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- Creators: Harrington Bioengineering Program
- Creators: Pizziconi, Vincent
- Status: Published
Description
One of the most prominent biological challenges for the field of drug delivery is the blood-brain barrier. This physiological system blocks the entry of or actively removes almost all small molecules into the central nervous system (CNS), including many drugs that could be used to treat diseases in the CNS. Previous studies have shown that activation of the adenosine receptor signaling pathway through the use of agonists has been demonstrated to increase BBB permeability. For example, regadenoson is an adenosine A2A receptor agonist that has been shown to disrupt the BBB and allow for increased drug uptake in the CNS. The goal of this study was to verify this property of regadenoson. We hypothesized that co-administration of regadenoson with a non-brain penetrant macromolecule would facilitate its entry into the central nervous system. To test this hypothesis, healthy mice were administered regadenoson or saline concomitantly with a fluorescent dextran solution. The brain tissue was either homogenized to measure quantity of fluorescent molecule, or cryosectioned for imaging with confocal fluorescence microscopy. These experiments did not identify any significant difference in the amount of fluorescence detected in the brain after regadenoson treatment. These results contradict those of previous studies and highlight potential differences in injection methodology, time windows, and properties of brain impermeant molecules.
ContributorsWohlleb, Gregory Michael (Author) / Sirianni, Rachael (Thesis director) / Stabenfeldt, Sarah (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2015-05
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 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
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 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
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 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
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 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
Description
A point of care glucose sensor using electrochemical impedance spectroscopy (EIS) with a glutaraldehyde-linked enzyme shows promise as an effective biosensor platform. This report details the characterization of various factors on optimal binding frequency (OBF) and sensor performance to better prepare the sensor for future experimentation. Utilizing a screen printed carbon electrode, the necessary amount of glucose oxidase was determined to be 10 mg/mL. Binding time trials ranging from 1-3 minutes demonstrated that 1.5 minutes was the optimal binding time. This timeframe produced the strongest impedance response at each glucose concentration. Using this enzyme concentration and binding time, the native OBF of the biosensor was found to be 1.18 Hz using vector analysis. Temperature testing showed little change in OBF in sensors exposed to 4 \u00B0C through 43.3 \u00B0C. Only exposure to 60 \u00B0C resulted in rapid OBF change which was likely due to glucose oxidase becoming denatured. Humidity tests showed little change in OBF and sensor performance between sensors prepared at the humidities of 7.5%, 10.625% and 16.5% humidity. Alternatively, solutions containing common interference molecules such as uric acid, acetaminophen, and ascorbic acid resulted in a highly shifted OBF and drastically reduced signal.
ContributorsMatloff, Daniel (Co-author) / Khanwalker, Mukund (Co-author) / Johns, Jared (Co-author) / LaBelle, Jeffrey (Thesis director) / Pizziconi, Vincent (Committee member) / Lin, Chi (Committee member) / Dean, W.P. Carey School of Business (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
The American Diabetes Association reports that diabetes costs $322 billion annually and affects 29.1 million Americans. The high out-of-pocket cost of managing diabetes can lead to noncompliance causing serious and expensive complications. There is a large market potential for a more cost-effective alternative to the current market standard of screen-printed self-monitoring blood glucose (SMBG) strips. Additive manufacturing, specifically 3D printing, is a developing field that is growing in popularity and functionality. 3D printers are now being used in a variety of applications from consumer goods to medical devices. Healthcare delivery will change as the availability of 3D printers expands into patient homes, which will create alternative and more cost-effective methods of monitoring and managing diseases, such as diabetes. 3D printing technology could transform this expensive industry. A 3D printed sensor was designed to have similar dimensions and features to the SMBG strips to comply with current manufacturing standards. To make the sensor electrically active, various conductive filaments were tested and the conductive graphene filament was determined to be the best material for the sensor. Experiments were conducted to determine the optimal print settings for printing this filament onto a mylar substrate, the industry standard. The reagents used include a mixture of a ferricyanide redox mediator and flavin adenine dinucleotide dependent glucose dehydrogenase. With these materials, each sensor only costs $0.40 to print and use. Before testing the 3D printed sensor, a suitable design, voltage range, and redox probe concentration were determined. Experiments demonstrated that this novel 3D printed sensor can accurately correlate current output to glucose concentration. It was verified that the sensor can accurately detect glucose levels from 25 mg/dL to 400 mg/dL, with an R2 correlation value as high as 0.97, which was critical as it covered hypoglycemic to hyperglycemic levels. This demonstrated that a 3D-printed sensor was created that had characteristics that are suitable for clinical use. This will allow diabetics to print their own test strips at home at a much lower cost compared to SMBG strips, which will reduce noncompliance due to the high cost of testing. In the future, this technology could be applied to additional biomarkers to measure and monitor other diseases.
ContributorsAdams, Anngela (Author) / LaBelle, Jeffrey (Thesis advisor) / Pizziconi, Vincent (Committee member) / Abbas, James (Committee member) / Arizona State University (Publisher)
Created2017
Description
Neurological disorders are difficult to treat with current drug delivery methods due to their inefficiency and the lack of knowledge of the mechanisms behind drug delivery across the blood brain barrier (BBB). Nanoparticles (NPs) are a promising drug delivery method due to their biocompatibility and ability to be modified by cell penetrating peptides, such as transactivating transciptor (TAT) peptide, which has been shown to increase efficiency of delivery. There are multiple proposed mechanisms of TAT-mediated delivery that also have size restrictions on the molecules that can undergo each BBB crossing mechanism. The effect of nanoparticle size on TAT-mediated delivery in vivo is an important aspect to research in order to better understand the delivery mechanisms and to create more efficient NPs. NPs called FluoSpheres are used because they come in defined diameters unlike polymeric NPs that have a broad distribution of diameters. Both modified and unmodified 100nm and 200nm NPs were able to bypass the BBB and were seen in the brain, spinal cord, liver, and spleen using confocal microscopy and a biodistribution study. Statistically significant differences in delivery rate of the different sized NPs or between TAT-modified and unmodified NPs were not found. Therefore in future work a larger range of diameter size will be evaluated. Also the unmodified NPs will be conjugated with scrambled peptide to ensure that both unmodified and TAT-modified NPs are prepared in identical fashion to better understand the role of size on TAT targeting. Although all the NPs were able to bypass the BBB, future work will hopefully provide a better representation of how NP size effects the rate of TAT-mediated delivery to the CNS.
ContributorsCeton, Ricki Ronea (Author) / Stabenfeldt, Sarah (Thesis director) / Sirianni, Rachael (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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 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