Medical Sciences Commons^™

Sage: Spatially Aware Gene Selection And Dual-View Embedding Fusion For Domain Identification In Spatial Transcriptomics, Yi He, Yunpei Xu, Liqing Ding, Hong-Dong Li, Yaohang Li, Shaokai Wang

Computer Science Faculty Publications

Despite enabling high-resolution mapping of gene expression within tissues, spatial transcriptomics (ST) still faces challenges in accurately segmenting spatial domains due to complex tissue architecture and limitations of current methods. Most approaches rely on local spatial priors, lack gene-level interpretability, and fall short in capturing structure-discriminative genes or long-range functional relationships, limiting their ability to resolve biologically meaningful architectures. We present Spatially Aware Gene selection and dual-view Embedding fusion (SAGE), a unified and reproducible framework for domain identification in spatial transcriptomics that combines topic-driven gene selection with dual-view embedding fusion to address these gaps. SAGE integrates non-negative matrix factorization (NMF)-based …

Gaze Transition Entropy And Automation Trust In Multitasking Workspace, Yusuke Yamani, Austin Jackson, Tetsuya Sato, Feyishola Ashimi, Michael S. Politowicz, Eric T. Chancey, Makoto Itoh

Psychology Faculty Publications

Safe flight operation requires visual scanning across multiple displays in a cockpit, which collectively represent the state of the aircraft and supporting automation. Trust is a crucial factor that drives human-automation interaction, and recent work has suggested a relationship between an operator's visual attention and automation trust. One index that captures predictability of eye movements between different areas of interest is gaze transition entropy. The current work reanalyzed data from Sato et al., which examined eye movement patterns and trust in automation associated with the system monitoring task of the Multi-Attribute Task Battery. Results showed credible positive correlations between the …

Reply To: S. N. Katkuri Et Al. And H. Liu Et Al. On Early And Sustained Improvements In Sense Of Smell With Tezepelumab Treatment In Patients With Chronic Rhinosinusitis With Nasal Polyps (Waypoint), Joaquim Mullol, Joseph K. Han, Tanya M. Laidlaw, Claire Hopkins, Anju T. Peters, Oliver Pfaar, Martin Desrosiers, Stella E. Lee, Andrew P. Lane, Claudia Chen, Yun Chon, Sandhia S. Ponnarambil, Andrew Foster, Andrew W. Lindsley, Christopher S. Ambrose

Department of Otolaryngology (ENT) Faculty Publications

[Introduction] We thank Dr. S. N. Katkuri, Dr. H. Liu, and their coauthors [1, 2] for their interest in our recent publication describing the improvements in loss of smell symptoms with tezepelumab versus placebo in patients with uncontrolled chronic rhinosinusitis with nasal polyps (CRSwNP) in the WAYPOINT trial (NCT04851964) [3]. We are grateful for the authors' feedback on the clinical significance of the data presented and their appreciation of the consistency observed across a range of baseline clinical characteristic and demographic subgroups.

Consensus And Controversies Of International Guidelines For The Diagnosis, Surveillance, And Management Of Fetal Growth Restriction: An Updated Comparison, Daniele Diane Mascio, Suneet P. Chauhan, Tullio Ghi, Asma Khalil, Juliana G. Martins, Sara Sorrenti, Tamara Stampalija, Fabrizio Zullo, Francesc Figueras

Department of Obstetrics & Gynecology Faculty Publications

OBJECTIVE: To compare areas of consensus and disagreements across contemporary international and national guidelines on the diagnosis, surveillance, and management of fetal growth restriction (FGR).

DATA SOURCES: Electronic searches of MEDLINE from database inception up to March 2026 using MeSH terms and keywords related to FGR and guidelines. STUDY ELIGIBILITY CRITERIA: Critical, structured comparison of national or international guidelines on FGR published since 2010. Final inclusion required unanimous agreement from all authors.

STUDY APPRAISAL AND SYNTHESIS METHODS: Pre-specified extraction across domains: definition; prediction/prevention; surveillance tools and frequency; delivery timing and mode; and labor induction methods. Dual data …

Panda-Plus: Improved Dataset Of Prostate Whole Slide Images From Panda Challenge With Pixel-Level Expert Annotations, Spencer Hopson, Carson Mildon, Corbyn Kubalek, Joshua L. Ebbert, Ryan Vance, Lauren Laverty, Paul Urie, Dennis Della Corte

Faculty Publications

Artificial intelligence (AI)-based prostate cancer detection through whole slide images (WSIs) offers promising potential to address the global pathologist shortage while improving clinical consistency. Digital slides and improving image analysis methods encourage the creation of tools to aid in WSI classification. Despite promising advances, these tools are still limited by available training data. Current publicly available datasets, such as Kaggle's PANDA Challenge, while large in scale, rely on slide-level labels that may introduce noise and limit model reliability. Others contain detailed annotations, but are smaller in size due to manual processing efforts. In this work, we introduce PANDA-PLUS, a 546-image …

Benchmarking Dna Foundation Models For Genomic And Genetic Tasks, Haonan Feng, Lang Wu, Bingxin Zhao, Chad Huff, Jianjun Zhang, Jia Wu, Lifeng Lin, Peng Wei, Chong Wu

School of Medicine Faculty Publications

The rapid evolution of DNA foundation models promises to revolutionize genomics, yet comprehensive evaluations are lacking. Here, we present a comprehensive, unbiased benchmark of five models (DNABERT-2, Nucleotide Transformer V2, HyenaDNA, Caduceus-Ph, and GROVER) across diverse genomic and genetic tasks including sequence classification, gene expression prediction, variant effect quantification, and topologically associating domain (TAD) region recognition, using zero-shot embeddings. Our analysis reveals that mean token embedding consistently and significantly improves sequence classification performance, outperforming other pooling strategies. Model performance varies among tasks and datasets; while general purpose DNA foundation models showed competitive performance in pathogenic variant identification, they were less …

Implementation And Assessment Of The Openbci Platform As An Accessible Brain- Computer Interface, Jewell Norris

Honors Theses

OpenBCI is a low-cost, open-source platform for alternative brain-computer interface (BCI) software and hardware. This thesis evaluates OpenBCI’s electroencephalogram (EEG) and electromyography (EMG) capabilities by constructing and testing a 16-channel EEG system using the Ultracortex Mark IV headset and Cyton + Daisy biosensing board. The viability of the system was assessed through real-time BCI control and comparison to a clinical-grade EEG system. Real-time BCI control of an online falling-block game was tested via the use of eye blinks EMG (channels Fp1/Fp2) and head-tilt accelerometer inputs. The BCI game demonstrated reliable control despite minor latency and artifact sensitivity. For clinical comparison, …

Probabilistic Modeling Of Antibody Kinetics Post Infection And Vaccination, Rayanne Luke, Prajakta Bedekar, Anthony J. Kearsley

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Elucidating The Relationship Between Water Quality And Antibiotic Resistance Of Rainwater Microbes Across Western Humboldt County, Theo P. Murphy, Eve Wendley, Justice Laskowski, Tyler Paredes

Humboldt Journal of Microbiology

Limited studies have been conducted regarding the fecal contamination and microbial dispersal through rainwater, with no known studies investigating the potential connection between antibiotic resistant microbes and water quality in rainwater. This study seeks to identify the presence of a correlation between water quality and incidence of antibiotic resistance in rainwater microbes. We hypothesize that there will be a correlation between these two variables. We additionally anticipate a high incidence of fecal contamination across rural and urban collection sites alike, with fecal contamination being more prevalent in rural areas than urban locals. To determine water quality, we measured the concentration …

Large Language Models As Information Providers For Appropriate Antimicrobial Use: Computational Text Analysis And Expert-Rated Comparison Of Chatgpt, Claude And Gemini, Marcello Di Pumpo, Maria Rosaria Gualano, Danilo Buonsenso, Francesca Raffaelli, Daniele Donà, Vittorio Maio, Patrizia Laurenti, Walter Ricciardi, Leonardo Villani

College of Population Health Faculty Papers

OBJECTIVES: Antimicrobial resistance is a critical public health threat. Large language models (LLMs) show great capability for providing health information. This study evaluates the effectiveness of LLMs in providing information on antibiotic use and infection management.

METHODS: Using a mixed-method approach, responses to healthcare expert-designed scenarios from ChatGPT 3.5, ChatGPT 4.0, Claude 2.0 and Gemini 1.0, in both Italian and English, were analysed. Computational text analysis assessed readability, lexical diversity and sentiment, while content quality was assessed by three experts via DISCERN tool.

RESULTS: 16 scenarios were developed. A total of 101 outputs and 5454 Likert-scale (1-5) scores were obtained …

Use Of Modified Z-Domain Peptides To Specifically Label The Constant Region Of Immunoglobulin G And Vegf, Madeline Allen

Department of Chemistry: Dissertations, Theses, and Student Research

Antibodies are increasingly being used in medicinal therapies due to their ability to specifically bind to a target antigen. This fundamental behavior of antibodies has been used as a key component in drug development to address common problems with traditional drug molecules such as off-targeting, fast drug clearance, and low efficacy. This thesis provides some background into how antibodies can be used as potential treatments for a few diseases. Chapter 1 begins with an overview of the human immune system, and the role antibodies play within it. Then, the variety of ways in which antibodies can be used as medicinal …

The Science Of Sound: Studying The Cognitive, Emotional, And Physiological Effects Of Frequency, Genre, And Music, Jett Yarborough

Senior Honors Theses

Music influences emotion, physiology, and cognition, yet little is known about how the frequency it is tuned to affects these influences. Prior research has shown that music tuned to 432 Hz can reduce stress, lower blood pressure, and improve sleep. My colleagues and I conducted two studies to investigate this. Our research found that music tuned to 432 Hz promotes a significant increase in memory retention and induces a state of focus and relaxation. These results suggest that shifting from the standard tuning frequency of 440 Hz to 432 Hz could have a profoundly positive impact on our daily lives. …

Association Of Estimated Plasma Volume Status With Invasive Hemodynamics And Adverse Clinical Outcomes In Patients With Pulmonary Hypertension And Chronic Kidney Disease, Andrew Geller, Jose Manuel Martinez Manzano, Esteban Kosak Lopez, Phuuwadith Wattanachayakul, John Malin, Raul Leguizamon, Tara John, Rasha Khan, Ian Mclaren, Alexander Prendergast, Simone Jarrett, Kevin Bryan Lo, Christian Witzke

Einstein Health Papers

Identifying noninvasive measures to assess intravascular volume status and risk stratify patients with pulmonary hypertension (PH) and chronic kidney disease (CKD) is needed. We assessed the predictive value of estimated plasma volume status (ePVS) using the Strauss-derived Duarte formula in PH-CKD patients. This single-center retrospective cohort analysis included patients with PH and CKD Stage 3b (CKD3b), Stage 4 (CKD4), or Stage 5 (CKD5) who underwent right heart catheterization from 2018 to 2023. Patients were categorized into low ePVS (< 6.2) and high ePVS (≥ 6.2) using Youden's J statistics. We used the Cox-proportional hazards model, adjusting for age, sex, and body mass index, to investigate the association between …

Emerging Clinical Role Of Tavapadon, A Novel Dopamine Partial Agonist, In The Treatment Of Parkinson’S Disease, Alan D. Kaye, Bennett M. Ford, Brennan M. Abbott, Kalob M. Broocks, Sofia Novacic, Sahar Shekoohi

School of Medicine Faculty Publications

Tavapadon, a novel oral dopamine-D1R/D5R partial agonist, has been studied in recent years for the treatment of late-stage development Parkinson’s disease (PD). Levodopa, a dopamine precursor that currently remains the gold-standard first-line therapy for PD motor symptoms, serves as a benchmark against emerging dopaminergic agents. By selectively activating D1-family receptors on direct-pathway medium neurons, Tavapadon differs in that it delivers levodopa-level motor benefit while avoiding its many D2R/D3R-mediated adverse effects. In placebo-controlled trials, Tavapadon produced clear, clinically meaningful gains in motor function and day-to-day activities, as captured by the Unified Parkinson’s Disease Rating Scale (UPDRS). Recent late-stage results have revealed …

Deep Learning-Driven Proteomics Analysis For Gene Annotation In The Renin-Angiotensin System, Mortaza Eivazi, Kamran Hosseini, Shahin Alipanahi, Huijing Xia, Luke Restivo, Ayushi Patel, Mahdieh Gozali, Tahereh Ebrahimi, Amy Scarborough, Vahideh Tarhriz, Eric Lazartigues

School of Medicine Faculty Publications

The renin-angiotensin system (RAS) is central to cardiovascular diseases such as hypertension and cardiomyopathy, yet the functions of many RAS genes remain unclear. This study developed a multi-label deep learning model to systematically annotate RAS gene functions and elucidate their roles in biological pathways. A total of 39,463 RAS-related publications from PubMed and PMC were processed into text format. Feature matrices were generated using TF-IDF and token processing, followed by dimensionality reduction via Principal Component Analysis (PCA). A Multi-Layer Perceptron (MLP) was applied for multi-label classification, with performance evaluated using Precision, F1-Score, Ranking Loss, and ROC-AUC metrics. The model outperformed …

Enhancing Cataract Surgery Outcomes: Optimal Use Of Pre- And Post-Operative Eye Drops, Keith Skolnick M.D., Anu Valiaveedu

Mako: NSU Undergraduate Student Journal

Many preoperative and postoperative cataract patients struggle with comprehending the use of prescription medication as directed. Language barriers and low health literacy levels are major factors contributing to improper use of prescriptions. To increase patients comprehension, the Fort Lauderdale Eye Institute employed an educational intervention consisting of a live presentation and an instructional video. Results found that 44% of patients were hesitant to ask questions to clinical staff, 32% felt overwhelmed, and nearly 70% lacked confidence in using their prescribed eye drops. Following the intervention, 91% of patients reported increased confidence in their medications, and most indicated that the video …

Identification Of Alkynyl Nicotinamide Hsn748 As A Ret Solvent-Front Mutant Inhibitor With Intracranial Efficacy, Ujjwol Khatri, Neetu Dayal, Kofi B Owusu, Mandeep Kaur Hunjan, Shriya Pandey, Haley Anne Harper, Carli Mcmahan, Bennett D Elzey, Tao Shen, Xueqing Hu, Kurt W Evans, Ahmed El-Sheikh, Seong Jun Jo, Frederick W Holtsberg, M Javad Aman, Funda Meric-Bernstam, Sukyung Woo, Herman O Sintim, Jie Wu

Faculty, Staff and Student Publications

RET solvent-front G810C/R/S mutations confer resistance to the currently approved RET protein tyrosine kinase inhibitors (TKIs) selpercatinib and pralsetinib. Moreover, RET fusion-positive lung adenocarcinoma frequently metastasizes to the brain. To address these challenges, it is imperative to develop a RET TKI that is effective against solvent-front mutations and exhibits intracranial activity. We synthesized alkynyl nicotinamide-based RET TKIs and tested their efficacy in cell cultures in inhibiting selpercatinib/pralsetinib-resistant RET solvent-front mutants G810C/R/S found in cancer patients, and in BaF3/KIF5B-RET(G810C) cell-derived subcutaneous and intracranial tumors in vivo. We also evaluated alkynyl nicotinamide RET TKIs in KIF5B-RET-induced lung tumors in immune competent …

Development And Validation Of Venous Thromboembolism-Bidirectional Encoder Representations From Transformers (Vte-Bert) Natural Language Processing Model, Omid Jafari, Shengling Ma, Barbara D Lam, Jun Y Jiang, Emily Zhou, Mrinal Ranjan, Justine Ryu, Raka Bandyo, Arash Maghsoudi, Bo Peng, Christopher I Amos, Abiodun Oluyomi, Nathanael R Fillmore, Jennifer La, Ang Li

Faculty, Staff and Students Publications

Background: Accurate and rapid phenotyping of venous thromboembolism (VTE) in longitudinal studies is important. A natural language processing (NLP) tool externally validated in representative patients is lacking.

Objectives: To train and validate an efficient NLP model to detect incident VTE event.

Methods: We designed a novel NLP platform, NLPMed, to assist thrombosis researchers with data preprocessing, phenotype annotation, language model finetuning, and NLP application. Using clinical notes, discharge summaries, and radiology reports from patients with cancer at 2 healthcare institutions, we finetuned Bio_Clinical Bidirectional Encoder Representations from Transformers (BERT) to develop VTE-BERT. The new model was trained to detect acute …

Machine Learning Based Medical Ultrasound Image Classification And Grad-Cam Interpretation, Victoria C. Hemphill

All Theses

This work takes a step in creating a diagnostic tool for the classification decision process of Achilles tendinopathy using ultrasound images. An attention-based multiple instance learning model is developed to classify the images. Typically, doctors capture multiple ultrasound images of the Achilles tendon during a study to determine a complete diagnosis. Multiple instance models adopt this behavior by providing a single label for a set of instances (images). The images are grouped into ”bags” at the study level and passed into the model. The MIL model then uses its attention property to assign an importance score to each image to …

Table Of Contents

Journal of the South Carolina Academy of Science

No abstract provided.

Dose Characterization Of Galactic Cosmic Rays In The Human Brain: A Phits-Based Study With A Segmented Anatomical Phantom, Shanice N. Manning

LSU Master's Theses

Astronauts aboard the International Space Station (ISS) are chronically exposed to a complex space radiation environment composed of galactic cosmic rays (GCR), solar particles, and Earth's trapped radiation. These high-energy particles interact with spacecraft shielding, producing secondary particles that can penetrate inside and deposit energy in sensitive tissues. Although animal studies have been useful in assessing space radiation risks, anatomical and physiological differences limit their applicability to human anatomy especially neuroanatomy. To address this gap, we developed a computational framework to model radiation dose distribution within the human brain using Monte Carlo simulations in Particle and Heavy Ion Transport code …

Investigation Of Subcellular Damage And Long-Term Non-Cancerous Cardiovascular Effects Following Whole-Body Photon Irradiation, Nousha Afshari

LSU Doctoral Dissertations

Current radiobiological models focus primarily on nuclear DNA damage and fail to fully explain radiation-induced noncancerous health effects. This dissertation investigates whether non-nuclear subcellular structures contribute to radiation-induced biological damage through experimental biomarker analysis and computational microdosimetry. The central hypothesis posits that whole-body photon irradiation results in subcellular non-nuclear damage, specifically increased circulating mitochondrial DNA concentrations, that correlates with radiation dose and long-term noncancerous effects such as cardiovascular disease.

Specific Aim 1 measured mitochondrial and nuclear DNA concentrations in banked blood samples from 72 Chinese rhesus macaques that previously received whole-body photon irradiation ($0-10$ Gy) from Cobalt-60 or LINAC sources. …

Biocomputing Approach To Modeling And Modulating Calcium Signaling, Sehee Sun

School of Computing: Dissertations, Theses, and Student Research

Biocomputing is an emerging field that seeks to perform computational tasks using biological substrates and processes. Unlike conventional computing systems based on silicon hardware, biocomputing leverages the parallelism, energy efficiency, and complex dynamics of living systems. Among various cellular mechanisms, calcium (Ca2+) signaling stands out as a central regulator of diverse biological functions, offering a promising basis for programmable logic and control in living cells.

This thesis introduces a novel framework for modeling and modulating Ca2+ dynamics using biologically inspired Boolean logic circuits. Specifically, we propose the Ca2+ Boolean Logic (CaBL) model, in which Ca2+ fluxes and interactions are abstracted …

Phytochemical Analysis And Antimicrobial Activity Of Micromeria Barbata Leaf And Stem Extracts Against Pseudomonas Aeruginosa: Insights From Molecular Docking And In Vitro Assays, Shiraz Rawas, Dalia El-Badan, Nawal Al Hakawati

BAU Journal - Science and Technology

The present study provides the characterization of the phytochemical content and the antibacterial activity of ethanolic extracts from the leaves (LM) and stems (SM) of Micromeria barbata (M.barbata) against Pseudomonas aeruginosa . Important functional groups were determined by analyzing the FTIR spectra of LM and SM. The phytochemical profiles were analyzed by GC-MS, and these characterized the chemicals according to retention periods and peak regions. The binding affinities of discovered compounds with the P. aeruginosa LasR and PqsD proteins were evaluated using molecular docking approach. Assays for biofilm formation, MIC, MBC, and agar well diffusion were used to assess the …

Multiscale Modeling Of Drug-Induced Liver Injury From Organ To Lobule, Alon Malka-Markovitz, Stelian Camara Dit Pinto, Mohammed Cherkaoui, Steven M Levine, Sharmila Anandasabapathy, Gagan K Sood, Sadhna Dhingra, Gao Yujia, John M Vierling, Nicolas R Gallo

Center for Medical Ethics and Health Policy Staff Publications

Drug-induced liver injury poses significant challenges in drug development and in clinical care. This study builds on prior work developing a Human Liver Virtual Twin by creating a Multiscale Computational Fluid Dynamics framework that integrates patient-specific anatomical data to predict acetaminophen-induced liver injury as a demonstration of its capability. The model bridges vascular, lobular, and cellular scales to simulate dynamic blood flow, drug transport, and injury mechanisms that accurately reflect clinically observed spatial heterogeneity. Results demonstrate accurate blood flow dynamics, predictions of hepatocellular damage, and a scalable framework for studying spatial heterogeneity applicable to other hepatic pathologies. This work establishes …

Histone Methyltransferase Ash1l Primes Metastases And Metabolic Reprogramming Of Macrophages In The Bone Niche, Chenling Meng, Kevin Lin, Wei Shi, Hongqi Teng, Xinhai Wan, Anna Debruine, Yin Wang, Xin Liang, Javier Leo, Feiyu Chen, Qianlin Gu, Jie Zhang, Vivien Van, Kiersten L Maldonado, Boyi Gan, Li Ma, Yue Lu, Di Zhao

Faculty, Staff and Student Publications

Bone metastasis is a major cause of cancer death; however, the epigenetic determinants driving this process remain elusive. Here, we report that histone methyltransferase ASH1L is genetically amplified and is required for bone metastasis in men with prostate cancer. ASH1L rewires histone methylations and cooperates with HIF-1α to induce pro-metastatic transcriptome in invading cancer cells, resulting in monocyte differentiation into lipid-associated macrophage (LA-TAM) and enhancing their pro-tumoral phenotype in the metastatic bone niche. We identified IGF-2 as a direct target of ASH1L/HIF-1α and mediates LA-TAMs' differentiation and phenotypic changes by reprogramming oxidative phosphorylation. Pharmacologic inhibition of the ASH1L-HIF-1α-macrophages axis elicits …

Human Endogenous Retroviruses (Hervs) Associated With Glioblastoma Risk And Prognosis, Harun Mazumder, Hui Yi Lin, Melody Baddoo, Wojciech Gałan, Diana Polania-Villanueva, Chindo Hicks, David Otohinoyi, Francesca Peruzzi, Zbigniew Madeja, Victoria P. Belancio, Erik K. Flemington, Krzysztof Reiss, Monika Rak

School of Medicine Faculty Publications

Emerging evidence suggests expression from human endogenous retrovirus (HERV) loci likely contributes to, or is a biomarker of, glioblastoma multiforme (GBM) disease progression. However, the relationship between HERV expression and GBM malignant phenotype is unclear. Applying several in silico analyses based on data from The Cancer Genome Atlas (TCGA), we derived a locus-specific HERV transcriptome for glioma that revealed 211 HERVs significantly dysregulated in the comparisons of GBM vs. normal brain (NB), GBM vs. low-grade glioma (LGG), and LGG vs. NB. Our analysis supported development of a unique HERV scoring algorithm that segregated GBM, LGG, and NB. Interestingly, lower HERV …

Identification And Preclinical Assessment Of Novel Therapeutic Modalities In Environmental Exposure-Induced Lung Disease, Aaron D. Schwab

Theses & Dissertations

Environmental lung diseases are preventable respiratory conditions either caused or made worse by inhaled environmental exposures. Contemporarily, environmental lung diseases are most associated with workplace exposures given specific occupational processes aerosolize inflammatory agents that can be inhaled at high concentrations. Although a considerable amount is known regarding immunoglobulin (Ig)E-mediated responses to environmental exposures, little is known about non-IgE mediated respiratory conditions resulting from lipopolysaccharide (LPS)-enriched organic dust exposure(s). Chronic respiratory diseases such as asthma, hypersensitivity pneumonitis, chronic obstructive pulmonary disease (COPD), and pulmonary fibrosis have all been identified as environmental lung diseases caused or exacerbated by such environmental dusts. Therapeutic …

Ai Model For Predicting Asthma Prognosis In Children, Elham Sagheb, Chung-Il Wi, Katherine S King, Bhavani Singh Agnikula Kshatriya, Euijung Ryu, Hongfang Liu, Miguel A Park, Hee Yun Seol, Shauna M Overgaard, Deepak K Sharma, Young J Juhn, Sunghwan Sohn

Faculty, Staff and Student Publications

BACKGROUND: Childhood asthma often continues into adulthood, but some children experience remission. Utilizing electronic health records (EHRs) to predict asthma prognosis can aid health care providers and patients in developing effective prioritized care plans.

OBJECTIVE: We aimed to develop artificial intelligence (AI) models using various clinical variables extracted from EHRs to predict childhood asthma prognosis (remission vs no remission) in different age groups.

METHODS: We developed AI models utilizing patients' EHRs during the first 6, 9, or 12 years of their lives to predict their asthma prognosis status at ages 6 to 9, 9 to 12, or 12 to 15 …

A Comparative Analysis Of Nf-Κb1 Gene Regulatory Sequence Methylation In Normotensive And Hypertensive Kenyans, Aaryan Barlas Piracha

Honors Theses

Accounting for the majority of deaths worldwide, non-communicable diseases (NCDs) present the greatest health challenge of the twenty-first century. Specifically, cardiovascular diseases (CVDs) exceed all other NCDs in annual deaths and especially affect low- and middle-income countries (LMICs). Hypertension, being the primary risk factor for CVD, affects over 75% of adults in LMICs due to inadequate health care and preventative measures. Additionally, epigenetic modifications of DNA are important mechanisms that regulate gene expression; DNA methylation, in particular, affects cytosine residues in cytosine-phosphate-guanine (CpG) islands on regulatory sequences. Previous research in our laboratory analyzed percent methylation at 8 different CpG islands …