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.

This report outlines the current methods and instrumentation used for diabetes monitoring and detection, and evaluates the problems that these methods face. Additionally, it will present an approach to remedy these problems. The purpose of this project is to create a potentiostat that is capable of controlling a diabetes meter that monitors multiple biological markers simultaneously. Glucose is the most commonly measured biomarker for diabetes. However, it provides only a limited amount of information. In order to give the user of the meter more information about the progression of his or her disease, the concentrations of several different biological markers for diabetes may be measured using a system that operates in a similar fashion to blood glucose meters. The potentiostat provides an input voltage into the electrode sensor and receives the current from the sensor as the output. From this information, the impedance may be calculated. The concentrations of each of the biomarkers in the blood sample can then be determined. In an effort to increase sensitivity, the diabetes meter forgoes the use of amperometric i-t in favor of the electrochemical impedance spectroscopy technique. A three-electrode electrochemical sensor is used with the meter. In order to perform simultaneous and rapid testing of biomarker concentration, a single multisine input wave is generated using a hardware implementation of a summing amplifier and waveform generators.

In this study, the entrainment of brain dynamics in epilepsy was investigated in a thorough, systematic way. In the first part of the study, diagnosis of epilepsy, elements from the theory of chaos were used to measure the brain dynamics over time from EEGs (electroencephalograms) recorded in humans with either epileptic or non-epileptic seizures. In the second part of the study, treatment of epilepsy, data from rats undergoing VNS (vagus nerve stimulation) treatment were analyzed in the same way. The results suggest that a) the differential diagnosis in humans with epileptic and non-epileptic seizures can be significantly improved by analysis of brain dynamics, and b) the Vagus Nerve Stimulation may be working by controlling the entrainment level of brain dynamics.

Volume depletion can lead to migraines, dizziness, and significant decreases in a subject's ability to physically perform. A major cause of volume depletion is dehydration, or loss in fluids due to an imbalance in fluid intake to fluid excretion. Because proper levels of hydration are necessary in order to maintain both short and long term health, the ability to monitor hydration levels is growing in clinical demand. Although devices capable of monitoring hydration level exist, these devices are expensive, invasive, or inaccurate and do not offer a continuous mode of measurement. The ideal hydration monitor for consumer use needs to be characterized by its portability, affordability, and accuracy. Also, this device would need to be noninvasive and offer continuous hydration monitoring in order to accurately assess fluctuations in hydration data throughout a specified time period. One particular method for hydration monitoring that fits the majority of these criteria is known as bioelectric impedance analysis (BIA). Although current devices using BIA do not provide acceptable levels of accuracy, portability, or continuity in data collection, BIA could potentially be modified to fit many, if not all, desired customer specifications. The analysis presented here assesses the viability of using BIA as a new standard in hydration level measurement. The analysis uses data collected from 22 subjects using an existing device that employs BIA. A regression derived for estimating TBW based on the parameters of age, weight, height, sex, and impedance is presented. Using impedance data collected for each subject, a regression was also derived for estimating impedance based on the factors of age, weight, height, and sex. The derived regression was then used to calculate a new impedance value for each subject, and these new impedance values were used to estimate TBW. Through a paired-t test between the TBW values derived by using the direct measurements versus the calculated measurements of impedance, the two samples were found to be comparable. Considerations for BIA as a noninvasive measurement of hydration are discussed.

A great deal of research has been done on communication barriers between patient and doctor, but due to the complexity of the relationship, little successful solutions have been suggested to bridge interdisciplinary communication between the two persons. This project explores a solution to aid both patient and doctor as they seek to communicate with each other regarding the patient's prognosis and treatment with a medical device. By creating a website, the information found therein can be accessed in the doctor's office by using a smartphone or tablet so that both patient and doctor can use it as a resource before, during, and after a doctor's visit. The website, Medical Devices 4 U (MD4U), gives background information on a large selection of medical devices, allows primary sources to share their information with potential consumers of the medical device, permits users to ask questions and comment on other user's comments, and gives a list of questions that a patient can ask a healthcare professional during a doctor's visit. In this report, the nature of doctor and patient communication is exposed and the steps taken to alleviate the communication barriers by way of creating a website are explained.

In this paper, β-estradiol was characterized utilizing electrochemical impedance spectroscopy (EIS) techniques for the purpose of developing a multi-marker fertility sensor. β-estradiol was immobilized onto the surface of gold disk electrodes to find the optimal binding frequency of estradiol and its respective antibody, anti-17β-estradiol, which was determined to be 37.46Hz. At this frequency a logarithmic relationship between concentration and impedance (Z/ohm) was established creating a concentration calibration curve with a slope of 211 ohm/ln(pg mL-1), an R-squared value of 0.986 and a lower limit of detection of 742 fg mL-1. The specificity and cross-reactivity of the antibody with other hormones was tested through interferent and non-target experiments. Signal-to-noise ratio analysis verified that anti-17β-estradiol exhibited minimal chemical reactions with other hormones (SNR< 3) in non-target experiments. Additionally, there were minimal changes in the amount of signal collected during interferent testing, with albumin and follicle stimulating hormone having SNR values greater than 3. These results, along with the unique frequency response of the antibody-target binding reaction, allow for the possibility of using anti-17β-estradiol and β-estradiol for detecting multiple fertility biomarkers on a single sensor.

The action/adventure game Grad School: HGH is the final, extended version of a BME Prototyping class project in which the goal was to produce a zombie-themed game that teaches biomedical engineering concepts. The gameplay provides fast paced, exciting, and mildly addicting rooms that the player must battle and survive through, followed by an engineering puzzle that must be solved in order to advance to the next room. The objective of this project was to introduce the core concepts of BME to prospective students, rather than attempt to teach an entire BME curriculum. Based on user testing at various phases in the project, we concluded that the gameplay was engaging enough to keep most users' interest through the educational puzzles, and the potential for expanding this project to reach an even greater audience is vast.

The purpose of this project was to examine the viability of protein biomarkers in pre-symptomatic detection of lung cancer. Regular screening has been shown to vastly improve patient survival outcome. Lung cancer currently has the highest occurrence and mortality of all cancers and so a means of screening would be highly beneficial. In this research, the biomarker neuron-specific enolase (Enolase-2, eno2), a marker of small-cell lung cancer, was detected at varying concentrations using electrochemical impedance spectroscopy in order to develop a mathematical model of predicting protein expression based on a measured impedance value at a determined optimum frequency. The extent of protein expression would indicate the possibility of the patient having small-cell lung cancer. The optimum frequency was found to be 459 Hz, and the mathematical model to determine eno2 concentration based on impedance was found to be y = 40.246x + 719.5 with an R2 value of 0.82237. These results suggest that this approach could provide an option for the development of small-cell lung cancer screening utilizing electrochemical technology.