Matching Items (124)
Description
The representation of a patient’s characteristics as the parameters of a model is a key component in many studies of personalized medicine, where the underlying mathematical models are used to describe, explain, and forecast the course of treatment. In this context, clinical observations form the bridge between the mathematical frameworks and applications. However, the formulation and theoretical studies of the models and the clinical studies are often not completely compatible, which is one of the main obstacles in the application of mathematical models in practice. The goal of my study is to extend a mathematical framework to model prostate cancer based mainly on the concept of cell-quota within an evolutionary framework and to study the relevant aspects for the model to gain useful insights in practice. Specifically, the first aim is to construct a mathematical model that can explain and predict the observed clinical data under various treatment combinations. The second aim is to find a fundamental model structure that can capture the dynamics of cancer progression within a realistic set of data. Finally, relevant clinical aspects such as how the patient's parameters change over the course of treatment and how to incorporate treatment optimization within a framework of uncertainty quantification, will be examined to construct a useful framework in practice.
ContributorsPhan, Tin (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric J (Committee member) / Crook, Sharon (Committee member) / Maley, Carlo (Committee member) / Bryce, Alan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Synthetic biology (SB) has become an important field of science focusing on designing and engineering new biological parts and systems, or re-designing existing biological systems for useful purposes. The dramatic growth of SB throughout the past two decades has not only provided us numerous achievements, but also brought us more timely and underexplored problems. In SB's entire history, mathematical modeling has always been an indispensable approach to predict the experimental outcomes, improve experimental design and obtain mechanism-understanding of the biological systems. \textit{Escherichia coli} (\textit{E. coli}) is one of the most important experimental platforms, its growth dynamics is the major research objective in this dissertation. Chapter 2 employs a reaction-diffusion model to predict the \textit{E. coli} colony growth on a semi-solid agar plate under multiple controls. In that chapter, a density-dependent diffusion model with non-monotonic growth to capture the colony's non-linear growth profile is introduced. Findings of the new model to experimental data are compared and contrasted with those from other proposed models. In addition, the cross-sectional profile of the colony are computed and compared with experimental data. \textit{E. coli} colony is also used to perform spatial patterns driven by designed gene circuits. In Chapter 3, a gene circuit (MINPAC) and its corresponding pattern formation results are presented. Specifically, a series of partial differential equation (PDE) models are developed to describe the pattern formation driven by the MINPAC circuit. Model simulations of the patterns based on different experimental conditions and numerical analysis of the models to obtain a deeper understanding of the mechanisms are performed and discussed. Mathematical analysis of the simplified models, including traveling wave analysis and local stability analysis, is also presented and used to explore the control strategies of the pattern formation. The interaction between the gene circuit and the host \textit{E. coli} may be crucial and even greatly affect the experimental outcomes. Chapter 4 focuses on the growth feedback between the circuit and the host cell under different nutrient conditions. Two ordinary differential equation (ODE) models are developed to describe such feedback with nutrient variation. Preliminary results on data fitting using both two models and the model dynamical analysis are included.
ContributorsHe, Changhan (Author) / Kuang, Yang (Thesis advisor) / Wang, Xiao (Committee member) / Kostelich, Eric (Committee member) / Tian, Xiaojun (Committee member) / Gumel, Abba (Committee member) / Arizona State University (Publisher)
Created2021
Description
Heart disease is the leading cause of death in the developed world and often occurs following myocardial infarction. Apelin is an endogenous prepropeptide that has been studied for its role in improving cardiac contractility and vasodilation but suffers from a short half-life in the body. By encasing apelin in a nanoparticle patch, we were able to slowly release apelin to cardiac tissue and observe its effects for one month following induced myocardial infarction surgery in mice. This study demonstrates that the apelin nanoparticles can protect the heart from myocardial-induced heart failure, observing overall improved cardiac function and reduction of fibrotic scarring associated with post-myocardial infarction compared to a nontreated group.
ContributorsHenderson, Adam (Author) / Chen, Qiang (Thesis director) / Zhu, Wuqiang (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2022-05
Description
Weight stigma is present in many aspects of society, and especially in medicine. Weight stigma has detrimental effects on individuals physical and mental health, as well as patient-physician interactions. Application of weight-neutral healthcare ideologies such as Health at Every Size (HAES) are promising ways of decreasing weight stigma within the medical field without reducing the focus on improving patient health. Most widely applicable interventions include changing the focus of interactions from weight to health-promoting behaviors and lab values, improving provider education, and improving the general population's awareness of the problem.
ContributorsBrouhard, Mya (Author) / Chen, Qiang (Thesis director) / Parker, Lynn (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / Department of Psychology (Contributor)
Created2024-05
Description
Platelet Rich Plasma (PRP) is an emerging procedure in regenerative medicine that offers a non-surgical minimally invasive way for tissue repair and regeneration. PRP has many different bioactive molecules that are able to influence and help achieve greater recovery and regenerative outcomes. Diet has many effects on platelets and looking at the mechanism in which platelet function and aggregation are affected with different diets shows how they are able to affect PRP therapy. Looking at these mechanisms allows for better physician recommendations for preprocedural diets to optimize efficacy. This paper conducts a systematic review to investigate the influence that diet can have on PRP outcomes. It was shown that high fat diets lower the efficacy of treatment while the Mediterranean diet helps promote platelet function and help efficacy. The future is to look at more diets while also integrating lifestyle choice before treatment for optimal outcomes.
ContributorsLaguna, Sebastian (Author) / Chen, Qiang (Thesis director) / Goyle, Ashu (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2024-05
Description
Since the 20th century, Arizona has undergone shifts in agricultural practices, driven by urban expansion and crop irrigation regulations. These changes present environmental challenges, altering atmospheric processes and influencing climate dynamics. Given the potential threats of climate change and drought on water availability for agriculture, further modifications in the agricultural landscape are expected. To understand these land use changes and their impact on carbon dynamics, our study quantified aboveground carbon storage in both cultivated and
abandoned agricultural fields. To accomplish this, we employed Python and various geospatial libraries in Jupyter Notebook files, for thorough dataset assembly and visual, quantitative analysis. We focused on nine counties known for high cultivation levels, primarily located in the lower latitudes of Arizona. Our analysis investigated carbon dynamics across not only abandoned and actively cultivated croplands but also neighboring uncultivated land, for which we estimated the extent. Additionally, we compared these trends with those observed in developed land areas.
The findings revealed a hierarchy in aboveground carbon storage, with currently cultivated lands having the lowest levels, followed by abandoned croplands and uncultivated wilderness. However, wilderness areas exhibited significant variation in carbon storage by county compared to cultivated and abandoned lands. Developed lands ranked highest in aboveground carbon storage, with the median value being the highest. Despite county-wide variations, abandoned croplands generally contained more carbon than currently cultivated areas, with adjacent wilderness lands containing even more than both. This trend suggests that cultivating croplands in the region reduces aboveground carbon stores, while abandonment allows for some replenishment, though only to a limited extent. Enhancing carbon stores in Arizona can be achieved through active restoration efforts on abandoned cropland. By promoting native plant regeneration and boosting aboveground carbon levels, these measures are crucial for improving carbon sequestration. We strongly advocate for implementing this step to facilitate the regrowth of native plants and enhance overall carbon storage in the region.
ContributorsGoodwin, Emily (Author) / Eikenberry, Steffen (Thesis director) / Kuang, Yang (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
Glioblastoma Multiforme is a prevalent and aggressive brain tumor. It has an average 5-year survival rate of 6% and average survival time of 14 months. Using patient-specific MRI data from the Barrow Neurological Institute, this thesis investigates the impact of parameter manipulation on reaction-diffusion models for predicting and simulating glioblastoma growth. The study aims to explore key factors influencing tumor morphology and to contribute to enhancing prediction techniques for treatment.
ContributorsShayegan, Tara (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution & Social Change (Contributor)
Created2024-05
Description
Coccidioidomycosis, or valley fever (VF), is a fungal infection caused by Coccidioides that is highly endemic in southern Arizona and central California. The antibody response to infection in combination with clinical presentation and radiographic findings are often used to diagnose disease, as a highly sensitive and specific antigen-based assay has yet to be developed and commercialized. In this dissertation, a panel of monoclonal antibodies (mAbs) was generated in an attempt to identify circulating antigen in VF-positive patients. Despite utilizing a mixture of antigens, almost all mAbs obtained were against chitinase 1 (CTS1), a protein previously identified as a main component in serodiagnostic reagents. While CTS1 was undoubtedly a dominant seroreactive antigen, it was not successfully detected in circulation in patient samples prompting a shift toward further understanding the importance of CTS1 in antibody-based diagnostic assays. Interestingly, depletion of this antigen from diagnostic antigen preparations resulted in complete loss of patient IgG reactivity by immunodiffusion. This finding encouraged the development of a rapid, 10-minute point-of-care test in lateral flow assay (LFA) format to exclusively detect anti-CTS1 antibodies from human and non-human animal patients with coccidioidal infection. A CTS1 LFA was developed that demonstrated 92.9% sensitivity and 97.7% specificity when compared to current quantitative serologic assays (complement fixation and immunodiffusion). A commercially available LFA that utilizes a proprietary mixture of antigens was shown to be less sensitive (64.3%) and less specific (79.1%). This result provides evidence that a single antigen can be used to detect antibodies consistently and accurately from patients with VF. The LFA presented here shows promise as a helpful tool to rule-in or rule-out a diagnosis of VF such that patients may avoid unnecessary antibacterial treatments, improving healthcare efficiency.
ContributorsGrill, Francisca J (Author) / Lake, Douglas F (Thesis advisor) / Magee, D Mitch (Committee member) / Grys, Thomas (Committee member) / Chen, Qiang (Committee member) / Arizona State University (Publisher)
Created2023
Description
Food insecurity is an economic and social condition involving limited or uncertain access to food. The problem of food insecurity in communities is influenced by economic conditions, food deserts, and barriers to accessing healthy food. Individuals experiencing food insecurity often endure concurrent problems of financial instability, hunger, and poor mental and physical health. Public and non-profit services in the U.S., such as the federally supported Supplemental Nutrition Assistance Program (SNAP) and community food banks, provide food-related assistance to individuals who are at a high risk of experiencing food insecurity. Unfortunately, many individuals who qualify for these services still experience food insecurity due to barriers preventing them from accessing food, which may include inadequate finances, transportation, skills, and information. Effective approaches for removing barriers that prevent individuals from accessing food are needed to mitigate the increased risk of hunger, nutritional deficiencies, and chronic disease among vulnerable populations. This dissertation tested a novel food insecurity intervention using informational nudges to promote food security through the elimination of information barriers to accessing food. The intervention used in this mixed-methods feasibility study consisted of informational nudges in the form of weekly text messages that were sent to food pantry clients experiencing food insecurity. The study aims were to test the efficacy and acceptability of the intervention by examining whether the informational nudges could enhance food pantry utilization, increase SNAP registration, and promote food security. Quantitative study results showed a lower prevalence of food insecurity in the intervention group than the control group. Qualitative findings revealed how the intervention group found the text messages to be helpful and informative. These study findings can enhance future food insecurity interventions aiming to eliminate barriers that prevent individuals who are food insecure from accessing healthy food.
ContributorsRoyer, Michael F. (Author) / Wharton, Christopher (Thesis advisor) / Buman, Matthew (Committee member) / Der Ananian, Cheryl (Committee member) / MacKinnon, David (Committee member) / Ohri-Vachaspati, Punam (Committee member) / Arizona State University (Publisher)
Created2023
Description
A description of numerical and analytical work pertaining to models that describe the growth and progression of glioblastoma multiforme (GBM), an aggressive form of primary brain cancer. Two reaction-diffusion models are used: the Fisher-Kolmogorov-Petrovsky-Piskunov equation and a 2-population model that divides the tumor into actively proliferating and quiescent (or necrotic) cells. The numerical portion of this work (chapter 2) focuses on simulating GBM expansion in patients undergoing treatment for recurrence of tumor following initial surgery. The models are simulated on 3-dimensional brain geometries derived from magnetic resonance imaging (MRI) scans provided by the Barrow Neurological Institute. The study consists of 17 clinical time intervals across 10 patients that have been followed in detail, each of whom shows significant progression of tumor over a period of 1 to 3 months on sequential follow up scans. A Taguchi sampling design is implemented to estimate the variability of the predicted tumors to using 144 different choices of model parameters. In 9 cases, model parameters can be identified such that the simulated tumor contains at least 40 percent of the volume of the observed tumor. In the analytical portion of the paper (chapters 3 and 4), a positively invariant region for our 2-population model is identified. Then, a rigorous derivation of the critical patch size associated with the model is performed. The critical patch (KISS) size is the minimum habitat size needed for a population to survive in a region. Habitats larger than the critical patch size allow a population to persist, while smaller habitats lead to extinction. The critical patch size of the 2-population model is consistent with that of the Fisher-Kolmogorov-Petrovsky-Piskunov equation, one of the first reaction-diffusion models proposed for GBM. The critical patch size may indicate that GBM tumors have a minimum size depending on the location in the brain. A theoretical relationship between the size of a GBM tumor at steady-state and its maximum cell density is also derived, which has potential applications for patient-specific parameter estimation based on magnetic resonance imaging data.
ContributorsHarris, Duane C. (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric J. (Thesis advisor) / Preul, Mark C. (Committee member) / Crook, Sharon (Committee member) / Gardner, Carl (Committee member) / Arizona State University (Publisher)
Created2023