Physical Sciences and Mathematics Commons^™

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 …

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.

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 …

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

MSU Graduate Theses

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 …

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 …

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 …

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 …

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 …

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 …

Frontiers In The Self-Assembly Of Charged Macromolecules, Khatcher O. Margossian

Doctoral Dissertations

The self-assembly of charged macromolecules forms the basis of all life on earth. From the synthesis and replication of nucleic acids, to the association of DNA to chromatin, to the targeting of RNA to various cellular compartments, to the astonishingly consistent folding of proteins, all life depends on the physics of the organization and dynamics of charged polymers. In this dissertation, I address several of the newest challenges in the assembly of these types of materials. First, I describe the exciting new physics of the complexation between polyzwitterions and polyelectrolytes. These materials open new questions and possibilities within the context …

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 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 …

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 …

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 …

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, …

Assessing The Reidentification Risks Posed By Deep Learning Algorithms Applied To Ecg Data, Arin Ghazarian, Jianwei Zheng, Daniele Struppa, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

ECG (Electrocardiogram) data analysis is one of the most widely used and important tools in cardiology diagnostics. In recent years the development of advanced deep learning techniques and GPU hardware have made it possible to train neural network models that attain exceptionally high levels of accuracy in complex tasks such as heart disease diagnoses and treatments. We investigate the use of ECGs as biometrics in human identification systems by implementing state-of-the-art deep learning models. We train convolutional neural network models on approximately 81k patients from the US, Germany and China. Currently, this is the largest research project on ECG identification. …

The Short-Term Effects Of Fine Airborne Particulate Matter And Climate On Covid-19 Disease Dynamics, El Hussain Shamsa, Kezhong Zhang

Medical Student Research Symposium

Background: Despite more than 60% of the United States population being fully vaccinated, COVID-19 cases continue to spike in a temporal pattern. These patterns in COVID-19 incidence and mortality may be linked to short-term changes in environmental factors.

Methods: Nationwide, county-wise measurements for COVID-19 cases and deaths, fine-airborne particulate matter (PM_2.5), and maximum temperature were obtained from March 20, 2020 to March 20, 2021. Multivariate Linear Regression was used to analyze the association between environmental factors and COVID-19 incidence and mortality rates in each season. Negative Binomial Regression was used to analyze daily fluctuations of COVID-19 cases …

Development Of Optical Nanomaterial For Enhanced Cerenkov Imaging, Qize Zhang

Dissertations, Theses, and Capstone Projects

Cancer is a significant public health problem worldwide and is the second leading cause of death in the United States. Imaging has increasingly been used over the last two decades to improve the diagnosis and guidance of tumor tissue removal surgery. Among the most widely used techniques for in vivo imaging are planar and tomographic fluorescence imaging and bioluminescence imaging. Despite their utility, these techniques are primarily restricted to preclinical use. Factors that have prevented translation from the bench to the bedside include depth-penetration considerations, regulatory issues, and toxicity. A recent development in nuclear imaging has been the ability to …

Markerless Tumor Tracking Using Kalman Filter And Deep Learning, Anisha Kapoor, Mark Albert

Computer Science Research Seminars and Symposia

According to the Center for Disease Control, more people die from lung cancer than any other cancer in the United States. A complication that arises from lung cancer treatment, radiation therapy, is radiation pneumonitis. Radiation pneumonitis can be fatal and affects over 23% of patients.

Spectroscopic Measurements Of Meibum Compositional, Structural, And Functional Relationships To Elucidate The Role Of Meibum In Dry Eye., Anthony Chigozie Ewurum

Electronic Theses and Dissertations

The major aim of my dissertation was to investigate the etiology of dry eye disease which affects about 7 million people in the United States, causing symptoms that can lead to visual disturbance. Correlation between dry eye and an abnormal lipid layer of the tear film has been found. Tear film lipids originate mostly from the meibomian glands. Cholesteryl ester (CE) and Wax ester (WE) lipids make up most of the human meibum lipidome and the CE/WE ratio has been shown to decrease in patients with meibomian gland dysfunction. Model studies using synthetic CE and WE, although providing some insight, …

“Translation Of Hdac6 Pet Imaging Using [18f]Ekz-001 – Cgmp Production And Measurement Of Hdac6 Target Occupancy In Nhps” – A Review, Dania Rahal

Chemistry & Biochemistry Undergraduate Honors Theses

The inhibition of histone deacetylase 6 (HDAC6) has been reported to alleviate the effects of neurodegenerative diseases such as Alzheimer’s disease. The brain-penetrant PET radioligand [¹⁸F]EKZ-001 has high affinity and selectivity towards HDAC6 and therefore suggests great promise in therapeutic treatment studies and development for neurodegenerative diseases. “Translation of HDAC6 PET imaging using [¹⁸F]EKZ-001 – cGMP production and measurement of HDAC6 target occupancy in NHPs” has achieved an effective, fully automated method of producing [¹⁸F]EKZ-001 in compliance with current good manufacturing practices (cGMP) to support the translation of [¹⁸F]EKZ-001 PET for …

Foundations Of Plasmas For Medical Applications, T. Von Woedtke, Mounir Laroussi, M. Gherardi

Electrical & Computer Engineering Faculty Publications

Plasma medicine refers to the application of nonequilibrium plasmas at approximately body temperature, for therapeutic purposes. Nonequilibrium plasmas are weakly ionized gases which contain charged and neutral species and electric fields, and emit radiation, particularly in the visible and ultraviolet range. Medically-relevant cold atmospheric pressure plasma (CAP) sources and devices are usually dielectric barrier discharges and nonequilibrium atmospheric pressure plasma jets. Plasma diagnostic methods and modelling approaches are used to characterize the densities and fluxes of active plasma species and their interaction with surrounding matter. In addition to the direct application of plasma onto living tissue, the treatment of liquids …

Estimating The Analytical Performance Of Raman Spectroscopy For Quantification Of Active Ingredients In Human Stratum Corneum, Hichem Kichou, Emilie Munnier, Yuri Dancik, Kamilia Kemel, Hugh Byrne, Ali Tfayli, Dominique Bertrand, Martin Soucé, Igor Chourpa, Franck Bonnier

Articles

Confocal Raman microscopy (CRM) has become a versatile technique that can be applied routinely to monitor skin penetration of active molecules. In the present study, CRM coupled to multivariate analysis (namely PLSR—partial least squares regression) is used for the quantitative measurement of an active ingredient (AI) applied to isolated (ex vivo) human stratum corneum (SC), using systematically varied doses of resorcinol, as model compound, and the performance is quantified according to key figures of merit defined by regulatory bodies (ICH, FDA, and EMA). A methodology is thus demonstrated to establish the limit of detection (LOD), precision, accuracy, sensitivity (SEN), and …