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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
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Description

The purpose of this study, which was done in conjunction with the Arizona Heart Foundation, was to evaluate whether pyridoxine accelerates ulcer wound healing in diabetic patients with ulcers in the lower extremities. In this study, 100 mg of pyridoxine per day was given to patients in the experimental grou

The purpose of this study, which was done in conjunction with the Arizona Heart Foundation, was to evaluate whether pyridoxine accelerates ulcer wound healing in diabetic patients with ulcers in the lower extremities. In this study, 100 mg of pyridoxine per day was given to patients in the experimental group (while they receive normal wound treatment) while patients in the control group received normal treatment of wounds without the pyridoxine. Over time, wound healing was evaluated by photographing and then measuring the size of patients' ulcer wounds on the photographs. Results from the experimental group were compared with those of the control group to evaluate the efficacy of the pyridoxine treatment. In addition, comparisons of the healing rates were made with respect to whether the patients smoked, had hypertension or hypotension, and the patients' body mass indexes. It has been found that there was no statistically significant difference in the mean healing rates between the control groups and experimental groups. In addition, it has been found that smoking, BMI and blood pressure did not have a statistically appreciable effect on the difference in mean healing rates between the control and experimental groups. This is evidence that pyridoxine did not have a statistically significant effect on wound healing rates.

ContributorsHaupt, Shawn Anthony (Author) / Caplan, Michael (Thesis director) / Pauken, Christine (Committee member) / Pagan, Pedro (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2013-05
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Description
Diabetes is a growing epidemic in developing countries, specifically in rural Kenya. In addition to the high cost of glucose testing, many diabetics in Kenya do not understand the importance of testing their blood glucose, let alone the nature of the disease. This project addresses the insufficiency of educational materials

Diabetes is a growing epidemic in developing countries, specifically in rural Kenya. In addition to the high cost of glucose testing, many diabetics in Kenya do not understand the importance of testing their blood glucose, let alone the nature of the disease. This project addresses the insufficiency of educational materials regarding diabetes in rural Kenya. The resulting documents can easily be adjusted for use in other developing countries.
ContributorsBuchak, Jacqueline (Author) / Caplan, Michael (Thesis director) / Snyder, Jan (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
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Description
Type II diabetes is a serious, chronic metabolic disease that has serious impacts on both the health and quality of life in patients diagnosed with the disease. Type II diabetes is also a very prevalent disease both in the United States and around the world. There is still a lot

Type II diabetes is a serious, chronic metabolic disease that has serious impacts on both the health and quality of life in patients diagnosed with the disease. Type II diabetes is also a very prevalent disease both in the United States and around the world. There is still a lot that is unknown about Type II diabetes, and this study will aim to answer some of these questions. The question posed in this study is whether insulin resistance changes as a function of time after the start of a high fat diet. We hypothesized that peripheral insulin resistance would be observed in animals placed on a high fat diet; and peripheral insulin resistance would have a positive correlation with time. In order to test the hypotheses, four Sprague-Dawley male rats were placed on a high fat diet for 8 weeks, during which time they were subjected to three intraperitonal insulin tolerance tests ((NovoLogTM 1 U/kg). These three tests were conducted at baseline (week 1), week 4, and week 8 of the high fat diet. The test consisted of serially determining plasma glucose levels via a pin prick methodology, and exposing a droplet of blood to the test strip of a glucometer (ACCUCHEKTM, Roche Diagnostics). Two plasma glucose baselines were taken, and then every 15 minutes following insulin injection for one hour. Glucose disposal rates were then calculated by simply dividing the glucose levels at each time point by the baseline value, and multiplying by 100. Area under the curve data was calculated via definite integral. The area under the curve data was then subjected to a single analysis of variance (ANOVA), with a statistical significance threshold of p<0.05. The results of the study did not indicate the development of peripheral insulin resistance in the animals placed on a high fat diet. Insulin-mediated glucose disposal was about 50% at 30 minutes in all four animals, during all three testing periods. Furthermore, the ANOVA resulted in p=0.92, meaning that the data was not statistically significant. In conclusion, peripheral insulin resistance was not observed in the animals, meaning no determination could be made on the relation between time and insulin resistance.
ContributorsBrown, Kellen Andrew (Author) / Caplan, Michael (Thesis director) / Herman, Richard (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
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Description

2D fetal echocardiography (ECHO) can be used for monitoring heart development in utero. This study’s purpose is to empirically model normal fetal heart growth and function changes during development by ECHO and compare these to fetuses diagnosed with and without cardiomyopathy with diabetic mothers. There are existing mathematical models describing

2D fetal echocardiography (ECHO) can be used for monitoring heart development in utero. This study’s purpose is to empirically model normal fetal heart growth and function changes during development by ECHO and compare these to fetuses diagnosed with and without cardiomyopathy with diabetic mothers. There are existing mathematical models describing fetal heart development but they warrant revalidation and adjustment. 377 normal fetuses with healthy mothers, 98 normal fetuses with diabetic mothers, and 37 fetuses with cardiomyopathy and diabetic mothers had their cardiac structural dimensions, cardiothoracic ratio, valve flow velocities, and heart rates measured by fetal ECHO in a retrospective chart review. Cardiac features were fitted to linear functions, with respect to gestational age, femur length, head circumference, and biparietal diameter and z-scores were created to model normal fetal growth for all parameters. These z-scores were used to assess what metrics had no difference in means between the normal fetuses of both healthy and diabetic mothers but differed from those diagnosed with cardiomyopathy. It was found that functional metrics like mitral and tricuspid E wave and pulmonary velocity could be important predictors for cardiomyopathy when fitted by gestational age, femur length, head circumference, and biparietal diameter. Additionally, aortic and tricuspid annulus diameters when fitted to estimated gestational age showed potential to be predictors for fetal cardiomyopathy. While the metrics overlapped over their full range, combining them together may have the potential for predicting cardiomyopathy in utero. Future directions of this study will explore creating a classifier model that can predict cardiomyopathy using the metrics assessed in this study.

ContributorsMishra, Shambhavi (Co-author) / Numani, Asfia (Co-author) / Sweazea, Karen (Thesis director) / Plasencia, Jonathan (Committee member) / Economics Program in CLAS (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

2D fetal echocardiography (ECHO) can be used for monitoring heart development in utero. This study’s purpose is to empirically model normal fetal heart growth and function changes during development by ECHO and compare these to fetuses diagnosed with and without cardiomyopathy with diabetic mothers. There are existing mathematical models describing

2D fetal echocardiography (ECHO) can be used for monitoring heart development in utero. This study’s purpose is to empirically model normal fetal heart growth and function changes during development by ECHO and compare these to fetuses diagnosed with and without cardiomyopathy with diabetic mothers. There are existing mathematical models describing fetal heart development but they warrant revalidation and adjustment. 377 normal fetuses with healthy mothers, 98 normal fetuses with diabetic mothers, and 37 fetuses with cardiomyopathy and diabetic mothers had their cardiac structural dimensions, cardiothoracic ratio, valve flow velocities, and heart rates measured by fetal ECHO in a retrospective chart review. Cardiac features were fitted to linear functions, with respect to gestational age, femur length, head circumference, and biparietal diameter and z-scores were created to model normal fetal growth for all parameters. These z-scores were used to assess what metrics had no difference in means between the normal fetuses of both healthy and diabetic mothers, but differed from those diagnosed with cardiomyopathy. It was found that functional metrics like mitral and tricuspid E wave and pulmonary velocity could be important predictors for cardiomyopathy when fitted by gestational age, femur length, head circumference, and biparietal diameter. Additionally, aortic and tricuspid annulus diameters when fitted to estimated gestational age showed potential to be predictors for fetal cardiomyopathy. While the metrics overlapped over their full range, combining them together may have the potential for predicting cardiomyopathy in utero. Future directions of this study will explore creating a classifier model that can predict cardiomyopathy using the metrics assessed in this study.

ContributorsNumani, Asfia (Co-author) / Mishra, Shambhavi (Co-author) / Sweazea, Karen (Thesis director) / Plasencia, Jon (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05