Diabetes affects millions of people globally and can lead to other severe health complications when undiagnosed or not properly managed. The incidence of diabetes has rapidly increased over the past several years, however, not all individuals have access to affordable or convenient healthcare. We hypothesize that wastewater-based epidemiology (WBE) has the potential to assess community health status by analyzing biomarkers indicative of human health and disease, including diabetes. Used in tandem with current methods, monitoring indicators of diabetes in community wastewater could provide a comprehensive assessment tool for disease prevalence in large and small populations. Specifically, the proposed targeted biomarker evaluated in this study to indicate population-wide diabetes prevalence was 8-hydroxy-2’-deoxyguanosine (8-OHdG). This work combines a rigorous literature review and initial laboratory studies to explore the possibility of diabetes monitoring at the community level using WBE. Here, 24-hour composite wastewater samples were collected from within two wastewater sub-catchments of Greater Tempe, AZ. Overall goals of this study were to: i) Determine the feasibility to detect endogenous markers of diabetes in community wastewater; ii) Assess the potential impact of confounding factors, such as smoking, cancer, and atherosclerosis, through a literature analysis; and iii) Evaluate the socioeconomic status and demographics of the study population. Preliminary results of the experiments suggest this methodology to be feasible, as indicated by the observation of detectable signals of 8-OHdG in community wastewater collected from the sewer infrastructure; however, future work and continued experimentation will be required to address low signal intensity and assay precision and accuracy. Thus, the work presented here provides valuable proof-of-concept data, with detailed information on the method employed and identified opportunities to further determine the relationship between 8-OHdG concentrations in municipal wastewater and diabetes prevalence at the community level.

Currently, there are a number of studies confirming the link between exposure to certain chemicals, notably pesticides (Costello et. al. 2009, Wang et. al 2014), heavy metals such as arsenic (Chen et. al. 2017), ambient air pollution (Chen et. al. 2016), and chemicals specific to certain industrial fields (Nielsen et. al. 2021). However, few papers have attempted to perform a widespread analysis of the factors associated with Parkinson’s disease to identify whether the risk of developing the disease is dependent on different factors regionally. The goal of my thesis project is to complete a meta-analysis of toxins- where exposure may occur in both residential and occupational settings- that are associated with Parkinson’s to determine such regional differences and to identify any gaps in current literature, which may direct the course of future research in the field. As seen in this paper, it appears that occupational exposure to toxins appears to have the greatest impact on the risk of developing Parkinson’s disease, particularly pesticides and industrial toxins. However, there are numerous gaps with regards to data collection, regions studied, and quantification of toxin concentrations. However, this data may be useful in identifying at-risk populations if more extensive incremental and biopsy data regarding these toxins is provided.

In this research, I surveyed existing methods of characterizing Epilepsy from Electroencephalogram (EEG) data, including the Random Forest algorithm, which was claimed by many researchers to be the most effective at detecting epileptic seizures [7]. I observed that although many papers claimed a detection of >99% using Random Forest, it was not specified “when” the detection was declared within the 23.6 second interval of the seizure event. In this research, I created a time-series procedure to detect the seizure as early as possible within the 23.6 second epileptic seizure window and found that the detection is effective (> 92%) as early as the first few seconds of the epileptic episode. I intend to use this research as a stepping stone towards my upcoming Masters thesis research where I plan to expand the time-series detection mechanism to the pre-ictal stage, which will require a different dataset.

The COVID-19 pandemic caused uncertainty and changing public health recommendations across the world as our understanding of the SARS-CoV-2 virus changed. Following a preliminary assessment by the World Health Organization, non-steroidal anti-inflammatory drugs were said to worsen symptoms and should be avoided before the recommendation was subsequently revoked. There also was pain associated with infection, leading to the hypothesis that use of over-the-counter pain medication increases may correlate with increases of SARS-CoV-2 infections. Wastewater samples were collected from two communities in Tempe, AZ from December 2019 to July 2020 (n = 35) and were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify levels of acetaminophen, ibuprofen and their metabolites, acetaminophen sulfate and carboxy-ibuprofen. Results showed 100% detection frequency of all analytes in all samples across the duration of the study. Mass loadings of acetaminophen (918.4 g day-1 +/- 354.8 g day-1) were higher than ibuprofen (182.9 g day-1 +/- 49.8 g day-1), potentially driven by flushing behaviors rather than consumption activities. However, ibuprofen was more heavily consumed than acetaminophen across all days of the study period. Comparisons to COVID-19 clinical cases data showed increased use in ibuprofen with increases in clinical cases loads, while acetaminophen showed no change, suggesting ibuprofen was the over the counter (OTC) medication of choice during the first wave of the pandemic.

Phthalates are ubiquitous in the built environment and are used across various fields, despite known endocrine disruptive properties, and other associated health hazards, including abnormalities in reproductive health and development. I investigated the presence of phthalates in the built environment using the Health Product Declaration (HPD) repository to survey for products containing these chemicals, investigated the literature for possible health effects and alternatives to phthalates, and conducted a laboratoy-based feasibility study of urinary biomarkers associated with phthalates using wastewater-based epidemiology (WBE) on a US university campus at the building-scale. Of the 5,278 products in the HPD repository, 73 contained phthalates and were most commonly found in windows, doors, flooring, sealants, insulations, and furnishings. Alternative plasticizers (cardanol, epoxidized soybean oil, hydrogenated castor oil) usage were identified in 10 products from HPD repository. The two wastewater samples analyzed by liquid chromatography-tandem mass spectrometry (LC-MS-MS) showed that dimethyl phthalate (DMP) was detectable, as well as its human metabolite, monomethyl phthalate (MMP), observed at a concentration of 163-202 ng/L. These results indicate low human exposure from the building materials in the limited convenience sample investigated. Future studies of building scale wastewater-based epidemiology are recommended to investigate these and other phthalates commonly found in the built environment, including diisononyl phthalate (DINP) and diisononyl hexahydrophthalate (DINCH).