Digital Commons Network^™

04.21.2025 Ored Connect, Liz Williamson

ORED Newsletter

ORED Stakeholder Meetings

SEC Travel Grants

Facilitating Better Literacy Outcomes In First And Second-Grade At-Risk Readers At An After-School Program, Brooke Goldhammer

Student Theses and Dissertations

This study aimed to examine the efficacy of literacy intervention in first and second-grade at-risk readers at an after-school program, CityYouth Ministries, in Jonesboro, AR. It was hypothesized that if children at risk for literacy difficulties receive literacy intervention at an after-school program, they would have better reading outcomes, narrowing the gap in academic achievement. If successful, it may resolve the need for long-term literacy support at school, which has been frequently observed with this population. The study utilized a quasi-experimental, within-group pretest-posttest design. Reading fluency and sight word knowledge were measured using DIBELS-8 Oral Reading Fluency passages and Dolch …

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) …

The Impact Of Sensory Sensitivity On Nutritional Outcomes In Children With Neurodevelopmental Disorders, Rachel M. Qualley

Theses and Dissertations

Background/Objective: Children with neurodevelopmental disorders (NDDs) are at increased risk of malnutrition due to restrictive dietary patterns and behavioral difficulties at mealtimes. Research suggests that heightened sensory sensitivity, a characteristic accompanying several of these disorders, may be a contributing factor. However, the effects of sensory sensitivity on nutritional outcomes across distinct NDDs have not been fully explored. This review aims to examine the relationship between sensory sensitivity and nutritional status, food preferences, and eating behaviors in children with NDDs, specifically investigating how atypical sensory processing might influence dietary intake.

Methods: A literature search was conducted using Google Scholar, PubMed, …

Effect Of Multiple Simulation Exposures On Nursing Students' Critical Thinking In First Direct Patient Care Practice, Dawn Veltre

Doctoral Dissertations and Projects

Critical thinking is a core competency essential for nursing practice, and nursing students must develop these skills to become competent nurses. The purpose of this quantitative quasi-experimental pretest-posttest design is to determine the effect of multiple simulation exposures on nursing students' critical thinking during their first direct patient care practice. A convenience sample of 39 first-year nursing students enrolled in an associate degree nursing program in the western United States participated in this study. Participants completed a demographic survey and the Health Sciences Reasoning Test (HSRT) to measure critical thinking skills before multiple simulation exposures, two exposures, or four exposures …

Nursing Faculty Perspectives On Students With Learning Disabilities: A Transcendental Phenomenological Study, Marlies Aanensen Hébert

Doctoral Dissertations and Projects

The purpose of this transcendental phenomenological study was to record the lived experiences of nursing faculty perspectives of students with learning disabilities in all learning environments. The theories that provide the framework for the study are John Dewey’s theory of education and Malcolm Knowles’ theory of adult learning. Both theories establish a foundation for understanding the challenges faced by adult learners with learning disabilities and how to offer effective support. The research question is: What are the lived experiences of nurse educators working with students with learning disabilities in all learning environments? The study employed a snowball sampling method to …

The Ability Of Algebra Final Grades And Teas Scores To Predict Undergraduate Nursing Student Grade Point Averages: A Quantitative, Non-Experimental, Predictive Correlational Study, Jeanna L. Kerchner

Doctoral Dissertations and Projects

A major concern for many nursing programs is student completion rates. This is due in large part to the demands and rigor of curriculum in accredited nursing programs. The purpose of this quantitative, non-experimental, predictive correlational study was to determine if final nursing program GPA can be predicted by the linear combination of college algebra final grades and TEAS scores in an undergraduate nursing program at a private university. A quantitative, non-experimental research design was used to address the proposed study’s purpose. The study’s sample of participants was purposive and convenient in nature, and was accessed using an existing, archival …

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, …

Nursing Educators’ Perceptions Of Using Virtual Simulated Games In Nursing Education, Ruhee Waheeda Mir

Walden Dissertations and Doctoral Studies

Nursing educators of Trinidad and Tobago have limited experience with virtual simulated games (VSGs) as a part of their teaching methodology. This lack of experience was highlighted during the COVID-19 pandemic as educators had to use virtual approaches in their classrooms and struggled with unfamiliar technologies. The purpose of this qualitative descriptive study, guided by the extended technology acceptance model, was to explore the perceptions of Trinidadian and Tobagonian nursing educators regarding the use of a VSG as a teaching tool. The sample consisted of eight nursing educators from an educational institute in Trinidad who met the criteria of being …

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

Machine Learning To Optimize Use Of Natriuretic Peptides In The Diagnosis Of Acute Heart Failure, Dimitrios Doudesis, Kuan Ken Lee, Mohamed Anwar, Adam J. Singer, Judd E. Hollander, Camille Chenevier-Gobeaux, Yann-Erick Claessens, Desiree Wussler, Dominic Weil, Nikola Kozhuharov, Ivo Strebel, Zaid Sabti, Christopher Defilippi, Stephen Seliger, Evandro Tinoco Mesquita, Jan C. Wiemer, Martin Möckel, Joel Coste, Patrick Jourdain, Komukai Kimiaki, Michihiro Yoshimura, Irwani Ibrahim, Shirley Beng Suat Ooi, Win Sen Kuan, Alfons Gegenhuber, Thomas Mueller, Olivier Hanon, Jean-Sébastien Vidal, Peter Cameron, Louisa Lam, Ben Freedman, Tommy Chung, Sean P. Collins, Christopher J. Lindsell, David E. Newby, Alan G. Japp, Anoop S. V. Shah, Humberto Villacorta, A. Mark Richards, John J V Mcmurray, Christian Mueller, James L. Januzzi, Nicholas L. Mills, Gordon Moe, Carlos Fernando, Hanna K. Gaggin, Antoni Bayes-Genis, Roland Rj Van Kimmenade, Yigal Pinto, Joost H. W. Rutten, Anton H. Van Den Meiracker, Luna Gargani, Nicola R. Pugliese, Pemberton Christopher, Michael Neumaier, Michael Behnes, Ibrahim Akin, Michele Bombelli, Guido Grassi, Peiman Nazerian, Giovanni Albano, Philipp Bahrmann

Department of Emergency Medicine Faculty Papers

AIMS: B-type natriuretic peptide (BNP) and mid-regional pro-atrial natriuretic peptide (MR-proANP) testing are guideline-recommended to aid in the diagnosis of acute heart failure. Nevertheless, the diagnostic performance of these biomarkers is uncertain.

METHODS AND RESULTS: We performed a systematic review and individual patient-level data meta-analysis to evaluate the diagnostic performance of BNP and MR-proANP. We subsequently developed and externally validated a decision-support tool called CoDE-HF that combines natriuretic peptide concentrations with clinical variables using machine learning to report the probability of acute heart failure. Fourteen studies from 12 countries provided individual patient-level data in 8493 patients for BNP and 3899 …

Machine Learning To Optimize Use Of Natriuretic Peptides In The Diagnosis Of Acute Heart Failure, Dimitrios Doudesis, Kuan Ken Lee, Mohamed Anwar, Adam J. Singer, Judd Hollander, Md, Camille Chenevier-Gobeaux, Yann-Erick Claessens, Desiree Wussler, Dominic Weil, Nikola Kozhuharov, Ivo Strebel, Zaid Sabti, Christopher Defilippi, Stephen Seliger, Evandro Tinoco Mesquita, Jan C. Wiemer, Martin Möckel, Joel Coste, Patrick Jourdain, Komukai Kimiaki, Michihiro Yoshimura, Irwani Ibrahim, Shirley Beng Suat Ooi, Win Sen Kuan, Alfons Gegenhuber, Thomas Mueller, Olivier Hanon, Jean-Sébastien Vidal, Peter Cameron, Louisa Lam, Ben Freedman, Tommy Chung, Sean P. Collins, Christopher J Lindsell, David E. Newby, Alan G. Japp, Anoop S V Shah, Humberto Villacorta, A Mark Richards, John J V Mcmurray, Christian Mueller, James L. Januzzi, Nicholas L. Mills

Department of Emergency Medicine Faculty Papers

AIMS: B-type natriuretic peptide (BNP) and mid-regional pro-atrial natriuretic peptide (MR-proANP) testing are guideline-recommended to aid in the diagnosis of acute heart failure. Nevertheless, the diagnostic performance of these biomarkers is uncertain.

METHODS AND RESULTS: We performed a systematic review and individual patient-level data meta-analysis to evaluate the diagnostic performance of BNP and MR-proANP. We subsequently developed and externally validated a decision-support tool called CoDE-HF that combines natriuretic peptide concentrations with clinical variables using machine learning to report the probability of acute heart failure. Fourteen studies from 12 countries provided individual patient-level data in 8493 patients for BNP and 3899 …

Natal And Neonatal Teeth In Mississippi Newborn, Keona Sanders, Anna Mcdowell, Dr. Sarah Mccrary

Undergraduate Research Conference

Title: Natal And Neonatal Teeth In Mississippi Newborns

Submission Type: Semester Long Research/ Oral Presentation

Abstract: In recent years, we have seen a growth in Mississippi infants being born with or growing teeth at an earlier stage. This may be caused by several things, like gestational age, maternal age, or possibly the mother’s diet. We are now looking for common similarities between these infants to see what the possible cause may be. Although this may not seem detrimental, this is still very harmful for infants, causing diseases like Riga Fede Disease. This is a disease that is caused by mandibular …

Paradigm Shifts In Medicine, David Meza, James Peter Meza

Clinical Research in Practice: The Journal of Team Hippocrates

Implications for history of science, scientific revolutions and paradigm shifts in the context of rapidly developing artificial intelligence.

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.

Bryant University Research And Engagement Day Program

Bryant REDay (Research and Engagement Day)

No abstract provided.

Research And Engagement Day Poster

Bryant REDay (Research and Engagement Day)

No abstract provided.

Prenatal Exposure To Pm2.5 Concentration And Risk Of Preterm Birth In New Mexico; A Seasonal Pattern And Spatio-Temporal Modeling., Onyedikachi J. Okeke

Mathematics & Statistics ETDs

This study investigates the association between prenatal PM2.5 exposure and preterm birth risk in New Mexico (2014–2021). Using descriptive statistics, time series models, and spatial analyses, findings show an average preterm birth rate of 14.87 per 1,000 live births, with moderate correlation (r = 0.727) between PM2.5 levels and very preterm births. While traditional models revealed no significant global effect, spatial methods such as Geographically Weighted Regression (GWR) and Multiscale GWR uncovered strong spatial heterogeneity. PM2.5 effects varied by county (coefficients: -0.00154 to 0.00150), with clustering evident in 2018–2019 (Moran’s I = 0.155–0.174). Results highlight the limitations of global models …

Identifying The Human Olfactory And Chemosignaling Neural Networks Using Event Related Fmri And Graph Theory, Saideh Ferdowsi, Tom Foulsham, Alireza Rahmani, Dimitri Ognibene, Luca Citi, Wen Li

Faculty, Staff and Student Publications

This study aims to characterize and compare the functional neural networks associated with different olfactory stimuli, including air, non-social odours, and human body odours. We introduce a novel processing pipeline based on event-related functional magnetic resonance imaging (fMRI) and graph theory for network identification. To ensure the stability and small worldness of the characterized networks, we conduct statistical validations, network modularity assessments, and robustness measurement against local attacks. The key hypothesis is that human body odours (so-called social odours) and non-social odours engage distinct neural networks, particularly in regions responsible for social processing. We found that the posterior medial orbitofrontal …

Protecting Global Health Partnerships In The Era Of Destructive Nationalism, Maya Adam, Desiree Labeaud, Nokwanele Mbewu, Jennifer Gates, Randall Waechter, Mercy Borbor-Cordova, Lydiah Kibe, Sonia Alvarez, Annie Dori, Anil Bilimale, Meggie Mwoka, Jessica Deffler, Michele Spring, Avriel Diaz, Rachael Farquhar, Willy Dunbar, Comfort Phiri, Valerie Luzadis, Nicole Redvers, Cristina Alonso, Rosemary Rochford, Rachel Lowe, Kareem Coomansingh, Moses Laman, Rebeca Sultana, Sadie Ryan, Amaya Bustinduy, Anita Hargrave-Bouagnon, Shazia Ruybal-Pesántez, Zebedee Kerry, Trevor Kelebi, Samuel Mcewen, Leanne Robinson, Maritza Salazar Campo, Till Bärnighausen

Department of Family & Community Medicine Faculty Papers

No abstract provided.