Medicine and Health Sciences Commons^™

Paper-Based Dna Biosensor For Rapid And Selective Detection Of Mir-21, Alexander Hunt, Sri Ramulu Torati, Gymama Slaughter

Electrical & Computer Engineering Faculty Publications

Cancer is the second leading cause of death globally, with 9.7 million fatalities in 2022. While routine screenings are vital for early detection, healthcare disparities persist, highlighting the need for equitable solutions. Recent advancements in cancer biomarker identification, particularly microRNAs (miRs), have improved early detection. MiR-21 is notably overexpressed in various cancers and can be a valuable diagnostic tool. Traditional detection methods, though accurate, are costly and complex, limiting their use in resource-limited settings. Paper-based electrochemical biosensors offer a promising alternative, providing cost-effective, sensitive, and rapid diagnostics suitable for point-of-care use. This study introduces an innovative electrochemical paper-based biosensor that …

Investigation Of The Effect Of Preparation Parameters On The Structural And Mechanical Properties Of Gelatin/Elastin/Sodium Hyaluronate Scaffolds Fabricated By The Combined Foaming And Freeze-Drying Techniques, Mansour Qamash, S. Misagh Imani, Meisam Omidi, Ciara Glancy, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

This paper aimed to evaluate the effects of different preparation parameters, including agitation speed, agitation time, and chilling temperature, on the structural and mechanical properties of a novel gelatin/elastin/sodium hyaluronate tissue engineering scaffold, recently developed by our research group. Fabricated using a combination of foaming and freeze-drying techniques, the scaffolds were assessed to understand how these parameters influence their morphology, internal microstructure, porosity, mechanical properties, and degradation behavior. The fabrication process used in this study involved preparing a homogeneous aqueous solution containing 8% gelatin, 2% elastin, and 0.5% sodium hyaluronate (w/v), which was then subjected to mechanical agitation at speeds …

Advancing Chronic Kidney Disease Prediction Through Machine Learning And Deep Learning With Feature Analysis, Shiddarth Dey Tusar, S. M. Ahad Ali Chowdhury, Md. Jalal Uddin Chowdhury, Rana M. Pir, H. M. Nur A. Alam, Muhammad Rezaur Rahman, Md. Nural Absar Siddiky, Muhammad Enayetur Rahman

Electrical & Computer Engineering Faculty Publications

Chronic Kidney Diesease (CKD) is a significant health issue, ranking as the fourth leading cause of mortality worldwide. The traditional diagnosis and treatment process, reliant on medical experts, is time-consuming. Therefore, thereis an urgent need for more efficient diagnostic methods to improve patient outcomes and reduce mortality rates. In this study, we employ Machine Learning (ML) and Deep Learning (DL) techniques to predict CKD based on important features. Feature analysis was performed using a correlation matrix and the LASSO algo-rithm to identify the most relevant features for model training. We evaluated several ML and DL classifiers, including Logistic Regression (LR), …

Comparative Analysis Of Machine Learning Models For Predicting Healthcare Traffic: Insights For Optimized Emergency Response, Shadman Mahmood Khan Pathan, Sakan Binte Imran, M. M. Shabab Iqbal, Muhammad Enayetur Rahman, Md. Nurul Absar Siddiky, Muhammad Rezaur Rahman, Md Rafid Hasan, Nondon Lal Dey, Md Sobuj Hossain

Electrical & Computer Engineering Faculty Publications

Efficient management of healthcare traffic is crucial for ensuring timely access to medical services, particularly in emergency situations where delays can have severe consequences. This study presents a comparative analysis of three widely used machine learning models—Linear Regression, Decision Trees, and Random Forests—aimed at predicting healthcare-related traffic volumes. A large dataset from a metropolitan traffic system was used to train and evaluate the models based on key performance indicators, including Mean Squared Error (MSE), R² Score, and computational efficiency. The results reveal that the Random Forest model offers the best performance, achieving higher predictive accuracy and faster execution times compared …

Mesoporous Silica Administration As A New Strategy In The Management Of Warfarin Toxicity: An In-Vitro And In-Vivo Study, Fatemeh Farjadian, Fatemeh Parsi, Reza Heidari, Khatereh Zarkesh, Hamid Reza Mohammadi, Soliman Mohammadi-Samani, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

Purpose: Warfarin is one of the most widely used anticoagulants that functions by inhibiting vitamin K epoxide reductase. Warfarin overdose, whether intentional or unintentional, can cause life-threatening bleeding. Here, we present a novel warfarin adsorbent based on mesoporous silica that could serve as an antidote to warfarin toxicity.

Method: Amino-functionalized mesoporous silica (MS-NH₂) was synthesized based on the cocondensation method through a soft template technique followed by template removal. The prepared structure and functional group were studied by Fourier transform infrared spectroscopy (FT-IR), and X-ray diffraction (XRD). Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) checked the morphology. The …

Integrated Machine Learning And Deep Learning Models For Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study, Shadman Mahmood Khan Pathan, Sakan Binte Imran

Electrical & Computer Engineering Faculty Publications

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the …

Predictors Of Occupational Distress Of Catholic Priests On The Eastern Seaboard Of The United States, Michael D. Kostick, Xihe Zhu, Justin A. Haegele, Pete Baker

Human Movement Studies & Special Education Faculty Publications

With ever-increasing demands placed upon active priests in the United States, insight into protecting their mental health may help strengthen vocational resilience for individual priests. The purpose of this study was to examine the association of individual variables, workplace characteristics, and physical activity participation with occupational distress levels among Catholic priests. A 22-question survey consisting of a demographic questionnaire, the Clergy Occupational Distress Index, and the International Physical Activity Questionnaire was employed to collect individual variables, workplace characteristics, physical activity participation, and occupational distress levels of Catholic priests from the Eastern seaboard of the United States. Regression analyses showed that …

The Associations Of Physical Activity And Sedentary Behavior With Self-Rated Health In Chinese Children And Adolescents, Yahan Liang, Youzhi Ke, Yang Liu

Human Movement Studies & Special Education Faculty Publications

Objective

The study aimed to analyze the independent and joint associations of physical activity (PA) and sedentary behavior (SB) with self-rated health (SRH) among Chinese children and adolescents.

Methods

Cross-sectional data on moderate-to-vigorous physical activity (MVPA), school-based PA, extracurricular physical activity (EPA), screen time (ST), homework time, and SRH were assessed through a self-report questionnaire in the sample of 4227 Chinese children and adolescents aged 13.04 ± 2.62 years. Binary logistic regression was used to compare gender differences in PA, SB, and SRH among children and adolescents, and analyses were adjusted for age and ethnicity.

Results

In independent associations, boys …

Teachers' Perception On Physical Activity Promotion In Kindergarten Children In China: A Qualitative Study Connecting Social Ecological Model, Yahan Liang, Fangyuan Ju, Yueran Hao, Jia Yang, Yang Liu

Human Movement Studies & Special Education Faculty Publications

Background

Globally, the majority of kindergarten-aged children face obesity issues and insufficient physical activity (PA) engagement. Regular PA participation can provide various health benefits, including obesity reduction, for kindergarten-aged children. However, limited studies have investigated the factors influencing kindergarten-aged children's PA engagement from the perspective of their teachers. This qualitative study aimed to identify factors that could help promote PA among kindergarten-aged children from teachers' perspectives, including facilitators, barriers, and teachers' recommendations.

Methods

Fifteen kindergarten teachers (age range: 28-50 years; mean age: 38.53 years) with teaching experience ranging from 2 to 31 years (mean: 16.27 years) were recruited from Shanghai …

Salmonella Detection In Food Using A Hek-Htlr5 Reported Cell-Based Sensor, Esma Eser, Victoria A. Felton, Rishi Drolia, Arun K. Bhunia

Biological Sciences Faculty Publications

The development of a rapid, sensitive, specific method for detecting foodborne pathogens is paramount for supplying safe food to enhance public health safety. Despite the significant improvement in pathogen detection methods, key issues are still associated with rapid methods, such as distinguishing living cells from dead, the pathogenic potential or health risk of the analyte at the time of consumption, the detection limit, and the sample-to-result. Mammalian cell-based assays analyze pathogens’ interaction with host cells and are responsive only to live pathogens or active toxins. In this study, a human embryonic kidney (HEK293) cell line expressing Toll-Like Receptor 5 (TLR-5) …

Receptor-Targeted Next-Generation Probiotics Ameliorate Mammalian Colitis, Nicholas L. F. Gallina, Vignesh Nathan, Akshay Krishnakumar, Dongqi Liu, Rishi Drolia, Nicole Irrizary Tardi, Yang Fu, Manalee Samadar, Shivendra Tenguria, Alvin Cai, Ruth Eunice Centeno-Martinez, Timothy A. Johnson, Abigail Cox, Lavanya Reddivari, Bruce Applegate, Xingjian Bai, Luping Xu, Deepti Tanjore, Ramesh Vemulapalli, Rahim Rahimi, Arun K. Bhunia

Biological Sciences Faculty Publications

Introduction: a loss of intestinal barrier function, inflammation, and an elevated expression of epithelial heat shock protein 60 (Hsp60) are features of an inflamed bowel. Probiotics have been used to alleviate colitis-induced pathologies, but offer poor adhesion and adaptation to the diseased gut. We hypothesize that enhancing probiotic adhesion in the inflamed bowel may ameliorate such pathologies. Listeria adhesion protein (LAP; 94-kDa acetaldehyde alcohol dehydrogenase) aids Listeria attachment to the epithelial cells by interacting with the mammalian receptor Hsp60. Bioengineered Lactobacillus casei probiotics (BLPs) expressing LAP showed strong interaction with epithelial Hsp60, a high immunomodulatory response, and sustained epithelial barrier …

Ai And Ml-Based Risk Assessment Of Chemicals: Predicting Carcinogenic Risk From Chemical-Induced Genomic Instability, Ajay Vikram Singh, Preeti Bhardwaj, Peter Laux, Prachi Pradeep, Madleen Busse, Andreas Luch, Akihiko Hirose, Christopher J. Osgood, Michael W. Stacey

Biological Sciences Faculty Publications

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, …

An Online Module To Promote Self-Care And Resiliency In Nursing Students, Karen Higgins, Janice Hawkins, Beth Tremblay, Lynn Wiles

Ellmer School of Nursing Faculty Publications

Because the demands of nursing education can impact the physical and mental health of nursing students, the American Association of Colleges of Nursing’s revised Essentials require inclusion of self-care and resilience education in nursing curricula. This article describes the development, implementation, and evaluation of a self-care module in a new online undergraduate course. Using the REST mnemonic (relationships, exercise, soul, and transformative thinking), students developed personalized self-care plans for the semester. End-of-course evaluations revealed an increase in self-care activities. The most used activities were exercise, humor, intentional rest, and healthy eating.

Impact Of A Design Thinking Educational Activity On Graduate Students’ Knowledge, Confidence, And Perceived Benefits, Janice Hawkins, John Baaki, Beth Tembley, Robert J. Hawkins

Ellmer School of Nursing Faculty Publications

Background: Design Thinking is gaining recognition as an innovative and creative approach to problem solving. Though nurse leaders need problem solving tools to address health care challenges, Design Thinking concepts are not commonly taught in nursing education. To introduce graduate level nursing students to Design Thinking, we held an educational activity focused on this content as part of required coursework.

Purpose: The purpose was to describe and compare outcomes of a Design Thinking educational activity on students’ perceived knowledge, confidence, and benefits to nursing practice.

Methods: Graduate level nursing students participated in a 3-hour educational activity. After the session, …

How Should Focus Be Shifted From Individual Preference To Collective Wisdom For Patients At The End Of Life With Antimicrobial-Resistant Infections?, Jeannie P. Cimiotti, Kimberly Adams Tufts, Lucia D. Wocial, Elizabeth Peter

Ellmer School of Nursing Faculty Publications

Despite growth in numbers of organizational antimicrobial stewardship programs, antimicrobial resistance continues to escalate. Interprofessional education and collaboration are needed to make these programs appropriately responsive to the ethically and clinically complex needs of patients at the end of life whose care plans still require antimicrobial management.

Exploring Angiotensin Ii And Oxidative Stress In Radiation-Induced Cataract Formation: Potential For Therapeutic Intervention, Vidya P. Kumar, Yali Kong, Riana Dolland, Sandra R. Brown, Kan Wang, Damian Dolland, David Mu, Milton L. Brown

Department of Biomedical and Translational Sciences Faculty Publications

Radiation-induced cataracts (RICs) represent a significant public health challenge, particularly impacting individuals exposed to ionizing radiation (IR) through medical treatments, occupational settings, and environmental factors. Effective therapeutic strategies require a deep understanding of the mechanisms underlying RIC formation (RICF). This study investigates the roles of angiotensin II (Ang II) and oxidative stress in RIC development, with a focus on their combined effects on lens transparency and cellular function. Key mechanisms include the generation of reactive oxygen species (ROS) and oxidative damage to lens proteins and lipids, as well as the impact of Ang II on inflammatory responses and cellular apoptosis. …

Image-To-Mesh Conversion Method For Multi-Tissue Medical Image Computing Simulations, Fotis Drakopoulos, Yixun Liu, Kevin Garner, Nikos Chrisochoides

Computer Science Faculty Publications

Converting a three-dimensional medical image into a 3D mesh that satisfies both the quality and fidelity constraints of predictive simulations and image-guided surgical procedures remains a critical problem. Presented is an image-to-mesh conversion method called CBC3D. It first discretizes a segmented image by generating an adaptive Body-Centered Cubic mesh of high-quality elements. Next, the tetrahedral mesh is converted into a mixed element mesh of tetrahedra, pentahedra, and hexahedra to decrease element count while maintaining quality. Finally, the mesh surfaces are deformed to their corresponding physical image boundaries, improving the mesh’s fidelity. The deformation scheme builds upon the ITK open-source library …

Enhancing Heart Disease Prediction With Reinforcement Learning And Data Augmentation, Gayathri R., Sangeetha S. K. B., Sandeep Kumar Mathivanan, Hariharan Rajadurai, Benjula Anbu Malar Mb, Saurav Mallik, Hong Qin

Computer Science Faculty Publications

The study presents a novel method to improve the prediction accuracy of cardiac disease by combining data augmentation techniques with reinforcement learning. The complex nature of cardiac data frequently presents challenges for traditional machine learning models, which results in subpar performance. In response, our fusion methodology improves predictive capabilities by augmenting data and utilizing reinforcement learning's skill at sequential decision-making. Our method predicts cardiac disease with an astounding 94 % accuracy rate, which is an outstanding result. This significant improvement outperforms existing techniques and shows a deeper comprehension of intricate data relationships. The amalgamation of reinforcement learning and data augmentation …

Enhanced Skin Cancer Diagnosis Through Grid Search Algorithm-Optimized Deep Learning Models For Skin Lesion Analysis, Rudresh Pillai, Neha Sharma, Sheifali Gupta, Deepali Gupta, Sapna Juneja, Saurav Malik, Hong Qin, Mohammed S. Alqahtani, Amel Ksibi

Computer Science Faculty Publications

Skin cancer is a widespread and perilous disease that necessitates prompt and precise detection for successful treatment. This research introduces a thorough method for identifying skin lesions by utilizing sophisticated deep learning (DL) techniques. The study utilizes three convolutional neural networks (CNNs)-CNN1, CNN2, and CNN3-each assigned to a distinct categorization job. Task 1 involves binary classification to determine whether skin lesions are present or absent. Task 2 involves distinguishing between benign and malignant lesions. Task 3 involves multiclass classification of skin lesion images to identify the precise type of skin lesion from a set of seven categories. The most optimal …

Flexible Fitting Of Alphafold2-Predicted Models To Cryo-Em Density Maps Using Elastic Network Models: A Methodological Affirmation, Maytha Alshammari, Jing He, Willy Wriggers

Computer Science Faculty Publications

Motivation: This study investigates the flexible refinement of AlphaFold2 models against corresponding cryo-electron microscopy (cryo-EM) maps using normal modes derived from elastic network models (ENMs) as basis functions for displacement. AlphaFold2 generally predicts highly accurate structures, but 18 of the 137 models of isolated chains exhibit a TM-score below 0.80. We achieved a significant improvement in four of these deviating structures and used them to systematically optimize the parameters of the ENM motion model.

Results: We successfully refined four AlphaFold2 models with notable discrepancies: lipid-preserved respiratory supercomplex (TM-score increased from 0.52 to 0.69), flagellar L-ring protein (TM-score increased from 0.53 …

Sccad: Cluster Decomposition-Based Anomaly Detection For Rare Cell Identification In Single-Cell Expression Data, Yunpei Xu, Shaokai Wang, Qilong Feng, Jiazhi Xia, Yaohang Li, Hong-Dong Li, Jianxin Wang

Computer Science Faculty Publications

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate …

Identifying Patterns For Neurological Disabilities By Integrating Discrete Wavelet Transform And Visualization, Soo Yeon Ji, Sampath Jayarathna, Anne M. Perrotti, Katrina Kardiasmenos, Dong Hyun Jeong

Computer Science Faculty Publications

Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities …

Triphlapan: Predicting Hla Molecules Binding Peptides Based On Triple Coding Matrix And Transfer Learning, Meng Wang, Chuqi Lei, Jianxin Wang, Yaohang Li, Min Li

Computer Science Faculty Publications

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding …

College Of Health Sciences Newsletter, College Of Health Sciences, Old Dominion University

Ellmer College of Health Sciences Newsletters

December 2023 issue of Old Dominion University's College of Health Sciences Newsletter.

Charting And Checking For Suicidality In A Family Medicine Residency Clinic, Bridget Murphy, Stacy Ogbeide

Journal of Human Services Scholarship and Interprofessional Collaboration

Suicide is a leading cause of death in the United States, and many individuals who die by suicide are likely to have seen a primary care physician (PCP) within the month of their death. Thus, the goal of this quality improvement (QI) project was to examine suicidality documentation practices of interprofessional clinicians within a Family Medicine residency clinic, thus providing rationale for continued research and a template for other clinics to emulate. The QI project used the Plan-Do-Study-Act cycle to survey 28 Family Medicine residents, faculty, and trainees for the Plan stage of the cycle in 2022 and assessed their …

College Of Health Sciences Newsletter, College Of Health Sciences, Old Dominion University

Ellmer College of Health Sciences Newsletters

November 2023 issue of Old Dominion University's College of Health Sciences Newsletter.

College Of Health Sciences Newsletter, College Of Health Sciences, Old Dominion University

Ellmer College of Health Sciences Newsletters

October 2023 issue of Old Dominion University's College of Health Sciences Newsletter.

Incremental Validity Of The Mmpi-A-Rf And Maci In A Clinical Outpatient Setting, Madison Smart-Mccarthy

Psychology Theses & Dissertations

With the persistent rise of depression and suicide in adolescents, it is imperative that clinicians select empirically supported measures that accurately assess these conditions (Van Orman, 2022; Sellbom & Suhr, 2020). One empirical method used to evaluate measures is incremental validity. Incremental validity examines the level of improvement in predicting a phenomenon when adding a test or procedure to a combination of other instruments (APA, 2020). Experts regard the Minnesota Multiphasic Personality Inventory—Adolescent Version—Restructured Form (MMPI-A-RF; Archer et al., 2016) and the Millon Adolescent Clinical Inventory (MACI; Millon et al., 1993) as psychometrically sound measures often used in clinical practice. …

A Reflection Of Experiences Of Adults With Type 1 Diabetes In Integrated Physical Education Classes, Kalleigh West

Human Movement Studies & Special Education Theses & Dissertations

The purpose of this study was to gain an understanding of the experiences of type 1 diabetics in integrated physical education classes. In this study, we interviewed young type 1 diabetic adults and asked them to reflect on their school-based physical education experiences. An interpretative phenomenological analysis (IPA) approach was adopted to guide data collection, analysis, and interpretation for this retrospective study. Eight participants (ages 19 to 32) were enrolled in this study, and semi-structured interviews focused on their physical education experiences acted as the primary data. Transcribed interview data were analyzed using an IPA approach. Three interrelated themes emerged …

Dental Hygiene Students Reported Physiological Symptoms Associated With Wearing An N95 Respirator Mask, Peyton Shea Butler

Dental Hygiene Theses & Dissertations

Purpose: Physiological symptoms and comfort levels while wearing an N95 respiratory mask has not been examined with dental hygienists. The purpose of this study was to investigate dental hygiene students reported physiological symptoms and comfort perception while wearing an N95 respirator mask during patient care appointments. Methods: After IRB approval (IRB #1987754-2), a 16-item questionnaire was distributed through email to a convenience sample of 65 dental hygiene students. Questions assessed respiratory, dermatologic, cardiac, mask mouth and general physiological symptoms, as well as comfort levels. Additionally, participants were asked to respond to demographic questions and one open ended question inquiring about …