Medicine and Health Sciences Commons^™

Use Of Machine Learning Models To Predict Microaspiration Measured By Tracheal Pepsin A., Annette Bourgault, Ilana Logvinov, Chang Liu, Rui Xie, Jan Powers Phd, Rn, Ccrn, Mary Lou Sole

Nursing Publications, Posters, & Presentations

BACKGROUND: Enteral feeding intolerance, a common type of gastrointestinal dysfunction leading to underfeeding, is associated with increased mortality. Tracheal pepsin A, an indicator of microaspiration, was found in 39% of patients within 24 hours of enteral feeding. Tracheal pepsin A is a potential biomarker of enteral feeding intolerance.

OBJECTIVE: To identify predictors of microaspiration (tracheal or oral pepsin A). It was hypothesized that variables predicting the presence of tracheal pepsin A might be similar to predictors of enteral feeding intolerance.

METHODS: In this secondary analysis, machine learning models were fit for 283 adults receiving mechanical ventilation who had tracheal and …

Three Wishes Project: Individualizing End Of Life Care, Sarah Seddon Rn

Nursing Publications, Posters, & Presentations

This poster was presented at the 2025 Nursing Research Symposium. •We used the journal article to create tangible ways to individualize the dying process •A binder ways created with the different “wishes” nurses can provide.

Disparities In Maternal Healthcare: Examining The Impact Of Social Determinants Of Health On Provider Care Quality, Rishita Anumukonda

Honors Undergraduate Theses

Poor maternal health outcomes among racially and socioeconomically marginalized populations are a major public health concern in the United States. Effective clinician discussions during prenatal and postpartum care play an essential role in promoting maternal and infant well-being. However, the content of provider-patient discussions in maternal care and how it varies for patients exposed to different social determinants of health has not yet been well explored. This study aims to examine whether healthcare provider discussions about key maternal health topics, including preventative health tips, risk factors, and available resources, differ based on social factors such as income level and race. …

Social Justice In Higher Education: The Forgotten Needs Of Students With Visual Impairments In Bangladesh, Mohammed Mozadded Hossen, Roy K. Chen, Nahal Salimi, Jane L. Nichols

School of Rehabilitation Services & Counseling Faculty Publications

Students with disabilities face myriad barriers and hurdles to success in higher education settings. Institutions in developing nations often lack the necessary resources to provide accessible instruction, and the absence of clearly defined policies further impedes upholding the educational rights of such a special population. The purpose of this study was to examine how undergraduate students with visual impairments in Bangladesh felt about their experiences related to social justice and challenges in learning during the COVID-19 pandemic. A convenience sample of 133 students was recruited from two public universities. The authors developed two instruments, namely, the Social Justice Experiences in …

Educational Case: Disseminated Intravascular Coagulation In A Patient With Cancer, Kripa Ahuja, Richard M. Conran

Department of Biomedical and Translational Sciences Faculty Publications

The following fictional case is intended as a learning tool within the Pathology Competencies for Medical Education (PCME), a set of national standards for teaching pathology. These are divided into three basic competencies: Disease Mechanisms and Processes, Organ System Pathology, and Diagnostic Medicine and Therapeutic Pathology. For additional information, and a full list of learning objectives for all three competencies, see https://www.sciencedirect.com/journal/academic-pathology/about/pathology-competencies-for-medical-education-pcme.

Protein Marker-Dependent Drug Discovery Targeting Breast Cancer Stem Cells, Ashley V. Huang, Yali Kong, Kan Wang, Milton L. Brown, David Mu

Department of Biomedical and Translational Sciences Faculty Publications

Breast cancer is one of the most common cancers globally. Unfortunately, many patients with breast cancer develop resistance to chemotherapy and tumor recurrence, which is primarily driven by breast cancer stem cells (BCSCs). BCSCs behave like stem cells and can self-renew and differentiate into mature tumor cells, enabling the cancer to regrow and metastasize. Key markers like CD44 and aldehyde dehydrogenase-1 (ALDH1), along with pathways like Wingless-related integration site (Wnt), Notch, and Hedgehog, are critical to regulating this stem-like behavior of BCSCs and, thus, are being investigated as targets for various new therapies. This review summarizes marker-dependent strategies for targeting …

Validation Of Nutrition Focus Physical Assessment Via Telehealth, Ann Flickinger Ms, Rd, Ldn, Melissa Faura Rd, Ldn, Shae Duka Mph

Clinical Nutrition Service

No abstract provided.

Synthesising Cross-Speaker Data For Low-Resource Pathological Speech Recognition With Peft, Kesego Mokgosi, Milad Dadgar, Cathy Ennis, Robert Ross

Conference papers

Dysarthric speech recognition is essential for enhancing communication and accessibility for individuals with speech impairments, yet its development is hindered by a scarcity of robust, speaker-specific datasets. This study explores low-resource dysarthric speech recognition through cross-speaker transfer using synthetic data and parameter-efficient fine-tuning (PEFT). We integrate SpeechT5 text-to-speech (TTS) synthesis with x-vector speaker embeddings to generate speaker-specific dysarthric speech, enabling model adaptation while preserving pathological speech characteristics such as prosodic irregularities. Experiments on the TORGO dataset show that mixed cross-synthetic data with LoRA fine-tuning achieves a WER of 0.17, representing a 71.7% improvement over the standard model (0.60 WER) without …

Effects Of Interventions For The Prevention And Management Of Maternal Anemia In The Advent Of The Covid-19 Pandemic: Systematic Review And Meta-Analysis, John Kyalo Muthuka, Diana Kageni Mbari-Fondo, Francis Muchiri Wambura, Kelly Oluoch, Japheth Mativo Nzioki, Everlyn Musanga Nyamar, Rosemary Nabaweesi

Faculty Publications

Background:The COVID-19 pandemic presented many unknowns for pregnant women, with anemia potentially worsening pregnancy outcomes due to multiple factors.

Objective:This review aimed to determine the pooled effect of maternal anemia interventions and associated factors during the pandemic.

Methods:Eligible studies were observational and included reproductive-age women receiving anemia-related interventions during the COVID-19 pandemic. Exclusion criteria comprised non-English publications, reviews, editorials, case reports, studies with insufficient data, sample sizes below 50, and those lacking DOIs. A systematic search of PubMed, Scopus, Embase, Web of Science, and Google Scholar identified articles published between December 2019 and August 2022. Risk of bias was evaluated …

Delivery Of Medical Supplies To Remote Locations Via Unmanned Aerial Vehicles: Approaches, Challenges, And Solutions, Meshari Aljohani, Ravi Mukkamala, Stephanie Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming more important in improving healthcare logistics, in particular due to their cost effectiveness, minimized risk, and versatile operational capabilities. This study explores the deployment of autonomous UAVs to deliver medical supplies to remote areas. Advances in ledger technology, smart contracts, and machine learning have transformed tasks previously managed by human teams or manually controlled UAVs into fully autonomous missions. We present a comprehensive analysis of the challenges and initial solutions vital for the effective use of autonomous UAVs in the delivery of medical supplies. In addition, we propose a machine-learning model to optimize UAV …

Lrtm Left-Right Transition Matrices For Molecular Interaction Prediction, Kaitlin Zheng, Guihua Duan, Mengyun Yang, Wei Wu, Yao-Hang Li, Jianxin Wang

Computer Science Faculty Publications

Molecular interactions are central to most biological processes. The discovery and identification of potential associations between molecules can provide insights into biological exploration, diagnostic and therapeutic interventions, and drug development. So far many relevant computational methods have been proposed, but most of them are usually limited to specific domains and rely on complex preprocessing procedures, which restricts the models’ ability to be applied to other tasks. Therefore, it remains a challenge to explore a generalized approach to accurately predicting potential associations. In this study, We propose Left-Right Transition Matrices (LRTM) for molecular interaction prediction. From the perspective on the diffusion …

Heterogeneous Clustering Of Multiomics Data For Breast Cancer Subgroup Classification And Detection, Joseph Pateras, Musaddiq Lodi, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation–maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene …

Gramseq-Dta: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information, Kasul Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance …

A Data-Driven Sliding-Window Pairwise Comparative Approach For The Estimation Of Transmission Fitness Of Sars-Cov-2 Variants And The Construction Of The Evolution Fitness Landscape, Md Jubair Pantho, Richard Annan, Landen Alexander Bauder, Sophia Huang, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Estimating the transmission fitness of SARS-CoV-2 variants and understanding their evolutionary fitness trends are important for epidemiological forecasting. Existing methods are often constrained by their parametric natures and do not satisfactorily align with the observations during COVID-19. Here, we introduce a sliding-window data-driven pairwise comparison method, the differential population growth rate (DPGR) that uses viral strains as internal controls to mitigate sampling biases. DPGR is applicable in time windows in which the logarithmic ratio of two variant subpopulations is approximately linear. We apply DPGR to genomic surveillance data and focus on variants of concern (VOCs) in multiple countries and regions. …

Cath-Ddg: Towards Robust Mutation Effect Prediction On Protein-Protein Interactions Out Of Cath Homologous Superfamily, Guanglei Yu, Xuehua Bi, Teng Ma, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Protein-protein interactions (PPIs) are fundamental aspects in understanding biological processes. Accurately predicting the effects of mutations on PPIs remains a critical requirement for drug design and disease mechanistic studies. Recently, deep learning models using protein 3D structures have become predominant for predicting mutation effects. However, significant challenges remain in practical applications, in part due to the considerable disparity in generalization capabilities between easy and hard mutations. Specifically, a hard mutation is defined as one with its maximum TM-score < 0.6 when compared to the training set. Additionally, compared to physics-based approaches, deep learning models may overestimate performance due to potential data leakage.

Results: We propose new training/test splits that mitigate data leakage according to the CATH homologous superfamily. Under the constraints of physical …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

Multi-Modal Mri Based Segmentation Of Brain Metastases Using Adaptive Self-Attention, Evan Savaria

Computer Science Faculty Publications

Brain metastases (BMs) are the most common adult central nervous system malignancy, affecting 20–40% of cancer patients. Accurate segmentation of metastatic lesions in multi-modal MRI is essential for treatment planning and prognosis however, manual delineation is time consuming and prone to variability. Traditional deep learning models such as U-Net, have improved segmentation accuracy but capture limited long-range dependencies and struggle with variations in metastasis size, shape, and distribution. This study introduces the Adaptive Integrated Multi-modal Segmentation (AIMS) model, an adaptive self-attention framework within a hybrid U-Net and Transformer architecture to enhance BM segmentation by leveraging multi-modal MRI integration. The proposed …

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

Optiselect And Enshap: Integrating Machine Learning And Game Theory For Ischemic Stroke Prediction, Pritam Chakraborty, Anjan Bandyopadhyay, Sricheta Parul, Sujata Swain, Partha Sarathy Banerjee, Tapas Si, Hong Qin, Saurav Mallik

Computer Science Faculty Publications

Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing …

A Survey On Deep Learning For Drug-Target Binding Prediction: Models, Benchmarks, Evaluation, And Case Studies, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug–target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug …

Benchmarking And Improving Foundation Model Dietary Estimates From Meal Images, Yongcheng Mu, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Accurate quantifying dietary contents, such as calories, proteins, carbohydrates, and fats, from an image of a meal plate is vital for managing diabetes. Recently, Large Multimodal Models (LMMs) have excelled in complex vision-language tasks due to their use of very large, highly diverse data. This study benchmarked the use of seven LMMs that include full and lightweight models of GPT, Gemini, and Llama for nutrition estimation based on Google's Nutrition5k dataset and our own phone-collected DonateAndLearn dataset. We analyzed the performance of LMMs and the RGB-D fusion model, in which the RGB-D model was specifically trained using Nutrition5k data. On …

Deepssetracer 2.0: Improved Deep Learning Model Performance For Protein Secondary Structure Segmentation From Cryo-Em Maps, Bryan Hawickhorst, Thu Nguyen, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

DeepSSETracer is a method for segmenting protein secondary structure from medium-resolution (5-10Å) cryogenic electron microscopy (cryo-EM) density maps. We conducted experiments and ablation studies to examine the effects of normalization methods, max-pooling, activation functions, and loss calculation region on DeepSSETracer. By combining multiple technical improvements, the performance of the new version, DeepSSETracer 2.0, was significantly enhanced compared to DeepSSETracer 1.1. On a set of 77 test cases, the weighted average per-voxel F1 score increased from 62.1% to 70.3% for helix detection, and from 47.8% to 62.5% for β-sheet detection. While each of the five modifications in the network enhanced the …

Icu-Length Of Stay Prediction On Electronic Health Records Using Graph Neural Networks And Homogeneous Similarity Graphs, Ahmad F. Al Musawi, Pratip Rana, Sibtanu Raha, Joshua Braunstein, William C. Sleeman Iv, Rishabh Kapoor, Preetam Ghosh

Computer Science Faculty Publications

Predicting the length of stay (LoS) is important for hospital administration, as it helps allocate proper resources, such as bed management and hospital staffing. Patients' Electronic Health Records (EHRs) contain highly relevant data for LoS prediction; however, their integration and effective use in predictive modeling for accurately estimating LoS remain challenging. To address this, we propose a homogeneous Graph Neural Network (GNN)-based framework for predicting LoS. This method employs a comprehensive data fusion strategy based on the hospital Visit-based Similarity Graph (VSG), which integrates diverse multi-modal clinical features into a coherent, homogeneous graph representation. Next, this VSG is fed into …

She Speaks: Amplifying Women's Voices For Wellbeing, Narelle Lemon

Reports accepted as research outputs

Executive Summary

The SheSpeaks project successfully transformed how 223 Western Australian women understand and practice self-care, demonstrating  that targeted wellbeing literacy interventions can create lasting change  in women’s relationship with their own wellbeing. Funded by the Women’s Grants for a Stronger Future program, this innovative initiative combined evidence-based workshops with authentic podcasting to challenge the pervasive cultural narrative that positions women’s self-care as selfish  or impossible.

Self-Awareness And Self-Efficacy In The Novice Professional Phase Of Marriage And Family Therapists After Person Of The Therapist Training, A Comparative Survey, Rubio Betsabé

Dissertations

The study focuses on examining the influence of person-of-the-therapist or self-of-the-therapist training on therapists’ self-reports of self-awareness (as a conscious awareness of one’s internal states and interactions with others) and self-efficacy (perceived beliefs in personal capabilities to perform in ways that give control over events that affect their lives) at the novice professional stage of their career. The study will examine the experiences reported by mental health providers actively providing clinical care by utilizing a survey design focusing on person of the therapist connecting through alumni lists and online solicitation. This research adds to the current literature by using a …

What Works In Telehealth Play Therapy? An Appreciative Heuristic Inquiry, Veronica Ann Ball

Dissertations

With the onset of coronavirus disease 2019 (COVID-19) in the United States in March 2020 (World Health Organization, 2020), professional counselors providing traditional in-person play therapy found themselves in a quandary regarding the delivery of services during a global pandemic (Evans, 2022; Mitchell, 2020; Whaibeh et al., 2020). Online counseling proffered a means to prevent the disruption of services, respond to the need for emerging counseling services, and comply with social restrictions (Evans, 2022). Prior to the pandemic, telehealth play therapy was used sparingly (Haslam & Harris, 2011; Smith et al., 2021), and many child-serving mental health professionals lacked training …

What About The Children? An Exploration Of Family Counselors' Play Therapy Training And Preparedness To Integrate Children Into Family Counseling, Patricia J. Walker Monical

Dissertations

Children are an important part of families, yet family counselor education programs often do not incorporate training for including children in family counseling. At some point in their careers, family counselors and therapists will encounter clients who have children 3–12 years old. The problem is that since these professionals often receive little to no training in play therapy and thus do not typically include young children in family therapy, these younger family members are often overlooked. Research has confirmed that, due to a lack of sufficient training and supervision in play therapy, many family therapists report feeling unprepared to work …

Defining Spine Cancer Pain Syndromes: A Systematic Review And Proposed Terminology, Markian Pahuta, Ilya Laufer, Sheng-Fu Larry Lo, Stefano Boriani, Charles Fisher, Nicolas Dea, Michael H Weber, Dean Chou, Arjun Sahgal, Laurence Rhines, Jeremy Reynolds, Aron Lazary, Alessandro Gasbarrinni, Jorrit-Jan Verlaan, Ziya Gokaslan, Chetan Bettegowda, Mohamed Sarraj, Ori Barzilai, Ao Spine Knowledge Forum Tumor

Faculty, Staff and Student Publications

STUDY DESIGN: Systematic Review.

OBJECTIVES: Formalized terminology for pain experienced by spine cancer patients is lacking. The common descriptors of spine cancer pain as mechanical or non-mechanical is not exhaustive. Misdiagnosed spinal pain may lead to ineffective treatment recommendations for cancer patients.

METHODS: We conducted a systematic review of pain terminology that may be relevant to spinal oncology patients. We provide a comprehensive and unbiased summary of the existing evidence, not limited to the spine surgery literature, and subsequently consolidate these data into a practical, clinically relevant nomenclature for spine oncologists.

RESULTS: Our literature search identified 3515 unique citations. Through …

The Influence Of The Microbiome On Radiotherapy And Dna Damage Responses, Aadil Sheikh, Michael A Curran

Faculty, Staff and Student Publications

Colorectal cancer (CRC) is one of the most prevalent cancers in terms of diagnosis and mortality. Radiotherapy (RT) remains a mainstay of CRC therapy. As RT relies on DNA damage to promote tumor cell death, the activity of cellular DNA damage repair pathways can modulate cancer sensitivity to therapy. The gut microbiome has been shown to influence intestinal health and is independently associated with CRC development, treatment responses and outcomes. The microbiome can also modulate responses to CRC RT through various mechanisms such as community structure, toxins and metabolites. In this review we explore the use of RT in the …

Patient-Reported Outcomes During Pelvic Radiation Therapy: A Secondary Analysis On Sexual Function From Nrg-Rtog 1203, Kelsey L Corrigan, Rebecca Paulus, Ann H Klopp, Lari B Wenzel, Anamaria R Yeung, J Spencer Thompson, Desiree E Doncals, Vijayananda Kundapur, Nancy H Wiggers, Dasarahally S Mohan, Sharad A Ghamande, Shannon N Westin, Kara L Schnarr, Michael L Haas, David K Gaffney, Steven E Waggoner, Pamela J Vanderwall, Noha T Jastaniyah, Stephanie L Pugh, Lisa A Kachnic

Faculty, Staff and Student Publications

Purpose: NRG-RTOG 1203 reported that intensity-modulated radiation therapy (IMRT) reduced patient-reported GI toxicities in patients with cervical/endometrial cancer receiving postoperative RT, compared with 3-dimensional conformal radiation therapy (3DRT). We conducted a secondary analysis of patient-reported sexual function (PR-SF) among treatment groups to identify factors associated with sexual dysfunction.

Methods and materials: Patients on NRG-RTOG 1203 were randomly assigned to 3DRT versus IMRT and completed Patient-Reported Outcomes (PRO)-Common Terminology Criteria for Adverse Events (CTCAE) and FACT-Cx surveys at baseline, week 5 of RT, and at 4-6 weeks, 1 year, and 3 years after RT. Patient responses to FACT-Cx sexual function questions …