Medicine and Health Sciences Commons^™

The Role Of Tgf-Beta Superfamily Members In Immune Homeostasis And Disease, Natalie Eva Nieuwenhuizen, Ioannis Eleftherianos, Piotr Jan Kraj, Maria Semitekolou

Biological Sciences Faculty Publications

[Introduction] The Transforming Growth Factor beta (TGF-beta) superfamily, encompassing molecules such as TGFs, activins, Bone Morphogenetic Proteins (BMPs), Growth/Differentiation Factor (GDFs) and Nodals, represents the largest family of growth and differentiation factors, playing crucial roles in developmental and physiological processes across animal species (1). These molecules are integral to tissue homeostasis and cell fate determination. Among them, TGF-beta is particularly noted for its regulatory influence on immune responses and tissue fibrosis (2, 3). Recent research has expanded our understanding of the immune functions of other superfamily members, including activin A and BMPs (4). These molecules signal through receptor complexes composed …

Long-Term Safety And Effectiveness Of The Treo Stent Graft For The Endovascular Treatment Of Infrarenal Abdominal Aortic Aneurysms, Matthew J. Eagleton, Michael C. Stoner, John Henretta, Maciej Dryjski, Jean M. Panneton, Apostolos Tassiopoulos, Manish Mehta, Benjamin Pearce, Mel J. Sharafuddin, Treo Investigators

Department Surgery Faculty Publications

Objective

Report on the long-term safety and effectiveness of the TREO stent graft in endovascular repair of AAA from a US investigational device clinical study.

Methods

Data from a multicenter, nonrandomized, prospective, US investigational device exemption pivotal study (NCT02009644) were used. From November 2013 to February 2016, 150 patients were enrolled at 29 US centers. Safety endpoints were defined as major adverse events (i.e., all-cause mortality, myocardial infarction, stroke, renal failure requiring renal replacement therapy, respiratory failure, paraplegia, bowel ischemia, and procedural blood loss of 1,000cc or greater). Effectiveness endpoints were defined as no aneurysm rupture, absence of sac increase …

Dengue Virus Modulates Critical Cell Cycle Regulatory Proteins In Human Megakaryocyte Cells, Swarnendu Basak, Shovan Dutta, Supreet Khanal, Girish Neelakanta, Hameeda Sultana

Biological Sciences Faculty Publications

Suppression of human megakaryocytes by dengue virus (DENV) infection significantly reduces the platelet count that eventually leads to thrombocytopenia, severe dengue and death. To understand DENV interactions with megakaryocytes, we investigated the cell cycle in leukemic human megakaryocytic in vitro cell line (MEG-01 cells). Megakaryocytes are known for complex endomitotic cell cycle leading to their polyploidy state. Our study shows that DENV uses these polyploid cells for its replication. Understanding the modulation of DENV-mediated cell cycle regulation in megakaryocytes is therefore highly important. We show that DENV2 (serotype 2) infection significantly modulates cell cycle signaling. Our protein profile microarray data …

Effect Of Family-Centered Care Interventions On Motor And Neurobehavior Development Of Very Preterm Infants: A Systematic Review And Meta-Analysis, Manasa Kolibylu Raghupathy, Shradha S. Parsekar, Shubha R. Nayak, Kalesh M. Karun, Sonia Khurana, Alicia J. Spittle, Leslie Edward S. Lewis, Bhamini Krishna Rao

Rehabilitation Sciences Faculty Publications

Aim

To assess the effectiveness of family-centered care (FCC) interventions on motor and neurobehavior development of very preterm infants.

Method

Randomized and quasi-randomized trials assessing the effect of FCC on motor and neurobehavioral outcomes in very preterm infants (28–32 wk gestation) were included. Five electronic databases and grey literature were searched from January 2010 to August 2022. Two reviewers independently screened the titles/abstracts and full texts, assessed the risk of bias, and extracted data. The Cochrane Risk of Bias 2.0 Tool and GRADE were used for risk and evidence certainty assessments. Meta-analysis or narrative synthesis was performed based on data …

Athletic Trainers' Perspectives On Resources And Preparation Needed For Successful Salary Negotiation, Kimberly R. Detwiler, Cailee E. Welch Bacon, Indigo B. White, Leanne E. Jones, Julie M. Cavallario

Rehabilitation Sciences Faculty Publications

Context

Many athletic trainers (ATs) work under high stress, long hours, and low pay. These conditions often lead ATs and athletic training students to leave the profession. Previous studies show that over half of ATs do not negotiate salary, often because they feel the offer is fair or fear losing the job. Additional support is needed to help ATs prepare for negotiations, as current resources are limited.

Objective

This study explored ATs’ views on the resources need to prepare for salary negotiations during the hiring process.

Design

Consensual qualitative research.

Setting

Individual video interviews.

Patients or Other Participants

Twenty-eight participants …

Educational Case: Disseminated Intravascular Coagulation In A Patient With Cancer, Kripa Ahuja, Richard M. Conran

Department of Biomedical and Translational Sciences Faculty Publications

The following fictional case is intended as a learning tool within the Pathology Competencies for Medical Education (PCME), a set of national standards for teaching pathology. These are divided into three basic competencies: Disease Mechanisms and Processes, Organ System Pathology, and Diagnostic Medicine and Therapeutic Pathology. For additional information, and a full list of learning objectives for all three competencies, see https://www.sciencedirect.com/journal/academic-pathology/about/pathology-competencies-for-medical-education-pcme.

Protein Marker-Dependent Drug Discovery Targeting Breast Cancer Stem Cells, Ashley V. Huang, Yali Kong, Kan Wang, Milton L. Brown, David Mu

Department of Biomedical and Translational Sciences Faculty Publications

Breast cancer is one of the most common cancers globally. Unfortunately, many patients with breast cancer develop resistance to chemotherapy and tumor recurrence, which is primarily driven by breast cancer stem cells (BCSCs). BCSCs behave like stem cells and can self-renew and differentiate into mature tumor cells, enabling the cancer to regrow and metastasize. Key markers like CD44 and aldehyde dehydrogenase-1 (ALDH1), along with pathways like Wingless-related integration site (Wnt), Notch, and Hedgehog, are critical to regulating this stem-like behavior of BCSCs and, thus, are being investigated as targets for various new therapies. This review summarizes marker-dependent strategies for targeting …

Delivery Of Medical Supplies To Remote Locations Via Unmanned Aerial Vehicles: Approaches, Challenges, And Solutions, Meshari Aljohani, Ravi Mukkamala, Stephanie Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming more important in improving healthcare logistics, in particular due to their cost effectiveness, minimized risk, and versatile operational capabilities. This study explores the deployment of autonomous UAVs to deliver medical supplies to remote areas. Advances in ledger technology, smart contracts, and machine learning have transformed tasks previously managed by human teams or manually controlled UAVs into fully autonomous missions. We present a comprehensive analysis of the challenges and initial solutions vital for the effective use of autonomous UAVs in the delivery of medical supplies. In addition, we propose a machine-learning model to optimize UAV …

Lrtm Left-Right Transition Matrices For Molecular Interaction Prediction, Kaitlin Zheng, Guihua Duan, Mengyun Yang, Wei Wu, Yao-Hang Li, Jianxin Wang

Computer Science Faculty Publications

Molecular interactions are central to most biological processes. The discovery and identification of potential associations between molecules can provide insights into biological exploration, diagnostic and therapeutic interventions, and drug development. So far many relevant computational methods have been proposed, but most of them are usually limited to specific domains and rely on complex preprocessing procedures, which restricts the models’ ability to be applied to other tasks. Therefore, it remains a challenge to explore a generalized approach to accurately predicting potential associations. In this study, We propose Left-Right Transition Matrices (LRTM) for molecular interaction prediction. From the perspective on the diffusion …

Heterogeneous Clustering Of Multiomics Data For Breast Cancer Subgroup Classification And Detection, Joseph Pateras, Musaddiq Lodi, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation–maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene …

Gramseq-Dta: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information, Kasul Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance …

A Data-Driven Sliding-Window Pairwise Comparative Approach For The Estimation Of Transmission Fitness Of Sars-Cov-2 Variants And The Construction Of The Evolution Fitness Landscape, Md Jubair Pantho, Richard Annan, Landen Alexander Bauder, Sophia Huang, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Estimating the transmission fitness of SARS-CoV-2 variants and understanding their evolutionary fitness trends are important for epidemiological forecasting. Existing methods are often constrained by their parametric natures and do not satisfactorily align with the observations during COVID-19. Here, we introduce a sliding-window data-driven pairwise comparison method, the differential population growth rate (DPGR) that uses viral strains as internal controls to mitigate sampling biases. DPGR is applicable in time windows in which the logarithmic ratio of two variant subpopulations is approximately linear. We apply DPGR to genomic surveillance data and focus on variants of concern (VOCs) in multiple countries and regions. …

Cath-Ddg: Towards Robust Mutation Effect Prediction On Protein-Protein Interactions Out Of Cath Homologous Superfamily, Guanglei Yu, Xuehua Bi, Teng Ma, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Protein-protein interactions (PPIs) are fundamental aspects in understanding biological processes. Accurately predicting the effects of mutations on PPIs remains a critical requirement for drug design and disease mechanistic studies. Recently, deep learning models using protein 3D structures have become predominant for predicting mutation effects. However, significant challenges remain in practical applications, in part due to the considerable disparity in generalization capabilities between easy and hard mutations. Specifically, a hard mutation is defined as one with its maximum TM-score < 0.6 when compared to the training set. Additionally, compared to physics-based approaches, deep learning models may overestimate performance due to potential data leakage.

Results: We propose new training/test splits that mitigate data leakage according to the CATH homologous superfamily. Under the constraints of physical …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

Multi-Modal Mri Based Segmentation Of Brain Metastases Using Adaptive Self-Attention, Evan Savaria

Computer Science Faculty Publications

Brain metastases (BMs) are the most common adult central nervous system malignancy, affecting 20–40% of cancer patients. Accurate segmentation of metastatic lesions in multi-modal MRI is essential for treatment planning and prognosis however, manual delineation is time consuming and prone to variability. Traditional deep learning models such as U-Net, have improved segmentation accuracy but capture limited long-range dependencies and struggle with variations in metastasis size, shape, and distribution. This study introduces the Adaptive Integrated Multi-modal Segmentation (AIMS) model, an adaptive self-attention framework within a hybrid U-Net and Transformer architecture to enhance BM segmentation by leveraging multi-modal MRI integration. The proposed …

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

Optiselect And Enshap: Integrating Machine Learning And Game Theory For Ischemic Stroke Prediction, Pritam Chakraborty, Anjan Bandyopadhyay, Sricheta Parul, Sujata Swain, Partha Sarathy Banerjee, Tapas Si, Hong Qin, Saurav Mallik

Computer Science Faculty Publications

Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing …

A Survey On Deep Learning For Drug-Target Binding Prediction: Models, Benchmarks, Evaluation, And Case Studies, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug–target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug …

Benchmarking And Improving Foundation Model Dietary Estimates From Meal Images, Yongcheng Mu, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Accurate quantifying dietary contents, such as calories, proteins, carbohydrates, and fats, from an image of a meal plate is vital for managing diabetes. Recently, Large Multimodal Models (LMMs) have excelled in complex vision-language tasks due to their use of very large, highly diverse data. This study benchmarked the use of seven LMMs that include full and lightweight models of GPT, Gemini, and Llama for nutrition estimation based on Google's Nutrition5k dataset and our own phone-collected DonateAndLearn dataset. We analyzed the performance of LMMs and the RGB-D fusion model, in which the RGB-D model was specifically trained using Nutrition5k data. On …

Deepssetracer 2.0: Improved Deep Learning Model Performance For Protein Secondary Structure Segmentation From Cryo-Em Maps, Bryan Hawickhorst, Thu Nguyen, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

DeepSSETracer is a method for segmenting protein secondary structure from medium-resolution (5-10Å) cryogenic electron microscopy (cryo-EM) density maps. We conducted experiments and ablation studies to examine the effects of normalization methods, max-pooling, activation functions, and loss calculation region on DeepSSETracer. By combining multiple technical improvements, the performance of the new version, DeepSSETracer 2.0, was significantly enhanced compared to DeepSSETracer 1.1. On a set of 77 test cases, the weighted average per-voxel F1 score increased from 62.1% to 70.3% for helix detection, and from 47.8% to 62.5% for β-sheet detection. While each of the five modifications in the network enhanced the …

Icu-Length Of Stay Prediction On Electronic Health Records Using Graph Neural Networks And Homogeneous Similarity Graphs, Ahmad F. Al Musawi, Pratip Rana, Sibtanu Raha, Joshua Braunstein, William C. Sleeman Iv, Rishabh Kapoor, Preetam Ghosh

Computer Science Faculty Publications

Predicting the length of stay (LoS) is important for hospital administration, as it helps allocate proper resources, such as bed management and hospital staffing. Patients' Electronic Health Records (EHRs) contain highly relevant data for LoS prediction; however, their integration and effective use in predictive modeling for accurately estimating LoS remain challenging. To address this, we propose a homogeneous Graph Neural Network (GNN)-based framework for predicting LoS. This method employs a comprehensive data fusion strategy based on the hospital Visit-based Similarity Graph (VSG), which integrates diverse multi-modal clinical features into a coherent, homogeneous graph representation. Next, this VSG is fed into …

An Innovative Community‐Led Education And Recruitment Program To Increase Clinical Research Participation Among Black Americans, Bahar Niknejad, Ethlyn Mcqueen Gibson, Travonia Brown-Hughes, Katie Mcdonough, Ebony Andrews, Deborah Hudson, Hamid Okhravi

Glennan Center for Geriatrics and Gerontology Faculty Publications

Background: Previous studies attest to a lack of awareness about Alzheimer’s Disease (AD) and limited participation of Black Americans in AD clinical trials. The AHEAD Study is a multicenter trial focused on preventing AD by evaluating the effectiveness and safety of Lecanemab in individuals with preclinical AD. The study aims to recruit at least 15% from underrepresented populations, including Black Americans. To achieve this goal, our study site utilizes a community-based participatory research (CBPR) approach and developed a Community Education and Recruitment Program (CERP) designed to increase the participation of Black individuals in the AHEAD Study.

Method: In a collaborative …

Unmeasured Confounding In Summary Estimates Of Maternal Periodontitis And Adverse Birth Outcomes: Meta-Analyses Of Observational Studies, May Salama, Abdullah Al-Taiar, Denise Mickenny

Graduate Student Government Association Research Conference

Abstract

Importance Maternal periodontitis has been consistently linked to adverse birth outcomes. However, a causal relationship is not established due to potential unmeasured confounding factors in observational studies and inconclusive results from randomized controlled trials.

Objective To assess the impact of unmeasured confounding factors in meta-analyses examining associations between maternal periodontal disease and preterm birth or low birth weight.

Data Sources PubMed/Medline, the Cochrane database for systematic reviews, Embase, Google Scholar, and a manual search of the reported references between 2002 and 2023.

Study Selection Systematic reviews with meta-analyses of observational studies were included if either preterm birth or low …

A Global Comparison Of Communication Intervention Strategies For Justice-Involved Youth, Sophia Janeiro Martinez

OUR Journal: ODU Undergraduate Research Journal

Purpose: This paper explores the intricate relationship between communication disorders and delinquent youth behavior. It will explore the detrimental impact of zero-tolerance policies and their contribution to the school-to-prison pipeline. It will introduce issues, such as complex Miranda warning diction, and the benefits of including speech-language pathologists (SLPs) to aid in youth comprehension. Additionally, it will propose the integration of SLPs within the juvenile justice system to assist in communication between justice-involved youth (JIY) and justice professionals during conversations, questioning, and trials or hearings. Furthermore, this paper examines the roles of SLPs within juvenile justice systems abroad, including Canada, …

Empowering Students Is “Perfectly Normal:" The Case For 𝘐𝘵'𝘴 𝘗𝘦𝘳𝘧𝘦𝘤𝘵𝘭𝘺 𝘕𝘰𝘳𝘮𝘢𝘭 In Middle Schools, Caroline H. Peabody

OUR Journal: ODU Undergraduate Research Journal

In September of 2023, a middle school teacher wrote an open letter to the Lexington County, Virginia school board expressing outrage over the “graphic sexual content” included in the county middle school’s library. Swiftly following the letter, the school board removed two books from the county’s Lylburn Middle School library without due process and avowed to reconsider the guidelines for including books in the library. One of the two books in question was It’s Perfectly Normal, a non-fiction illustrated book intended to educate readers ten and up on sex, intercourse, and puberty. It’s Perfectly Normal and should be available …

An Examination Of Food And Alcohol Disturbance Based On Sexual Orientation In A Sample Of Women Who Binge Eat, Alicia M. Moulder

Psychology Theses & Dissertations

Food and alcohol disturbance (FAD), characterized by disordered eating behaviors in the context of drinking alcohol to enhance intoxication, counteract alcohol’s calories, or both, is associated with increased risk of negative drinking consequences. Previously established as a phenomenon occurring in college students, research has begun to support that FAD may also occur in nonstudents. Three particularly vulnerable groups for FAD may include women, individuals with history of binge eating, as well as sexual minority individuals. Consequently, the goals of the current study were to elucidate differences in FAD engagement between sexual minority women (SMW) and heterosexual women using a sample …

An Integrated Theoretical Socio-Technical Framework For Implementing Service Robots’ Integration In Healthcare, Sujatha Alla

Engineering Management & Systems Engineering Theses & Dissertations

Healthcare workers, either clinical or non-clinical, are obligated to serve patients. However, lack of a sufficient number of professionals leads to burnout, severe stress, and, consequently, decreased quality of services. In this context, very few countries have been successful in employing service robots to perform dull, dirty, and/or dangerous tasks related to patient wellbeing/healthcare, while most countries are still skeptical about it. As robotics advances, there is an opportunity for healthcare to take advantage of this technology to reduce personnel workload and to reduce the possibility of exposure to contagious pathogens. However, healthcare is a vulnerable environment and requires critical …

Intercellular Mitochondrial Transfer Contributes To Microenvironmental Fate Redirection Of Mammary Cancer Cells, Julie Sofie Bjerring

Biomedical Sciences Theses & Dissertations

The dynamic cellular microenvironment, made up of resident cells and various macromolecules, differs markedly across tissues in the body. The multidirectional signals that cells receive from this microenvironment, along with the signaling they send back, heavily influence their physiology and behavior. Recent important findings have further validated this notion as the mammary microenvironment has been shown to suppress tumor progression by redirecting cancer cells to adopt a normal mammary epithelial progenitor fate in vivo. However, the mechanism(s) driving changes in metabolic reprogramming and cancer fate redirection is understudied. The work presented in this dissertation focuses on exploring the impact of …

Real-Time Navigation System For Breast Cancer Surgery With Pre- And Intra-Operative Imaging Using Neural Networks, Motaz Alqaoud

Biomedical Engineering Theses & Dissertations

Breast cancer is one of the most frequently diagnosed malignancies in women worldwide, necessitating precise diagnosis and treatment. Breast-conserving surgery (BCS) is the primary treatment for nonpalpable cases, yet current approaches often lack accuracy due to the absence of real-time 3D imaging during surgery. This limitation impairs surgeons’ ability to visualize tumor locations, compromising outcomes and potentially leading to repeat surgeries with higher risks, undesirable cosmetic results, increased costs, and, in some cases, mastectomy. Thus, there is a critical need for a navigation system to facilitate accurate tumor excision in nonpalpable breast cancer through real-time patient-specific 3D tracking.

This study …

Does Job Satisfaction Take A Hit? The Relationships Between Employee Cannabis Use, Job Satisfaction, And Absenteeism, Caroline Jordan Moughan

Psychology Theses & Dissertations

Despite the frequency of cannabis use, limited research has examined how employees’ cannabis use relates to job attitudes and behaviors. This study responds to calls to examine the effects of employee cannabis use on work-relevant outcomes by exploring how employee cannabis use relates to job satisfaction and voluntary absenteeism by analyzing a sample of 526 employed young adults from the most recent three waves of the National Longitudinal Survey of Youth (NLSY) Child and Young Adult Cohort. Through estimating cross-lagged panel models, this study clarifies the direction of relationships between employee cannabis use and job satisfaction, and how these relate …