucidating these mechanisms has been the difficulty encountered in the development of experimental whole animal models of arsenic carcinogenesis.
Results: Three methods accounted for 93% of the residential estimates of arsenic concentration: direct measurement of water samples (27%; median, 0.3 µg/L; range, 0.1 11.5), statistical models of water utility measurement data (49%; median, 0.4 µg/L; range, 0.3 3.3), and statistical models of arsenic concentrations in wells using aquifers in New England (17%; median, 1.6 µg/L; range, 0.6 22.4).
Three sources of data accounted for 93% of the EYs with arsenic estimates: direct measurement at residences of the subjects (27%), statistical modeling of PWS measurement data (49%), and predictive models of arsenic concentrations in private wells in New England (17%).
Statistical modeling of arsenic concentrations.
Data sources for statistical modeling of arsenic concentrations in water supplies.
The most cited conceptual model of arsenic methylation involves the reduction of pentavalent iAs (iAsV) to trivalent iAs (iAsIII), with subsequent methylation (Drobna et al. 2009).
Thus, the in vitro prostate model of arsenic carcinogenesis (Benbrahim-Tallaa et al. 2005a) duplicates this key aspect of the corollary disease in humans (Table 2).
Methods: Estimates covered the lifetimes of most study participants and were based on a combination of arsenic measurements at the homes of the participants and statistical modeling of arsenic concentrations in the water supply of both past and current homes.
Our work represents, to our knowledge, the first kinetic analysis of transcription pattern in bacteria exposed to arsenic, leading to propose a global model of arsenic response in H. arsenicoxydans.
