Biomedical Informatics Commons^™

Agentic Scientific Machine Learning For Autonomous Model Discovery In Systems Pharmacology, Nazanin Ahmadi, George Karniadakis

Biology and Medicine Through Mathematics Conference

No abstract provided.

Re-Purposing Pre-Existing Tilapia Aquaculture Open-Design System For Zebrafish Biomedical Research Purposes, Luke Cantu, David Sierra, Skyleigh Curtis, Maya Avila

ATU Scholars Symposium

Title: Re-purposing pre-existing tilapia open-design aquaculture systems for zebrafish biomedical research purposes.

Background: Zebrafish (Danio rerio) models are gaining popularity in biomedical research; however, the high infrastructure cost, at approximately $20,000 per unit, is a significant hurdle for primary undergraduate institutions (PUIs) establishing zebrafish facilities. This study assesses if repurposing existing tilapia open-design aquaculture systems could be a cost-effective method for zebrafish facility establishment.

Methods: Between November-2025 to February-2026, the study used a pre-existing tilapia aquaculture open-design system with personnel modified outlet filters. The water quality of this system was monitored and recorded. The study compared the stabilization phase or …

Validating Pharmacogenomics Generative Artificial Intelligence Query Prompts Using Retrieval-Augmented Generation (Rag), Ashley Rector, Beth Breeden, Jay Dorris

Student Scholar Symposium

This study evaluated the performance of Sherpa Rx, an artificial intelligence platform leveraging large language models and retrieval-augmented generation (RAG) for pharmacogenomics, by validating its performance across key response metrics. Sherpa Rx integrated Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines with Pharmacogenomics Knowledgebase (PharmGKB) data to generate contextually relevant responses. A dataset (N=260 queries) spanning 26 CPIC guidelines was used to evaluate drug-gene interactions, dosing recommendations, and therapeutic implications. In Phase 1, only CPIC data was embedded; Phase 2 additionally incorporated PharmGKB. Responses were scored on accuracy, relevance, clarity, completeness (5-point Likert scale), and recall. Wilcoxon signed-rank tests compared accuracy between …

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.

Machine Learning Quantification Of High-Resolution Tissue Microarray (Tma) Image On Muc13 Ihc Analysis, Beibei Huang, Aiko Yamaguchi, Jianbo Wang, Shilpa Sharma, Zhiwen Liu, Henry Charles Manning

Research Symposium

Background High-resolution tissue microarray (TMA) technology allows prompt molecular profiling of multiple tissue specimens, making it ideal for analyzing candidate biomarkers quickly and effectively [1, 2]. Integrating TMA with digital pathology and machine learning enhances high-throughput, cost-effective studies, offering advanced image analysis and improved diagnostic accuracy. MUC13 (Mucin 13) is a transmembrane glycoprotein frequently overexpressed in colorectal cancer (CRC) [3]. MUC13 contributes to colonic tumorigenesis, progression and metastasis [4, 5], making it an attractive target for antibody-guided radiotheranostics in CRC. This study investigates the expression pattern of MUC13 and its association with patients' clinical characteristics in primary and metastatic CRC …

Gene Co-Expression Networks And Descriptive Statistical Patterns In Cancer Subtypes, Arely Solis, Marzieh Ayati

Research Symposium

There are many cancers that are affecting the human population, with some being more common and studied than others. These cancers have mostly been studied individually until 2012 when scientists began studying through comparison analysis of different cancers to see possible connections on the genomic and molecular level and have resulted in the categorization of tumors into types. The result from molecular analysis has re-classified types of tumors into new clusters, which aid doctors in deciding the optimal way of treating tumors. In this study, we analyze a dataset comprising over 2,000 cancer samples, focusing on six cancer types: breast, …

Investigating Fetalgro Ex As An Alternative To Fetal Bovine Serum In Supplementing Cell Culture Growth, Porter Fife

Annual Research Symposium

No abstract provided.

Machine Learning Approaches For Predicting Dental Caries In Permanent Molars Of Children And Adolescents Using Nhanes 2011-16 Data, Pritam Deb, Christina Scherrer Phd, Lin Li Phd

Symposium of Student Scholars

Dental caries remains a prevalent chronic disease among children, significantly affecting their quality of life, educational outcomes, and school attendance. Between 2011 and 2016, caries affected 17.4% of children aged 6-11 and 56.8% of adolescents aged 12-19, with higher incidences among non-Hispanic Black and Mexican American youth, and those from lower-income families. This study aims to develop a robust machine learning model to predict the presence of decayed, missing, or filled permanent molars (DMFT) in children and adolescents using demographic, dietary, and oral health examination data from the National Health and Nutrition Examination Survey (NHANES) for the years 2011 to …

Genomics Insights Into Anti-Platelets Therapy: Unraveling Of Variants And The Implementation Of Clopidogrel-Guided Therapy In The United Arab Emirates, Lubna Qasem Khasawneh

Thesis/ Dissertation Defenses

Anti-platelet therapy is a cornerstone in the management of cardiovascular diseases such as acute coronary syndrome (ACS) and stroke. These medications are essential for reducing the risk of thrombotic events by preventing platelet aggregation. However, variations in cardiovascular pharmacogenes can significantly impact the metabolism, efficacy, and safety of anti-platelet therapies, making personalized medicine increasingly important for optimizing treatment outcomes. Pharmacogenomic testing has emerged as a crucial tool in tailoring anti-platelet therapy, particularly for patients with subtherapeutic responses due to genetic factors. While extensive research has been conducted globally to identify and study the effect of the pharmacogenomic variants that influence …

Investigation Of The Utility Of A Ptsd Coaching Mobile App To Address Mental Health Services Demand In A Primary Care Clinic: Analysis Of Caps-5 Measures, Sindhura Nemani, Danielle Rae Schweitzer, Anne C. Jones

Rowan-Virtua Research Day

The COVID-19 pandemic affected countless people globally, resulting in a greater need for mental health professionals and resources¹. The demand for mental health care is soaring yet the limitation of resources has strained the healthcare system, making it challenging to help patients in a timely manner². This poster represents a snapshot of a larger project that is striving to investigate an intervention to help bridge the gap between patient need and prompt referral through a warm handoff approach and use of the PTSD Coach mobile application as well as exploring patients’ satisfaction^3,4,5,6,8,9. In this …

Unveiling The Potential: The Role Of Ai-Enhanced Ecg In Cardiovascular Disease Detection, Alisha Vincent

Rowan-Virtua Research Day

Background: The Electrocardiogram (ECG) is a widely utilized, non-invasive, cost-effective cardiac test. Its integration with Artificial Intelligence (AI) has empowered it to become a potent screening tool and a predictor for various cardiovascular diseases, especially in asymptomatic individuals. Objective: This review investigates the utility of AI-powered ECG in early detection of cardiac conditions, focusing on conditions such as low ejection fraction (LEF), atrial fibrillation (AF), aortic valve stenosis (AVS), and cardiac amyloidosis (CA). Methods: A literature review spanning 2018 to 2024 was conducted, analyzing 10 articles - 3 on AF, 3 on AVS, 3 on LEF, and …

Clinical Outcomes For Impella Patients Associated With Hyperlipidemia: An Analysis Of The National Inpatient Sample, Tony Elias, Sonika Vatsa, Kyrillos Girgis, Taha Syed, Rafail Beshai

Rowan-Virtua Research Day

The Impella, a ventricular assist device, is crucial for managing severe heart failure and cardiogenic shock. Despite its widespread use, there's scant information on how hyperlipidemia affects Impella patients. To address this gap, we delved into the National Inpatient Sample Database from 2019 and 2020. Our aim was to scrutinize in-hospital outcomes among these patients. We identified 8233 Impella patients, among whom 1012 (12.3%) had hyperlipidemia. Those with hyperlipidemia displayed higher rates of hypertension, diabetes mellitus, and chronic kidney disease compared to their counterparts without hyperlipidemia. Shockingly, in-hospital mortality was notably elevated in the hyperlipidemia group, emphasizing its clinical significance. …

Annotation Of Hypothetical Genes In Lactococcus Lactis Ssp. Il403, Jennifer A. Tangires

Student Scholar Showcase

The human gastrointestinal tract (GIT) harnesses various microbial organisms involved in almost all processes of physiological homeostasis, among these are lactic acid bacteria (LAB). These bacteria, almost all of which belong to the order Lactobacillales, are able to produce lactic acid, and play an important role in food preservation because they produce bacteriocins. Bacteriocins are antimicrobial proteins that are used to fight off related bacteria in their environment that are competing for the same resources. This study focuses on a specific LAB strain, Lactococcus lactis ssp. IL1403 where 21.9% of its predicted genes have not yet been assigned a function. …

Hgs-3 The Influence Of A Tandem Cycling Program In The Community On Physical And Functional Health, Therapeutic Bonds, And Quality Of Life For Individuals And Care Partners Coping With Parkinson’S Disease, Leila Djerdjour, Jennifer L. Trilk

SC Upstate Research Symposium

Purpose Statement: Several studies have shown that aerobic exercise can have a positive impact on alleviating symptoms experienced by individuals with Parkinson's disease (PD). Despite this evidence, the potential benefits of exercise for both PD patients and their care partners (PD dyad) remain unexplored. This research project investigates the effectiveness, therapeutic collaborations, and physical outcomes of a virtual reality (VR) tandem cycling program specifically designed for PD dyads.

Methods: Following approval from the Prisma Health Institutional Review Board, individuals with PD were identified and screened by clinical neurologists. The pre-testing measures for PD dyads (N=9) included emotional and cognitive status …

Optimization Of Gait Analysis System For Clinical Applications, Gerardo Medellin, Katherine S. Bolado, Daniel Salinas, Kelsey Potter-Baker

Research Symposium

Background: Adequate gait function is pivotal for many activities of daily living and high quality of life. Following many neurodegenerative diseases, such as Parkinson’s Disease, gait abnormalities can manifest and range from reduced stride length, inability to turn, foot drop or shuffling. To track and monitor such changes in gait, gait analysis techniques are gaining clinical popularity and have the ability to gather a range of data in a short duration. Gait analysis techniques go beyond simple visual observation and include instrumental gait analysis and weight distribution of the gait cycle. Here, we sought to optimize the gait analysis …

Exploring The Impact Of Trusted Patient-Physician Relationship And Effective Health Communication On Patient Empowerment, Mingming Song

UNO Student Research and Creative Activity Fair

Abstract

Empowered patients actively participate in their healthcare decision making, clearly understanding their health conditions, and effectively managing their health care plan. Traditionally, health education has served as the primary method for educating and empowering patients within the healthcare services model, including the care provided by physicians....

Sepsis Treatment: Reinforced Sequential Decision-Making For Saving Lives, Dipesh Tamboli, Jiayu Chen, Kiran Pranesh Jotheeswaran, Denny Yu, Vaneet Aggarwal

Graduate Industrial Research Symposium

Sepsis, a life-threatening condition triggered by the body's exaggerated response to infection, demands urgent intervention to prevent severe complications. Existing machine learning methods for managing sepsis struggle in offline scenarios, exhibiting suboptimal performance with survival rates below 50%. Our project introduces the "PosNegDM: Reinforcement Learning with Positive and Negative Demonstrations for Sequential Decision-Making" framework utilizing an innovative transformer-based model and a feedback reinforcer to replicate expert actions while considering individual patient characteristics. A mortality classifier with 96.7% accuracy guides treatment decisions towards positive outcomes. The PosNegDM framework significantly improves patient survival, saving 97.39% of patients and outperforming established machine learning …

Assessing Gait Metrics For Early Parkinson's Disease Prediction: A Preliminary Analysis Of Underfit Models, Daniel Salinas, Gerardo Medellin, Katherine Bolado, Tomas Gomez, Kelsey Potter-Baker, Nawaz Khan Abdul Hack, Ramu Vadukapuram

Research Symposium

Background: Parkinson's Disease (PD) is characterized by both motor and non-motor symptoms, and its diagnosis primarily relies on clinical presentation. There is a growing need for diagnostic tools to identify the early signs of PD, particularly the initial motor impairments often manifested as gait abnormalities. Here we seek to present preliminary findings to address this need. Our study focuses on using Machine Learning techniques (ML) to predict the PD clinical stage most efficiently and accurately. Specifically, we have sought to evaluate how spatiotemporal characteristics and other locomotor performance variables obtained on a walkway system can be utilized to identify the …

Molecular Diagnostics - Biomarker Based Diagnosis Of Human Papillomavirus (Hpv), Lilly Hivner

Harrisburg University Research Symposium: Highlighting Research, Innovation, & Creativity

Research on how HPV-16 E6 identifies cervical cancer more often than others.

Cardiovascular Disease Prediction Modelling: A Machine Learning Approach, Usmaan Al-Shehab, Maduka Gunasinghe, Yousuf Elkhoga, Nimay Patel, Juliana Yang

Rowan-Virtua Research Day

The objective of this project is to utilize the UCI Heart Disease dataset to identify physiological biomarkers that are highly correlated with heart disease incidence. A predictive model can then be developed using these biomarkers to estimate the likelihood of someone having or developing a heart-related condition. This study compares the efficacy of predicting cardiovascular disease as an outcome using three machine learning algorithms: Support Vector Machine, Gaussian Naive Bayes, and logistic regression. Support Vector Machine works by creating hyperplanes between data points to conduct classification. Gaussian Naive Bayes works by using the conditional probabilities of events to classify the …

2d Respiratory Sound Analysis To Detect Lung Abnormalities, Rafia Sharmin Alice, Kc Santosh

SDSU Data Science Symposium

Abstract. In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. The primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep Learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients …

Gene Therapy And Biomedical Applications, Sarah Dundore, Leena Pattarkine

Harrisburg University Research Symposium: Highlighting Research, Innovation, & Creativity

Gene therapy has many uses today through the new treatment options and its potential to treat uncurable diseases. (Class Project)

Sustainability In Biomedical Engineering, Kyle Borror, Christopher Hartemink

Summer Research

Boston Scientific (BSC) is a biomedical engineering company that specializes in designing and manufacturing devices that are used to help diagnose or treat health conditions in the human body. Primarily based in Europe and North America, BSC has recently been faced with the need to adhere to new sustainability protocols. Many of these protocols are addressed through a product accounting system called a Life Cycle Analysis (LCA), which is a standardized method for analyzing a product life cycle. A product life cycle for a medical device typically includes material extraction and refinement, component manufacturing, assembly, storage and distribution, use, and …

Behavioral Analysis Of Zebrafish (Danio Rerio) As A Model For Bjornstad Syndrome, Luke Schellenberg, Amy Wilstermann, Rachael Baker

Summer Research

Bjornstad Syndrome

• Autosomal recessive disease caused by single mutations in the BCS1L gene
• Characterized by sensorineural hearing loss and pili torti (brittle hair susceptible to falling out)

Zebrafish as a Model

• 70% of the same genes as humans, 84% of the same genes associated with human disease
• High fecundity and quick development