Medicine and Health Sciences Commons^™

Standardizing Social Determinants Of Health Factors From Heterogeneous Sources To Improve Data Fairness, Yifang Dang

Dissertations and Theses (Open Access)

Social Determinants of Health (SDoH) significantly influence health outcomes, yet their representation in computational models remains fragmented. This dissertation addresses this gap by constructing an SDoH ontology (SDoHO), leveraging it to improve large language model (LLM) performance, and using LLMs to extract new knowledge and enrich the ontology. The overarching goal is to establish a feedback loop where ontology development enhances LLM-based extraction, and LLM-derived insights refine and expand the ontology. The methodology is structured across three aims: (1) ontology construction, (2) ontology-assisted LLM enhancement, and (3) LLM-driven ontology enrichment, with a focus on Alzheimer’s Disease and Related Dementias (ADRD). …

Computer-Aided Integrated Rehabilitation For Post-Stroke Patients Using Deep Learning Techniques, Kaichen Tang

Dissertations and Theses (Open Access)

Stroke is a leading cause of long-term disability, often requiring intensive rehabilitation and frequent clinical assessments such as the Fugl-Meyer Assessment. However, traditional in-person evaluations are resource-heavy and difficult to scale, limiting access for many patients. Additionally, monitoring vital signs like blood pressure and oxygen saturation typically depends on specialized equipment, leading to fragmented care and incomplete recovery insights. Even when assessments and monitoring are available, patients often struggle to maintain high-quality, intensive exercise routines without supervision, further limiting rehabilitation outcomes.

To address these challenges, this dissertation proposes an AI-driven, smartphone-based framework that integrates automated motor function assessment, non-invasive vital …

Precision Drug Dosing And Disease Risk Prediction: Advanced Deep Learning Artificial Intelligence Approaches For Precision Medicine Using Structured Electronic Health Records Data, Bingyu Mao

Dissertations and Theses (Open Access)

Advancements in precision medicine increasingly rely on data-driven approaches to improve clinical decision-making. This work presents three interconnected studies that leverage deep learning, reinforcement learning, and comparative modeling to address key challenges in precision drug dosing and disease risk prediction. Together, these contributions offer novel computational frameworks that enhance model performance, improve dosing strategies, and guide model selection for clinical predictive tasks.

To improve individualized vancomycin therapeutic drug monitoring (TDM), the first study introduces PKRNN-2CM, a novel deep learning framework that integrates a two-compartment pharmacokinetic (PK) model with recurrent neural networks (RNNs). While one-compartment models are commonly used in clinical …

Computational Methods For Enhancing The Quality And Interoperability Of Biomedical Terminologies, Xubing Hao

Dissertations and Theses (Open Access)

Biomedical ontologies or terminologies not only serve as a part of the metadata standards for describing data in the FAIR Data Principles (Findable, Accessible, Interoperable, Reusable), but also play a vital role in downstream applications such as cohort identification from electronic health records (EHR). However, there are two critical barriers that may lead to ambiguity, complexity, and inaccuracies in such ontology-based downstream applications. The first barrier is the quality of the ontology. Despite efforts by ontology curators to ensure ontology accuracy and comprehensiveness, errors and inconsistencies are unavoidable. The second barrier is the semantic heterogeneity since human experts may use …

Advancing Genetic Association Discovery In Brain Mri Using Unsupervised And Self-Supervised Deep Learning: Exploring Learning Dynamics, Region-Specific Features, And Spatially Resolved Representations, Sheikh Muhammad Saiful Islam

Dissertations and Theses (Open Access)

Deep learning has unlocked significant potential for advancing the discovery of genetic associations in imaging genetics, particularly in brain imaging using T1-weighted Magnetic Resonance Imaging (MRI). Traditional methods for this task often relied on hand-crafted or semi-automated feature extraction, followed by genetic association studies utilizing these features. While effective, these approaches are limited by their lack of data-driven exploration and the generation of features with limited informativeness.

Unsupervised deep learning, in particular, has emerged as a powerful alternative, addressing some of these limitations by enabling automated and data-driven feature discovery. Recent research in this domain has primarily focused on adapting …

Accelerating Drug Repurposing And Target Discovery Using Deep Learning, Kohong Lin

Dissertations and Theses (Open Access)

Drug discovery is a long-lasting and expensive process. Computational approaches, particularly deep learning techniques, offer the potential to accelerate this process by integrating diverse perspectives from drug discovery theories and capturing intricate patterns within large, multimodal datasets. This dissertation explores deep learning methodologies to accelerate drug repurposing and genetic target discovery. The first aim focuses on integrating multimodal data, including chemical structures, disease genetics, and systems biology, into comprehensive disease knowledge graphs, followed by applying graph neural networks (GNNs) to prioritize repurposable drug candidates. The second aim is to develop a heterogeneous GNN-based approach capable of modelling distinct semantic relationships …

Harnessing Knowledge And Data For Clinical Information Extraction In The Era Of Large Language Models, Yan Hu

Dissertations and Theses (Open Access)

The rapid digitization of healthcare records has led to the widespread adoption of Electronic Health Records (EHRs), which contain rich, unstructured clinical notes. These notes hold valuable information for patient care and clinical research, but their complexity and unstructured nature pose significant challenges for effective utilization. Clinical Information Extraction (IE) aims to bridge this gap by transforming unstructured text into structured data, enabling automated analysis. Traditional Natural Language Processing (NLP) techniques, such as Named Entity Recognition (NER), have been widely used for clinical IE, but recent advancements in Large Language Models (LLMs) like GPT-3.5, GPT-4, and LLaMA have opened new …

Leveraging And Advancing The Foundation Models For Clinical Trajectory Analysis Based On Electronic Health Record Data, Jianping He

Dissertations and Theses (Open Access)

Enhancements in foundation models have provided significant potentials for clinical applications. This dissertation leverages and advances foundation models for a range of clinical tasks using electronic health record (EHR) data, demonstrating their applicability in Clinical Temporal Relation Extraction (CTRE), disease prediction, and patients trajectory analysis.

Aim 1 is to efficiently adapt large language models (LLMs) for CTRE in both full data and few-shot settings. This study leveraged four LLMs: GatorTron-Base, GatorTron-Large, LLaMA3.1, and MeLLaMA. The proposed fine-tuning strategies include: (a) standard fine-tuning; (b) hard-prompting; (c) soft-prompting; (d) Low-Rank Adaptation (LoRA). We found that nearly all proposed fine-tuning strategies outperformed existing …

Addressing The Public Health Concern Of Maternal Mortality In Houston, Texas, Lucy Couture

Dissertations and Theses (Open Access)

Maternal mortality remains a pressing public health concern in the United States, with disproportionate consequences for marginalized communities. Despite being a developed nation, the U.S. reports one of the highest maternal mortality rates among its peers, with approximately 80% of maternal deaths deemed preventable. Texas reflects this national crisis, particularly affecting Black women, whose maternal mortality rate is significantly higher than that of other racial and ethnic groups. This thesis explores maternal mortality trends in Houston, Texas, analyzing the roles of systemic inequities, social determinants of health, and disparities in access to quality care. Drawing on data from the Texas …

Pharmacological Inhibition Of Lsd1/Kdm1a In Diffuse Midline Glioma Models And Impact On Kinase Signaling, Faith Angelina Hernandez, Faith Angelina Hernandez

Dissertations and Theses (Open Access)

Diffuse Midline Gliomas (DMG) occur in the midline structures of the brain such as the brainstem. DMG, a subset of pediatric high-grade gliomas (pHGGs), confers poor prognosis in children with survival rates below two years post-diagnosis. This is heavily due to limited treatment options as radiation and chemotherapy are only temporarily effective and surgery pose high risks. DMG are characterized by histone mutations typically occurring in histone 3.1 and 3.3 alleles, with about more than 80% of patients containing the H3K27 mutation. Recent publications, highlight epigenetic modulators as promising cancer-specific targets emphasizing their potential as a novel therapeutic strategy. Epigenetic …

Smooth Muscle Cell Stress Response: Essential In Atherosclerotic Pathogenesis, Pujun Guan

Dissertations and Theses (Open Access)

Atherosclerosis remains the leading cause of death worldwide. Recent research using lineage tracing and single-cell RNA sequencing unveiled a previously undescribed and prominent role of smooth muscle cells (SMCs) in atherosclerotic plaque formation. SMCs undergo complex phenotypic modulation characterized by de-differentiation of SMCs and heterogeneously increased expression of markers for macrophages, fibroblasts, chondrocytes, and mesenchymal stem cells as plaques form. Cultured SMCs exposed to exogenous cholesterol mimic many of the phenotypic changes associated with plaque formation, and our lab determined this SMC modulation is driven by cholesterol entering the endoplasmic reticulum (ER) and triggering ER stress and PERK signaling. Further, …

Comprehensive Analysis Of Adverse Events Following Covid-19 Vaccination: Insights From Diverse Data Sources, Yiming Li

Dissertations and Theses (Open Access)

This dissertation investigates adverse events (AEs) associated with COVID-19 vaccinations through a multi-faceted approach involving structured and unstructured data. The study is organized into three specific aims, each contributing to a comprehensive understanding of vaccine safety. Aim 1 focuses on the temporal and spatial analysis of AEs reported in the Vaccine Adverse Event Reporting System (VAERS). We performed a detailed temporal analysis to detect patterns over time, revealing a significant increase in reported AEs shortly after the vaccine rollout. Spatial analysis highlighted regional variations in AE reporting, with higher frequencies observed in middle and north regions of the United States …

Cardiovascular Biomarkers Using Deep Learning And Retinal Imaging For The Assessment Of Systemic Disease, Ivan Coronado

Dissertations and Theses (Open Access)

Fundus photography is a radiation-free, non-invasive imaging modality widely employed to assess both ocular and systemic diseases in population-level applications. Traditional methods for extracting cardiovascular biomarkers rely on quantifying vascular features in these images to identify indicators of disease. However, these approaches often depend on manual clinical expertise, which can be costly and time-consuming, making them impractical for large-scale datasets. Deep learning offers a compelling alternative to traditional techniques by enabling the automated processing of extensive datasets with high accuracy. Despite its advantages, deep learning models often function as "black boxes," making it challenging to interpret the features they prioritize …

A Quantitative Examination Of Tobacco And Marijuana Use On Oral Health Outcomes Among Young Adults In Texas, Meenakshi Dasagi

Dissertations and Theses (Open Access)

This dissertation aimed to quantify the relationship between methods of tobacco and marijuana use and their impact on oral health. Tobacco use was defined as smoking combustible tobacco (cigarettes, cigars, and hookah) or electronic aerosolized vaping nicotine with an e-cigarette.5,9 Similarly, marijuana use was defined as smoking combustible marijuana (joints, blunts, and spliffs) or vaping marijuana with an e-cigarette.8 Despite the prevalence of nicotine vaping and marijuana use among young adults1,3-4,7,14-15, there is limited evidence of their association with oral health in Texas. To address this issue, this dissertation comprising three studies analyzed longitudinal data from the Texas Adolescent Tobacco …

Building Futures: How The Built Environment, Out-Of-School Time Programs, And Community Service Participation Relate To Social-Emotional Development Of U.S. Youth, Bailey A. Perez

Dissertations and Theses (Open Access)

Background: This dissertation aimed to explore the relationship between built environment features, participation in out-of-school time (OST) programs, participation in community service activities, and the social-emotional development of youth aged 6 to 17 years residing within the United States (US). Previous research indicates that built environment features, as well as participation in OST programs and community service activities, could impact youth’s social-emotional development. However, few studies have explored these effects on individual social-emotional development measures in this specific age group. In addition, little is known regarding whether built environment features and youth’s sex moderates the relationship between youth participation in …

Investigating The Role Of Insulin-Like Signaling In Paclitaxel-Induced Nociceptive Hypersensitivity Using Drosophila Melanogaster, Sreepradha Sridharan

Dissertations and Theses (Open Access)

Paclitaxel (PTX), a chemotherapeutic that alters microtubule dynamics, is known to induce nociceptive hypersensitivity and sensory neuron damage in human patients and in Drosophila larvae. We used a modified feeding protocol to establish a genetically tractable Drosophila model of PTX-induced hypersensitivity. Larvae fed PTX exhibited hypersensitivity to thermal and mechanical stimuli. Thermal hypersensitivity was observed at low concentrations of PTX (even below 1 μM per ml of food), begins within 8 hours of PTX feeding, and did not completely resolve at the larval stage. Live imaging of peripheral thermal nociceptors showed that this hypersensitivity precedes observable neuronal damage. At low …

Functional Contribution Of Epithelial To Mesenchymal Transition Program On Krasg12d Targeting Efficacy In Pancreatic Cancer, Ana S. Maldonado

Dissertations and Theses (Open Access)

Functional contribution of epithelial to mesenchymal transition program on KRAS^G12D targeting efficacy in pancreatic cancer

Ana S. Maldonado, BS

Advisory Professor: Raghu Kalluri, M.D., Ph.D.

Pancreatic Ductal Adenocarcinoma (PDAC), a highly aggressive, metastatic, and therapeutically resistant form of pancreatic cancer has been projected to become the second-leading cause of cancer related mortality in the United States. This is driven by its detection difficulty, treatment efficacy, and asymptomatic nature, among other factors. PDAC is driven predominantly by KRAS^G12D mutations, which are responsible for the initial development of pancreatic intraepithelial neoplasia (Pan-IN) lesions, that are further maintained and progressed until …

Consistency Of Advanced Radiation Therapy Qa Methods And Global Auditing Systems, Fre'etta Brooks

Dissertations and Theses (Open Access)

Consistency of Advanced Radiation Therapy QA Methods and Global Auditing Systems

Fre’Etta M.D. Brooks, B.S.

Advisory Professor: Stephen F. Kry, Ph.D.

Abstract

Auditing systems provide a means to verify the implementation and operation of all equipment and radiation parameters used by an institution providing radiation therapy treatments. The systems are important tools that are used to determine if an institution can accurately deliver radiation therapy that requires complex planning in the form of IMRT or VMAT and credential radiotherapy treatment institutions for specific clinical trial protocols; to assure the quality of treatment to all patients. Auditing techniques vary by region …

Delineating Metastasis In Mss Colorectal Cancer By Single - Cell Rna Sequencing, Shuangjie You

Dissertations and Theses (Open Access)

Colorectal cancer Colorectal Cancer (CRC) is the leading cause of cancer-related deaths, with the majority of these deaths occurring after metastases. In this study, we used single-cell RNA transcriptome sequencing to characterize the cellular and molecular features of primary CRC and colorectal liver metastases (CRLM). Analysis of 130 samples revealed significant heterogeneity in the tumor microenvironment (TME) characterized by a diversity of cancer-associated fibroblasts, immune cells, and endothelial cell populations. Notably, we identified different CAF subtypes, including inflammatory (iCAFs), matrix(mCAFs), complement (cCAFs), and vascular (vCAFs). iCAFs were enriched in primary tumors expressing higher levels of inflammation-associated genes, whereas mCAFs were …

Single Cell And Spatial Analysis Characterize Metabolic Dpep1+ Fibroblasts In Pdac, Yuanyuan Zhu

Dissertations and Theses (Open Access)

Pancreatic ductal adenocarcinoma (PDAC) is highly malignant and exhibits aggressive progression patterns, resists conventional treatments, and poses significant challenges to patient survival, necessitating urgent research and development of novel therapeutic approaches. Understanding cancer-associated fibroblasts (CAF) metabolism reprogramming in PDAC tumor microenvironment (TME) may uncover novel therapeutic targets. Here, we used single cell and spatial transcriptomics to comprehensively characterize the TME content of human PDAC. We characterized CAFs metabotypes and a novel CAF population expressing DPEP1, COMP, CST1, which involved in glutathione metabolism, termed "gluCAFs” and was associated with poor clinical outcomes and advanced PDAC stages. We also validate the existence …

Exploring The Role Of The Arginine-Methylation Writer-Reader Pair Prmt5/Snd1 In Jak2-Mutant Myeloproliferative Neoplasms, Rocio Rubiano

Dissertations and Theses (Open Access)

Myeloproliferative neoplasms (MPNs) are a hematopoietic disease characterized by hyperproliferation of cells of the myeloid lineage for which current therapeutic options are limited. Discovered in 2005, the JAK2V617F mutation is the most common driver mutation in BCR-ABL negative MPNs, resulting in constitutive activation of the JAK2 protein and the JAK-STAT signaling pathway. A role for the methyltransferase activity of Protein Arginine Methyltransferase 5 (PRMT5) has been proposed in JAK2-mutant MPN, highlighting both a mechanism through which this mutation can drive disease progression and a potential mode of therapeutic intervention. Staphylococcal Nuclease Domain-Containing Protein 1 (SND1) is the effector molecule responsible …

Influential Factors And Predicting Dose Delivery Accuracy For Imaging And Radiation Oncology Core’S Phantom Program Using Machine Learning, Hunter Mehrens

Dissertations and Theses (Open Access)

IROC’s mission is to help ensure consistent and comparable, high-quality radiotherapy across clinics that participate in national clinical trials. To obtain this mission, IROC’s phantom program provides a third-party end-to-end check of the clinical workflow of a patient receiving radiotherapy. The goal of the phantom audit is to compare the dose delivered to the dose planned by the treatment system ensuring dose delivery accuracy. While IROC’s phantoms are better equipped to catch dose delivery errors compared to a clinic’s QA process, the end-to-end process and reporting of results is time-consuming creating a bottleneck for clinical trial participation. Furthermore, IROC’s passing …

The Role Of An Ultraconserved Long Non-Coding Rna In B-Cell Lymphomagenesis, Swati Mohapatra

Dissertations and Theses (Open Access)

Ultraconserved regions (UCRs) are genomic segments with perfect (100%) conservation between the orthologous regions of human, rat, and mouse genomes. UCRs can be transcribed into mono-exonic long non-coding RNAs (lncRNAs) known as transcribed ultraconserved regions (T-UCRs). These regions, despite lacking protein-coding potential, are increasingly recognized for their regulatory roles in gene expression, including the modulation of non-coding RNA (ncRNA) transcripts. NcRNAs play crucial roles in cellular processes, including oncogenic transformation, with emerging evidence revealing their ability to encode small peptides known as ncRNA-encoded peptides (ncPEPs). These peptides, originating from small open reading frames (smORFs), contribute to diverse cellular functions and …

Genetic Analysis Of Kmt2d Loss In Tumorigenesis Using Genetically Engineered Mouse Models, Yingda Jiang

Dissertations and Theses (Open Access)

The Cancer Genome Atlas Program (TCGA) has discovered multiple tumor suppressors that are commonly mutated across a variety of human cancers, such as chromatin regulators in COMPASS and SWI/SNF complexes. KMT2D, a histone H3K4 methyltransferase in the COMPASS complex, is frequently mutated in human lymphoma, melanoma, and epithelial carcinoma. Biochemically, KMT2D shapes the cellular enhancer landscape as the major writer of the putative enhancer mark H3K4me1. Given the critical role of cellular enhancer landscape in determining cellular lineage in embryonic development, it is unknown whether enhancer dysregulation caused by KMT2D loss leads lineage switch of tumor cells, which has …

Vesicle-Associated Membrane Proteins In Airway Mucin Secretion, Boxuan Yang

Dissertations and Theses (Open Access)

Mucus plays a vital role in shielding the lungs against harmful substances such as particles, pathogens, and chemicals inhaled from the environment. It captures these harmful elements, and ciliary action helps to expel them from the lungs. Paradoxically, malfunctioning of mucus induces the development of various respiratory illnesses, including asthma, cystic fibrosis, chronic obstructive pulmonary disease (COPD), and interstitial lung diseases. Despite extensive research on controlling mucin production and hydration, the process of secretion is not yet fully comprehended.

Mucin undergoes regulated secretion at two rates: a low baseline rate and a high stimulated rate. Over the past 25 years, …

Evaluation Of Myogenic Differentiation Potential Of Bmd Ipscs In Vitro And The Effect Of Ubiquitination Inhibitors On Dystrophin Stability In Vivo, Muchen Liu

Dissertations and Theses (Open Access)

Background: Muscular dystrophies are heterogeneous groups of inherited diseases leading to progressive muscular weakness and degeneration. In the case of Becker muscular dystrophy (BMD), non-disrupting mutations of the DMD gene reading frame is the causative defect. By the age of 30s, about 60 to 70% of BMD patients develop cardiomyopathy, which is often lethal. Unfortunately, there is currently no cure for BMD. Meanwhile, improvement of dystrophin stability by repressing polyubiquitination is a promising strategy. The application of human iPSCs in tissue regeneration also serves as a potential therapeutic strategy. This study aims to test the efficacy of selected drugs interfering …

In Vivo Examination Of Peripheral Drivers Of Alzheimer’S Disease, Celso Catumbela

Dissertations and Theses (Open Access)

Alzheimer’s disease (AD) is the leading cause of dementia worldwide, and predominantly affects elderly populations. This disease is well known for its effects on the brain, but a wealth of clinical reports show that dementia can also modify, or be modified by, various peripheral and systemic processes. Yet, the pathological contribution of peripheral comorbidities to AD remains to be fully understood. In an attempt to address some knowledge gaps, we characterized the cerebral and peripheral pathology in mice with history of either liver injury or sepsis. In the former subjects, we found that even in the absence of genetic risk …

Multi-Omic Single-Cell Integration For Understanding T Cell Responses In Acute Myeloid Leukemia, Poonam Desai

Dissertations and Theses (Open Access)

In recent years, translational initiatives have gained significant traction within the medical community. Clinical parameters alone have been insufficient to fully understand the intricate mechanisms governing patient responses to disease and therapy. Additionally, single-cell technologies have rapidly advanced to a point where generation of large patient-based datasets is financially and logistically feasible. The integration of clinical information with multi-omic single-cell technologies enables the formation of data-driven hypotheses pertaining to the mechanisms of disease progression and therapeutic resistance. Acute Myeloid Leukemia (AML) is a rapidly-progressing cancer of the bone marrow with poor prognoses and dismal outcomes. Of the adult patients that …

Improving Distributed Health Information Technology Integration Through Enriched Context In Sociotechnical System Components, Jeritt Thayer

Dissertations and Theses (Open Access)

The design and architecture of health information technology (HIT) are in the process of a transformative shift. Monolithic code bases, which are several systems intertwined into a single program, are being split into multiple distributed programs that communicate across information networks. Thus far, only sparse knowledge exists about the challenges of distributed HIT, and the interaction of these architectures with other factors of the sociotechnical system, such as clinical content, is not well understood. Existing sociotechnical models describe the components within a healthcare system and the connections between components, but the types of relationships between each component are underspecified. Within …

Designing Recommendation Systems For Identification And Reduction Of Unnecessary Blood Tests, Tongtong Huang

Dissertations and Theses (Open Access)

Blood testing is an indispensable tool to check the patient's health status. However, the overutilization of lab tests in healthcare settings is a prevalent issue that can lead to redundant information, increased patient risk, and financial strain on healthcare resources. This dissertation presents novel approaches to optimizing lab test utilization by applying deep learning models. The primary objective is to develop and validate methods that accurately predict and reduce unnecessary blood tests.

The study introduces a “selective” mechanism that quantifies the predictability of future lab tests, allowing for the selective reduction of unnecessary tests in the context of specific clinical …