Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

Recent Insight And Perspective Of Marine-Inspired Biopolymers For Wound Healing Applications - A Review, Muhammad Umar Aslam Khan, Saiqa Yousaf, Abdalla Abdal-Hay, Mohd Faizal Bin Abdullah, Sahar Madani, Muhammad Shahzad Zafar, Goran M. Stojanović, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

There is an increasing necessity for advanced, sustainable, and biocompatible materials for wound healing as therapeutic and diagnostic products. Marine environments, characterized by high biodiversity, offer an underutilized source of natural resources with enormous potential for creating novel materials for dressings. This review highlights the revolutionary nature of polymeric biomaterials of marine origin, with a focus on polysaccharides, like alginate, chitosan, and carrageenan; proteins, such as collagen and gelatin. These biopolymers are outstanding in their physicochemical properties, such as biodegradability, bioactivity, and modifiable mechanical strength, which enable their use in wound-healing systems. Besides, these biomaterials may be easily chemically and …

Pin-Plane Electrical Discharge Driven By A Mosfet Dc Current Source, Myles Perry, Sidmar Holoman, Daniel Wozniak, Shirshak Kumar Dhali

Electrical & Computer Engineering Faculty Publications

The generation of atmospheric pressure nonequilibrium plasma using electrical discharges is an active area of research due to its significance in a wide spectrum of applications including medicine, combustion, and manufacturing. In our attempt to create a helium plasma jet in a pin-plane discharge with a constant current source, we observed self-pulsating behavior. We present the results of the electrical, optical, and spectroscopic measurements carried out to characterize the discharge. The duration of the discharge is a few tens of nanoseconds, and the repetition rate is in the few tens of kHz. The effect of the gap distance and gas …

Multimodal Machine Learning For Alzheimer's Disease Classification Using Adni Biomarker Fusion, Hesameddin Mostaghimi, Hamid R. Okhravi, Bahar Niknejad, Daniel A. Cohen, Michel A. Audette, Alzheimer's Disease Neuroimaging Initiative

Electrical & Computer Engineering Faculty Publications

Despite ongoing advances, accurate diagnosis of Alzheimer’s disease (AD) remains challenging due to its multifactorial nature, comorbidities, and clinical heterogeneity. Accordingly, approaches that combine multimodal data may improve AD classification by integrating complementary information. To investigate this, we evaluated classification performance using a preprocessed ADNI-3 dataset comprising a shared set of clinical/cognitive features along with four imaging modality-based cohorts: trimodal (MRI + amyloid PET + tau PET), MRI + amyloid PET, MRI + tau PET, and MRI-only. We trained a range of supervised machine learning (ML) and deep learning (DL) classifiers using stratified five-fold cross-validation and evaluated performance using accuracy, …

A Mass Cytometry-Based Blood Cell Phenotyping Workflow Enabling Inclusion Of Resource-Limited And Rural Sites In Immune System Studies, Natalie J. Smith, Michael Cohen, Lauren Tracey, Julie Alipaz, Christina Loh, David King, Neha Pulyani, Rebecca Auzins, Elin Gray, Sandra Taylor, Rajat Rai, Steven Kao, Barbara Fazekas De St Groth, Helen M. Mcguire

Research outputs 2022 to 2026

Insight into disease detection and treatment through comprehensive immune phenotyping relies on the generation of high-quality data. However, the execution of robust immune monitoring in clinical trials by flow cytometry is complex due to logistical challenges in sample preparation and reagent stability. These challenges are exacerbated when considering remote and rural communities in Australia, which are burdened by both an increased prevalence and a worse prognosis of many chronic and infectious pathologies. This is not unique to the Australian context as globally remote and rural communities are underrepresented in biomedical research studies, contributing to persistent health inequities. To address these …

The Crucial Role Of Machine Learning Models In Predicting Current Childhood Asthma: Model Comparison, Calibration, And Shap-Based Interpretation, Aditya Chakraborty, A. K.M. Raquibul Bashar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Asthma is one of the most prominent chronic diseases in children and one of the most challenging ailments to diagnose in infants and preschoolers in the United States. Predictive models can be instrumental in improving early diagnosis, personalized treatment strategies, and disease progression. By utilizing nationalized data, this study focuses on building and comparing high-performing analytical predictive models based on the relevant risk factors and identifying the most influential predictors.

Methods: We analyzed cross-sectional BRFSS Asthma Call-Back Survey data (2011-2020; N = 9,813) and randomly split participants into training and testing sets. An XGBoost model (hyperparameters tuned via grid …

Microarray Analysis Of Human Abdominal Aortic Aneurysm With Emphasis On Cardiovascular Genes Revealed Differentially Expressed Genes, Song Lu, Li Ping Li, John V. White, Xiaoying Zhang, Ifeyinwa Nwaneshiudu, Adaobi Nwaneshiudu, Nectaria Ntaoula, John Gaughan, Dimitri S. Monos, Wan-Lu Lin, Charalambos C. Solomides, Emilia L. Oleszak, Chris D. Platsoucas

Biological Sciences Faculty Publications

Background/Aim: We examined gene expression profiles in abdominal aortic aneurysm (AAA) lesions vs. normal aortas by cDNA microarray and real-time quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR).

Materials and Methods: Phosphorus (32P)-labeled cDNA from AAA specimens (mean AAA size 6.65 cm) and normal aortas were hybridized with a 588-gene microarray primarily of the cardiovascular system. The results were validated by qRT-PCR.

Results: A total of 35 out of the 588 genes were differentially expressed, with either log2 ratio of AAAs/controls ≥1 (upregulated; 20 genes) or ≤−1 (downregulated; 15 genes) in AAA lesions vs. normal aorta, and 25 of these were significantly …

All-Inside Arthroscopic Technique For Repairing Radial Meniscus Tears At The Anterior Horn-Body Junction, Brandon Omari Boyd, Robert M. Walker, Harrison A. Volaski, Ferdinand J. Chan

Department of Medicine Faculty Publications

Radial meniscus tears are a common tear pattern that, if left untreated, can ultimately lead to accelerated degenerative changes and disability. There is well-established evidence that supports meniscal preservation in patients with radial meniscus tears. Various surgical techniques are used to repair radial tears, including inside-out, outside-in, and all-inside methods. Self-retrieving all-inside devices are a safe option when repairing radial tears. However, radial tears at the junction of the anterior horn and body present a unique surgical challenge, as surgeons may have difficulty maneuvering the jaws of the self-retrieving suture device around the anterior limb of the meniscal tear. We …

Maxgrnet: A Multi-Axis Vision Transformer With Improved Generalization For Eye Disease Classification Using Explainable Ai With Insertion-Deletion Operations On Fundus Images, Md Mehedi Hasan Santo, Fuyad Hasan Bhoyan, Fuad Ibne Jashim Farhad, Fahmid Al Farid, Sovon Chakraborty, Md Humaion Kabir Mehedi, Jia Uddin, Hezerul Bin Abdul Karim

Computer Science Faculty Publications

Eye diseases, including diabetic retinopathy (DR), glaucoma, and cataracts, represent a major global health concern and can lead to severe visual impairment or blindness if not identified in a timely manner. This study proposes a novel eye disease classification framework based on a multi-axis vision transformer (MaxViT) applied to color fundus images with Explainable Artificial Intelligence (XAI) techniques to enhance model transparency. The proposed architecture integrates transformer-based attention mechanisms with Global Response Normalization (GRN)-based multi-layer perceptron (MLP) layers to capture complex spatial and contextual relationships within fundus images effectively. The model was evaluated on a publicly available eye disease classification …

Privacy-Preserving Federated Learning With Optimized Ensemble Weighting And Knowledge Distillation For Covid-19 Detection From Non-Iid Medical Imaging Data, Richard Annan, Hong Qin, Robert Newman, Madhuri Siddula, Letu Qingge

Computer Science Faculty Publications

Medical imaging enables rapid and accurate diagnosis of COVID-19, with CT scans proving especially effective. However, data privacy concerns limit collaborative model development across hospitals. To address this issue, we introduce a novel federated learning framework. It is referred to as Independent Knowledge Distillation with post-Ensemble Federated Learning (IKDEFL). Differential Privacy (DP) is integrated into the framework to improve privacy guarantees. Three DP mechanisms are evaluated. These include Fixed Gaussian, Gaussian Adaptive, and Tree Adaptive. The evaluation has been conducted on heterogeneous and Non-Independent and Identically Distributed (Non-IID) datasets. These datasets reflect real-world hospital scenarios. Results show that IKDEFL significantly …

A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification, Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana

Computer Science Faculty Publications

Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and …

Near Real-Time Adaptive Isotropic And Anisotropic Image-To-Mesh Conversion For Cerebral Aneurysm Simulations, Kevin Garner, Chander Sadasivan, Nikos Chrisochoides

Computer Science Faculty Publications

This paper presents two performance optimization techniques for a mesh adaptation method that is designed to help streamline the discretization of complex vascular geometries within the numerical modeling process. This method is integrated into a pipeline with an image-to-mesh conversion tool to generate adaptive anisotropic meshes from segmented medical images. The pipeline is shown to satisfy quality, fidelity, smoothness, and robustness requirements while providing near real-time performance for medical image-to-mesh conversion. Tested with two brain aneurysm cases and utilizing up to 96 CPU cores within a single, multicore node on Purdue University’s Anvil supercomputer, the parallel adaptive anisotropic meshing method …

Adaptive Self-Attention For Enhanced Segmentation Of Adult Gliomas In Multi-Modal Mri, Evan P. Savaria, Jiangwen Sun

Computer Science Faculty Publications

Every year there are an estimated 80,000–90,000 new glioma cases, highlighting the need for reliable imaging-based decision support. Although deep learning has improved tumor sub-region segmentation, many state-of-the-art models fail to fully capture complementary information across T1, T1Gd, T2, and FLAIR MRI modalities and often operate as “black boxes,” limiting physician trust when precise delineation is critical for surgical planning, radiation targeting, and treatment monitoring. To address these limitations, we propose AIMS, an Adaptive Integrated Multi-Modal Segmentation framework that maintains modality-specific feature streams and employs adaptive self-attention within a hierarchical CNN-Transformer architecture to prioritize and fuse multi-modal MRI features. We …

Serum Protein Signatures For Breast Cancer Detection In Treatment-Naïve African American Women Using Integrated Proteomics And Pattern Analysis, Padma Tadi Uppala, Elmer Rivera, Hyun J. Kwon, Sharon S. Lum

Faculty Publications

Breast cancer is the leading cause of cancer-related mortality in African American (AA) women. In this study we evaluated the serum proteomic profile of AA women with breast cancer using an integrated proteomic framework with multivariate pattern analysis. Using 2D-DIGE, thousands of serum protein spots were detected across 33 gels; 46 spots met criteria for presence, statistical significance, and differential expression. Proteins from the spots were identified by MALDI-TOF/TOF and matched in curated databases, highlighting serum biomarkers including ceruloplasmin, alpha-2-macroglobulin, complement component C3 and C6, alpha-1-antitrypsin, alpha-1B-glycoprotein, alpha-2-HS-glycoprotein and haptoglobin-related protein. LC-MS/MS analysis revealed 163 differentiating peptides after imputing and …

Hyperglycemia Detection From Sigle - Lead Ecg Using A Hybrid Cnn & Transformer Model, Adam Ayomikun Ogunjembola

Graduate Theses, Dissertations, and Problem Reports (ETD)

Abstract
Hyperglycemia Detection from Single-Lead ECG using a Hybrid CNN & Transformer Model
Adam Ogunjembola

Diabetes Mellitus is known as high blood glucose. This high blood glucose level happens when the body has a problem with producing or using insulin. Insulin is a very important hormone that the pancreas makes to control how much glucose gets into the bloodstream and cells. Diabetes Mellitus has an effect on the body if it is not treated, such as damaging the blood vessels and nerves which can lead to stroke, kidney failure, heart attack and permanent loss of vision. Since people with diabetes …

Thermoregulation And Associated Disorders: 3pm-Guided Holistic Approach Bridging Innovative And Traditional Chinese Medicine, Zhuo Wang, Liu, Yueqiang Xu, Weijie Cao, Youxin Wang, Haifeng Hou, Xiuhua Guo, Olga Golubnitschaja, Wei Wang, Yuguang Du, Suboptimal Health Study Consortium, European Association For Predictive, Preventive, Personalised Medicine

Research outputs 2022 to 2026

Accurately performed thermoregulation is life-important for the human body. Therefore, a relatively narrow temperature range of 36.5–37 °C, which all our biochemical reactions are adapted to, is rigorously kept by the body allowing for the most effective kinetics of all physiological processes. In contrast, feeling inappropriately cold or too hot in the environment with comfortable temperature ranges are symptoms of an altered or even disordered thermoregulation described for a number of syndromes as well as patient cohorts. The rationale of the paper is to contribute to the paradigm shift from reactive to proactive healthcare considering thermoregulation deficits as an important …

Advances In Survival Analyses: Machine Learning Methods And Model Comparison, Ryan Gately, Dharshana Sabanayagam, Wai H. Lim, Lin Zhu, Farzaneh Boroumand, Shuvo Bakar, Armando Teixeira-Pinto, Germaine Wong

Research outputs 2022 to 2026

This is the second article in a 2-part series on survival analysis. In part 1, we discussed the core concepts and traditional methods used in survival analysis. Part 2 explores the novel approaches to predict survival outcomes and evaluate model performance. To facilitate hands-on learning and practical implementation, the R code used in these analyses is provided in the supplementary materials, along with instructions to help readers apply these methods to their data.

Bridging The Gap: Why Circulating Micrornas Have Yet To Become Clinical Biomarkers For Endometriosis, Anisha Addepalli, Mary C. Boyes

Undergraduate Research Posters

Endometriosis is a chronic inflammatory disorder in which endometrial-like tissue grows  outside the uterus, leading to pelvic pain, infertility, and diagnostic delays due to the current  reliance on invasive laparoscopy. Although imaging and hormonal assays offer limited accuracy in diagnosing the disease, circulating microRNAs (miRNAs) in arterial blood have emerged as a  promising alternative method as noninvasive biomarkers because they regulate gene expression,  reflect inflammatory and angiogenic pathways, and remain stable in circulation. I propose  investigating miRNAs such as miR-17-5p, miR-451a, and the let-7 family because of their direct  involvement in the regulation of processes such as cell proliferation, immune …

The Importance Of Hippotherapy: Improving The Lives Of The Disabled Through Alternative Therapy, Jennifer N. Stapleton

Undergraduate Research Posters

For centuries, horses have been suggested as “healers” and “helpers,” although it wasn’t until the 1900s that horseback riding was scientifically proven as a treatment and rehabilitation method. The exploitation of hippotherapy, the implementation of the three-dimensional pelvic movement stimulated by the oscillation and gait of the horse to combat symptoms of diseases and disorders, has since become a flourishing field of study, but one that is often overlooked. Benefits of hippotherapy often include increased motor neuron function, facilitating proper anatomical alignment, and reducing spasticity. This research analyzes the diverse and dynamic nature of treatment that hippotherapy facilitates, exploring the …

The Myopia Epidemic: Integrating Genetic, Environmental, And Epigenetic Pathways To Define A Critical Window For Intervention, Pranavika Balaji

Undergraduate Research Posters

Myopia, or nearsightedness, has increased rapidly worldwide and is now a major public health concern caused by both genetic and environmental factors. Current understanding shows that too much near work, limited time spent outdoors, and prolonged screen use contribute to abnormal eye growth in individuals who may already have a genetic predisposition. At the molecular level, epigenetic changes and shifts in DNA activity, often influenced by environmental conditions, affect the signaling pathways that regulate eye development and may help explain why myopia is increasingly common among children. This paper synthesizes recent environmental, genetic, and epigenetic research to explain how these …

Oxytocin Dosing During Trial Of Labor After Cesarean To Minimize The Risk Of Uterine Rupture: A Systematic Review And Meta-Analysis, Pierpaolo Nicolì, Moti Gulersen, Jordan Beacham, Hila Hochler, Alessandra Familiari, Anna Locatelli, Cynthia Abraham, Ettore Cicinelli, Amerigo Vitagliano, Vincenzo Berghella

Department of Obstetrics and Gynecology Faculty Papers

OBJECTIVE: Oxytocin remains the mainstay for induction and for the management of labor arrest during trial of labor after cesarean (TOLAC). Therefore, the clinical question should be not whether to use oxytocin, but how to optimize its administration. This meta-analysis aimed to assess which oxytocin protocol, in terms of initial dose, amount of increase, timing to next increase, and maximum dose, may minimize the risk of uterine rupture (UR) during TOLAC.

DATA SOURCES: PubMed, Embase and Clinicaltrials.gov were searched up to September 21, 2024.

STUDY ELIGIBILITY CRITERIA: Randomized and nonrandomized studies evaluating the association between induction and/or augmentation with oxytocin …