Medicine and Health Sciences Commons^™

Survival On Image Regression With Application To Partially Functional Distributional Representation Of Physical Activity, Rahul Ghosal, Sunwoo Emma Cho, Marcos Matabuena

Faculty Publications

Technological advancements in wearable devices and medical imaging often lead to high-dimensional physiological signals in the form of images or surfaces. To address these data structures, we develop a novel survival on image regression model with a specific focus on partially functional distributional representation of wearable data. The existing approaches for functional data and survival outcomes have been primarily developed for uni-dimensional functional predictors. Drawing on recent developments in distributional data analysis, we model temporally varying distributional patterns of physical activity (PA) as a partially functional distributional predictor within a semiparametric Cox model framework. We use tensor product splines to …

Environmental Chemicals And Maternal Depression During And After Pregnancy: A Scoping Review, Pengfei Guo, Yunyue Shi, Cindy Nguyen, Haoran Zhuo, Tormod Rogne, Zeyan Liew

Faculty Publications

Purpose of Review

There is increasing evidence that several environmental exposures may pose a risk for depression, including maternal depression. We conducted a scoping review of epidemiological evidence regarding maternal exposure to environmental chemicals and perinatal depression.

Recent Findings

We searched PubMed, Embase, Web of Science, Dimensions, and Scopus, and summarized the findings from 27 articles that examined environmental chemical exposures and maternal depression. Studies of ambient air pollutants (N = 11) showed exposure to NO₂ and PM10 to be most consistently associated with antenatal or postnatal depression. Studies of endocrine-disrupting chemicals, including phthalates (n = 6), …

Protecting ‘Punks’: The Shortcomings Of The Prison Rape Elimination Act, Rachel Goldsmith

Binghamton University Undergraduate Journal

Prisoner-on-prisoner sexual abuse is widespread in US male prisons, with inmates “turning each other out” by sexually assaulting each other. While the Prison Rape Elimination Act (PREA) received unanimous support in Congress in 2003, motivated by the Human Rights Watch Report “No Escape: Male Rape in Prisons,” scholars argue that the PREA fails to protect prisoners from prison rape: for example, its standards may be ineffective or criminalize consensual sex between prisoners. This essay will examine the shortcomings of the PREA, drawing on current legal scholarship, and propose solutions to enhance the law's effectiveness in protecting prisoners. The US government …

Pediatric Inflammatory Bowel Disease Tissue Classification From Pathology Slide Images: Detecting Phenotypes Using Computer Vision, Chloe Martin-King, Ali Nael, Louis Ehwerhemuepha, Blake Calvo, Quinn Gates, Jamie Janchoi, Elisa Ornelas, Melissa Perez, Andrea Venderby, John Miklavcic, Peter Chang, Aaron Sassoon, Brian Rubio, Ghislaine Barrigan, Kenneth Grant

Food Science Faculty Articles and Research

Background and Aims

Methods

Results …

Finding Research Datasets And Evaluating Data Quality, Ibis Anette Moreno-Lozano Phd.

Day Family Research Lab Workshop Series

No abstract provided.

Ai Scribe Use In Residency Training: A Call For Specialty Society Guidance In Graduate Medical Education, Julia A. Giordano, Elizabeth Jones

Department of Dermatology and Cutaneous Biology Faculty Papers

Artificial intelligence (AI) is increasingly used for documentation purposes in clinical practice, yet guidance for resident use is limited. Given the substantial documentation burden on medical trainees, AI-powered scribing tools may offer benefits, but their integration into residency training raises educational, supervisory, and patient safety considerations. This study aimed to assess the availability of resident-specific guidance on AI scribe use from major medical and specialty organizations and to summarize current evidence on AI scribes in residency. We reviewed five major medical and specialty society websites (AAD, AMA, ACGME, AAMC, ABMS) via website searches and direct emails and conducted a PubMed …

Data Management Plans For Grant Proposals, Rubab Shahzad

Day Family Research Lab Workshop Series

Fundamentals of research data management and how to create effective Data Management Plans (DMPs) and Data Management Sharing Plans (DMSP)

Ecologic Factors Contributing To West Nile Virus Hyperendemicity In Central South Carolina: An Integrated Vector–Human–Environmental Study, Elba S. Fridriksson, Ahayla Muraleedharan, Kyndall C. Dye-Braumuller, Madeleine M. Meyer, Kia Zellars, Hiuxuan Li, Melissa S. Nolan Ph.D., Mph

Faculty Publications

West Nile virus (WNV) is an endemic arboviral infection in the United States that has undergone phyloge-netic evolution since its introduction 25 years ago. An integrated vector–human–pathogen study was conducted in the summer of 2023 to unearth contemporary Culex quinquefasciatus habitat patterns and human transmission spillover foci in South Carolina, a state with historically little WNV data. A serosurvey revealed WNV seroprevalence 10 times the national average (22% versus 2%, respectively), with unusual epidemiologic risk factors. Female Culex quinquefas-ciatus WNV positivity was low (2.7%), with viral phylogenetics 100% homologous to the WN02 clade. Mosquito vectors clustered in affluent urban neighborhoods …

Fedda-Tsformer: Federated Domain Adaptation With Vision Timesformer For Left Ventricle Segmentation On Gated Myocardial Perfusion Spect Image, Yehong Huang, Chen Zhao, Rochak Dhakal, Min Zhao, Guang-Uei Hung, Zhixin Jiang, Weihua Zhou

Michigan Tech Publications

BACKGROUND: Accurate assessment of left ventricular function is essential for diagnosing and managing cardiovascular disease. Gated myocardial perfusion SPECT (MPS) enables simultaneous evaluation of perfusion and function, but reliable contour extraction is challenged by image noise, resolution limits, and anatomical variability. Multi-center validation is further restricted by data privacy concerns, underscoring the need for robust and privacy-preserving contouring methods. METHODS: In this study, we propose a novel approach, FedDA-TSformer, which integrates Federated Domain Adaptation with the TimeSformer model for the task of left ventricle segmentation using MPS images. The proposed model captures spatial and temporal features through a Divide-Space-Time-Attention mechanism, …

Biologically-Inspired Multiscale Neuromorphic Architecture, Christian O'Reilly, Ramtin Zand

Publications

This white paper proposes a biologically-inspired multiscale neuromorphic architecture that bridges key gaps between artificial neural networks (ANNs), spiking neural networks (SNNs), and biological neural networks (BNNs). While SNNs offer promising energy efficiency, their broader adoption remains limited by suboptimal performance and the need for novel learning paradigms. To address these challenges, the proposed framework integrates structural and functional principles observed in the brain, including hierarchical organization, sparse and modular connectivity, predictive coding, and diverse neuronal dynamics.

The architecture operates across micro-, meso-, and macro-scales, incorporating neuron-level diversity (e.g., excitatory/inhibitory and principal/support cells), canonical microcircuits (CMCs), and large-scale hierarchical organization. …

Detecting Stigmatizing Language In Clinical Notes With Large Language Models For Addiction Care, Rohan Sethi, John Caskey, Yanjun Gao, Matthew M. Churpek, Timothy A. Miller, Anoop Mayampurath, Elizabeth Salisbury-Afshar, Majid Afshar, Dmitriy Dligach

Computer Science: Faculty Publications and Other Works

Intensive care units (ICU) produce numerous progress notes that may contain stigmatizing language that perpetuate negative biases and punitive approaches against patients. Patients with substance use disorders are particularly vulnerable to stigma. This study examined the performance of Large Language Models (LLMs) in the identification of stigmatizing language. We annotated a dataset with over 77,000 stigmatizing and non-stigmatizing notes from the MIMIC-III database. We utilized Meta's Llama-3 8B Instruct LLM to run the following experiments for stigma detection: zero-shot; in-context learning; in-context learning with a selective retrieval; supervised fine-tuning (SFT); and keyword search. All approaches were evaluated on a held-out …

Monosaccharide Binding To Synthetic Carbohydrate Receptor Microarrays, Milan A. Shlain

Dissertations, Theses, and Capstone Projects

Chapter 1: A glycan detection platform comprised of synthetic carbohydrate receptors (SCRs) immobilized onto polymer brushes was prepared. SCR043, an alkene-containing SCR, was incorporated into grafted-from polymer brushes using hypersurface photolithography, resulting in microarrays of SCR043-functionalized polymer brushes, where brush height (h) and SCR grafting density (Γ) is controlled precisely at each feature in the array. The influence of h and Γ on the binding to five fluorescently labelled monosaccharides – α-glucose (α-Gluc-FL), α-galactose (α-Gal-FL), α-mannose (α-Man-FL), β-glucose (β-Gluc-FL), and β-galactose (β-Gal-FL) – in aqueous buffer …

Obstructive Sleep Apnea Prediction: A Comprehensive Review And Comparative Study, Thi Khanh Chi Huynh, Amonae Dabbs-Brown, Anna Jurek-Loughrey, James Mulhall, Tuan Dung Pham, Ngoc Phu Doan, Viet Hung Tran, Zichi Zhang, Xuan Hoang Nguyen, Yimeng An, Peixin Li, Phi Hung Nguyen, Thi Linh Hoang, Xinming Shi, Hans Vandierendonck, Sebastien Bailly, Jean-Louis Pépin, Thai Son Mai

Research Collection School Of Computing and Information Systems

Obstructive Sleep Apnea (OSA) is a highly prevalent sleep disorder linked to considerable public health burdens and comorbidities. However, its heterogeneous presentation and the limited accessibility of traditional diagnostic tools such as polysomnography (PSG) lead to widespread underdiagnosis. As a result, artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) models, have attracted attention as an alternative pathway to detection. This paper first provides a comprehensive review of AI-driven OSA diagnosis, covering different diagnosis problems, input-data types, data biases, pre-processing techniques, and model performance. We then leverage the largest clinical dataset used in OSA prediction to date, …

Demographic And Other Correlates Of Non-Prescription Drug Use Among College Students During The Covid-19 Pandemic, Subi Gandhi, Sidketa Fofana, Md Rafiul Islam, Tamer Oraby

School of Mathematical & Statistical Sciences Faculty Publications

Background and objectives: Substance use among college students in the U.S. remains a pressing concern and may have intensified during the COVID-19 pandemic due to increased stress, uncertainty, and academic disruptions. This study investigates the relationship between non-prescription drug use and various demographic, mental health, and behavioral factors among college students during the pandemic's early stages.

Methods: Data were collected through online and in-person surveys in the summer semester of 2021. Behavioral health was assessed using validated instruments: the Patient Health Questionnaire-9 (PHQ-9) for depression and the Drug Abuse Screening Test-20 (DAST-20) for substance use. Demographic and behavioral variables were …

Evaluation Of Large Language Models As Decision Support Tools For Head And Neck Cancer Management: A Blinded Multidisciplinary Simulation Study, Sholem Hack, Ron J. Karni, Antonino Maniaci, Christopher E. Fundakowski, Luca Castellani, Fabiola Incandela, Remo Accorona, Miguel Mayo-Yanez, Martina Violati, Lorenzo Giannini, Niccolo' Mevio, Alberto Maria Saibene

Department of Otolaryngology - Head and Neck Surgery Faculty Papers

BACKGROUND: The management of head and neck cancer relies on multidisciplinary expertise; however, access to tumor boards remains variable. Large language models (LLMs) may support guideline-based decision-making, although performance in complex oncologic scenarios is not well defined.

METHODS: Fourteen synthetic cases based on real tumor board encounters were evaluated. Five blinded comparator arms produced recommendations: a human expert, Non-RAG-GPT-4, Non-RAG-GPT-5, RAG-GPT-4, and RAG-GPT-5. Eight head and neck oncologic surgeons scored each recommendation for appropriateness, clarity, specificity, and feasibility using 5-point Likert scales. Paired permutation testing and inter-rater reliability were assessed.

RESULTS: LLM outputs showed close alignment with expert recommendations. RAG-based …

A Virtual-Reality-Based Dental Simulator For Endodontics With Automated Formative Feedback, Yousef Salah Abo El Ela

Theses and Dissertations

Advancements in virtual reality (VR) and haptic technology are transforming the landscape of medical and dental education, offering new avenues for safe, immersive, and repeatable training experiences. Within dentistry, endodontics presents unique challenges for preclinical education due to anatomical complexity, limited access to extracted teeth, ethical concerns, and the shortcomings of conventional plastic models. Despite endodontics specific plastic teeth being available, they fall short of replicating the hardness of real extracted teeth, are relatively costly compared to generic plastic teeth, and are ultimately a disposable item which makes them inadequate as a sustainable long-term solution. Extracted teeth do a much …

Novel R Shiny Tool For Survival Analysis With Time-Varying Covariate In Oncology Studies: Overcoming Biases And Enhancing Collaboration, Yimei Li, Yang Qiao, Fei Gao, Jordan Gauthier, Qiang Ed Zhang, Jenna Voutsinas, Wendy Leisenring, Ted Gooley, Corinne Summers, Alexandre Hirayama, Cameron Turtle, Rebecca Gardner, Jarcy Zee, Qian Vicky Wu

Wills Eye Hospital Papers

PURPOSE: Our study is motivated by evaluating the role of hematopoietic cell transplantation (HCT) after chimeric antigen receptor T-cell (CAR-T) therapy for ALL, a debated topic. Because patients may receive HCT at different times after CAR-T infusion or never, HCT post-CAR-T should be considered as a time-varying covariate (TVC).

METHODS: Standard Cox models and Kaplan-Meier (KM) curves (naïve method) assume that TVC status is known and fixed at baseline, which can yield biased estimates. Landmark analysis is a popular alternative but depends on a chosen landmark time. Time-dependent (TD) Cox model is better suited for TVC although visualizing survival curves …

Use Of Electrocardiograms To Identify Coronary Artery Disease: Cross-Validation Of An Artificial Intelligence Model, Michael Leasure, Indu Poornima, Adam Butchy, Utkars Jain, Devin Vasoya, Michael Warnick, Brent Williams, John Rehder, Prahlad Menon, Veronica A. Covalesky, Gary S. Mintz

Department of Medicine Faculty Papers

BACKGROUND: The current gold standard for the diagnosis of coronary artery disease (CAD) is invasive angiography; however, it is an invasive procedure. Therefore, we developed an artificial intelligence model designed to predict significant CAD from a resting digital 12-lead electrocardiogram (ECG).

OBJECTIVES: This retrospective study assessed the model's ability to predict clinically significant CAD in a patient population presenting for coronary angiography.

METHODS: From 2019 to 2021, 16,476 patients had a resting 12-lead digital ECG recorded within 90 days prior to coronary angiography. The artificial intelligence model was developed using 10-fold cross-validation methodology. Clinically significant disease was defined as angiographic …

Interpretable Linear Models For Heart Disease Prediction: A Comparative Study, Dipok Deb, Emran Hossain

Data Science and Data Mining

Heart disease remains a leading cause of mortality worldwide, underscoring the importance of accurate and transparent methods for early diagnosis. While many machine learning and artificial intelligence models have demonstrated strong predictive performance, their limited interpretability poses challenges for clinical adoption. In this study, we evaluate three interpretable linear classification models—Generalized Linear Model (GLM) logistic regression, L1-regularized (Lasso) logistic regression, and Linear Discriminant Analysis (LDA)—for heart disease prediction using the Cleveland Heart Disease dataset. Following comprehensive data preprocessing, the models are assessed on a held-out test set using standard evaluation metrics, including accuracy, precision, recall, F1-score, and the area under …

From Latent Manifolds To Targeted Molecular Probes: An Interpretable, Kinome-Scale Generative Machine Learning Framework For Family-Based Kinase Ligand Design, Gennady M. Verkhivker, Ryan Kassab, Keerthi Krishnan

Mathematics, Physics, and Computer Science Faculty Articles and Research

Scaffold-aware artificial intelligence (AI) models enable systematic exploration of chemical space conditioned on protein-interacting ligands, yet the representational principles governing their behavior remain poorly understood. The computational representation of structurally complex kinase small molecules remains a formidable challenge due to the high conservation of ATP active site architecture across the kinome and the topological complexity of structural scaffolds in current generative AI frameworks. In this study, we present a diagnostic, modular and chemistry-first generative framework for design of targeted SRC kinase ligands by integrating ChemVAE-based latent space modeling, a chemically interpretable structural similarity metric (Kinase Likelihood Score), Bayesian optimization, and …

Handling Missing Data In Copd Research, Chia-Ying Chiu

ETDs from 2020-2029

Chronic Obstructive Pulmonary Disease (COPD) remains a major global health concern and one of the leading causes of death in the United States, affecting approximately 4.6% of adults and reaching a prevalence of 9.4% in Alabama according to 2024 National Health Interview Survey. The disease imposes a substantial burden on quality of life and healthcare costs and contributes to increased disability-adjusted life years and years of life lost, as highlighted by the 2022 Lancet Commission report. Despite its im-pact, COPD is frequently diagnosed only after irreversible lung damage has occurred, largely due to under-recognized symptoms and limited diagnostic tools. Early …

Lead Levels In Fingernails Of Hemodialysis Patients And Healthy Individuals In Karbala, Iraq: A Biomonitoring Study Using Icp Oes, Baker A. Joda, Rusul Jaafar, Rana Hameed, Hiba Sadeq Alheloo, Sadeq Jaafar Hasan Dwenee

Karbala International Journal of Modern Science

The use of non-invasive media to evaluate the association between trace elements and diseases has spread in recent centuries. Fingernail samples were collected from 103 individuals, including both healthy participants and those undergoing hemodialysis, residing in Karbala, Iraq, and ranging in age from 21 to 75 years. The concentrations of lead were determined using an Inductively Coupled Plasma Optical Emission Spectrometry (ICP OES). Statistical analyses using F-tests and two-tailed t-tests were conducted to examine the effects of health status, gender, smoking status, and their interactions on lead levels in washed fingernails. The concentrations of Pb in washed fingernails of healthy …

Dietary Inflammatory Index And Risk Of Colorectal Cancer In Japanese Men, Ayaka Kotemori, Kumiko Kito, Motoki Iwasaki, Taiki Yamaji, James Hébert Scd, Junko Ishihara, Manami Inoue, Shoichiro Tsugane, Norie Sawada On The Behalf Of Jphc Study Group

Faculty Publications

Background/Objectives: Unhealthy lifestyles lead to chronic low-grade inflammation, increasing the risk of colorectal cancer. Few studies in East Asia have examined the association between the dietary inflammation potential and colorectal cancer incidence. Therefore, we aimed to investigate this association further in the Japanese population. Methods: This study included 38,807 men aged 45–74 years who participated in the Japan Public Health Center-based prospective study (JPHC Study). The energy-adjusted dietary inflammatory index (E-DII) was derived from a food frequency questionnaire. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards regression models. Differences in risk due to a …

Table Of Contents

Journal of the South Carolina Academy of Science

No abstract provided.

Protocol For The Development Of The Who Gestational Weight Gain Charts, Thais Rangel Bousquet Carrilho, Olufemi Taiwo Oladapo, Jennifer A. Hutcheon, Giovanna Gatica-Domínquez, Kathleen M. Rasmussen, Monica C. Flores-Urrutia, Richard Kumapley, Ӧzge Tunçalp, Dang Bahya-Batinda, Amel A. Fayed, Annick Bogaerts, Aris T. Papageorghiou, Cinthya Muñoz-Manrique, Dayana Rodrigues Farias, Eric Ohuma, Harshpal Sachdev, Hayfaa A. Wahabi, Helena J. Teede, Molin Wang, Nandita Perumal Phd

Faculty Publications

Introduction Gestational weight gain (GWG) is an important indicator of maternal nutrition to be monitored during pregnancy. However, there is no evidence-based tool that can be used to monitor it across all geographic locations and pre-pregnancy body mass index (BMI) categories. The WHO is undertaking a project to develop GWG charts by pre-pregnancy BMI category, and to identify GWG ranges associated with the lowest risks of adverse maternal and infant outcomes. This protocol describes all the steps that will be used to accomplish the development of these GWG charts.

Methods and analysis This project will involve the analysis of individual …

Confluence, Vol. 4, Iss. 2, Full Issue

The Confluence

No abstract provided.

Sociodemographic And Hiv-Related Characteristics Associated With Mental Health Diagnoses Among People Living With Hiv, Monique J. Brown Ph.D., Mph, Jiayang Xiao, Xueying Yang Ph.D., Bankole Olatosi Ph.D., Sharon Weissman, Xiaoming Li Ph.D., Jiajia Zhang Ph.D.

Faculty Publications

Mental health diagnoses have been linked to poor HIV treatment outcomes and poorer quality of life among people living with HIV (PLWH). Therefore, this study aimed to investigate the association between sociodemographic and HIV-related characteristics, and common and serious mental health disorders among PLWH in South Carolina (SC). Data were obtained from the integrated system of statewide electronic health record (EHR) data in SC (2006–2019; N = 8,124). Multivariable logistic regression models were used to determine the associations between sociodemographic and HIV-related characteristics, and common mental health disorders and serious mental health disorders. Among the study population, 4% were 60 …

Utilizing Machine Learning Techniques For Computer-Aided Covid-19 Screening Based On Clinical Data, Honglun Xu, Andrews T. Anum, Michael Pokojovy, Sreenath Chalil Madathil, Yuxin Wen, Md. Fashiar Rahman, Tzu-Liang Bill Tseng, Scott Moen, Eric Walser

Engineering Faculty Articles and Research

The COVID-19 pandemic has highlighted the importance of rapid clinical decision-making to facilitate the efficient usage of healthcare resources. Over the past decade, machine learning (ML) has caused a tectonic shift in healthcare, empowering data-driven prediction and decision-making. Recent research demonstrates how ML was used to respond to the COVID-19 pandemic. This paper puts forth new computer-aided COVID-19 disease screening techniques using six classes of ML algorithms (including penalized logistic regression, random forest, artificial neural networks, and support vector machines) and evaluates their performance when applied to a real-world clinical dataset containing patients’ demographic information and vital indices (such as …

Multiple Myeloma Risk Linked To Dna Damage Response Genes, Michael Conry, Irina Ostrovnaya, Yelena Kemel, Saloni Sinha, Linda B. Baughn, Brian Avery, Kylee Maclachlan, Victoria Groner, Lauren Banaszak, Aaron Norman, Nicholas J. Boddicker, Alyssa I. Clay-Gilmour Ph.D., Shaji Kumar, Ellen Kim, Sita Dandiker, Mitul Waghmare, Susan Slager, Douglas Sborov, Judy Garber, Elizabeth E. Brown, Michelle Hildebrandt, Et. Al.

Faculty Publications

Background DNA damage response genes (DDRG), implicated in several cancers as both predisposing risk factors as well as biomarkers for aggressiveness, have not been fully explored in multiple myeloma (MM).

Methods Herein, we analyzed disease associations of pathogenic variations in nine putative candidate genes using 3 446 MM cases and 323 233 cancer-free controls.

Results Increased MM risk was found to be associated with inherited rare pathogenic mutations in TP53, ATM, CHEK2, KDM1A, and ARID1A, with an enrichment of these variants among individuals with early onset or family history of MM. Individuals with TP53 or ATM germline mutations are also …

A Phase 1, First-In-Human, Dose Escalation Study Of Jnj-80038114, A Psmaxcd3 Bispecific Antibody, In Participants With Metastatic Castration-Resistant Prostate Cancer, Andrew Hudson, Anuradha Jayaram, Benjamin Garmezy, Nicholas Zorko, Kevin Zarrabi, Ligi Mathews, Brent Rupnow, Mengjie Li, Debopriya Ghosh, Karen Urtishak, Peter Francis, Sherry Wang, Edward Attiyeh, Johann De Bono

Department of Medical Oncology Faculty Papers

PURPOSE: Prostate-specific membrane antigen (PSMA) has been identified as a therapeutic target for metastatic castration-resistant prostate cancer (mCRPC). The recent success of radioligands targeting PSMA spurred development of new PSMA-targeting agents including immunotherapy. JNJ-80038114 is a bispecific antibody that binds PSMA on tumor cells and CD3 on T cells to induce anti-tumor activity.

METHODS: This was a phase 1, open-label, multicenter study of JNJ-80038114 in participants with mCRPC and ≥ 1 prior systemic therapy. JNJ-80038114 was administered subcutaneously every 3 weeks (Q3W), starting at 0.1 mg. The primary endpoint was safety. Secondary endpoints included pharmacokinetics (PK), immunogenicity, and prostate-specific antigen …