Digital Commons Network^™

The Evolution Of Public Health Statistical Modeling Approaches And How To Advance Their Incorporation Into Modern Arboviral Surveillance, Maggie Mccarter, Stella C W Self, Alex Ewing, Mufaro Kanyangarara, Sarah M Gunter, Melissa S Nolan

Faculty, Staff and Students Publications

Statistical modeling of infectious disease transmission patterns has been in existence since the mid-1700s, evolving in their utility as the scientific and technological revolutions progressed. Despite the expansion of emerging mathematical and statistical methodologies over the past 250 yr, their usage has largely remained restricted to academic settings. This forum article will discuss the evolution of disease modeling techniques, the most common types of models in use today, and recommendations on how key archetypes can be incorporated into future public health practice. With the recent global impetus to predict and forecast novel pathogens, this article raises the question: Why are …

Identifying Priority Research Questions For Decentralized Wastewater, Bryan W Brooks, Timothy Callahan, Jacob K Stanley, Jamie Holodak, Kevin M Stroski, Alissa H Cox, Thomas W Groves, Anish Jantrania, Jeff C Moeller, Kris Neset, Christopher Walker, Harry Zhang, Amal Bakchan, Kelly D Alley, Jim Bell, Allison Blodig, Eric Casey, Donna Cosper, Victor D'Amato, Mark A Elliott, Greg Graves, Roxanne Groover, Robert Himschoot, Mary G Lusk, Jillian Maxcy-Brown, David Meints, Rojelio Mejia, Matt Pace, Benjamin J Ryan, Brian Scheffe, Tom Schimelfenig, June E Wolfe, Sara F Heger

Faculty, Staff and Students Publications

Decentralized wastewater treatment and reuse represent critical infrastructure across the rural-periurban-urban continuum around the world. Effective, efficient, resilient, and equitable implementation of on-premise technologies and management systems is part of a One Water approach and necessary to protect public health and the environment, yet inconsistent delivery of essential public health services persists in many regions, including across states, tribes, and territories of the United States. We initiated a groundbreaking effort to understand challenges and to identify research opportunities related to the science and practice of decentralized wastewater. A horizon scanning exercise using a bottom up and transparent key questions approach …

Daily Locomotor Activity Declines With Tumor Growth And Disease Progression In Glioblastoma, Maria F Gonzalez-Aponte, Sofia V Salvatore, Anna R. Damato, Ruth Gn Katumba, Grayson R. Talcott, Omar H. Butt, Jian L. Campian, Jingqin Luo, Joshua B. Rubin, Olivia J. Walch, Erik D. Herzog

2020-Current year OA Pubs

Glioblastoma (GBM) is an aggressive brain tumor that often progresses despite resection and treatment. Timely and continuous assessment of GBM progression is critical to expedite secondary surgery or enrollment in clinical trials. However, current progression detection requires costly and specialized MRI examinations, which, in the absence of new symptoms or signs, are usually scheduled every 2-3 months. Here, we hypothesized that changes in daily activity are associated with GBM growth and disease progression. We found that wheel-running activity in GBM-bearing mice declined as tumors grew and preceded weight loss and circadian breakdown by over a week. Temozolomide treatment in the …

3d Reconstruction Of Spatial Transcriptomics With Spatial Pattern Enhanced Graph Convolutional Neural Network, Chen Tang, Yuansheng Zhou, Xue Xiao, Lei Dong, Lei Yu, Qiwei Li, Guanghua Xiao, Lin Xu

Faculty, Staff and Student Publications

Spatially resolved transcriptomics (SRT) is a promising new technology that enables simultaneous analysis of gene expression and spatial information for biomedical research. However, the existing statistical and deep learning algorithms used for analyzing SRT data rely solely on two-dimensional (2D) spatial coordinates, which limits their ability to accurately identify spatial domains, spatially variable genes (SVGs), cell-to-cell communications, and developmental trajectories in a three-dimensional (3D) spatial manner. To address these limitations, we introduced Spa3D, which utilized the anti-leakage Fourier transform and graph convolutional neural network model to reconstruct 3D-based spatial structures from multiple 2D SRT slices. We demonstrate that Spa3D is …

Pathways, Outputs And Impact Of Nih-Supported Bioinformatics And Genomics Graduate Trainees In Africa, Daudi Jjingo, Andrew Walakira, Suhaila Hashim, Cisse Cheickna, Ronald Galiwango, Caleb Kibet, Florence N Kivunike, Gerald Mboowa, Fredrick Elishama Kakembo, Babajide Ayodele, Jean-Baka Domelevo Entfellner, Santie De Villiers, Karen Wambui, Segun Fatumo, Tinashe Chikowore, John Mukisa, Alfred Ssekagiri, Nicholas Bbosa, Julius Mulindwa, Samuel Kyobe, Mike Nsubuga, Grace Kebirungi, Eric Katagirya, Savannah Mwesigwa, Ibra Lujumba, Rogers Kamulegeya, Samuel Kirimunda, Stephen Kanyerezi, Shahiid Kiyaga, Ivan Sserwadda, Davis Kiberu, Bernard S Bagaya, Julius Okwir, Patricia Nabisubi, Grace Nabakooza, Mugume Twinamatsiko Atwine, Ricard Sserunjogi, Rolanda Julius, Mariam Quiñones, Meghan Mccarthy, Phillip Cruz, Karlynn Noble, Christopher J Whalen, Darrell Hurt, Maria Y Giovanni, Michael Tartakovsky, Deogratius Ssemwanga, John M Kitayimbwa, Steven J Reynolds, Christopher C Whalen, Andrew Kambugu, Neil A Hanchard, Li Jian, Peter Amoako-Yirenkyi, Graeme Mardon, I King Jordan, Samson Pandam Salifu, Mamadou Wele, Ezekiel Adebiyi, Jeffrey G Shaffer, Seydou Doumbia, David Patrick Kateete, Michelle Skelton, Nicola Mulder, Jonathan K Kayondo, Daniel Masiga, H3africa Consortium

Faculty, Staff and Students Publications

Global biomedical and health research is increasingly relying on genomic and computational approaches, largely driven by the increasing volumes of nucleic acid sequencing. Concurrently, epidemiological studies and clinical records are generating enormous amounts of data amenable to disease modeling, machine learning, and artificial intelligence techniques. Bioinformatics and data science expertise is therefore essential for improved population health. Accordingly, in 2012, the US National Institutes of Health (NIH) in partnership with the Wellcome Trust, and with support from the African Society for Human Genetics, initiated the H3Africa (Human Heredity and Health in Africa) consortium. One of its key goals was to …

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 …

Towards A Unified Gating Scheme For The Cnbd Ion Channel Family, Jenna L Lin, Baron Chanda

2020-Current year OA Pubs

Cyclic nucleotide-binding domain (CNBD) channels are critical components of numerous bioelectrical processes, including cardiac pacemaking, neuronal signaling, phototransduction in the eye, and stomatal regulation in plants. While members of this channel family share a conserved overall structure, they exhibit striking differences in voltage sensitivity. Hyperpolarization-activated cyclic nucleotide-gated channels are activated by membrane hyperpolarization, whereas ether-à-go-go channels open upon depolarization. Mutagenesis and chimeragenesis studies have revealed that some mutants display bipolar gating behavior-remaining closed at intermediate membrane potentials but capable of opening in response to both hyperpolarization and depolarization. Remarkably, in certain cases, just a few mutations are sufficient to reverse …

An Association Test For Ordinal Outcomes In Clustered Data With Informative Cluster Size, Hasika K. Wickrama Senevirathne, Sandipan Dutta

Mathematics & Statistics Faculty Publications

In cluster-correlated data, the number of observations in a cluster can be associated with the outcome from that cluster. This phenomenon is known as informative cluster size which can occur in cluster-randomized clinical trial data. Several studies have found that ignoring the issue of informative cluster size can produce biased results in the analysis of clustered data. Most of the existing methods for addressing informative cluster size are suited to continuous outcomes. However, ordinal outcomes and covariates are often encountered in clustered data obtained from large clinical studies. The existing methods for ordinal association testing in clustered data can produce …

Maxgrnet: A Multi-Axis Vision Transformer With Improved Generalization For Eye Disease Classification Using Explainable Ai With Insertion-Deletion Operations On Fundus Images, Md Mehedi Hasan Santo, Fuyad Hasan Bhoyan, Fuad Ibne Jashim Farhad, Fahmid Al Farid, Sovon Chakraborty, Md Humaion Kabir Mehedi, Jia Uddin, Hezerul Bin Abdul Karim

Computer Science Faculty Publications

Eye diseases, including diabetic retinopathy (DR), glaucoma, and cataracts, represent a major global health concern and can lead to severe visual impairment or blindness if not identified in a timely manner. This study proposes a novel eye disease classification framework based on a multi-axis vision transformer (MaxViT) applied to color fundus images with Explainable Artificial Intelligence (XAI) techniques to enhance model transparency. The proposed architecture integrates transformer-based attention mechanisms with Global Response Normalization (GRN)-based multi-layer perceptron (MLP) layers to capture complex spatial and contextual relationships within fundus images effectively. The model was evaluated on a publicly available eye disease classification …

An Ensemble Classifier For Ordinal Outcomes In High-Dimensional Genomics Data, Heranga K. Rathnasekara, Sinjini Sikdar

Mathematics & Statistics Faculty Publications

Analysis of genomics data for predicting disease outcomes is a fast-growing field in medical research. There often exist categorical, specifically, ordinal outcomes that need to be predicted based on genomic profiles. This has led to recent development of some high-dimensional ordinal classification methods that can address the large dimensionality of the genomic covariate set. These high-dimensional ordinal models tend to vary widely in their performance depending on the data they are applied to and the evaluation criteria used. In this article, we outline an ensemble ordinal classifier that integrates different ordinal modeling approaches through bootstrap-based model evaluation, multi-metric performance assessment, …

Wearable Sensor-Based Phase Segmentation Analysis Of Front Crawl Swimming: A Scoping Review, Jonathan Simoes, Samuel Aylward, Daniel Hamze, Daniel James Goble, Daniel M. Russell, Joshua Haworth

Exercise Science Faculty Publications

Front crawl swimming stroke phase segmentation has historically relied on video analysis, but the development of wearable sensor technology has created new opportunities for automated phase segmentation. This scoping review mapped the available evidence on wearable sensor-based stroke phase segmentation methods in front crawl swimming, following PRISMA-ScR guidelines. A systematic search of SPORTDiscus, Web of Science, and IEEE Xplore conducted from January to June 2026, identified 15 eligible peer-reviewed studies published between 2000 and 2024. The review revealed an emerging field of research that has converged methodologically around inertial measurement units (IMUs) and the Chollet phase segmentation framework while remaining …

Greater Body Dissatisfaction At Admission Is Associated With Lower Bmi At Discharge In Anorexia Nervosa: Predictive Validity Of The Eating Pathology Symptoms Inventory., Danielle A N Chapa, Marianna L. Thomeczek, Brianne N. Richson, Alan Duffy, Kara A. Christensen Pacella, Kelsie T. Forbush, Renee D. Rienecke, Dan V. Blalock, Sara R. Gould, Victoria L. Perko, Philip S. Mehler

Manuscripts, Articles, Book Chapters and Other Papers

People with anorexia nervosa (AN) engage in dietary restriction and other weight loss behaviors that result in dangerously low body weight, leading to an increased risk for mortality and medical complications. Weight gain is one of the most important indicators of treatment progress and recovery for AN. There are limited predictors of weight gain for patients with AN, making it difficult for clinicians to anticipate which patients are likely to respond favorably to treatment. Thus, there is a need to identify additional, potentially modifiable predictors of weight gain within a higher level of care for AN. This study tested the …

Identification Of Neural Crest And Melanoma Cancer Cell Invasion And Migration Genes Using High-Throughput Screening And Deep Attention Networks., J C Kasemeier-Kulesa, S Martina Perez, R E Baker, Paul M. Kulesa

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND: Cell migration and invasion are well-coordinated in development and disease but remain poorly understood. We previously showed that the neural crest (NC) cell migratory wavefront shares a 45-gene panel with other cell invasion phenomena. To rapidly and systematically identify critical genes, we performed a high-throughput siRNA screen and statistical and deep learning analyses to determine changes in NC- versus non-NC-derived human cell line behaviors.

RESULTS: We find 14 out of 45 genes significantly reduced c8161 melanoma cell migration; four of the 14 genes altered leader cell motility (BMP4, ITGB1, KCNE3, and RASGRP1). Deep learning identified marked disruptions in cell-neighbor …

Differences In Biologic Clinical Trials For Chronic Rhinosinusitis With Nasal Polyps—Are We Comparing Apples With Oranges?, Marjolein Cornet, Peter W. Hellings, Martin Desrosiers, Martin Wagenmann, Richard Follows, Laura Walrave, Luz Adriana Jimenez, Lee Tombs, Dawn Edwards, Peter Howarth, Joseph K. Han

Department of Otolaryngology (ENT) Faculty Publications

In recent years, several biologics targeting Type 2 inflammation have been developed for treating chronic rhinosinusitis with nasal polyps (CRSwNP). These have been studied in registrational randomized controlled trials (RCTs), which vary in their patient populations, trial design, endpoints, geography, timing, or data-handling processes. While (in)direct treatment comparisons and meta-analyses have been carried out to compare efficacy results from RCTs, often these fail to properly account for these between-study differences. Here, we summarize the key between-study differences that can influence trial outcomes and highlight the resulting challenges faced when comparing outcomes from different Phase III RCTs of biologics in CRSwNP.

Clinical Subtypes Of Co-Morbid Insomnia And Obstructive Sleep Apnea (Comisa): Results Of A Cluster Analysis, Yuan Shi, Xujun Feng, Fengyi Hao, Yuru Nie, Yihui Zhang, Zhaohua Chen, Siqi Guan, Larry D. Sanford, Michael V. Vitiello, Xiangdong Tang

Department of Pathology & Anatomy Faculty Publications

Background

Variations in the bidirectional relationship between obstructive sleep apnea (OSA) and insomnia in co-morbid insomnia and OSA (COMISA) may form distinct subtypes of COMISA, which have not been previously characterized. This study aims to identify and characterize subtypes of COMISA.

Methods

From a community-recruited COMISA cohort 256 individuals who met diagnosis for COMISA were used to identify subtypes using a two-step clustering methodology. Demographics and multidimension clinical characteristics were collected and compared among obtained subtypes. Logistic models were used to evaluate whether these subtypes were associated with cardiometabolic and mental disorders. A clinical cohort of 1816 COMISA patients was …

Eeg And Imu Gait Signal Processing: A Comparative Assessment Of The "Reza" Exponential Filter And Classic Filters, Reza Pousti, Daniel M. Russell, Derek C. Monroe, Christopher K. Rhea

Rehabilitation Sciences Faculty Publications

Noise degrades both EEG and gait signals, and classical IIR filters (Butterworth, Chebyshev, elliptic) involve trade-offs between passband flatness, ripple, and roll-off. This study compared a novel exponential "Reza" filter with these designs for neural and locomotor data. We analyzed an open-source mobile brain-body imaging dataset with EEG and gait data from 49 healthy adults (EEG: 256-channel, 512 Hz; IMUs: six APDM Opals, 128 Hz). EEG channels were grand-averaged and band-pass filtered at 0.5-50 Hz, while IMU axes were averaged and band-pass filtered at 0.5-5 Hz. The outcomes were signal-to-noise ratio SNR (dB) and band-integrated Welch PSD (EEG:0.5-50 Hz; IMU:0.5-5 …

Sustainable Community-Wide Model To Enhance Cancer Data Usage And Utility, Emily S Boja, Mousumi Ghosh, Ying Huang, Tung-Shing Mamie Lih, Rawan Shraim, Gregory Wheeler, Diana Thomas, Rakesh Khanna, Stephanie Sandor, Jinghui Zhang, Sapna Oberoi, Nicolas J Llosa, Aditya Suru, Fernanda Silva Michels, Eric Durbin, Anna Fernandez, Lucy Han, James Galbraith, Yin Lu, Brian Furner, James H Tanis, Michael Watkins, David Higgins, Gavriel Matt, Xin Zhou, Yang Li, Marcin Cieslik, Jamie Estill, Michael Sierk, Yanling Sun, Weiping Ma, Joseph Flores-Toro, Freddie Pruitt, Subhashini Jagu, Elmer A Fernández, Daniela Orschanski, Stephanie J Spielman, Jaclyn Taroni, Trinh Nguyen, Brian Capaldo, Erin Beck, Tanja Davidsen, Johanna Goderre Jones, Minghong Ward, Anne Sturcke, Jennifer M Torres Del Valle, Sean Hanlon, Danielle Daee, Brandon Wright, Nathaniel Boyd, Heather Basehore, Liang Liu, Ying Hu, Daoud Meerzaman, Huiqing Li, Ying Wu, Gregory H Reaman, Erin Rudzinski, Jack F Shern, Brigitte Widemann, Warren Kibbe, Jaime Guidry Auvil

Children’s Nutrition Research Center Staff Publications

Despite vast investments in data collection, generation, and sharing from childhood cancer studies, for example, Gabriella Miller Kids First Research Program, Childhood Cancer Data Initiative, and other efforts, secondary use of data remains a challenge especially for rare diseases. The National Cancer Institute (NCI) Office of Data Sharing (ODS) aimed to promote FAIR (Findable, Accessible, Interoperable, Reusable) data sharing practices to enhance data utility, accelerate discovery, and foster interdisciplinary collaboration. NCI ODS launched its inaugural Data Jamboree focusing on childhood cancer alongside its third Annual Symposium in September 2025. Over 120 participants with diverse backgrounds coalesced into 23 multidisciplinary teams …

Latent Classification Of Time-Dependent Transition Rates In Longitudinal Binary Outcome Data, Joonha Chang, Wenyaw Chan

Faculty, Staff and Student Publications

Continuous-time Markov chain (CTMC) models and latent classification methods are commonly used to analyze longitudinal categorical outcomes in medical research. While CTMC models are popular for their simplicity and effectiveness, their assumption of constant transition rates presents limitations in capturing dynamic behaviors. To address this, non-homogeneous continuous-time Markov chains (NH-CTMCs) have been developed, incorporating time-varying transition rates to enhance model flexibility. In this study, we leverage closed-form transition probabilities for a fully ergodic two-state NH-CTMC model and propose a latent class clustering approach to identify heterogeneous transition rate patterns within the population. We emphasize the potential advantages of these models …

Safety And Efficacy Of 6% Hydroxyethyl Starch In Patients Undergoing Major Surgery: The Randomised Controlled Phoneics Trial, Wolfgang Buhre, Óscar Díaz-Cambronero, Simon Schaefer, Martin Novacek, Marina Soro Domingo, Bjorn Stessel, Aurelio Rodríguez-Pérez, Torsten Richter, Georg Rohe, Bernard Cholley, Matthias Gruenewald, Gerhardus Kuiper, Samir Jaber, Dianne De Korte, Javier Belda, Marcelo Gama De Abreu, Robert Baronica, Thomas Scheeren, Carlos Ferrando-Ortolá, Wojciech Szczeklik, Dana Tomescu, Tomas Vyzamal, Zejka Gavranovic, María Pilar Argente-Navarro, Guido Mazzinari, Sarah Thaler, Nuria García-Gregorio, Jeroen Vandenbrande, Diane Zlotnik, Jakob Wittenstein, Sonja Schmier, Susanne Rohn, Christoph Glasmacher, Martin Holler, Cornelius Jungheinrich, Ulf Niess, Daniel I Sessler, Martin Westphal

Faculty, Staff and Student Publications

Abstract

Background: Hydroxyethyl starch (HES) is often used for maintaining vascular volume during major surgery. Long-term high-dose HES in septic patients promotes renal injury, whereas meta-analyses of current HES products in surgical patients do not show such effects.

Objective: We studied if the peri-operative use of HES is noninferior to crystalloids in terms of acute kidney injury. Secondary outcome was the noninferiority of HES on worsening of renal injury and/or the incidence of a composite endpoint of major complications and mortality until postoperative day 90.

Design: Randomised double-blind trial in patients undergoing elective abdominal surgery with expected blood loss at …

Explainable Convolutional Neural Network Model Provides An Alternative Genome-Wide Association Perspective On Mutations In Sars-Cov-2, Parisa C. Hatami, Richard Annan, Luis Miranda, Jane L. Gorman, Mengjun Xie, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Identifying informative genomic features in SARS-CoV-2 can help clarify patterns of viral evolution. In this study, we developed an explainable convolutional neural network (CNN) model to classify SARS-CoV-2 genomic sequences into the WHO-designated Variants of Concern (VOCs), Alpha, Beta, Gamma, Delta, and Omicron. Using a balanced dataset of genomes, the classification CNN achieved 99.96% accuracy on the held-out test set. To interpret the model’s predictions, we applied SHapley Additive exPlanations (SHAP) to estimate the contribution of each nucleotide position to VOC-label prediction and compared aggregated attributions with a chi-square GWAS baseline applied to the same categorical labels. SHAP prioritized several …

A Hybrid Vit-L/32-Maxvit-L Architecture With Adaptive Gated Fusion For Multiclass Gastrointestinal Disease Detection And Multi-Method Post-Hoc Explainability, Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Zhongming Zhao

Faculty, Staff and Student Publications

Introduction: Accurate detection of gastrointestinal diseases from endoscopic images remains challenging due to substantial inter-class similarity, intra-class variability, and heterogeneous lesion presentation. While convolutional neural networks (CNNs) effectively capture fine mucosal textures, their limited receptive field restricts global contextual reasoning. Conversely, transformer architectures model long-range dependencies but may underrepresent localized structural detail.

Methods: This study proposes a dual-branch hybrid transformer framework that integrates global token-level reasoning from ViT-L/32 with hierarchical spatial modeling from MaxViT-L. An adaptive gated fusion mechanism is introduced to dynamically regulate the relative contribution of local and global representations on a per-sample basis, enabling content-aware cross-scale integration. …

Grading Melanocytic Dysplasia: Updated Histopathologic Criteria, Christopher R Shea, Victor G Prieto, Catherine M Shachaf, Scott R Florell

Faculty, Staff and Student Publications

Dr. Martin C. Mihm, Jr.'s innovative work on the dysplastic nevus achieved a milestone in his chapter in the World Health Organisation Classification of Skin Tumours (WHO-C). WHO-C presents a dichotomous classification (high-grade versus low-grade dysplastic nevi) and a quantitative metric to assess melanocytic nuclear enlargement. The Duke classification is a related approach that provides mostly quantitative histopathologic criteria for dysplastic nevi and gives due weight to architectural features as well as cytology. This paper proposes and illustrates updated criteria for scoring and grading melanocytic dysplasia, incorporating some of the definitions and categories of WHO-C, while refining the quantitative and …

Preclinical Ischemic Stroke Multicenter Trials (Prism) Collective Statement: Opportunities, Challenges, And Recommendations For A New Era, Cenk Ayata, Philip M Bath, Anna M Planas, Stuart M Allan, Johannes Boltze, Ryan P Cabeen, Claire L Gibson, Marilyn J Cipolla, Marcio A Diniz, Stefano Fumagalli, Fahmeed Hyder, Raymond C Koehler, Arthur Liesz, Sarah K Mccann, Tim Magnus, Louise D Mccullough, Emily S Sena, Simone Beretta, Jaroslaw Aronowski, Francesca Bosetti, Clinton B Wright, Patrick D Lyden, Lauren H Sansing

Faculty, Staff and Student Publications

Preclinical stroke research faces a critical translational gap, with animal studies failing to reliably predict clinical efficacy. To address this, the field is moving toward rigorous, multicenter preclinical randomized controlled trials (mpRCTs) that mimic phase 3 clinical trials in several key components. This collective statement, derived from experts involved in mpRCTs, outlines considerations for designing and executing such trials. mpRCTs offer advantages such as increased sample sizes, robust statistical design, incorporation of heterogeneity, and standardized protocols, but they face challenges in finding the right balance between standardization and heterogeneity, appropriate stroke model selection, and outcome measures, as well as the …

First-Generation Medical School Applicants: A Quantitative Study Designed To Identify Areas Of Educational Support, Bethsabe Romero, Amanda K. Burbage

EVMS School of Health Professions Faculty Publications

First-generation (First Gen) students are unique medical school applicants. Due to their lived experience, they approach patient care by prioritizing trust, comfort and understanding. They have proven ability to overcome obstacles and were found to be more resilient than their continuing generation (Cont Gen) peers. Despite these notable attributes, they face unique challenges in gaining medical school acceptance. There are very few quantitative studies examining this student subpopulation, and our study identifies characteristics of first-generation medical school applicants while highlighting areas of needed support. This cross-sectional study used deidentified Application and Matriculating Student Questionnaire survey data that was obtained from …

Crossing The Theory Threshold: The Pedagogical Potential Of Generative Artificial Intelligence In Educational Research, Amanda Burbage, Jennifer L. Styron

EVMS School of Health Professions Faculty Publications

Purpose

This paper presents findings from an educational research graduate course in which generative artificial intelligence (AI) was incorporated to strengthen learners' understanding of threshold concepts related to theoretical frameworks. Medical and health professionals often struggle with the transition from a clinical role into the educational research role.

Methods

The study posits that the use of generative AI will help learners understand and apply theoretical frameworks beyond a superficial level, furthering their understanding, constructing new knowledge, and strengthening their ability to develop sound educational research studies. Journal and AI transcripts were analyzed for 37 participants.

Results

Open-ended codes were grouped …

Predicting Oral Hiv Pre-Exposure Prophylaxis Efficacy In Cisgender Women Across Pregnancy And Postpartum, Yifan Yu, Kristina M. Brooks, Gustavo F. Doncel, Brookie M. Best, Mark A. Marzinke, Mark Mirochnick, Peter L. Anderson, Landon Myer, Connie Celum, Renee Heffron, Jenell Coleman, Dvora L. Joseph Davey, Lanxin Zhang, Max Von Kleist, Craig W. Hendrix, Jeremiah D. Momper, Robert Bies, Rachel K. Scott

CONRAD Publications

Cisgender women face an increased risk of HIV acquisition during pregnancy and postpartum. Oral pre-exposure prophylaxis (PrEP) with emtricitabine and tenofovir disoproxil fumarate (F/TDF) is recommended in pregnancy despite limited published pharmacokinetic (PK) and efficacy data during pregnancy. Altered PK during pregnancy may reduce prophylactic efficacy and necessitate higher adherence. Tenofovir alafenamide (TAF) is a newer tenofovir prodrug with higher tenofovir diphosphate (TFV-DP) exposure in peripheral blood mononuclear cells (PBMCs), but the prophylactic efficacy of F/TAF across different reproductive stages in women is understudied. We adapted a multiscale modeling framework to predict the prophylactic efficacy of F/TDF and F/TAF across …

Perceived Hiv Risk, Barriers, And Preferences For Hiv Testing In Structurally Vulnerable Communities In St. Louis: A Best-Worst Scaling Survey, Emmanuel K Tetteh, Noelle Le Tourneau, Gregory Gross, Mckenzie Swan, Tyrell Manning, Justin Cole, Katie Wolf, Lawrence Hudson-Lewis, Julia D López, Todd Combs, Virginia R Mckay

2020-Current year OA Pubs

BackgroundPersistent gaps in efforts to end the HIV epidemic in the United States highlight the need for community-specific, evidence-based HIV testing strategies. This study investigated barriers and preferences around HIV testing among underserved populations, including Black, queer, and young adults in the St. Louis, Missouri area.MethodsWe conducted a Best-Worst Scaling (BWS) survey with adults (16+ years) recruited at community events, collecting responses electronically and providing a $25 incentive. The survey assessed the relative importance of 13 potential barriers to HIV testing such as stigma, structural barriers, and perceived HIV risk, chosen based on literature review and community input. Mean preference …

Differential Impact Of Admission Type And Clinical Complexity On Diabetes Hospitalization Costs Among African American And Hispanic Patients In Southeastern Virginia, Ismail El Moudden, Asra Amidi, Reem Sharaf-Alddin, Michael C. Bittner, Qi Zhang

Department of Obstetrics & Gynecology Faculty Publications

Background

Diabetes mellitus (DM) imposes substantial healthcare costs with documented disparities among African Americans and Hispanic patients. To inform care delivery and resource allocation, this study identified hospitalization cost predictors among African American and Hispanic patients with diabetes in Southeastern Virginia.

Methods

We analyzed 6,011 hospital discharges from the Virginia Health Information database (2016-2020) for adults aged 18-85 with diabetes. Discharges were classified by Medicare Severity Diagnosis-Related Groups: DM with complications/comorbidities (DCC, n = 3,328), DM with major complications/comorbidities (DMCC, n = 1,518), and DM without major complications/comorbidities (DWO, n = 1,165). Because cost distributions were right-skewed (skewness 3.5-8.24), we …

Uncertainty-Guided Test-Time Optimization For Personalizing Segmentation Models In Longitudinal Medical Imaging, Jaehee Chun, Austin Castelo, Mckell Woodland, Caleb O'Connor, Mais Al Taie, Mohamed Eltaher, Aashish Gupta, Bilel Daoud, Shanli Ding, Jeddy Bennett, Anirban Maitra, Matthew A Firpo, Kimberly Kirkwood, Eugene J Koay, Kristy K Brock

Faculty, Staff and Student Publications

Background: Accurate and consistent image segmentation across longitudinal scans is essential in many clinical applications, including surveillance, treatment monitoring, and adaptive interventions. While personalized model adaptation using patient-specific prior scans has shown promise, current approaches typically rely on fixed training durations and lack mechanisms to determine optimal stopping points on a per-patient basis, particularly in the absence of validation labels.

Purpose: We propose an uncertainty-guided test-time optimization (TTO) framework that dynamically adjusts the personalization duration for each patient using a validation-free stopping criterion based on predictive uncertainty.

Methods: Our framework personalizes a generalized segmentation model using patient-specific prior imaging and …

The Crucial Role Of Machine Learning Models In Predicting Current Childhood Asthma: Model Comparison, Calibration, And Shap-Based Interpretation, Aditya Chakraborty, A. K.M. Raquibul Bashar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Asthma is one of the most prominent chronic diseases in children and one of the most challenging ailments to diagnose in infants and preschoolers in the United States. Predictive models can be instrumental in improving early diagnosis, personalized treatment strategies, and disease progression. By utilizing nationalized data, this study focuses on building and comparing high-performing analytical predictive models based on the relevant risk factors and identifying the most influential predictors.

Methods: We analyzed cross-sectional BRFSS Asthma Call-Back Survey data (2011-2020; N = 9,813) and randomly split participants into training and testing sets. An XGBoost model (hyperparameters tuned via grid …