Digital Commons Network^™

Computational Sensitivity Evaluation Of Ultrasound Neuromodulation Resolution To Brain Tissue Sound Speed With Robust Beamforming, Boqiang Fan, Wayne Goodman, Sameer A Sheth, Richard R Bouchard, Behnaam Aazhang

Faculty, Staff and Student Publications

Low-intensity focused ultrasound (LIFU) neuromodulation requires precise targeting and high resolution enabled by phased array transducers and beamforming. However, focusing optimization usually relies on phantom measurements or simulations with inaccurate acoustic properties to degrade neuromodulation resolution. Therefore, this work analyzes the sensitivity of neuromodulation resolution, measured by off-target activation area (OTAA), to brain tissue sound speed. A Robust Optimal Resolution (ROR) beamforming method is proposed to minimize the worst-case OTAA with restricted sound speed inaccuracy and propagation information estimated with deviated sound speed. The propagation estimation model utilizes equivalent source method (ESM) to map sound field between different acoustic parameter …

General Characteristics Of Patients With Primary Biliary Cholangitis From Türkiye And Denmark, Cumali Efe, Ersin Batıbay, Tugrul Purnak

Faculty, Staff and Student Publications

No abstract provided.

The Microbiome-Restorative Potential Of Ibezapolstat For The Treatment Of Clostridioides Difficile Infection Is Predicted Through Variant Polc-Type Dna Polymerase Iii In Lachnospiraceae And Oscillospiraceae, Jacob K Mcpherson, Julian G Hurdle, Matthew L Baker, Tahir Hussain, Ashok Kumar, Kevin W Garey

Faculty, Staff and Student Publications

Ibezapolstat (IBZ), a first-in-class antibiotic targeting the PolC-type DNA polymerase III alpha-subunit (PolC) in low G + C bacteria, is in clinical development for the treatment of Clostridioides difficile infection (CDI). In the phase 2 trials, IBZ had potent activity against C. difficile while sparing or causing regrowth of Lachnospiraceae, Oscillospiraceae, and Erysipelotrichales, common commensal low G + C bacteria. The purpose of this study was to utilize in silico approaches to better interpret the narrower than expected IBZ spectrum of activity. IBZ susceptibility to human commensal microbiota was predicted using genomic analysis and PolC phylogenetic tree construction in relation …

Identifiability And Model Selection Frameworks For Models Of High-Grade Glioma Response To Chemoradiation, Khushi C Hiremath, Kenan Atakishi, Ernesto A B F Lima, Maguy Farhat, Bikash Panthi, Holly Langshaw, Mihir D Shanker, Wasif Talpur, Sara Thrower, Jodi Goldman, Caroline Chung, Thomas E Yankeelov, David A Hormuth Ii

Faculty, Staff and Student Publications

We have developed a family of biology-based mathematical models of high-grade glioma (HGG), capturing the key features of tumour growth and response to chemoradiation. We now seek to quantify the accuracy of parameter estimation and determine, when given a virtual patient cohort, which model was used to generate the tumours. In this way, we systematically test both the parameter and model identifiability. Virtual patients are generated from unique growth parameters whose growth dynamics are determined by the model family. We then assessed the ability to recover model parameters and select the model used to generate the tumour. We then evaluated …

Ven In Combination With 10-Day Dec In Newly Diagnosed Elderly Or Relapsed/Refractory Acute Myeloid Leukemia, And High-Risk Myelodysplastic Syndrome: Long Term Follow-Up Of A Phase 2 Trial, Mahesh Swaminathan, Courtney D Dinardo, Abhishek Maiti, Naveen Pemmaraju, Maro Ohanian, Navel G Daver, Guillermo Garcia-Manero, Ghayas C Issa, Gautam Borthakur, Farhad Ravandi, Guillermo Montalban-Bravo, Tapan M Kadia, Yesid Alvarado, Elias J Jabbour, Nicholas J Short, William G Wierda, Nitin Jain, Steven M Kornblau, Lucia Masarova, Sherry A Pierce, Wei Qiao, Jing Ning, Hagop Kantarjian, Marina Y Konopleva

Faculty, Staff and Student Publications

No abstract provided.

Prdm1 Is A Key Regulator Of The Nkt-Cell Central Memory Program And Effector Function, Gengwen Tian, Gabriel A Barragan, Hangjin Yu, Claudia Martinez-Amador, Akshaya Adaikkalavan, Xavier Rios, Linjie Guo, Janice M Drabek, Osmay Pardias, Xin Xu, Antonino Montalbano, Chunchao Zhang, Yanchuan Li, Amy N Courtney, Erica J Di Pierro, Leonid S Metelitsa

Faculty, Staff and Students Publications

Natural killer T cells (NKTs) are a promising platform for cancer immunotherapy, but few genes involved in the regulation of NKT therapeutic activity have been identified. To find regulators of NKT functional fitness, we developed a CRISPR/Cas9-based mutagenesis screen that uses a guide RNA (gRNA) library targeting 1,118 immune-related genes. Unmodified NKTs and NKTs expressing a GD2-specific chimeric antigen receptor (GD2.CAR) were transduced with the gRNA library and exposed to CD1d+ leukemia or CD1d-GD2+ neuroblastoma cells, respectively, over six challenge cycles in vitro. Quantification of gRNA abundance revealed enrichment of PRDM1-specific gRNAs in both NKTs and GD2.CAR NKTs, a result …

Bone-Induced Her2 Promotes Secondary Metastasis In Hr+/Her2- Breast Cancer, Rahat Alam, Anna Reva, David G Edwards, Bree M Lege, Laura S Munoz-Arcos, Carolina Reduzzi, Swarnima Singh, Xiaoxin Hao, Yi-Hsuan Wu, Zeru Tian, Laura M Natalee, Gargi Damle, Deniz Demircioglu, Yixian Wang, Ling Wu, Elisabetta Molteni, Dan Hasson, Bora Lim, Zbigniew Gugala, Jerry E Chipuk, Julie E Lang, Joseph A Sparano, Chonghui Cheng, Massimo Cristofanilli, Han Xiao, Xiang H-F Zhang, Igor L Bado

Faculty, Staff and Students Publications

Bone metastases can disseminate to secondary sites and promote breast cancer progression creating additional clinical challenges. The mechanisms contributing to secondary metastasis are barely understood. Here, we evaluate the prediction power of Her2-expressing (Her2E) circulating tumor cells (CTCs) after analyzing over 13,000 CTCs from a cohort of 137 metastatic breast cancer (MBC) patients with initial HR+/Her2− status and employ preclinical models of bone metastasis (BM) to validate the role of Her2E CTCs in multi-organ metastases. While Her2 expression was higher in patients with bone metastasis, experimental analyses revealed that Her2E CTCs derived from bone lesions were more dependent on Her2 …

Repair Drive Improves Gene Editing In The Liver, Marco De Giorgi, So Hyun Park, Gang Bao, William R Lagor

Faculty, Staff and Students Publications

No abstract provided.

Zp4: A Novel Target For Car-T Cell Therapy In Triple Negative Breast Cancer, Lauren K Somes, Jonathan T Lei, Xinpei Yi, Diego F Chamorro, Paul Shafer, Ahmed Z Gad, Lacey E Dobrolecki, Emily Madaras, Nabil Ahmed, Michael T Lewis, Bing Zhang, Valentina Hoyos

Faculty, Staff and Students Publications

Triple-negative breast cancer (TNBC) remains one of the most challenging subtypes of breast cancer to treat due to a lack of effective targeted therapies. Chimeric antigen receptor (CAR)-T cells hold promise, but their efficacy in solid tumors is often limited by on-target/off-tumor toxicities. Through comprehensive bioinformatic analysis of public RNA and proteomic data, we identified zona pellucida glycoprotein 4 (ZP4) as a novel target for TNBC. ZP4 RNA and protein were detected in a subset of TNBC patient samples and patient-derived xenograft (PDX) models, with expression otherwise restricted to oocytes. We generated 89 ZP4-specific novel monoclonal antibodies and used the …

Improving The Oral Pathology Referral Process In An Academic Dental Institution, Gregory Olson

Translational Projects (Open Access)

Introduction: Inefficiencies in the oral cancer referral process pose serious risks, including delayed diagnoses and poor patient outcomes. This project aimed to improve the reliability and efficiency of referrals between dental students and oral pathology residents for patients with suspected oral cancer. Although early detection is widely acknowledged as critical, significant process breakdowns were identified, including the absence of a standardized workflow, inconsistent follow-up practices, and a lack of accountability and tracking mechanisms. As a result, approximately half of the patients recommended for biopsy did not undergo the procedure, leading to delayed diagnoses and potentially worse prognoses.

Methodology: …

Leveraging Observational And Rct Data For Understanding Interventions’ Efficacy: Applications In Progressive And Acute Neurological Diseases, Yaobin Ling

Dissertations and Theses (Open Access)

The advancement of drug repurposing for progressive and acute neurological diseases is hampered by the limitations of randomized clinical trials (RCTs) and observational data. This dissertation presents a comprehensive framework to integrate data-driven insights from multiple sources, including observational studies, RCTs, and synthetic data generation, to overcome these challenges.

The first study focuses on estimating treatment effects on the population level, which investigates the effects of routine and high-dose influenza vaccines on the risk of Alzheimer’s Disease and Related Dementias (ADRD) through a trial emulation framework applied to health claims data, addressing biases inherent in observational studies. The second study …

The Structural Inequality Of Status Quo Clinical Communication, Joelle Robertson-Preidler

Faculty and Staff Publications

No abstract provided.

Implementing A Social Determinants Of Health Screening Tool To Improve Appointment Attendance In Primary Care Practice, Phuong Truong

Doctor of Nursing Practice Final Project Abstract

Purpose

The purpose of this quality improvement (QI) project was to implement a screening tool to identify the social determinants of health (SDoH) that may prevent patients from attending their scheduled appointment and to improve patient’s attendance adherence. The project was conducted at a primary care clinic in La Grange, Texas

Background

Missed appointments negatively impact a person’s health status. Missed appointments have been associated with a variety of social factors such as transportation access, socioeconomic status, health literacy, and insurance status.

Methodology

The Logic Model and PDSA model for improvement were used to guide this project. The project utilized …

Improving Bone Density Screening Rates In Patients At Risk For Falls Using Chart Screening And Protocol Orders, Lona Winnegan

Doctor of Nursing Practice Final Project Abstract

PURPOSE: This quality improvement project’s purpose was to identify those at risk for low bone density from a clinic’s population of positive fall risk screenings, to encourage bone density screening and identify those who would benefit from bone health optimization.

BACKGROUND: Spine fractures result in serious morbidity and mortality. The type of treatment options available for spine fractures and the timing of intervention is affected by the patient’s bone density.

METHODOLOGY: The aim was for patients diagnosed with spine fractures and degenerative spine conditions to be screened for osteoporosis/osteopenia according to screening recommendation standards. The goals were to maintain …

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 …

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 …

The Relationship Between Physical Activity And Telomere Length In Women: A Systematic Review, Jeni Page, Catherine Stephens, Melissa Richard, Elizabeth Lyons, Elizabeth Baumler, M Terese Verklan, Elizabeth Lorenzo

Faculty, Staff and Student Publications

Telomere length (TL) is a biomarker of cellular aging with variations observed by sex, age, race, and ethnicity. Prior studies have suggested that physical activity (PA) may positively impact TL by potentially elongating telomeres and slowing cellular aging. However, research examining the optimal type and intensity of PA needed to elicit these changes specific to women remains limited. This systematic review aimed to investigate variations in TL in response to PA among women, exploring how these effects differ by age, race, or ethnicity. Following PRISMA guidelines, searches across five databases identified 17 relevant studies published from 2008 to 2022. A …

Using Musical Interventions As A Non-Pharmacological Strategy To Improve Chronic Pain Levels And Satisfaction Among Patients With Sickle Cell Disease, Fredeline Metellus

Doctor of Nursing Practice Final Project Abstract

Background

Sickle Cell Disease (SCD) is a chronic, inherited blood disorder characterized by severe pain episodes that are often poorly managed with standard pharmacological treatments. In response to patient dissatisfaction with pain control and the desire for holistic alternatives, this quality improvement project explored music therapy as a non-pharmacological pain management strategy.

Purpose

The primary aim was to reduce chronic pain by at least 20% and improve patient satisfaction with pain management among adults with SCD using a structured musical intervention.

Methodology

This project was implemented at a comprehensive outpatient SCD clinic using the Plan-Do-Study-Act (PDSA) model. Five adult patients …

An Examination Of Ambulatory Care Code Specificity Utilization In Icd-10-Cm Compared To Icd-9-Cm: Implications For Icd-11 Implementation, Susan H Fenton, Cassandra Ciminello, Vickie M Mays, Mary H Stanfill, Valerie Watzlaf

Faculty, Staff and Student Publications

Objective: The ICD-10-CM classification system contains more specificity than its predecessor ICD-9-CM. A stated reason for transitioning to ICD-10-CM was to increase the availability of detailed data. This study aims to determine whether the increased specificity contained in ICD-10-CM is utilized in the ambulatory care setting and inform an evidence-based approach to evaluate ICD-11 content for implementation planning in the United States.

Materials and methods: Diagnosis codes and text descriptions were extracted from a 25% random sample of the IQVIA Ambulatory EMR-US database for 2014 (ICD-9-CM, n = 14 327 155) and 2019 (ICD-10-CM, n = 13 062 900). Code …

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 …

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 …

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 …

Llm-Ie: A Python Package For Biomedical Generative Information Extraction With Large Language Models, Enshuo Hsu, Kirk Roberts

Faculty, Staff and Student Publications

Objectives: Despite the recent adoption of large language models (LLMs) for biomedical information extraction (IE), challenges in prompt engineering and algorithms persist, with no dedicated software available. To address this, we developed LLM-IE: a Python package for building complete IE pipelines.

Materials and methods: The LLM-IE supports named entity recognition, entity attribute extraction, and relation extraction tasks. We benchmarked it on the i2b2 clinical datasets.

Results: The sentence-based prompting algorithm resulted in the best 8-shot performance of over 70% strict F1 for entity extraction and about 60% F1 for entity attribute extraction.

Discussion: We developed a Python package, LLM-IE, …

An Interoperability Framework Across Legacy Emr Systems, Sheena Rogers

Translational Projects (Open Access)

The 21st Century Cures Act (H.R.34 – 114th Congress, 2016) specifies that patients and authorized providers must be able to receive copies of their medical records with standard medical history details upon request. While the data archival system, DRIS (Data Retention Interoperability Solution), has comprehensive patient summary reports centralizing over 100 legacy electronic health records (EHR) with broad data elements, it is not 100% compliant with the upcoming 21st Century Cures Act’s interoperability standards. As a result, patients and providers are not receiving copies of their medical records with all of the required medical history and fields in this new …

Strategies To Improve Nurse Documentation Compliance And Confidence In The Delivery Of Preprocedural Education, Danielle A. Burris

Doctor of Nursing Practice Final Project Abstract

Strategies to Improve Nurse Documentation Compliance and Confidence in the Delivery of Preprocedural Education:

Abstract

Introduction:This project aimed to implement tools to improve preprocedural education compliance between nurses and patients in an interventional radiology department. The secondary aim was increasing staff confidence in providing education, reducing the potential for avoidable harm and adverse procedural-related outcomes. The project was implemented within an interventional radiology department of a healthcare system in the Texas Medical Center, Houston, Texas.

Methodology: The project utilized the Model for Improvement and followed the steps of the Plan-Do-Study-Act cycle to implement change. Strategies included checklists, reminder tools, …

Otolith And Genomic Data Reveal Temporal Insights Into Stocking Across A Large River Basin In A Mobile, Long-Lived Australian Freshwater Fish Species, Katherine A Harrisson, Luciano B Beheregaray, Christopher M Bice, Emily J Booth, Chris J Brauer, Gavin L Butler, David Dawson, Olga Dudchenko, Benjamin G Fanson, Graeme Hackett, Annique Harris, Matthew J Jones, Parwinder Kaur, Wayne M Koster, Kyne Krusic-Golub, Erez Lieberman Aiden, Jason A Lieschke, Jarod Lyon, John R Morrongiello, Nicholas P Murphy, James O'Dwyer, Scott M C Raymond, Meaghan L Rourke, Arron Strawbridge, Jason D Thiem, Zeb Tonkin, Jian D L Yen, Brenton P Zampatti

Staff and Researcher Publications

Freshwater ecosystems and their biota are under increasing pressure from anthropogenic stressors. In response to declining fish stocks, hatchery and stocking programmes are widely implemented as core components of restoration and management strategies, with positive outcomes for some wild populations. Despite this, stocking remains contentious due to potential genetic and ecological risks to wild populations. Monitoring and evaluation of stocking outcomes are critical to ensuring the long-term sustainability of wild populations, but identification of stocked individuals post-release remains a key challenge, particularly for mobile species. In this study, we combined otolith (natal origin and age) and genomic data to identify …

Differentiation Stage Predicts Radiosensitivity In Mesenchymal-Like Pancreatic Cancer, Tingshi Su, Xinjian Yu, Mojtaba Hoseini-Ghahfarokhi, David B Flint, Scott J Bright, Joana I D S Antunes, David K J Martinus, Mandira Manandhar, Mariam Ben Kacem, Poliana C Marinello, Eurico J G Pereira, Hua-Sheng Chiu, Uwe Titt, David R Grosshans, Jan Schuemann, Henning Willers, Harald Paganetti, Pavel Sumazin, Gabriel O Sawakuchi

Faculty, Staff and Student Publications

Purpose: To derive a genomic classifier to predict radiosensitivity of pancreatic cancer cell lines and patients with pancreatic cancer to allow genomic-guided radiation therapy.

Methods and materials: We collected a comprehensive data set of full clonogenic cell survival curves of 45 pancreatic cancer cell lines irradiated with clinical photon and proton beams. We derived classifiers based on data from human embryonic and fetal pancreas single-cell RNA-sequencing to distinguish between epithelial and mesenchymal cells and to predict pancreas cell-line differentiation stage. Independent testing was done with an embryonic mouse pancreas single-cell RNA-sequencing data set. We then used bulk RNA-seq profiles from …