Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

Head And Neck Tumor Histopathological Image Representation With Pre- Trained Convolutional Neural Network And Vision Transformer, Ranny Rahaningrum Herdiantoputri, Daisuke Komura, Tohru Ikeda, Shumpei Ishikawa

Journal of Dentistry Indonesia

Image representation via machine learning is an approach to quantitatively represent histopathological images of head and neck tumors for future applications of artificial intelligence-assisted pathological diagnosis systems. Objective: This study compares image representations produced by a pre-trained convolutional neural network (VGG16) to those produced by a vision transformer (ViT-L/14) in terms of the classification performance of head and neck tumors. Methods: W hole-slide images of five oral t umor categories (n = 319 cases) were analyzed. Image patches were created from manually annotated regions at 4096, 2048, and 1024 pixels and rescaled to 256 pixels. Image representations were …

Moral Injury To Inform Analysis Of Post-Traumatic Stress Disorder, Amanda Julia Manea

Senior Theses

Post-traumatic stress disorder (PTSD) is a mental health condition that almost one out of ten veterans struggle with. Although the National Center for PTSD has made extensive progress in characterizing and developing new treatments for PTSD, most veterans still experience symptoms of PTSD following treatment. Novel avenues of investigation, such as developing algorithms to review electronic health record (EHR) data and better understanding moral injury, are being pursued to address the gap that still exists when it comes to treating veterans. Moral injury is the individual evaluation of exposure to a potentially morally injurious event (PMIE) and can lead to …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Sulopenem For The Treatment Of Complicated And Uncomplicated Bacterial Urinary Tract Infection (Uti), Aiyi Chen, Thanh-Liem Vu, Daniel J. Yang, Landon Olsen

Annual Research Symposium

No abstract provided.

Synthesis And Biomedical Applications Of Hollow Iron Oxide Nanoparticles, Aloka S. Paragoda Arachchilage

Dissertations, Theses, and Capstone Projects

Nano-scale materials have gained much attention during the past few decades due to the stark differences in their properties compared to bulk material. Thus, they are being studied for a myriad of applications ranging from harnessing solar energy to diagnostics. This thesis focuses on the synthesis of hollow iron oxide nanoparticles using Galvanic replacement reactions and their application in drug delivery. Moreover, the use of a peptide precursor for the enhancement of exosomes is also discussed.

Chapter 1 discusses a simple and economical Galvanic approach used for the synthesis of hollow one-dimensional iron oxide nanotubes. In the initial reaction, the …

An Explainable Deep Learning Prediction Model For Severity Of Alzheimer's Disease From Brain Images, Godwin O. Ekuma

Graduate Theses/Dissertations

Deep Convolutional Neural Networks (CNNs) have become the go-to method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. The effectiveness of deep learning models are hampered by limited data sets, skewed class distributions, and the undesirable "black box" of neural networks, which decreases their understandability and usability in precision medicine applications. This thesis addresses the challenge of building an explainable deep learning model for a clinical application: predicting the severity of Alzheimer's disease (AD). AD …

Identifying The Serious Clinical Outcomes Of Adverse Reactions To Drugs By A Multi-Task Deep Learning Framework, Haochen Zhao, Peng Ni, Qichang Zhao, Xiao Liang, Di Ai, Shannon Erhardt, Jun Wang, Yaohang Li, Jiianxin Wang

Computer Science Faculty Publications

Adverse Drug Reactions (ADRs) have a direct impact on human health. As continuous pharmacovigilance and drug monitoring prove to be costly and time-consuming, computational methods have emerged as promising alternatives. However, most existing computational methods primarily focus on predicting whether or not the drug is associated with an adverse reaction and do not consider the core issue of drug benefit-risk assessment-whether the treatment outcome is serious when adverse drug reactions occur. To this end, we categorize serious clinical outcomes caused by adverse reactions to drugs into seven distinct classes and present a deep learning framework, so-called GCAP, for predicting the …

Enhancing Health Data Representation For Older Adults: Unlocking Opportunities, Peterson Jean

Academic Posters Collection

The prevalence of off-the-shelf wearable devices increases the monitoring and measurement of critical physiological parameters like activity, sleep, heart rate, and blood pressure. However, the accessibility of health data representations poses challenges for older adults, who often struggle to understand the criticality of their own health data without assistance. This poster highlights the challenges older adults face in accessing their health data from wearable technologies, specifically focusing on data representations.

To address these challenges, it proposes a methodology that involves a heuristic evaluation of existing data representations with experts and accessibility studies with older adults using a mixed methods approach …

Investigating Visinin-Like Protein 1 And Ubiquitinated Visinin-Like Protein 1 As Mild Traumatic Brain Injury Biomarkers, Yueming Wu

Theses and Dissertations--Chemistry

Traumatic brain injury (TBI) continues to be a significant cause of morbidity and mortality worldwide. Despite significant progress in understanding the complex pathophysiology of TBI, the underlying mechanisms remain poorly understood. The primary brain damage is acute and irreversible. However, secondary brain injuries often develop gradually over months to years, creating an opportunity for critical therapeutic interventions. In the past decade, research on TBI biomarkers has seen significant progress. This progress has been driven by the diverse nature of TBI pathologies and the challenges they present for evaluation, management, and prognosis. TBI biomarker proteins resulting from axonal, neuronal, or glial …

Prediction Of Rapid Early Progression And Survival Risk With Pre-Radiation Mri In Who Grade 4 Glioma Patients, Walia Farzana, Mustafa M. Basree, Norou Diawara, Zeina Shboul, Sagel Dubey, Marie M. Lockheart, Mohamed Hamza, Joshua D. Palmer, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Rapid early progression (REP) has been defined as increased nodular enhancement at the border of the resection cavity, the appearance of new lesions outside the resection cavity, or increased enhancement of the residual disease after surgery and before radiation. Patients with REP have worse survival compared to patients without REP (non-REP). Therefore, a reliable method for differentiating REP from non-REP is hypothesized to assist in personlized treatment planning. A potential approach is to use the radiomics and fractal texture features extracted from brain tumors to characterize morphological and physiological properties. We propose a random sampling-based ensemble classification model. The proposed …

Medical Diagnosis Via Refined Neutrosophic Fuzzy Logic: Detection Of Illness Using Neutrosophic Sets, K. Hemabala, B. Srinivasa Kumar, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

The objective of the paper is to implement and validate diagnosis in the medical field via refined neutrosophic fuzzy logic (RNFL). As such, we have proposed a Max-Min composition (MMC) method in RNFL. This method deals with the diagnosis under certain constraints like uncertainty and indeterminacy. Further, we have considered the diagnosis problems to validate the sensitivity analysis of the novel multi attribute decision-making technique. Finally, we gave the graphical representations and compared the obtained results with other existing measures in refined neutrosophic fuzzy sets.

Opioid Use Disorder Prediction Using Machine Learning Of Fmri Data, A. Temtam, Liangsuo Ma, F. Gerard Moeller, M. S. Sadique, K. M. Iftekharuddin, Khan M. Iftekharuddin (Ed.), Weijie Chen (Ed.)

Electrical & Computer Engineering Faculty Publications

According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical …

Msdrp: A Deep Learning Model Based On Multisource Data For Predicting Drug Response, Haochen Zhao, Xiaoyu Zhang, Qichang Zhao, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell …

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette

Electrical & Computer Engineering Faculty Publications

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has …

Analysis Of Biologically Effective Dose For Retroactive Yttrium-90 Trans-Arterial Radioembolization Treatment Optimization, Mj Lindsey

CMC Senior Theses

Trans-arterial radioembolization (TARE) is a protracted modality of radiation therapy where radionuclides labeled with Yttrium-90 (⁹⁰Y) are inserted inside a patient's hepatic artery to treat hepatocellular carcinoma (HCC). While TARE has been shown to be a clinically effective and safe treatment, there is little understanding of the radiobiological relationship between absorbed dose and tissue response, and thus there is no dosimetric standard for treatment planning. The Biologically Effective Dose (BED) formalism, derived from the Linear-Quadratic model of radiobiology, is used to weigh the absorbed dose by the time pattern of delivery. BED is a virtual dose that can …

Therapies For Mitochondrial Disorders, Kayli Sousa Smyth, Anne Mulvihill

SURE Journal: Science Undergraduate Research Experience Journal

Mitochondria are cytoplasmic, double-membrane organelles that synthesise adenosine triphosphate (ATP). Mitochondria contain their own genome, mitochondrial DNA (mtDNA), which is maternally inherited from the oocyte. Mitochondrial proteins are encoded by either nuclear DNA (nDNA) or mtDNA, and both code for proteins forming the mitochondrial oxidative phosphorylation (OXPHOS) complexes of the respiratory chain. These complexes form a chain that allows the passage of electrons down the electron transport chain (ETC) through a proton motive force, creating ATP from adenosine diphosphate (ADP). This study aims to explore current and prospective therapies for mitochondrial disorders (MTDS). MTDS are clinical syndromes coupled with abnormalities …

Speciated Isotope Dilution Mass Spectrometry In Combination With Thor’S Hammer Metaspike Technology For The Assessment Of Low-Level Biomarkers For Human Health Evaluation, Ashley Ebert

Electronic Theses and Dissertations

Our research team has been focused on quantifying important biological analytes in human blood samples to delineate the biochemical processes underlying certain states of dysfunction and evaluate treatment efficacy. These efforts have been guided by laboratory measurements with the input of medical experts. Speciated isotope dilution mass spectrometry (SIDMS) explained in detail in EPA Method 6800 has been successfully applied in the quantification of an important biomarker analyte for immune function and detoxification processes: glutathione. Past research has proven the molecule a biomarker for autism spectrum disorder (ASD) in the form of the reduced/oxidized glutathione (GSH/GSSG) ratio, and literature has …

Designing And Synthesizing A Warhead-Fragment Inhibitory Ligand For Ivyp1 Through Fragment-Based Drug Discovery, Samuel Moore

Symposium of Student Scholars

Fragment-based drug discovery (FBDD) is a powerful tool for developing anticancer and antimicrobial agents. Within this, magnetic resonance spectroscopy (NMR) provides a comprehensive qualitative and quantitative approach to screening and validating weak and robust binders with targeted proteins, making NMR among the most attractive strategies in FBDD. Inhibitor of vertebrate lysozyme (Ivyp1) of P. aeruginosa serves as an excellent target because of its active cellular location and implications in clinical prognosis for cystic fibrosis and immunocompromised patients. This study uses current NMR and biophysical techniques to develop a covalent, fragment-linked warhead inhibitor for Ivyp1 through synthetic methods, warhead linking, and …

A High-Accuracy Detection System: Based On Transfer Learning For Apical Lesions On Periapical Radiograph, Yueh Chuo, Wen-Ming Lin, Tsung-Yi Chen, Mei-Ling Chan, Yu-Sung Chang, Yan-Ru Lin, Yuan-Jin Lin, Yu-Han Shao, Chiung-An Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold …

A Human Oral Fluid Assay For D- And L- Isomer Detection Of Amphetamine And Methamphetamine Using Liquid-Liquid Extraction, Brian Robbins, Rob E. Carpenter, Mary Long, Jacob Perry

Human Resource Development Faculty Publications and Presentations

Medical providers are increasingly confronted with clinical decision-making that involves (meth)amphetamines. And clinical laboratories need a sensitive, efficient assay for routine assessment of D- and L-isomers to determine the probable source of these potentially illicit analytes. This paper presents a validated method of D- and L-isomer detection in human oral fluid from an extract used for determination of a large oral fluid assay (63 analytes) on an older AB SCIEX 4000 instrument. Taken from the positive extract, D- and L-analytes were added. The method for extraction included addition of internal standard and a 2-step …

Alpha, Beta-Unsaturated Aldehydes: The Underrepresented Markers Of Disease., Saurin Sutaria

Electronic Theses and Dissertations

The peroxidation of unsaturated fatty acids is a widely recognized metabolic process that creates a complex mixture of volatile organic compounds including aldehydes. Elevated levels of reactive oxygen species in cancer cells promote random lipid peroxidation, which leads to an increase in a variety of aldehydes. Many of these volatile aldehydes are exhaled and are of interest as potential markers of disease. Chapter 1 presents a review of reported aldehydes in the exhaled breath of lung cancer patients. alpha,beta-Unsaturated aldehydes, detected primarily when derivatized during exhaled breath preconcentration, are underreported in the reviewed articles. Chapter 1 concludes with our hypothesis …

Infrastructure Development For Personalized Risk Prediction To Reduce Cardiovascular Disease In Childhood Cancer Survivors, Suman Shrestha

Dissertations and Theses (Open Access)

Although childhood cancer survivors have lengthy life expectancies, they run the risk of experiencing long-term health issues as a result of their treatment. The most frequent non-cancerous cause of morbidity and mortality for these survivors is cardiac disease. Radiation therapy (RT) has been linked in numerous cohort studies to a higher chance of developing a late cardiac disease in these survivors, and this risk rises with higher mean heart doses and increased RT exposure to larger cardiac volumes. Since, the heart is a heterogeneous organ made up of several distinct substructures, RT dose received by the entire heart does not …

Artificial Intelligence And The Situational Rationality Of Diagnosis: Human Problem-Solving And The Artifacts Of Health And Medicine, Michael W. Raphael

Publications and Research

What is the problem-solving capacity of artificial intelligence (AI) for health and medicine? This paper draws out the cognitive sociological context of diagnostic problem-solving for medical sociology regarding the limits of automation for decision-based medical tasks. Specifically, it presents a practical way of evaluating the artificiality of symptoms and signs in medical encounters, with an emphasis on the visualization of the problem-solving process in doctor-patient relationships. In doing so, the paper details the logical differences underlying diagnostic task performance between man and machine problem-solving: its principle of rationality, the priorities of its means of adaptation to abstraction, and the effects …

Classification Of Breast Cancer Histopathological Images Using Semi-Supervised Gans, Balaji Avvaru, Nibhrat Lohia, Sowmya Mani, Vijayasrikanth Kaniti

SMU Data Science Review

Breast cancer is diagnosed more frequently than skin cancer in women in the United States. Most breast cancer cases are diagnosed in women, while children and men are less likely to develop the disease. Various tissues in the breast grow uncontrollably, resulting in breast cancer. Different treatments analyze microscopic histopathology images for diagnosis that help accurately detect cancer cells. Deep learning is one of the evolving techniques to classify images where accuracy depends on the volume and quality of labeled images. This study used various pre-trained models to train the histopathological images and analyze these models to create a new …

Evaluation And Clinical Implementation Of A Dual-Energy Ct Stopping-Power Ratio Mapping Technique For Proton-Therapy Treatment Planning, Maria Jose Medrano Matamoros

McKelvey School of Engineering Graduate Student Theses & Dissertations

Proton radiotherapy has the potential to treat tumors with better conformal dose distribution than competing modalities when the rapid dose falloff at the end of the proton-beam range is correctly aligned to the edge of the clinical target volume (CTV). However, its clinical potential is dependent on the accurate localization of the Bragg-peak position from predicted stopping-power ratio maps. The method that is most commonly used in today’s clinical practice for predicting stopping-power ratio (SPR) consists of a stoichiometric calibrationtechnique based on single-energy CT (SECT) for direct estimation of patient-specific SPR distribution from vendor-reconstructed Hounsfield Unit (HU) images. Unfortunately, this …

Machine Learning And Scalable Informatics Methods To Predict Disease Status From Multimodal Biomedical Data, Hossein Mohammadian Foroushani

McKelvey School of Engineering Graduate Student Theses & Dissertations

Biological understanding of complex diseases such as stroke and obesity is critical for the advancement of medicine. Further knowledge discovery can provide effective biomarkers to improve disease diagnosis and prognosis, identify driver mutations, predict individual genetic susceptibility for early prevention and effective disease management, and facilitate development of personalized drugs. Stroke is the second leading cause of death and long-term disability in the world. Thus, stroke management is a time-sensitive emergency. The initial hours after stroke onset map the trajectory of subsequent neurologic complications. Cerebral edema develops hours to days after acute ischemic stroke and may result in midline shift …

Advancing The Portability, Field Of View, And Spatiotemporal Resolution Of Multi-Parametric Photoacoustic Microscopy, Fenghe Zhong

McKelvey School of Engineering Graduate Student Theses & Dissertations

Capable of structural, functional, molecular, and metabolic imaging with high spatial resolution in vivo, photoacoustic microscopy (PAM) is an emerging tool in biomedical research. Further, recent advances in multi-parametric acquisition and analysis make PAM uniquely capable of simultaneously mapping the total concentration of hemoglobin (CHb), oxygen saturation of hemoglobin (sO2), and blood flow speed. However, there are multiple major limitations, including, the large footprint, low imaging speed, limited imaging contrast, and insufficient axial resolution, which impedance its applications in basic and translational research. In this dissertation, I presented potential solutions to overcome the limitations above.In the first part of this …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Monte Carlo Simulation Of Thallium-Bromide Semiconductor Detector For Range Verification Of A Carbon Ion Radiotherapy Beam Through Prompt Gamma-Ray Detection, Peter Dimpfl

UNLV Theses, Dissertations, Professional Papers, and Capstones

Thallium-Bromide is a semiconductor material which is well suited for photon detection. Composed of high atomic number elements and a high mass density of 7.56 g/cm3, TlBr has superb photon stopping power. External beam radiation therapy is an ever-changing environment with technology rapidly improving. With proton therapy facilities becoming more widely available, and carbon ion facilities showcasing the benefits of carbon ion radiation therapy, there is an industry need toprovide accurate dosimetric treatment plans to verify patients are receiving proper care. Current clinical practices apply safety margins to clinical target volumes to safe treatment. Through research to improve detection techniques, …

Radioluminescence Based Biochemical Sensing And Imaging Strategies To Measure Local Drug Release And Ph, Gretchen B. Schober

All Dissertations

In this dissertation we describe methods for measuring infection relevant biochemical analytes using radioluminescent and ultrasound luminescent materials. Films and nanoparticles fabricated with europium doped gadolinium oxysulfide (Gd₂O₂S:Eu³⁺) are used to quantitatively measure radiolabeled pharmaceutical concentration, specifically tritium labeled vancomycin (³H-vancomycin). Europium and dysprosium doped strontium aluminate is used to fabricate an ultrasound modulated, pH sensing film. These methods are indicated for theranostic evaluation of implant associated infection. Bacterial biofilms are inherently resistant to traditional antibiotic treatment and can coat biomedical implants. These biofilm related infections are difficult or impossible to eradicate …