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In the last decade, the use of engineered nanomaterials (ENMs) such as titanium dioxide (TiO2), carbon nanotubes (CNTs), carbon nanofibers (CNFs), as well as a variety of other materials have become increasingly popular in commerce because of their many beneficial properties (e.g. ability to manufacture products that are lighter, stronger, and/or more compact). However, according to the National Institute of Occupational Safety and Health, with the development of new nanotechnology it is prudent to ensure the health and safety of workers who are producing or using these materials at the forefront. For many ENMs, occupational exposure limits (OELs) are not available and the OELs developed for microscale materials may not be adequate for ENMs. In the absence of human data, rodent assays are often used to find a dose estimate which can then be used as a point of departure (POD) to extrapolate to humans. Some bioassays report summary statistics, which can be used to determine benchmark dose (BMD) estimates – the dose that corresponds to a specified level of increased response called a benchmark response or BMR [4]. Pooling data across studies with a small number of dose groups (as in many of the studies in this dataset) provides a more robust dataset by increasing the sample size, although also adding variability across different experimental designs (i.e. species, strain, gender). Thus, the aim of this project was to examine the influence of material type on the dose-response relationship using statistical regression modeling in R (statistical software) since the EPA’s Benchmark Dose Software (BMDS) does not allow for covariates, and building upon these regression models by adding covariates to account for experimental design features which add variability that may obscure these relationships.


Course Name: Mathematics Senior Project

Faculty Advisor: Dr. Max Buot



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