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Undergraduate Research Opportunities Program

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Publication Search Results

Now showing 1 - 10 of 214

Developing a Recommendation-Based Application to Help Endocrinologists Treat Type II Diabetes Mellitus

(Georgia Institute of Technology, 2023-01-18) Mithaiwala, Aamir

Diabetes Mellitus type II is a disease characterized by abnormally high levels of glucose in the bloodstream (hyperglycemia) due to decreased insulin secretion, insulin resistance, or both. It affects approximately 425 million adults worldwide and is the 7th most common chronic condition according to the CDC (Figure 1).[1] Patients with this disease typically have increased urination, increased thirst, and fatigue and can even be vulnerable to many types of infections. Patients with type II diabetes see diabetes specialists and endocrinologists to effectively treat their disease. Currently, however, there is a massive shortage of endocrinologists in the United States due to a growing demand of chronic diseases such as diabetes and osteoporosis.[2] In one study, the majority of endocrinologists surveyed believed the process of treating diabetes is difficult for these four reasons: the shortage of physicians, constantly evolving diabetes research, rapidly changing medication guidelines, and the rate at which medications are being added to the market.[3] Another major problem in the diabetes community is the risk of potentially inappropriate medications (PIMs), which are defined as prescribing medications that have a greater risk of potentially severe adverse effects. 74% of elderly patients with type II diabetes are prescribed at least one PIM when hospitalized.[4] The studies conducted by Healy et al. and Sharma et al. reveal that the process of treating type II diabetes is difficult because of 3 main reasons: The shortage of endocrinologists, rapidly evolving medication recommendations by diabetes associations, and the health risk to elderly diabetic patients due to PIMs. There is a growing need for technology that assists endocrinologists in prescribing medication based on factors that adjust to the evolving recommendations by the American Diabetes Association and uses patient biomarkers along with other factors to recommend appropriate medications for patients.
Predicting Metabolic Cost Using Cumulative Muscle Activation Per unit Distance

(Georgia Institute of Technology, 2023-01-18) Carter, Jacob M.

Many studies are currently being conducted in order to optimize the functions of exoskeletons in a way that generates the greatest benefit for the user. However, this research is hampered by the traditional methodology of measuring metabolic cost, indirect calorimetry. This study proposes an alternative method, based not on the overall gas exchange of the body, but rather the relative activity of the relevant muscle groups in a method known as Cummulative Muscle Activation Per unit Distance(CMAPD) analysis.
Providing Proprioceptive Feedback Via A Simultaneous Skin Stretch And Vibrotactile Haptic Display

(Georgia Institute of Technology, 2022-05) Lima, Bryanna

This paper presents the development and testing of a non-invasive haptic display that will provide proprioceptive information to a user about the location of the prosthetic device. This is accomplished using a previously designed skin stretch display combined with a vibrotactile display to communicate the location of the imaginary target. In this paper, the haptic display was studied to determine how much the cutaneous feedback improves the accuracy of finding a target location when no visual cues are available. The haptic display was controlled using a potentiometer, where the dial input would be mapped directly to the haptic display output. The accuracy was tested by providing random target locations for participants to navigate the potentiometer to, and the participants tried to accurately find those locations under four feedback conditions: no feedback from the display, skin stretch only feedback, vibration only feedback, and skin stretch and vibration feedback simultaneously. The expected outcome was that the absolute error in accuracy would be less when using the skin stretch and vibration feedback combined compared to navigating without it or with the feedback individually. Future research would include integrating the haptic display with a prosthetic device, and eventually developing an array of skin stretch displays to enable communication of multiple degrees of freedom.
MACHINE LEARNING MODELING OF COVID-19 AND CORONARY HEART DISEASE FOR PUBLIC HEALTH MONITORING AND CLINICAL DECISION SUPPORT

(Georgia Institute of Technology, 2022-05) Epperson, Rachel Elizabeth

Prediction of healthcare trends to improve public health and clinical decision making is a challenge affecting all lives and requires state-of-art mathematical modeling to solve. The use of artificial intelligence (AI) in clinical informatics is becoming more prevalent as healthcare data analytics advances. The goal is to make a better life for humans overall, whether that be physically or mentally. AI can be used to make accurate data driven predictions from the effects of pandemics to coronary heart disease risk assessment. Both of these challenges are important to solve, as we currently live in the COVID-19 pandemic, and many Americans experience heart problems due to the increase in unhealthy trends in the average diet and lifestyle. Through the application of AI, the prevalence of COVID cases across space, time, and populations can be predicted from analysis of available COVID data. Specifically, we use multiple linear models and time series data analysis, such as linear regression and deep/machine learning methods to predict future trends. Pandemic forecasting can be used as a tool for medical professionals to prepare for what is to come. Forecasting is also an essential tool to predict individual health outcomes, especially in one of the most important organs, the heart. For this challenge, we use logistic regression and advanced AI techniques to identify important interactions between clinical features to create a tool for clinical decision support to estimate risk of coronary heart disease. Oftentimes, it is difficult for clinicians to optimize treatment if they are unsure whether their patient is at risk for coronary heart disease. Our risk calculator can aid in this clinical decision making challenge. Both forecasting approaches offer solutions to current healthcare challenges while using state of art tools and data analysis approaches in AI. Through these methods and applications, mankind can work through problems together by taking advantage of the tools created by using AI in forecasting data. We face many problems as a society, and the goal of this research is to alleviate the stress of those involved, as well as potentially lessening the effects on humans physically. Through AI, it is now possible to answer the question of predicting the result of an initial COVID-19 test, as well as calculating the risk that a patient will contract coronary heart disease based on their characteristics, which in turn will aid medical professionals in making clinical decisions for treatment accordingly.
The effects of AT-RvD1 delivery on SPM metabolism, myeloid recruitment, and myogenesis in a murine model of Volumetric Muscle Loss injury

(Georgia Institute of Technology, 2022-05) Pittman, Frank S.

Volumetric Muscle Loss injury (VML) is the partial ablation of skeletal muscle, usually on the extremities, sustained through traumatic or surgical means, such as motor vehicle accidents, military combat, or surgical resection. The frank loss of musculature characteristic of VML sufficiently disrupts or eliminates the wound’s endogenous repair mechanisms such that healing becomes virtually impossible 1,2. VML patients must deal with permanent functional impairments, chronic inflammation, and chronic pain 1. Current clinical strategies for VML treatment include muscle flap autografts and free tissue transfer that, while salvaging the injured limb, are often no better than amputation in terms of functional improvement and patient quality of life 3,4. Much research in the field has been focused on overcoming the challenges and deficits associated with this clinical gold-standard. Biomaterial strategies using decellularized extracellular matrix (ECM) derived from skeletal muscle, porcine small intestinal submucosa (SIS), and urinary bladder matrix (UBM) have been extensively studied, with multiple FDA-approved products available for clinical use 5–7. However, these studies continue to show that minimal levels of physiologically-relevant muscle fibers are regenerated in both human and animal trials of acellular matrices. Instead, regenerated tissue has been overwhelmingly composed of non-functional and non-contractile fibrotic and adipose tissue 6. Common between both the clinical gold standards and the acellular matrix strategies being studied is the over-looking of the inhospitable microenvironment caused by persistent inflammation that serves to activate fibrotic pathways of regeneration 1,7. Thus, the need for an alternative strategy that targets this pathological inflammation and results in better long-term functional outcomes for patients after severe extremity trauma is clear.
The role of action observation in prosthesis learning

(Georgia Institute of Technology, 2022-05) Gale, Mary Katherine

When portions of upper limbs are lost, persons with new amputations must re-learn how to exist within the world. This involves training patients on prosthetic devices that aim to replace limb functionality; however, these prostheses are difficult to use and are often taught poorly. It has been established that the most effective method of teaching prostheses involves having patients observe a teacher who is also using a prosthetic device, but the reasons why are unclear. This work sought to explore the relationship between teacher status and learner outcome through the use of a fictive amputee modeling device (FAMS). Ten subjects attempted to learn use of the FAMS while watching a teacher using their intact limb, and ten subjects attempted the same FAMS task while observing a teacher also using the FAMS. During action observation, gaze positioning was recorded; during action execution, basic kinematic parameters were recorded. Then, kinematic and gaze parameters were examined for how they varied together using canonical correlation analysis. We discovered that those in the matched group had a more streamlined learning process with higher correlation between visual and kinematic variables; on the other hand, the mismatched group experienced a more chaotic learning process with lower correlation between visual and kinematic variables. This suggests that the route to prosthesis learning is more obvious in the presence of a matched teacher, which serves to further emphasize the importance of a matched protocol being the default in a rehabilitation setting.
3D Culture of Mesenchymal Stem Cells in PEG-4MAL Hydrogels Increases Exosome Production with Bacterial Sphingomyelinase Treatment

(Georgia Institute of Technology, 2022-05) York, William

Exosomes are becoming increasingly popular in the fields of regenerative medicine and tissue engineering for their immunomodulatory potential to enhance tissue regeneration. Exosomes are a membrane-bound extracellular vesicle with a diameter between 30-150nm that act as a non-immunogenic delivery method for biological molecules to communicate between cells, regulating cell function and activity. Despite the growing interest in exosomes, the lack of an established production method limits further research. Our lab has previously modulated sphingolipid signaling in human mesenchymal stem cells (MSCs) via sphingomyelinase (SMase), an enzyme that converts cell membrane sphingomyelin to ceramide, increasing cell membrane curvature and subsequent endosome production, the initiating step in exosome biogenesis. We showed that SMase treated MSCs cultured in tissue culture plastic increases overall exosome production compared to untreated MSCs, likely from SMase-mediated sphingomyelin to ceramide conversion. Here, we demonstrate increased exosome production in a 3D microenvironment by encapsulating MSCs into 4-arm polyethylene glycol-maleimide (PEG-4MAL) hydrogels with and without SMase compared to tissue culture plastic controls. We utilize nanoparticle tracking analysis to quantify changes in exosome production induced by a 3D biogenesis. We confirmed the immunomodulatory potential of these exosomes by evaluating their effect on TNF-a levels in LPS-stimulated Macrophages and found anti-inflammatory effects among all exosomes. Not only do these findings contribute to more effective methods for extracellular vesicle production, but further, this platform can be leveraged as a delivery vehicle of exosome-producing-MSCs to a range of pre-clinical injury and disease models as a novel immunotherapy.
Modeling Aortic Valvular Leaflet Thrombosis Risk Following Transcatheter Aortic Valve Replacement

(Georgia Institute of Technology, 2022-05) Venkatesh, Aniket

Calcific aortic stenosis (AS) is the thickening of the aortic valve leaflets in the heart due to calcium buildup. Currently, the most widespread treatment option is transcatheter aortic valve replacement (TAVR), a minimally invasive interventional procedure where a prosthetic valve is delivered to replace the diseased one. Despite TAVR’s effectiveness in treating AS, there have been reported instances of leaflet thrombosis (LT), which is characterized by the formation of blood clots around a leaflet of a prosthetic valve. Therefore, this study aimed to predict the risk of LT following different, controllable prosthetic valve deployment orientations through computational simulations. Normalized circulation, a quantity found to be inversely related to expected thrombus volume, was calculated following each deployment orientation and it was determined that different prosthetic valve commissural alignments may lead to higher NCs and lower risk of LT, but neither different stent rotational orientations nor different stent expansion volumes had a significant effect on NCs, and therefore risk of LT development. Additionally assessment of blood flow profiles post-TAVR deployments demonstrated that the maximum blood speed reached and the maximum pressure drop through the aortic valve were both decreased towards their respective healthy ranges.
Differentiation and Proliferation of Therapeutic T cells from umbilical cord blood derived cells

(Georgia Institute of Technology, 2022-05) Holzworth, Peyton

Cancer is among the leading causes of death worldwide; in 2018, there were 18.1 million new cases and 9.5 million cancer-related deaths. Current methods, such as chemotherapy, have been used for decades and while it is a decent treatment, it is not feasible in the long run for consistent reference because of the effects of radiation and the growing resistance that cancer cells develop. As a result of cancer cells having mutations that can result in “hidden” receptors, making them undiscoverable by the body's T cells, immunotherapy has emerged as a new approach to cancer treatments. Adoptive cell therapies using chimeric antigen receptor (CAR) T cells, more specifically, have shown promise due to their ability to incorporate the receptors and antibodies necessary to discover the cancer cells despite mutated patterns. Umbilical Cord Blood (UCB) T cells are a new, up and coming technology that are obtained through derivation from umbilical cord blood. In this thesis, UCB T cells are combined with 3 variable expansion systems and cultured with and without transduction to determine proliferation rate, phenotypic qualities, and CAR expression to determine the capabilities of their usage as an allogeneic adoptive cell therapy.
The Biomechanics of Elephant Trunk Elongation

(Georgia Institute of Technology, 2022-05) Boyle, Madeline A.

The elephant trunk is one of the largest, most versatile boneless appendages, dwarfing similar muscular hydrostats such as octopus tentacles and chameleon tongues. Little is known about how an elephant’s large size influences the function of the trunk. Analyses of high-speed videography revealed that elephant trunks could extend up to 125% of their resting length, a small percentage compared to the chameleon tongue’s 1000%. Seemingly restricted by their massive size, elephants may employ unique strategies to accomplish complex reaching movements grounded in morphological asymmetries. Using a digital analysis, we determine asymmetric relationships between trunk morphology and elongation mechanics. Not only does the trunk exhibit a forward wave mechanism during elongation, extending one segment at a time, but the dorsal portion can also stretch 15% more than the ventral portion. We discuss how elephant trunks stretch section by section and why the trunk’s skin is as important as the muscles when analyzing reaching tasks.