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Description
Diabetes mellitus is a disease characterized by many chronic and acute conditions. With the prevalence and cost quickly increasing, we seek to improve on the current standard of care and create a rapid, label free sensor for glycated albumin (GA) index using electrochemical impedance spectroscopy (EIS). The antibody, anti-HA, was

Diabetes mellitus is a disease characterized by many chronic and acute conditions. With the prevalence and cost quickly increasing, we seek to improve on the current standard of care and create a rapid, label free sensor for glycated albumin (GA) index using electrochemical impedance spectroscopy (EIS). The antibody, anti-HA, was fixed to gold electrodes and a sine wave of sweeping frequencies was induced with a range of HA, GA, and GA with HA concentrations. Each frequency in the impedance sweep was analyzed for highest response and R-squared value. The frequency with both factors optimized is specific for both the antibody-antigen binding interactions with HA and GA and was determined to be 1476 Hz and 1.18 Hz respectively in purified solutions. The correlation slope between the impedance response and concentration for albumin (0 \u2014 5400 mg/dL of albumin) was determined to be 72.28 ohm/ln(mg/dL) with an R-square value of 0.89 with a 2.27 lower limit of detection. The correlation slope between the impedance response and concentration for glycated albumin (0 \u2014 108 mg/dL) was determined to be -876.96 ohm/ln(mg/dL) with an R-squared value of 0.70 with a 0.92 mg/dL lower limit of detection (LLD). The above data confirms that EIS offers a new method of GA detection by providing unique correlation with albumin as well as glycated albumin. The unique frequency response of GA and HA allows for modulation of alternating current signals so that several other markers important in the management of diabetes could be measured with a single sensor. Future work will be necessary to establish multimarker sensing on one electrode.
ContributorsEusebio, Francis Ang (Author) / LaBelle, Jeffrey (Thesis director) / Pizziconi, Vincent (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
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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
Currently, the management of diabetes mellitus (DM) involves the monitoring of only blood glucose using self-monitoring blood glucose devices (SMBGs) followed by taking interventional steps, if needed. To increase the amount of information that diabetics can have to base DM care decisions off of, the development of an insulin biosensor

Currently, the management of diabetes mellitus (DM) involves the monitoring of only blood glucose using self-monitoring blood glucose devices (SMBGs) followed by taking interventional steps, if needed. To increase the amount of information that diabetics can have to base DM care decisions off of, the development of an insulin biosensor is explored. Such a biosensor incorporates electrochemical impedance spectroscopy (EIS) to ensure an extremely sensitive platform. Additionally, anti-insulin antibody was immobilized onto the surface of a gold disk working electrode to ensure a highly specific sensing platform as well. EIS measurements were completed with a 5mV sine wave that was swept through the frequency spectrum of 100 kHz to 1 Hz on concentrations of insulin ranging from 0 pM to 100 μM. The frequency at which the interaction between insulin and its antibody was optimized was determined by finding out at which frequency the R2 and slope of the impedance-concentration plot were best. This frequency, otherwise known as the optimal binding frequency, was determined to be 459 Hz. Three separate electrodes were developed and the impedance data for each concentration measured at 459 Hz was averaged and plotted against the LOG (pM insulin) to construct the calibration curve. The response was calculated to be 263.64 ohms/LOG(pM insulin) with an R2 value of 0.89. Additionally, the average RSD was determined to be 19.24% and the LLD was calculated to be 8.47 pM, which is well below the physiological normal range. These results highlight the potential success of developing commercial point-of-care insulin biosensors or multi-marker devices operating with integrated insulin detection.
ContributorsDecke, Zachary William (Author) / LaBelle, Jeffrey (Thesis director) / Pizziconi, Vincent (Committee member) / Cook, Curtiss (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

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
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Description

Carbohydrate counting has been shown to improve HbA1c levels for people with diabetes. However, the learning curve and inconvenience of carbohydrate counting make it difficult for patients to adhere to it. A deep learning model is proposed to identify food from an image, where it can help the user manage

Carbohydrate counting has been shown to improve HbA1c levels for people with diabetes. However, the learning curve and inconvenience of carbohydrate counting make it difficult for patients to adhere to it. A deep learning model is proposed to identify food from an image, where it can help the user manage their carbohydrate counting. This early model has a 68.3% accuracy of identifying 101 different food classes. A more refined model in future work could be deployed into a mobile application to identify food the user is about to consume and log it for easier carbohydrate counting.

ContributorsCarreto, Cesar (Author) / Pizziconi, Vincent (Thesis director) / Vernon, Brent (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05