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Monitoring complex diseases and their comorbidities requires accurate and convenient measurements of multiple biomarkers. However, many state-of-the-art bioassays not only require complicated and time-consuming procedures, but also measure only one biomarker at a time. This noncomprehensive single-biomarker monitoring, as well as the cost and complexity of these bioassays advocate for

Monitoring complex diseases and their comorbidities requires accurate and convenient measurements of multiple biomarkers. However, many state-of-the-art bioassays not only require complicated and time-consuming procedures, but also measure only one biomarker at a time. This noncomprehensive single-biomarker monitoring, as well as the cost and complexity of these bioassays advocate for a simple, rapid multi-marker sensing platform suitable for point-of-care or self-monitoring settings. To address this need, diabetes mellitus was selected as the example complex disease, with dry eye disease and cardiovascular disease as the example comorbidities. Seven vital biomarkers from these diseases were selected to investigate the platform technology: lactoferrin (Lfn), immunoglobulin E (IgE), insulin, glucose, lactate, low density lipoprotein (LDL), and high density lipoprotein (HDL). Using electrochemical techniques such as amperometry and electrochemical impedance spectroscopy (EIS), various single- and dual-marker sensing prototypes were studied. First, by focusing on the imaginary impedance of EIS, an analytical algorithm for the determination of optimal frequency and signal deconvolution was first developed. This algorithm helped overcome the challenge of signal overlapping in EIS multi-marker sensors, while providing a means to study the optimal frequency of a biomarker. The algorithm was then applied to develop various single- and dual-marker prototypes by exploring different kinds of molecular recognition elements (MRE) while studying the optimal frequencies of various biomarkers with respect to their biological properties. Throughout the exploration, 5 single-marker biosensors (glucose, lactate, insulin, IgE, and Lfn) and one dual-marker (LDL and HDL) biosensor were successfully developed. With the aid of nanoparticles and the engineering design of experiments, the zeta potential, conductivity, and molecular weight of a biomarker were found to be three example factors that contribute to a biomarker’s optimal frequency. The study platforms used in the study did not achieve dual-enzymatic marker biosensors (glucose and lactate) due to signal contamination from localized accumulation of reduced electron mediators on self-assembled monolayer. However, amperometric biosensors for glucose and lactate with disposable test strips and integrated samplers were successfully developed as a back-up solution to the multi-marker sensing platform. This work has resulted in twelve publications, five patents, and one submitted manuscripts at the time of submission.
ContributorsLin, Chi En (Author) / La Belle, Jeffrey T (Thesis advisor) / Caplan, Michael (Committee member) / Cook, Curtiss B (Committee member) / Stabenfeldt, Sarah (Committee member) / Spano, Mark (Committee member) / Arizona State University (Publisher)
Created2018