Digital Commons Network^™

Understanding The Challenges And Satisfaction Among The Medical Professional Preceptors, Cornelia Ko

West Chester University Doctoral Projects

This study evaluates the Penn Medicine Family Medicine Clerkship program through a mixed-methods approach, exploring the challenges and factors influencing the satisfaction of medical professional preceptors when precepting students. A quantitative survey with 54 preceptor participants reveals the top challenges preceptors face: workload, conflicts with patient care, and limited clinical space. Despite this, there is strong support for the program, with 89% of preceptors enjoying the role and 85% expressing willingness to continue to precept for the next three years. Finally, data analysis shows no statistical correlation between preceptor motivation with gender, ethnicity, age group, years of serving, or total …

The Contributions Of Language And Inhibitory Control To False Belief Reasoning Over Time, Jill De Villiers, Peter A. De Villiers

Psychology: Faculty Publications

Introduction: The role of language in false belief reasoning has been much debated for twenty-five years or more, especially the relative contributions of general language development, complement syntax, vocabulary, and executive function. However, the empirical studies so far have fallen short, in that they generally have too few participants for adequate statistical modeling; they do not include control variables; or they are cross-sectional rather than longitudinal, making inferences about causal direction much more tenuous.

Methods: The present study considers the role of these different variables in the development of false belief reasoning over several months of testing, with 258 children …

Neural Correlates Of Attentional Biases In Dietary Choice: Role Of Childhood Socioeconomic Status, Justine Jamie N. Gotico

CMC Senior Theses

Childhood poverty has been shown to increase adult risk for obesity above and beyond its direct effects on adult socioeconomic status (SES). One proposed mechanism of these effects is by shifting behavioral patterns of dietary consumption and choice, for example by increasing rapid attention to high-calorie unhealthy foods. Yet, whether such neural mechanisms can explain observed differences in dietary behavior based on childhood SES remains an open question. Here we used event-related potentials (ERPs) to examine early attentional correlates of low childhood SES during a dietary choice task, based on research suggesting that early attentional biases toward high-calorie foods emerge …

Determination Of Structural Factors Contributing To Protection Of Zinc Fingers In Estrogen Receptor Α Through Molecular Dynamic Simulations, Patricia B. Lutz, Wesley R. Coombs, Craig A. Bayse

Chemistry & Biochemistry Faculty Publications

The ERα transcription factor that induces tumor growth is a potential target for breast cancer treatment. Each monomer of the ERα DNA-binding domain (ERαDBD) homodimer has two conserved (Cys)₄-type zinc fingers, ZF1 (N-terminal) and ZF2 (C-terminal). Electrophilic agents release Zn²⁺ by oxidizing the coordinating Cys of the more labile ZF2 to inhibit dimerization and DNA binding. Microsecond-length molecular dynamics (MD) simulations show that greater flexibility of ZF2 in the ERαDBD monomer leaves its Cys more solvent accessible and less shielded from electrophilic attack by sulfur-centered hydrogen bonds than ZF1 which is buried in the protein. In the …

Phytochemical Analysis, Antioxidant, And Antibacterial Properties Of Partition Fractions Of Adansonia Digitata And Annona Muricata Extracts Using Chloroform, Ethyl Acetate, Ethanol, And Aqueous Solvent Systems, Fagbohun Oyenike Bushirat, Hassan Abdusalam Adewuyi, Sakariyau Adio Waheed, Maryam Nana Musa, Timothy God-Giveth Olusegun, Ayomide Babatunde Ishola, Agumage Idoko, Adeola Victor Kolawole, Adesanmi Adefunmilayo Oluwatuyi, Sarah Ngozi Agwasim

Chemistry & Biochemistry Faculty Publications

Adansonia digitata and Annona muricata are traditionally used medicinal plants with reported pharmacological properties. In this study, we aimed to elucidate the phytochemical composition, antioxidant activity, and antibacterial properties of partition fractions of Adansonia digitata and Annona muricata extracts obtained using chloroform, ethyl acetate, ethanol, and aqueous solvent systems. The plant extracts were successively partitioned using chloroform, ethyl acetate, ethanol, and aqueous solvent systems. Phytochemical analysis was performed using standard methods. Antioxidant activity was evaluated using FRAP and DPPH assays. Antibacterial activity was assessed using agar well diffusion and MIC determination. Phytochemical analysis revealed the presence of alkaloids (10.2-15.6%), flavonoids …

The Importance Of Solution Studies For The Structural Characterization Of The Enterovirus 5' Cloverleaf, Morgan G. Daniels, Meagan E. Werner, Xiaobing Zuo, Steven M. Pascal

Chemistry & Biochemistry Faculty Publications

Enteroviruses initiate genomic replication via a highly conserved mechanism that is controlled by an RNA platform, also known as the 5' cloverleaf (5'CL). Here, we present a biophysical analysis of the 5'CL conformation of three enterovirus serotypes under various ionic conditions, utilizing CD spectroscopy, size-exclusion chromatography, and small-angle X-ray scattering. In general, a tendency toward a smaller monomeric hydrodynamic radius in the presence of salts was observed, but the exact structural signature of each 5'CL varied depending upon the serotype. Rhinovirus B14 (RVB14) exhibited at least two monomeric conformations and a low propensity for dimerization, while poliovirus 1 (PV1) showed …

Environment Scan Of Generative Ai Infrastructure For Clinical And Translational Science, Hua Xu, Jiang Bian, Chunhua Weng, Yifan Peng, Betina Idnay, Zihan Xu, William G. Adams, Mohammad Adibuzzaman, Nicholas R. Anderson, Neil Bahroos, Douglas S. Bell, Cody Bumgardner, Thomas Campion, Mario Castro, James J. Cimino, I. Glenn Cohen, David Dorr, Peter L. Elkin, Jungwei W. Fan, Todd Ferris, David J. Foran, David Hanauer, Mike Hogarth, Kun Huang, Jayashree Kalpathy-Cramer, Manoj Kandpal, Niranjan S. Karnik, Avnish Katoch, Albert M. Lai, Christophe G. Lambert, Lang Li, Christopher Lindsell, Jinze Liu, Zhiyong Lu, Yuan Luo, Peter Mcgarvey, Eneida A. Mendonca, Parsa Mirhaji, Shawn Murphy, John D. Osborne, Ioannis C. Paschalidis, Paul A. Harris, Fred Prior, Nicholas J. Shaheen, Nawar Shara, Ida Sim, Umberto Tachinardi, Lemuel R. Waitman, Rosalind J. Wright, Adrian H. Zai, Kai Zheng, Sandra Soo-Jin Lee, Bradley A. Malin, Karthik Natarajan, Nicholson Price, Rui Zhang, Yiye Zhang

Articles

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of …

Training Set Augmentation And Harmonization Enables Radiomic Models To Detect Early Onset Of Lung Cancer, Claire Huchthausen, Menglin Shi, Gabriel L.A. Sousa, James Larner, Einsley Janowski, Jonathan Colen, Krishni Wijesooriya

Data Science Faculty Publications

Radiomics-based machine learning models have the potential to detect lung cancer at inception from CT scans and transform patient outcomes. Low malignancy rates in early-development pulmonary nodules (PNs) and variable image acquisition hinder development of clinically applicable radiomics-based early detection models. To address these challenges, we augmented training using later-development PNs and harmonized for acquisition effects. We first trained machine learning models to predict PN malignancy using radiomic features from scans of early-development benign and malignant PNs (n = 187) harmonized using ComBat. Observing near-chance performance, we augmented training with later-development benign and malignant PNs (n = 225). We evaluated …

Motion Artifacts Removal From Measured Arterial Pulse Signals At Rest: A Generalized Sdof-Model-Based Time-Frequency Method, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

Motion artifacts (MA) are a key factor affecting the accuracy of a measured arterial pulse signal at rest. This paper presents a generalized time–frequency method for MA removal that is built upon a single-degree-of-freedom (SDOF) model of MA, where MA is manifested as time-varying system parameters (TVSPs) of the SDOF system for the tissue–contact-sensor (TCS) stack between an artery and a sensor. This model distinguishes the effects of MA and respiration on the instant parameters of harmonics in a measured pulse signal. Accordingly, a generalized SDOF-model-based time–frequency (SDOF-TF) method is developed to obtain the instant parameters of each harmonic in …

An Analytical Model Of Motion Artifacts In A Measured Arterial Pulse Signal—Part I: Accelerometers And Ppg Sensors, Md Mahfuzur Rahman, Subodh Toraskar, Mamun Hasan, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

This paper, the first of two parts, presents an analytical model of motion artifacts (MAs) in measured pulse signals by accelerometers and photoplethysmography (PPG) sensors. As the transmission path from the true pulse signal in an artery to the sensor output (measured pulse signal), the tissue-contact-sensor (TCS) stack is modeled as a 1DOF (degree-of-freedom) system. MAs cause baseline drift of the mass and simultaneously time-varying system parameters (TVSPs) of the TCS stack. With arterial wall displacement and pulsatile pressure serving separately as the true pulse signal, an analytical model is developed to mathematically relate baseline drift and TVSP to a …

An Analytical Model Of Motion Artifacts In A Measured Arterial Pulse Signal—Part Ii: Tactile Sensors, Md Mahfuzur Rahman, Subodh Toraskar, Mamun Hasan, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

This paper, the second of two parts, presents an analytical model of motion artifacts (MA) in measured pulse signals by a tactile sensor, which contains a deformable microstructure sitting on a substrate. While the tissue-contact-sensor (TCS) stack and the sensor are both treated as a 1DOF (degree-of-freedom) system, tissue–sensor contact joins their mass together to form a 1DOF system with springs and dampers on both sides. MA on the sensor substrate causes baseline drift and time-varying system parameters (TVSP) of the TCS stack simultaneously. An analytical model is developed to mathematically relate baseline drift and TVSP to a measured pulse …

Gamified Mhealth System For Evaluating Upper Limb Motor Performance In Children: Cross-Sectional Feasibility Study, Md Raihan Mia, Sheikh Iqbal Ahamed, Samuel Nemanich

Computer Science Faculty Research and Publications

Background: Approximately 17% of children in the United States have been diagnosed with a developmental or neurological disorder that affects upper limb (UL) movements needed for completing activities of daily living. Gold-standard laboratory assessments of the UL are objective and precise but may not be portable, while clinical assessments can be time-intensive. We developed MoEvGame, a mobile health (mHealth) gamification software system for the iPad, as a potential advanced technology to assess UL motor functions.

Objective: This feasibility study examines whether MoEvGame can assess children’s whole-limb movement, fine motor skills, manual dexterity, and bimanual coordination. The specific aims were to …

A Bibliometric Analysis Of Ai-Driven Healthcare Literature Containing Kos Keywords: Trends, Themes, And Gaps, Julaine Clunis, Eric Asare

STEMPS Faculty Publications

As artificial intelligence (AI) becomes increasingly embedded in healthcare applications, concerns have emerged around the trustworthiness, interpretability, and context-awareness of these systems. Knowledge Organization Systems (KOS) hold considerable potential to address these challenges by supporting semantic standardization, explainability, and domain alignment. This study presents a bibliometric analysis of scholarly publications referencing both AI and healthcare concepts to examine how KOS are positioned within this evolving discourse. The findings indicate that while early literature frequently and explicitly referenced KOS—such as ontologies, controlled vocabularies, and classification systems—their visibility has declined relative to newer paradigms such as machine learning and large language models. …

Understanding Physiological Responses For Intelligent Posture Detection Using Wearable Technology, Chaitanya Vardhini Anumula, Tanvi Banerjee, Anuradha Oak

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

This study investigates the physiological impact of Iyengar yoga at the pose-level using EmbracePlus wearable smartwatch, for data recording and personalized yoga pose detection for tracking.

Generative Ai (Gan & Vae) In Motion Sickness Research, Harigovind Harikumar, Tomojit Ghosh

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

No abstract provided.

A Systematic Study Of Freezing Behavior In Earthworms In Response To Auditory And Vibratory Stimuli, Navjot Singh, A. Burton, Dragana Ivkovich Claflin

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

While earthworms have largely been studied for their role in eliminating toxic metals from soil, less is known about their behavior overall. Previous work from our lab found that a predator-like auditory stimulus (grunting) reliably induced fear-related freezing behavior (Worthen et al., 2024). The present studies further explore earthworm behaviors in response to audio-vibratory stimuli. In Experiment 1, we manipulated amplitude levels and speaker location to examine the parameters needed to reliably induce a freezing fear response to the grunting sound. It was hypothesized that when the speaker was touching the apparatus and producing an added mechanical vibration, there would …

Increasing Hpv Vaccination Adherence In Floyd County, Ky: Give Teens Vaccines, Taylor M. Stumbo

Theses and Dissertations--Public Health (M.P.H. & Dr.P.H.)

Cervical cancer remains a pressing public health issue, particularly in underserved and rural areas where access to preventive care is limited. This capstone addresses the urgent need to increase human papillomavirus (HPV) vaccination adherence in Floyd County, Kentucky—part of the central Appalachian region that experiences disproportionately high cervical cancer incidence and mortality. Despite the proven effectiveness of HPV vaccines in preventing the virus strains most responsible for cervical cancer, uptake in this region remains below national targets due to socioeconomic barriers, limited healthcare access, and lack of awareness. Give Teens Vaccines is a two component intervention leveraging existing healthcare systems …

Motion Artifacts (Ma) At-Rest In Measured Arterial Pulse Signals: Time-Varying Amplitude In Each Harmonic And Non-Flat Harmonic-Ma Coupled Baseline, Md Mahfuzur Rahman, Mamun Hasan, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

Motion artifacts (MA) cause great variability in a measured arterial pulse signal, and treatment of MA solely as a baseline drift (BD) fails to eliminate its effect on the measured signal. This paper presents a study on the effect of MA at rest (< 0.7 Hz) on measured arterial pulse signals using a microfluidic-based tactile sensor. By taking full account of the dynamic behavior of the transmission path from the true pulse signal in an artery to a measured pulse signal at the sensor, the tissue-contact-sensor (TCS) stack, an analytical model of MA in a measured pulse signal is developed. In this model, the TCS stack is treated as a 1DOF system for its dynamic behavior; MA is quantified as the displacement (i.e., BD) and time-varying system parameters (TVSP) of the TCS stack. The mathematical expression of MA in a measured pulse signal reveals that while BD remains as low-frequency additive noise, TVSP causes time-varying harmonics in a measured pulse signal. Further time-frequency analysis (TFA) of measured pulse signals validates the existence of TVSP and, for the first time, reveals its effect on a measured pulse signal: time-varying amplitude in each harmonic and non-flat harmonic-MA-coupled baseline.

Nutritional Management Of The Child With Chronic Kidney Disease And On Dialysis., Vanessa Shaw, Caroline Anderson, An Desloovere, Larry A. Greenbaum, Lyndsay Harshman, Christina L. Nelms, Pearl Pugh, Nonnie Polderman, José Renken-Terhaerdt, Evelien Snauwaert, Stella Stabouli, Jetta Tuokkola, Johan Vande Walle, Bradley A. Warady, Fabio Paglialonga, Rukshana Shroff

Manuscripts, Articles, Book Chapters and Other Papers

While it is widely accepted that the nutritional management of the infant with chronic kidney disease (CKD) is paramount to achieve normal growth and development, nutritional management is also of importance beyond 1 year of age, particularly in toddlers, to support the delayed infantile stage of growth that may extend to 2-3 years of age. Puberty is also a vulnerable period when nutritional needs are higher to support the expected growth spurt. Inadequate nutritional intake throughout childhood can result in failure to achieve full adult height potential, and there is an increased risk for abnormal neurodevelopment. Conversely, the rising prevalence …

Synthetic Data Generation Of Health And Demographic Surveillance Systems Data: A Case Study In A Low- And Middle-Income Country, Dorcas G. Mwigereri, Nigel T. Kamotho, Akbar K. Waljee, Ryan T. Rego, Eileen M. Weinheimer-Haus, Farhana Alarakhiya, Anthony K. Ngugi, W. Nicholson Price, Ji Zhu, Stephen Peter Wong, Geoffrey H. Siwo

Articles

Objective: To evaluate effectiveness of open-source generative models in producing high-quality tabular synthetic data using a Health and Demographic Surveillance System (HDSS) dataset from rural Kenya, as a proof of concept in a low- and middle-income (LMIC) setting.

Materials and Methods: Three open-source models (CTGAN, TableGAN, and CopulaGAN) were used to generate synthetic data from the Kaloleni/ Rabai HDSS dataset. To assess the quality of the synthetic datasets generated by each model, we performed fidelity, utility, and privacy tests.

Results: CTGAN outperformed the other models, producing synthetic data that closely mirrored the statistical properties of the real dataset while preserving …

Peak Teaching: Exploring And Fostering Sacred Experiences In University Classrooms, Charles Burack

International Journal of Transpersonal Studies

Supporting the spiritual growth of college and university students with diverse backgrounds and orientations is both important and challenging. It is important not only because spirituality is a crucial dimension of human life but also because the majority of students at both private and public schools want their teachers to address spiritual issues in the classroom. A powerful way to address spirituality is to explore and foster sacred experiences in university classrooms. Sacred experiences include religious, spiritual, mystical, transcendent, numinous, and peak experiences as well as many other nonordinary experiences. The author offers guidance to educators on how to: 1) …

Schistosomiasis Interventions In Africa: Assessment And Systematic Review, Christopher Yaw Dumevi, George Boateng Kyei, Patience B Tetteh-Quarcoo, James-Paul Kretchy, Irene Ayi, Patrick F Ayeh-Kumi

2020-Current year OA Pubs

No abstract provided.

Qualitative And Quantitative Analysis Of A Novel Dental Health Education Program To Improve Dental Care Utilization By Bhutanese-, Burmese-, And Swahili-Speaking Refugees In A Midwestern United States City, Chandra Swanson, Katherine Gordon, Kelly Kreisler, Elizabeth Montgomery Collins

2020-Current year OA Pubs

No abstract provided.

Adapting Data Science Competencies By Role And Purpose: Voice Ai, David A Dorr, Andrea Krussel, Rachel Hauck, Christie Jackson, Abhijeet Dalal, Steven Bedrick, Philip R O Payne, Bridge2ai-Voice Consortium;, William Hersh

2020-Current year OA Pubs

Competencies help define the skills and knowledge needed by learners. Often broad, educators integrate competencies to provide a framework for curricula or professional standards. For data science, the rate of change in the field, role variations, and specificity in key applications can be challenging. Our objective was to adapt general data science competencies for different learner roles in an emerging area: the clinical utility of Voice, Language, and Speech-based Artificial Intelligence/Machine Learning (AI/ML). Using a persona-inductive approach, we adapted competencies to support learners from varying professional and educational backgrounds and implemented these adaptations in a multi-institutional summer school. Results from …

Applications Of Quantum Computing In Clinical Care, Stevan C Fairburn, Lara Jehi, Brenton T Bicknell, Beckley G Wilkes, Bharat Panuganti

2020-Current year OA Pubs

INTRODUCTION: This review examines quantum computing (QC) applications in clinical care, emphasizing advancements directly impacting patient outcomes. QC holds transformative potential in medicine, particularly through enhancing diagnostic accuracy, optimizing treatment plans, and enabling real-time decision-making.

METHODS: A systematic analysis of 35 studies published between 2015 and 2024 was conducted. The studies were evaluated for their contributions to diagnostic, therapeutic, and decision-support improvements in clinical care enabled by quantum computing technologies.

RESULTS: The analysis revealed QC's promise in improving diagnostic accuracy in medical imaging, optimizing treatments in oncology, and enhancing real-time clinical decision-making. QC-driven algorithms demonstrated potential to enhance diagnostic accuracy …

Zika Virus Encephalitis Causes Transient Reduction Of Functional Cortical Connectivity, Shannon C Agner, Lindsey M Brier, Jeremy D Hill, Ethan Y Liu, Annie Bice, Rachel M Rahn, Shengxuan Chen, Joseph P Culver, Robyn S Klein

2020-Current year OA Pubs

SIGNIFICANCE: Determining the long-term cognitive impact of infections is clinically challenging. Using functional cortical connectivity, we demonstrate that interhemispheric cortical connectivity is decreased in individuals with acute Zika virus (ZIKV) encephalitis. This correlates with decreased presynaptic terminals in the somatosensory cortex. During recovery from ZIKV infection, presynaptic terminals recover, which is associated with recovered interhemispheric connectivity. This supports the contribution of synapses in the cortex to functional networks in the brain, which can be detected by widefield optical imaging. Although myeloid cell and astrocyte numbers are still increased during recovery, RNA transcription of multiple proinflammatory cytokines that increase during acute …

Development Of An Ecg-Based Deep Learning Model For Pediatric Congenital Heart Disease (Chd) Diagnosis, Annbar Mekouar

Selected Full-Text Master Theses 2021-

Congenital heart disease (CHD) stands as the leading congenital anomaly which affects pediatric populations throughout the world. The effectiveness of treatment depends on both early diagnosis and accurate identification but echocardiography requires manual interpretation which proves time-consuming and inconsistent especially when examining pediatric patients with their distinct cardiac systems. The research aims to create a deep learning-based diagnostic framework which uses ECG data to identify coronary artery disease subtypes in pediatric patients. The model uses high-quality datasets from Dr. Ignacio Lugones to extract R-R intervals and QRS durations through convolutional neural networks (CNNs). The system addresses pediatric-specific challenges while enhancing …

Adverse Childhood Experiences, Depression And Subjective Cognitive Decline By Gender: A Moderated Mediation Analysis, Monique J. Brown, Darlingtina K. Esiaka, Jaya Viswanathan

Behavioral Science Faculty Publications

Studies assessing depression as a mediating factor between adverse childhood experiences (ACEs) and subjective cognitive decline (SCD) are lacking. Therefore, the aims of this study were to: (1) determine the mediating role of depression in the association between ACEs and SCD; and (2) assess the moderating role of gender. Data were obtained from the 2023 Behavioral Risk Factor Surveillance Study (BRFSS) survey (N = 38,600). Crude and adjusted path analyses were used to determine the mediating role of depression between ACEs and SCD. Adjusted analyses controlled for sociodemographic confounders. ACEs were positively associated with depression (B = 0.129, p …

Leveraging Data Science For Resilience: Improving Trauma-Informed Care Practice For Adverse Childhood Experience With Ai & Data Science Application, Mohmmad Arif Shaik

Master's Theses

Adverse Childhood Experiences (ACEs) have long-lasting effects on physical health, mental well-being, education, and socioeconomic outcomes. Resilient Georgia (RG), a statewide initiative, seeks to address ACEs through trauma-informed care and data-driven strategies. However, challenges in data collection, analysis, and tracking set back the effectiveness of these efforts. This study explores the role of data science and interactive visualization tools in improving outcomes for individuals and communities affected by ACEs. A key focus of this research is the development of a data science management application designed to enhance data collection and improve real-time decision-making. The application features interactive dashboards that allow …

The Importance Of Atomic Charges For Predicting Site-Selective Ir-, Ru-, And Rh-Catalyzed C-H Borylations, Shannon M. Stephens, Kyle M. Lambert

Chemistry & Biochemistry Faculty Publications

A supervised machine learning model has been developed that allows for the prediction of site selectivity in late-stage C-H borylations. Model development was accomplished using literature data for the site-selective (≥95%) C-H borylation of 189 unique arene, heteroarene, and aliphatic substrates that feature a total of 971 possible sp² or sp³ C-H borylation sites. The reported experimental data was supplemented with additional chemoinformatic descriptors, computed atomic charges at the C-H borylation sites, and data from parameterization of catalytically active tris-boryl complexes resulting from the combination of seven different Ir-, Ru-, and Rh-based precatalysts with eight different ligands. Of the over …