Medicine and Health Sciences Commons^™

The Mediating Role Of Remote Communication On The Relationship Between Electronic Human Resource Management Practices And Organizational Performance In Iraqi Commercial Banks, Shuhub Basel Ahmad Taqa

Middle East Journal of Communication Studies

This study aimed to measure the impact of remote communication as a mediating variable on the relationship between electronic human resources (e-HRM) and organizational performance in Iraqi commercial banks. The study adopted a descriptive-analytical approach, with the study population consisting of managers, their deputies, and department heads in Iraqi commercial banks, totaling 242 individuals distributed across 24 commercial banks by the end of 2023. A total of 234 questionnaires were analyzed, achieving a response rate of 96.7%. The study employed Smart PLS software for data analysis and result extraction. The study revealed several key findings, the most significant of which …

Analysis And Monitoring Of A Robotics Curriculum: Are Simnow Modules Valuable?, Jacob Applegarth, Ibrahim Baida, Anthony Iacco, Ngan Nguyen, Nathan Novotny

Posters

No abstract provided.

From The Ground Up: Community-Based Participatory Research Reclaiming The Science Of Lead, Juan Manuel Rubio, Bavisha Kaylan, Anthony Diaz, Patricia Flores, Maya Cheav, David C. Bañuelas, Ashley Green, Annika Hjelmstad, Ariane Jong-Levinger, Tim Schütz, Maya Carrasquillo, Alana M. W. Lebron, Jun Wu

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

For decades, the dominant approach to lead poisoning has been to focus on homes affected by lead paint and to treat children who are already suffering from lead poisoning. This individualizing approach developed in the context of the defunding and deregulation of government agencies in the 1980s. In recent years, however, community–academic partnerships have reframed lead as an environmental issue produced by the development of the lead industry in the twentieth century and connected to overlapping histories of exploitation, discrimination, and inaction. These community-based projects have contributed to shifting research agendas (by emphasizing historical analysis and the study of the …

The Attitudes And Perspectives Of Laboratory Professionals On The Use Of Machine Learning Combined With Maldi For Viral Identification: A Qualitative Study, Grace Johnson

Honors Projects

The use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with machine learning (ML) has been proposed by numerous studies as a novel approach for viral identification. However, the development and implementation of this instrumentation is still in its early stages, and laboratory professionals' perspectives on its feasibility, accuracy, implementation, and effect on current laboratory operating procedures remain underexplored.

This study aimed to investigate laboratory professionals’ attitudes and opinions regarding the use of MALDI-TOF-MS coupled with machine learning for viral identification, focusing on perceived benefits, barriers, and factors that would affect participants’ opinions on implementation.

A qualitative descriptive research …

The Dialogue Dilemma: The Role Of Patient-Clinician Communication For Low-Income People Who Smoke And Manage Multiple Conditions, Monique T. Cano, Michael R. Lindstrom, Ricardo F. Muñoz

School of Mathematical & Statistical Sciences Faculty Publications

Introduction: Adults from low-income backgrounds who smoke face significant health disparities related to tobacco use, often at disproportionately high rates. These individuals are more likely to endure multiple mental and physical (MP) health conditions, which can negatively influence their self-rated health (SRH). The quality and effectiveness of patient-clinician communication (PCC) can influence how patients perceive their own health. Understanding how PCC influences SRH among low-income adults who smoke and suffer from multiple MP conditions is essential for clinical care as multimorbidity is on the rise. This study examines how PCC may influence the health perceptions of low-income adults who smoke …

Autoradai: A Versatile Artificial Intelligence Framework Validated For Detecting Extracapsular Extension In Prostate Cancer, Pegah Khosravi, Shady Saikali, Abolfazl Alipour, Saber Mohammadi, Maxwell Boger, Dalanda M. Diallo, Christopher J. Smith, Marcio C. Moschovas, Iman Hajirasouliha, Andrew J. Hung, Srirama S. Venkataraman, Vipul Patel

Publications and Research

Preoperative identification of extracapsular extension (ECE) in prostate cancer (PCa) is crucial for effective treatment planning, as ECE presence significantly increases the risk of positive surgical margins and early biochemical recurrence following radical prostatectomy. AutoRadAI, an innovative artificial intelligence (AI) framework, was developed to address this clinical challenge while demonstrating broader potential for diverse medical imaging applications. The framework integrates T2-weighted MRI data with histopathology annotations, leveraging a dual convolutional neural network (multi-CNN) architecture. AutoRadAI comprises two key components: ProSliceFinder, which isolates prostate-relevant MRI slices, and ExCapNet, which evaluates ECE likelihood at the patient level. The system was trained and …

The Analysis Of Reference Standard Storage Failures Through The Use Of Ibuprofen, Hope Vonder Brink

Honors Projects

Reference standards are commonly used in drug chemistry. They are used to calibrate the instruments and serve as a known point of reference for analysts’ ability to compare unknowns. Drugs can degrade over time or through environmental factors, causing contaminants to be present. In the event of environmental degradation while in storage, such as the exposure to high heat, it would be beneficial to have a database of known breakdown products in common drug reference standards for analysts’ reference. Using ibuprofen as an exemplar drug, this study attempted to identify the thermal breakdown products of ibuprofen and introduce the proposed …

Evaluating The Causal Effect Of Receiving Transthoracic Echocardiography On 28-Day Mortality In Mimic-Iii Intensive Care Unit Patients With Sepsis, Monserrath Velez, Jack O'Connor, Nicholas Della Pesca, Rio Baliga

Research & Creative Achievement Day

❏ Sepsis (an infection associated with vital organ dysfunction) is an emergent

medical condition estimated to occur in about 30% of intensive care unit

(ICU) patients[1] and is responsible for 20% of all deaths worldwide[2]

❏ Transthoracic echocardiography (TTE; an imaging modality that uses

ultrasound technology to record heart structure and function) is widely used in

medical treatment [3]

❏ 28-day mortality (whether or not a patient dies within 28 days after receiving a

treatment) is a measure considered to closely approximate ICU mortality[4]

Objectives

❏ Assess and quantify the causal effect of receiving a TTE on …

A Hybrid Deep Learning-Based Approach For Visual Field Test Forecasting, Ashkan Abbasi, Sowjanya Gowrisankaran, Wei-Chun Lin, Xubo Song, Bhavna Josephine Antony, Gadi Wollstein, Joel Schuman, Hiroshi Ishikawa

Wills Eye Hospital Papers

OBJECTIVE: Longitudinal assessment of visual field (VF) testing is essential in glaucoma management. Conventional VF forecasting methods require numerous prior tests, while deep learning techniques have shown promising results with fewer tests. This study introduces a hybrid deep learning framework to enhance flexibility and accuracy in VF test forecasting.

DESIGN: A retrospective longitudinal study using deep learning-based VF forecasting models.

SUBJECTS AND CONTROLS: A total of 1750 subjects (healthy and glaucoma patients) with 19 437 Humphrey VF (24-2 Swedish Interactive Threshold Algorithm) tests collected from longitudinal glaucoma cohorts at the University of Pittsburgh and New York University.

METHODS: Three deep …

Sexually Transmitted Infection (Sti) Incidence And Risk Factors Among People With Hiv (Pwh): Insights From A 13-Year Cohort Study In South Carolina, Salome-Joelle Gass, Shujie Chen, Jiajia Zhang Ph.D., Bankole Olatosi

Faculty Publications

The Ending the HIV Epidemic (EHE) initiative aims to reduce new HIV infections by 90% by 2030 in the United States (US). However, rising sexually transmitted infection (STI) rates exacerbate the bidirectional infection risk between HIV and STIs. Most research on STIs among people with HIV (PWH) has focused on high-risk groups, resulting in limited data on broader populations. This study addresses that gap by examining the incidence and risk factors for gonorrhea, chlamydia, and syphilis in a statewide cohort of PWH in South Carolina. Data from South Carolina’s HIV and STI surveillance systems were linked, and all PWH aged …

2025 Acssc Program, Acssc Planning Committee

Annual Celebration for Student Scholarship and Creativity

No abstract provided.

Environmental Risk And Alpha-Gal Syndrome (Ags) In The Mid-Atlantic United States, Brandon D. Hollingsworth Ph.D., Margaret Wiener, Dana A. Giandimenico, Scott P. Commins, Ross M. Boyce

Faculty Publications

Alpha-gal syndrome (AGS), commonly referred to as the tick bite red meat allergy, has been reported worldwide with the number of suspected cases in the United States increasing from 24 in 2009 to over 34,000 in 2019. Within the US, AGS is associated with the bite of two tick species, Amblyomma americanum and Ixodes scapularis, and has particularly high incidence rates in the mid-Atlantic region. Because AGS is associated with tick bites, the risk of developing AGS is affected by the environment individuals visit. Despite this, as well as the numerous studies associating the environment with Am. americanum, …

Volume 16, Maggie Duncan, Madeline Little, Alicia Hoffman, Megan Livesay, Gabrielle Quaresma, Serenity Allen, Laina Pfountz, Ainslie Allred, Sabrina Robles, Nicholas J. Duellman, Trinity L. Deguzman, Melissa H. Savage, Margaret Dudley, Jocelyn Escobar, Olivia Hildreth, Olivia Hopkins, Benjamin Gettier, Lee Kassay, Jade Riddle, Ashley Seiders

Incite: The Journal of Undergraduate Scholarship

Introduction Dr. Amorette Barber, Director, Office of Student Research

From the Editor Dr. Hannah Dudley-Shotwell

Artist’s Statement Maggie Duncan

On Mentoring Dr. Lee Millar Bidwell

The Hujum Campaign in Uzbekistan and its Consequences by Madeline Little

Wet Cupping Compared to Dry Needling for Treatment of Patients with Low Back Pain: A Critically Appraised Topic by Alicia Hoffman and Megan Livesay

Optimization of eDNA Air Sampling Via 3D Printed Fan by Gabrielle Quaresma

Beyond the Classroom: A Qualitative Study of Teacher Attrition and Retention by Serenity Allen and Laina Pfountz

The Treatment of Subacromial Impingement Syndrome with Platelet-Rich plasma Injections Verses …

Ensemble Learning With Explainable Ai For Improved Heart Disease Prediction Based On Multiple Datasets, Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Zhongming Zhao

Faculty, Staff and Student Publications

Heart disease is one of the leading causes of death worldwide. Predicting and detecting heart disease early is crucial, as it allows medical professionals to take appropriate and necessary actions at earlier stages. Healthcare professionals can diagnose cardiac conditions more accurately by applying machine learning technology. This study aimed to enhance heart disease prediction using stacking and voting ensemble methods. Fifteen base models were trained on two different heart disease datasets. After evaluating various combinations, six base models were pipelined to develop ensemble models employing a meta-model (stacking) and a majority vote (voting). The performance of the stacking and voting …

Speed Of Shear Waves As A Function Of Frequency In Micellar Fluid, Michael Basha

Honors Theses

The purpose of this experiment was to investigate the frequency dependence of shear wave speed in a micellar fluid. A high-concentration CTAB-NaSal micellar fluid seeded with microspheres was used in the experiment. Shear waves were generated using a wave driver and a function generator, while a high-speed camera captured video recordings. Since the frequency of the wave driver producing the shear waves in the micellar fluid is a constant value set by the experimenter, changes in shear wave speed can be determined by measuring their wavelengths at different frequencies.The captured video was then broken down into frames using MATLAB. These …

04.21.2025 Ored Connect, Liz Williamson

ORED Newsletter

ORED Stakeholder Meetings

SEC Travel Grants

2025 Academic Conference Program

Holy Cross Undergraduate Academic Conference

No abstract provided.

Data-Driven Survival Modeling For Breast Cancer Prognostics: A Comparative Study With Machine Learning And Traditional Survival Modeling Methods, Theophilus Gyedu Baidoo, Hansapani Rodrigo

School of Mathematical & Statistical Sciences Faculty Publications

Background This investigation delves into the potential application of data-driven survival modeling approaches for prognostic assessments of breast cancer survival. The primary objective is to evaluate and compare the ability of machine learning (ML) models and conventional survival analysis techniques, to identify consistent key predictors of breast cancer survival outcomes.

Methods This study employs data-driven survival modeling approaches to predict breast cancer survival, including survival-specific methods such as the Cox Proportional Hazards (CPH) model, Random Survival Forests (RSF), and Cox Proportional Deep Neural Networks (DeepSurv), as well as machine learning models like Random Forests (RF), XGBoost, Support Vector Machines (SVM) …

Application For Prediction Of Heart Failure; The Next Step In Machine Learning For Healthcare, Amy Adyanthaya, Dawn Bowerman, Rachel Liercke, Robert Slater

SMU Data Science Review

Heart failure (HF) is a serious medical condition affecting approximately 6.7 million U.S. adults and is expected to impact 8.5 million Americans by 2030 [1]. Heart failure is a complicated clinical ailment and characterizes the final course of numerous heart diseases [2]. This paper introduces a machine-learning-based application that utilizes Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and XGBoost models, implemented through the Python Flask framework, to predict HF risk using clinical data. The results indicate high model performance, with precision and recall metrics underscoring the application’s reliability in identifying at-risk patients. By providing real-time, accessible insights, this tool aims …

Research Week 2025, Michael Schueth

Research Week

Welcome to Undergraduate Research Week at Collin College!

This week celebrates the innovative research and scholarly work of our undergraduate students across all disciplines. From our informative webinars to Cultivating Scholars, our student poster presentation, the week offers opportunities to explore new ideas, share discoveries, and connect with fellow researchers and faculty mentors.

We invite all students, faculty, and staff to participate in these events designed to highlight the importance of undergraduate research in academic and professional development. Whether you're presenting your work or simply curious about your peers' projects, your engagement helps foster our college's culture of inquiry and …

Cultivating Scholars 2025, Elizabeth Hamner

Research Week

Cultivating Scholars is an interdisciplinary event promoting undergraduate students and their special interest topics. It is a research-based setting meant to imitate a professional conference. Students work with Faculty Mentors to conduct a high-quality project and present it to the community.

Date: April 24, 2025, 5:30-7:30 p.m.

Place: Collin College, Frisco Campus Conference Center

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning With Biosensor Signals, Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Anand Paul

School of Public Health Faculty Publications

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in diabetes management is maintaining blood glucose levels (BGLs) within a safe range to prevent complications such as renal failure, cardiovascular disease, and neuropathy. Traditional methods, such as finger-prick testing, often result in low patient adherence due to discomfort, invasiveness, and inconvenience. Consequently, there is an increasing need for non-invasive techniques that provide accurate BGL measurements. Photoplethysmography (PPG), a photosensitive method that detects blood volume variations, has shown promise for non-invasive glucose monitoring. Deep neural networks (DNNs) applied to …

Human Capital And Lifetime Income Gains Of Scaling-Up Small-Quantity Lipid Nutrient Supplements Among Children Under 2 Years: A Modelling Analysis, Nandita Perumal Phd, Goodrarz Danaei, Günther Fink, Mark Lambiris, Christopher R. Sudfeld

Faculty Publications

Undernutrition in early childhood is associated with adverse health and developmental outcomes later in life and remains a persistent global public health problem. Providing small-quantity lipid nutrient supplements (SQ-LNS) to children aged 6-24 months improves child growth and neurodevelopmental outcomes, but the potential long-term benefits to human capital have not been previously estimated. We estimated the potential returns to schooling and lifetime income attributable to increasing coverage of SQ-LNS for children < 2 years of age from 0% to 50% or 90% per five-year birth cohort in five countries (Bangladesh, Burkina Faso, Ethiopia, Pakistan, and Uganda) with a high burden of undernutrition. Random-effects meta-analyses were used to estimate the effect of SQ-LNS on child development using evidence from randomized controlled trials, and to estimate the returns to lifetime income as a function of change in development based on a de novo meta-analysis of observational economic studies. Gains in school years attributable to scaling-up SQ-LNS to 90% coverage ranged from 0.14 million school years (95% uncertainty interval [UI]: 0.064, …

Health Risk Assessment Of Heavy Metal(Loid)S Intake From Beverages In The United States, Hannah T.V. Stoner, Tewodros Rango Godebo, Pornpimol Kodsup Taylor

School of Public Health Faculty Publications

Heavy metals in beverages can pose health risks in an exposure-dependent manner, however, few studies in the United States have evaluated their metal content and health risks. This study determined the concentrations of eight metal(loid)s, As, Al, Cd, Cr, Mn, Ni, Pb, and Zn, in 60 beverages via inductively coupled plasma[sbnd]mass spectrometry (ICPMS). The highest median concentrations (µg/kg) were found in mixed fruit juices for Ni (45.7), Cr (14.8), and As (4.5); tea for Mn (5,300), Al (730), and Pb (1.4); and plant-based milk for Zn (835) and Cd (1.1). Chronic daily intake (CDI) across age groups was calculated using …

04.14.2025 Ored Connect, Liz Williamson

ORED Newsletter

Updated CITI Training Courses

Science Celebration

IHL Prepayment Approval

Ahr Activation At The Air-Blood Barrier Alters Systemic Microrna Release After Inhalation Of Particulate Matter Containing Environmentally Persistent Free Radicals, Ankit Aryal, Ashlyn C. Harmon, Alexandra Noël, Qingzhao Yu, Kurt J. Varner, Tammy R. Dugas

School of Public Health Faculty Publications

Particulate matter containing environmentally persistent free radicals (EPFRs) is formed when organic pollutants are incompletely burned and adsorb to the surface of particles containing redox-active metals. Our prior studies showed that in mice, EPFR inhalation impaired vascular relaxation in a dose- and endothelium-dependent manner. We also observed that activation of the aryl hydrocarbon receptor (AhR) in the alveolar type-II (AT-II) cells that form the air-blood interface stimulates the release of systemic factors that promote endothelial dysfunction in vessels peripheral to the lung. AhR is a recognized regulator of microRNA (miRNA) biogenesis, and miRNA control diverse signaling pathways. We thus hypothesized …

Securing Biometric Data, Alyssa F. Carroll

Cybersecurity Undergraduate Research Showcase

Biometric data has been widely adopted across various sectors, including digital identity, artificial intelligence (AI), border control, digital wallets, and national identification systems. While biometric identifiers—such as fingerprints, retina scans, and facial recognition—offer reliable and convenient authentication, they also raise significant concerns regarding privacy and security. This paper examines how biometric data is stored, the vulnerabilities it faces, and the most effective methods for safeguarding it. By highlighting the critical importance of biometric data protection, this study reviews current research on approaches, strategies, and policies that enhance security while preserving the functionality and efficiency of biometric systems.

Research And Engagement Day Poster

Bryant REDay (Research and Engagement Day)

No abstract provided.

Bryant's Ai Hack-A-Thon

Bryant REDay (Research and Engagement Day)

No abstract provided.

Research And Engagement Day Poster

Bryant REDay (Research and Engagement Day)

No abstract provided.