Candidate Name: Hussian Maanaki
Program: Bioinformatics and Computational Biology
Committee Chairs: Dr. Jun Wang and Dr. Xiuxia Du
Committee Members: Dr. Anthony Fodor, Dr. Jun-tao Guo, and Dr. Terry Xu
Abstract:

The early and accurate detection of metabolic imbalances is essential for managing chronic diseases such as diabetes mellitus (DM) and chronic kidney disease (CKD), which are heavily interconnected and exacerbated by oxidative stress (OS). Specifically, DM is an epidemiological disease afflicting ~12% of the United States population and is the largest driving factor for the development of CKD, accounting for approximately 40-50% of new cases. Due to the high prevalence of diabetes and CKD, and their bidirectional interactions through OS, it is of great importance to develop reliable point-of-care (POC) screening tools to prevent associated outcomes such as cardiovascular disease, end-stage renal disease, and death. In this dissertation, we propose the development of a rapid, portable, and sensitive multiplexed biosensor for POC assessment of CKD, DM, and OS induced diabetic nephropathy through measurements of creatinine/urea, glucose, and hydrogen peroxide, respectively. This work presents the first development of a multiplexed biosensor under a unified platform for holistic detection of key metabolites associated with diabetes-induced CKD. In chapters 3 and 5, the resistive nanosensor platform leverages the proton-sensitive transport properties of polyaniline nanofibers (PAnNFs) or multi-walled carbon nanotube (MWCNT)/PAnNF nanocomposite for the detection of creatinine, urea, and glucose, through hydroxyl generation by creatinine deiminase and urease or proton generation by glucose oxidase, respectively. Specifically, generated hydroxyls or protons results in dedoping or doping of PAnNFs, thereby decreasing, or increasing conductance across the nanosensor, respectively. Furthermore, in chapter 4, a copper-decorated zirconia (Cu@ZrO2) nanozyme is integrated with PAnNFs to produce a Cu@ZrO2/PAnNF nanocomposite for non-enzymatic detection of hydrogen peroxide. This preliminary approach takes advantage of the peroxidase-mimicking ability of Cu@ZrO2 and the redox-sensitive properties of PAnNF(i.e., oxidation results in resistance change). As such, under all approaches, conductance changes can be used to quantify creatinine, urea, glucose, and hydrogen peroxide. Further, the resistive nanosensor platform integrates sample pre-treatments and catalyzing components (i.e., enzymes) with the nanosensor, enabling the detection of creatinine and urea from low volume whole blood samples with no external sample pre-processing. Under optimal conditions, the nanosensor exhibited exceptional analytical performances for: urea in water, dynamic range between 0.03 and 0.2 mM, with limit of detection (LOD) of 0.0059 mM, and relative standard deviation (RSD) of <10%; creatinine in water, dynamic range between 0.05 and 1.0 mM, with LOD of 0.018 mM, and RSD of <10%; glucose in water, dynamic range between 1.25 and 10 mM, with LOD of 0.13 mM, and RSD of <2%; and hydrogen peroxide, dynamic range between 0.05 and 5 mM, with LOD of 0.017 mM. In the last chapter, we provide the development of a preliminary resistive meter and an integrated smartphone mobile app for resistive data collection, processing, and sharing, in applications for health and environmental monitoring. As such, this work shows future promise as a multiplexed system to monitor the progression of renal disease and for pre-screening of diabetes/oxidative stress for the likelihood of developing diabetic nephropathy, improving the health of diabetics.