Matching Items (8)
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- All Subjects: medicine
- All Subjects: Traumatic Brain Injury
- Creators: LaBelle, Jeffrey
Description
In the search for chemical biosensors designed for patient-based physiological applications, non-invasive diagnostic approaches continue to have value. The work described in this thesis builds upon previous breath analysis studies. In particular, it seeks to assess the adsorptive mechanisms active in both acetone and ethanol biosensors designed for breath analysis. The thermoelectric biosensors under investigation were constructed using a thermopile for transduction and four different materials for biorecognition. The analytes, acetone and ethanol, were evaluated under dry-air and humidified-air conditions. The biosensor response to acetone concentration was found to be both repeatable and linear, while the sensor response to ethanol presence was also found to be repeatable. The different biorecognition materials produced discernible thermoelectric responses that were characteristic for each analyte. The sensor output data is presented in this report. Additionally, the results were evaluated against a mathematical model for further analysis. Ultimately, a thermoelectric biosensor based upon adsorption chemistry was developed and characterized. Additional work is needed to characterize the physicochemical action mechanism.
ContributorsWilson, Kimberly (Author) / Guilbeau, Eric (Thesis advisor) / Pizziconi, Vincent (Thesis advisor) / LaBelle, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2011
DescriptionMy main goal for my thesis is in conjunction with the research I started in the summer of 2010 regarding the creation of a TBI continuous-time sensor. Such goals include: characterizing the proteins in sensing targets while immobilized, while free in solution, and while in free solution in the blood.
ContributorsHaselwood, Brittney (Author) / LaBelle, Jeffrey (Thesis director) / Pizziconi, Vincent (Committee member) / Cook, Curtiss (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2011-12
Description
This paper summarizes the [1] ideas behind, [2] needs, [3] development, and [4] testing of 3D-printed sensor-stents known as Stentzors. This sensor was successfully developed entirely from scratch, tested, and was found to have an output of 3.2*10-6 volts per RMS pressure in pascals. This paper also recommends further work to render the Stentzor deployable in live subjects, including [1] further design optimization, [2] electrical isolation, [3] wireless data transmission, and [4] testing for aneurysm prevention.
ContributorsMeidinger, Aaron Michael (Author) / LaBelle, Jeffrey (Thesis director) / Frakes, David (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2014-05
Description
Concussions and traumatic brain injuries are mechanical events which can derive from no specific activity or event. However, these injuries occur often during athletic and sporting events but many athletes experiencing these symptoms go undiagnosed and continue playing without proper medical attention. The current gold standard for diagnosing athletes with concussions is to have medical professionals on the sidelines of events to perform qualitative standardized assessments which may not be performed frequently enough and are not specialized for each athlete. The purpose of this report is to discuss a study sanctioned by Arizona State University's Project HoneyBee and additional affiliations to validate a third-party mouth guard device product to recognize and detect force impacts blown to an athlete's head during athletic activity. Current technology in health monitoring medical devices can allow users to apply this device as an additional safety mechanism for early concussion awareness and diagnosis. This report includes the materials and methods used for experimentation, the discussion of its results, and the complications which occurred and areas for improvement during the preliminary efforts of this project. Participants in the study were five non-varsity ASU Wrestling athletes who volunteered to wear a third-party mouth guard device during sparring contact at practice. Following a needed calibration period for the devices, results were recorded both through visual observation and with the mouth guard devices using an accelerometer and gyroscope. This study provided a sound understanding for the operation and functionality of the mouth guard devices. The mouth guard devices have the capability to provide fundamental avenues of research for future investigations.
ContributorsTielke, Austin Wyatt (Author) / Ross, Heather (Thesis director) / LaBelle, Jeffrey (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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
Intracranial aneurysms, which form in the blood vessels of the brain, are particularly dangerous because of the importance and fragility of the human brain. When an intracranial aneurysm gets large it poses a significant risk of bursting and causing subarachnoid hemorrhaging (SAH), a possibly fatal condition. One possible treatment involves placing a stent in the vessel to act as a flow diverter. In this study we look at the hemodynamics of two geometries of idealized basilar tip aneurysms, at 2,3, and 4 ml/s pulsatile flow, at three different points in the cardiac cycle. The smaller model had neck and dome diameters of 2.67 mm and 4 mm respectively, while the larger aneurysm had neck and dome diameters of 3 mm and 6 mm respectively. Both diameters and the dome to neck ratio increased in the second model, representing growth over time. Flow was analyzed using stereoscopic particle image velocimetry (PIV) for both geometries in untreated models, as well as after treatment with a high porosity Enterprise stent (Codman and Shurtleff Inc.). Flow in the models was characterized by root mean square velocity in the aneurysm and neck plane, cross neck flow, max aneurysm vorticity, and total aneurysm kinetic energy. It was found that in the smaller aneurysm model (model 1), Enterprise stent treatment reduced all flow parameters substantially. The smallest reduction was in max vorticity, at 42.48%, and the largest in total kinetic energy, at 75.69%. In the larger model (model 2) there was a 52.18% reduction in cross neck flow, but a 167.28% increase in aneurysm vorticity. The other three parameters experienced little change. These results, along with observed velocity vector fields, indicate a noticeable diversion of flow away from the aneurysm in the stent treated model 1. Treatment in model 2 had a small flow diversion effect, but also altered flow in unpredictable ways, in some cases having a detrimental effect on aneurysm hemodynamics. The results of this study indicate that Enterprise stent treatment is only effective in small, relatively undeveloped aneurysm geometries, and waiting until an aneurysm has grown too large can eliminate this treatment option altogether.
ContributorsLindsay, James Bryan (Author) / Frakes, David (Thesis director) / LaBelle, Jeffrey (Committee member) / Nair, Priya (Committee member) / Barrett, The Honors College (Contributor) / School of Humanities, Arts, and Cultural Studies (Contributor)
Created2013-05
Description
The purpose of this research was to determine and evaluate glutamate oxidase's ability to detect levels of glutamate as part of a working sensor capable of quantifying and detecting stress within the body in the case of adverse neurological events such as traumatic brain injury. Using electrochemical impedance spectroscopy (EIS), a linear dynamic range of glutamate was detected with a slope of 36.604 z/ohm/[pg/mL], a lower detection limit at 12.417 pg/mL, correlation of 0.97, and an optimal binding frequency of 117.20 Hz. After running through a frequency sweep the binding frequency was determined based on the highest consistent reproducibility and slope. The sensor was found to be specific against literature researched non-targets glucose, albumin, and epinephrine and working in dilutions of whole blood up to a concentration of 25%. With the implementation of Nafion, the sensor had a 250% improvement in signal and 155% improvement in correlation in 90% whole blood, illustrating the promise of a working blood sensor. Future work includes longitudinal studies and utilizing mesoporous carbon as the immobilization platform and incorporating this as part of a continuous, multiplexed blood sensor with glucose oxidase.
ContributorsLam, Alexandria Nicole (Author) / LaBelle, Jeffrey (Thesis director) / Ankeny, Casey (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Statistical process control (SPC) and predictive analytics have been used in industrial manufacturing and design, but up until now have not been applied to threshold data of vital sign monitoring in remote care settings. In this study of 20 elders with COPD and/or CHF, extended months of peak flow monitoring (FEV1) using telemedicine are examined to determine when an earlier or later clinical intervention may have been advised. This study demonstrated that SPC may bring less than a 2.0% increase in clinician workload while providing more robust statistically-derived thresholds than clinician-derived thresholds. Using a random K-fold model, FEV1 output was predictably validated to .80 Generalized R-square, demonstrating the adequate learning of a threshold classifier. Disease severity also impacted the model. Forecasting future FEV1 data points is possible with a complex ARIMA (45, 0, 49), but variation and sources of error require tight control. Validation was above average and encouraging for clinician acceptance. These statistical algorithms provide for the patient's own data to drive reduction in variability and, potentially increase clinician efficiency, improve patient outcome, and cost burden to the health care ecosystem.
ContributorsFralick, Celeste (Author) / Muthuswamy, Jitendran (Thesis advisor) / O'Shea, Terrance (Thesis advisor) / LaBelle, Jeffrey (Committee member) / Pizziconi, Vincent (Committee member) / Shea, Kimberly (Committee member) / Arizona State University (Publisher)
Created2013