Medicine and Health Sciences Commons^™

Amperometric Bio-Sensing Of Lactate And Oxygen Concurrently With Local Field Potentials During Status Epilepticus, Eliana Fernandes, Ana Ledo, Greg A. Gerhardt, Rui M. Barbosa

Neurology Faculty Publications

Epilepsy is a prevalent neurological disorder with a complex pathogenesis and unpredictable nature, presenting limited treatment options in >30 % of affected individuals. Neurometabolic abnormalities have been observed in epilepsy patients, suggesting a disruption in the coupling between neural activity and energy metabolism in the brain. In this study, we employed amperometric biosensors based on a modified carbon fiber microelectrode platform to directly and continuously measure lactate and oxygen dynamics in the brain extracellular space. These biosensors demonstrated high sensitivity, selectivity, and rapid response time, enabling in vivo measurements with high temporal and spatial resolution. In vivo recordings in the …

Epigenetic Regulation In Cancer, Minzhi Gu, Bo Ren, Yuan Fang, Jie Ren, Xiaohong Liu, Xing Wang, Feihan Zhou, Ruiling Xiao, Xiyuan Luo, Lei You, Yupei Zhao

Faculty, Staff and Student Publications

Epigenetic modifications are defined as heritable changes in gene activity that do not involve changes in the underlying DNA sequence. The oncogenic process is driven by the accumulation of alterations that impact genome's structure and function. Genetic mutations, which directly disrupt the DNA sequence, are complemented by epigenetic modifications that modulate gene expression, thereby facilitating the acquisition of malignant characteristics. Principals among these epigenetic changes are shifts in DNA methylation and histone mark patterns, which promote tumor development and metastasis. Notably, the reversible nature of epigenetic alterations, as opposed to the permanence of genetic changes, positions the epigenetic machinery as …

Clustering Of Patients With Heart Disease, Mukadder Cinar

Dissertations, Theses, and Capstone Projects

Heart disease, a leading cause of mortality worldwide, presents complex challenges in public health due to its varied manifestations. Accurate diagnosis and patient stratification are essential for effective management and improved outcomes. In response, this study employed machine learning techniques to analyze heart disease data obtained from UCI Machine Learning Repository, aiming to enhance patient care through advanced data analysis.

The study began with the application of K-Nearest Neighbors (KNN) classification, which categorized patients into 'Disease' and 'No Disease' groups. This preliminary step provided initial insights into the structure of the dataset. Subsequently, K-means clustering was applied in two rounds, …

Modeling Of Covid-19 Clinical Outcomes In Mexico: An Analysis Of Demographic, Clinical, And Chronic Disease Factors, Livia Clarete

Dissertations, Theses, and Capstone Projects

This study explores COVID-19 clinical outcomes in Mexico, focusing on demographic, clinical, and chronic disease variables to develop predictive models. In the binary classification task, the Ada Boost Classifier distinguishes survivors from non-survivors, with age, sex, ethnicity, and chronic medical conditions influencing outcomes. In multiclass classification, the Gradient Boosting Classifier categorizes patients into outcome groups.

Demographic variables, especially age, are crucial for predicting COVID-19 outcomes for both the binary and multiclass classification tasks. Clinical information about previous conditions, including chronic diseases, also holds relevance, especially diabetes, immunocompromise, and cardiovascular diseases. These insights inform public health measures and healthcare strategies, emphasizing …

Public Acceptance Of Using Artificial Intelligence-Assisted Weight Management Apps In High-Income Southeast Asian Adults With Overweight And Obesity: A Cross-Sectional Study, Han Shi Jocelyn Chew, Palakorn Achananuparp, Palakorn Achananuparp, Nicholas W. S. Chew, Yip Han Chin, Yujia Gao, Bok Yan Jimmy So, Asim Shabbir, Ee-Peng Lim, Kee Yuan Ngiam

Research Collection School Of Computing and Information Systems

Introduction: With in increase in interest to incorporate artificial intelligence (AI) into weight management programs, we aimed to examine user perceptions of AI-based mobile apps for weight management in adults with overweight and obesity. Methods: 280 participants were recruited between May and November 2022. Participants completed a questionnaire on sociodemographic profiles, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Self-Regulation of Eating Behavior Questionnaire. Structural equation modeling was performed using R. Model fit was tested using maximum-likelihood generalized unweighted least squares. Associations between influencing factors were analyzed using correlation and linear regression. Results: 271 participant responses were …

Predictive Algorithm For Surgery Recommendation In Thoracolumbar Burst Fractures Without Neurological Deficits, Charlotte Dandurand, Nader Fallah, Cumhur F. Öner, Richard J. Bransford, Klaus Schnake, Alex R. Vaccaro, Lorin M. Benneker, Emiliano Vialle, Gregory D. Schroeder, Shanmuganathan Rajasekaran, Mohammad El-Skarkawi, Rishi M. Kanna, Mohamed Aly, Martin Holas, Jose A. Canseco, Sander Muijs, Eugen Cezar Popescu, Jin Wee Tee, Gaston Camino-Willhuber, Andrei Fernandes Joaquim, Ory Keynan, Harvinder Singh Chhabra, Sebastian Bigdon, Ulrich Spiegel, Marcel F. Dvorak

Department of Orthopaedic Surgery Faculty Papers

STUDY DESIGN: Predictive algorithm via decision tree.

OBJECTIVES: Artificial intelligence (AI) remain an emerging field and have not previously been used to guide therapeutic decision making in thoracolumbar burst fractures. Building such models may reduce the variability in treatment recommendations. The goal of this study was to build a mathematical prediction rule based upon radiographic variables to guide treatment decisions.

METHODS: Twenty-two surgeons from the AO Knowledge Forum Trauma reviewed 183 cases from the Spine TL A3/A4 prospective study (classification, degree of certainty of posterior ligamentous complex (PLC) injury, use of M1 modifier, degree of comminution, treatment recommendation). Reviewers' regions …

Artificial Intelligence Model Predicts Sudden Cardiac Arrest Manifesting With Pulseless Electric Activity Versus Ventricular Fibrillation, Lauri Holmstrom, Bryan Bednarski, Harpriya Chugh, Habiba Aziz, Hoang Nhat Pham, Arayik Sargsyan, Audrey Uy-Evanado, Damini Dey, Angelo Salvucci, Jonathan Jui, Kyndaron Reinier, Piotr J Slomka, Sumeet S Chugh

Faculty, Staff and Student Publications

BACKGROUND: There is no specific treatment for sudden cardiac arrest (SCA) manifesting as pulseless electric activity (PEA) and survival rates are low; unlike ventricular fibrillation (VF), which is treatable by defibrillation. Development of novel treatments requires fundamental clinical studies, but access to the true initial rhythm has been a limiting factor.

METHODS: Using demographics and detailed clinical variables, we trained and tested an AI model (extreme gradient boosting) to differentiate PEA-SCA versus VF-SCA in a novel setting that provided the true initial rhythm. A subgroup of SCAs are witnessed by emergency medical services personnel, and because the response time is …

Analysis Of The Outer Retinal Bands In Abca4 And Prph2-Associated Retinopathy Using Oct, Rachael C. Heath Jeffery, Johnny Lo, Jennifer A. Thompson, Tina M. Lamey, Terri L. Mclaren, John N. De Roach, Lauren N. Ayton, Andrea L. Vincent, Abhishek Sharma, Fred K. Chen

Research outputs 2022 to 2026

Purpose: To evaluate the outer retinal bands using OCT in ABCA4- and PRPH2-associated retinopathy and develop a novel imaging biomarker to differentiate between these 2 genotypes.

Design: Multicenter case-control study. Participants: Patients with a clinical and genetic diagnosis of ABCA4- or PRPH2-associated retinopathy and an age-matched control group.

Methods: Macular OCT was used to measure the thickness of the outer retinal bands 2 and 4 by 2 independent examiners at 4 retinal loci.

Main Outcome Measures: Outcome measures included the thicknesses of band 2, band 4, and the band 2/band 4 ratio. Linear mixed modeling was used to make comparisons …

Edsuch: A Robust Ensemble Data Summarization Method For Effective Medical Diagnosis, Mohiuddin Ahmed, A. N. M. B. Rashid

Research outputs 2022 to 2026

Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data. Data summarization can create a concise version of the original data that can be used for effective diagnosis. In this paper, we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns. To the best of our knowledge, there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis. The performance of popular anomaly detection …

Safety, Immunogenicity, And Mechanism Of A Rotavirus Mrna-Lnp Vaccine In Mice, Chenxing Lu, Yan Li, Rong Chen, Xiaoqing Hu, Qingmei Leng, Xiaopeng Song, Xiaochen Lin, Jun Ye, Jinlan Wang, Jinmei Li, Lida Yao, Xianqiong Tang, Xiangjun Kuang, Guangming Zhang, Maosheng Sun, Yan Zhou, Hongjun Li

Faculty, Staff and Student Publications

Rotaviruses (RVs) are a major cause of diarrhea in young children worldwide. The currently available and licensed vaccines contain live attenuated RVs. Optimization of live attenuated RV vaccines or developing non-replicating RV (e.g., mRNA) vaccines is crucial for reducing the morbidity and mortality from RV infections. Herein, a nucleoside-modified mRNA vaccine encapsulated in lipid nanoparticles (LNP) and encoding the VP7 protein from the G1 type of RV was developed. The 5' untranslated region of an isolated human RV was utilized for the mRNA vaccine. After undergoing quality inspection, the VP7-mRNA vaccine was injected by subcutaneous or intramuscular routes into mice. …

Dynamic Prognosis Prediction For Patients On Dapt After Drug-Eluting Stent Implantation: Model Development And Validation, Fang Li, Laila Rasmy, Yang Xiang, Jingna Feng, Ahmed Abdelhameed, Xinyue Hu, Zenan Sun, David Aguilar, Abhijeet Dhoble, Jingcheng Du, Qing Wang, Shuteng Niu, Yifang Dang, Xinyuan Zhang, Ziqian Xie, Yi Nian, Jianping He, Yujia Zhou, Jianfu Li, Mattia Prosperi, Jiang Bian, Degui Zhi, Cui Tao

School of Medicine Faculty Publications

BACKGROUND: The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug-eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. METHODS AND RESULTS: We developed and validated a new AI-based pipeline using retrospective data of drug-eluting stent-treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum’s de-identified Clinformatics Data Mart Database (n=9978). The 36 months following drug-eluting stent implantation were …

Promises And Risks Of Applying Ai Medical Imaging To Early Detection Of Cancers, And Regulation For Ai Medical Imaging, Yiyao Zhang

The Journal of Purdue Undergraduate Research

No abstract provided.

De Novo Drug Design Using Transformer-Based Machine Translation And Reinforcement Learning Of An Adaptive Monte Carlo Tree Search, Dony Ang, Cyril Rakovski, Hagop S. Atamian

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder–Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards …

A Tale Of Two Viruses: Why Smallpox Was Eradicated And Polio Persists, Katherine G. Mcgough, Erin N. Bodine

Spora: A Journal of Biomathematics

The smallpox and poliomyelitis (polio) viruses were, at a time, one of the largest threats to global public health killing millions until global eradication campaigns were put into effect. Vaccination led to the eradication of smallpox and the elimination of polio for most of the world. However, polio continues to persist at endemic levels in Pakistan and Afghanistan. We developed ODE models of smallpox and polio to explore differences in transmission dynamics and determine if the underlying biology has made poliomyelitis more difficult to eradicate. Our model analysis shows there are multiple factors which should allow polio to have a …

Prevention And Potential Treatment Strategies For Respiratory Syncytial Virus, Bo-Wen Sun, Peng-Peng Zhang, Zong-Hao Wang, Xia Yao, Meng-Lan He, Rui-Ting Bai, Hao Che, Jing Lin, Tian Xie, Zi Hui, Xiang-Yang Ye, Li-Wei Wang

Faculty, Staff and Student Publications

Respiratory syncytial virus (RSV) is a significant viral pathogen that causes respiratory infections in infants, the elderly, and immunocompromised individuals. RSV-related illnesses impose a substantial economic burden worldwide annually. The molecular structure, function, and in vivo interaction mechanisms of RSV have received more comprehensive attention in recent times, and significant progress has been made in developing inhibitors targeting various stages of the RSV replication cycle. These include fusion inhibitors, RSV polymerase inhibitors, and nucleoprotein inhibitors, as well as FDA-approved RSV prophylactic drugs palivizumab and nirsevimab. The research community is hopeful that these developments might provide easier access to knowledge and …

Dementia Prediction In Older Adults Using Sex-Specific Health Trajectory Clustering, Omar A Ibrahim, Muskan Garg, Sunyang Fu, Maria Vassilaki, Ronald C Petersen, Jennifer St Sauver, Sunghwan Sohn

Faculty, Staff and Student Publications

With increasing number of people living with dementia, the problem of late diagnosis significantly impacts a person's quality of life while early signs of dementia may provide useful insights to facilitate better treatment plans. With time, this progressive neurodegenerative syndrome could progress from mild cognitive impairment to dementia. A pattern of health conditions can be characterized in unsupervised manner to help predict this progress. As a significant extension to our previous work with streaming clustering model, we consider additional information for predicting dementia onset. With empirical observations, we discover the importance of examining sex and age to predict dementia onset. …

Virgin Coconut Oil (Vco) Supplementation Relieves Symptoms And Inflammation Among Covid-19 Positive Adults: A Single-Blind Randomized Trial, Imelda Angeles-Agdeppa, Jacus S. Nacis, Fabian M. Dayrit, Keith V. Tanda

Chemistry Faculty Publications

A clinical study conducted in 2020 showed that virgin coconut oil (VCO) has been found effective in the rapid relief of COVID-19 symptoms and normalization of the C-reactive protein (CRP) concentration among probable and suspected cases of COVID-19. This present study aimed to validate those results and to evaluate the effects of VCO among COVID-19 patients through a 28-day randomized, single-blind trial conducted among 76 SARS-CoV-2 RT-PCR (reverse transcription-polymerase chain report)-confirmed adults, with VCO given as a COVID-19 adjunct therapy. The results showed that VCO recipients were free from symptoms and had normal CRP concentrations by day 14. In comparison, …

Table Of Contents

Journal of the South Carolina Academy of Science

No abstract provided.

3rd Annual Hu Research Symposium Program 2021, Akeisha Belgrave, Erica Ward, Rebekah Harriger

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

3rd Annual HU Research Symposium program and schedule of presenters. Presentations were held April 21st, 2021 virtually on the Teams platform.

Fusionnw, A Potential Clinical Impact Assessment Of Kinases In Pan-Cancer Fusion Gene Network, Chengyuan Yang, Himansu Kumar, Pora Kim

Faculty, Staff and Student Publications

Kinase fusion genes are the most active fusion gene group in human cancer fusion genes. To help choose the clinically significant kinase so that the cancer patients that have fusion genes can be better diagnosed, we need a metric to infer the assessment of kinases in pan-cancer fusion genes rather than relying on the sample frequency expressed fusion genes. Most of all, multiple studies assessed human kinases as the drug targets using multiple types of genomic and clinical information, but none used the kinase fusion genes in their study. The assessment studies of kinase without kinase fusion gene events can …

The Santa Clara, 2024-01-19, Santa Clara University

The Santa Clara

No abstract provided.

Effect Of Zwitterionic Additives On Solvation And Transport Of Sodium And Potassium Cations In (Ethylene Oxide)10: A Molecular Dynamics Simulation Study, Manh Tien Nguyen, Yuhua Duan, Qing Shao

Markey Cancer Center Faculty Publications

Sodium- (Na+) and potassium- (K+) ion batteries are cost-effective alternatives to lithium- ion (Li+) batteries due to the abundant sodium and potassium resources. Solid polymer electrolytes (SPEs) are essential for safer and more efficient Na+ and K+ batteries because they often exhibit low ionic conductivity at room temperature. While zwitterionic (ZW) materials enhance Li+ battery conductivity, their potential for Na+ and K+ transport in batteries remains unexplored. In this study, we investigated the effect of three ZW molecules (ChoPO4, i.e., 2-methacryloyloxyethyl phosphoryl- choline, ImSO3, i.e., sulfobetaine ethylimidazole, and ImCO2, i.e., carboxybetaine ethylimidazole) on the dissociation of Na+ and K+ coordination …

Ai-Enhanced Detection Of Clinically Relevant Structural And Functional Anomalies In Mri: Traversing The Landscape Of Conventional To Explainable Approaches, Pegah Khosravi, Saber Mohammadi, Fatemeh Zahiri, Masoud Khodarahmi, Javad Zahiri

Publications and Research

Anomaly detection in medical imaging, particularly within the realm of magnetic resonance imaging (MRI), stands as a vital area of research with far-reaching implications across various medical fields. This review meticulously examines the integration of artificial intelligence (AI) in anomaly detection for MR images, spotlighting its transformative impact on medical diagnostics. We delve into the forefront of AI applications in MRI, exploring advanced machine learning (ML) and deep learning (DL) methodologies that are pivotal in enhancing the precision of diagnostic processes. The review provides a detailed analysis of preprocessing, feature extraction, classification, and segmentation techniques, alongside a comprehensive evaluation of …

The Shifting Landscape Of Adolescent Wellness In Boarding Schools: Can Time Spent Off Screens And Outdoors Improve Adolescent Wellbeing?, Kristen H. Peterson

Dartmouth College Master’s Theses

For nearly twenty years I have worked directly with adolescents as an independent school educator. Whether in the classroom, on the field, or in the dorm, I have observed and supported students through their middle and high school experiences. During this time, I have witnessed an alarming shift in adolescent physical, emotional, and social wellbeing. Concurrently, I have observed a dramatic increase in the amount of time students spend using screen-based devices, and a decrease in their time spent outdoors.

Using research to ground my anecdotal accounts in empirical understanding, my thesis examines whether or not screen use might help …

Enabling The Clinical Application Of Artificial Intelligence In Genomics: A Perspective Of The Amia Genomics And Translational Bioinformatics Workgroup, Nephi A Walton, Radha Nagarajan, Chen Wang, Murat Sincan, Robert R Freimuth, David B Everman, Derek C Walton, Scott P Mcgrath, Dominick J Lemas, Panayiotis V Benos, Alexander V Alekseyenko, Qianqian Song, Ece Gamsiz Uzun, Casey Overby Taylor, Alper Uzun, Thomas Nate Person, Nadav Rappoport, Zhongming Zhao, Marc S Williams

Faculty, Staff and Student Publications

OBJECTIVE: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space.

PROCESS: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members.

CONCLUSIONS: Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of …

Revealing Chronic Disease Progression Patterns Using Gaussian Process For Stage Inference, Yanfei Wang, Weiling Zhao, Angela Ross, Lei You, Hongyu Wang, Xiaobo Zhou

Faculty, Staff and Student Publications

OBJECTIVE: The early stages of chronic disease typically progress slowly, so symptoms are usually only noticed until the disease is advanced. Slow progression and heterogeneous manifestations make it challenging to model the transition from normal to disease status. As patient conditions are only observed at discrete timestamps with varying intervals, an incomplete understanding of disease progression and heterogeneity affects clinical practice and drug development.

MATERIALS AND METHODS: We developed the Gaussian Process for Stage Inference (GPSI) approach to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. We tested the ability of the GPSI to reliably …

Development And Validation Of A Nomogram For Predicting Pulmonary Infection In Patients Receiving Immunosuppressive Drugs, Chuxuan Luo, Yue Zhang, Jiajie Zhang, Chen Jin, Xiaolan Ye, Yan Ren, Huajuan Shen, Maosheng Chen, Yiwen Li, Qiang He, Guangbiao Xu, Lina Shao

Faculty, Staff and Student Publications

Objective: Pulmonary infection (PI), a severe complication of immunosuppressive therapy, affects patients' prognosis. As part of this study, we aimed to construct a pulmonary infection prediction (PIP) model and validate it in patients receiving immunosuppressive drugs (ISDs). Methods: Totally, 7,977 patients being treated with ISDs were randomised 7:3 to the developing (n = 5,583) versus validation datasets (n = 2,394). Our predictive nomogram was established using the least absolute shrinkage and selection operator (LASSO) and multivariate COX regression analyses. With the use of the concordance index (C-index) and calibration curve, the prediction performance of the final model was …

Expanded T Lymphocytes In The Cerebrospinal Fluid Of Multiple Sclerosis Patients Are Specific For Epstein-Barr-Virus-Infected B Cells, Assaf Gottlieb, H Phuong T Pham, Jerome G Saltarrelli, J William Lindsey

Faculty, Staff and Student Publications

Epstein-Barr virus (EBV) infection has long been associated with multiple sclerosis (MS), but the role of EBV in the pathogenesis of MS is not clear. Our hypothesis is that a major fraction of the expanded clones of T lymphocytes in the cerebrospinal fluid (CSF) are specific for autologous EBV-infected B cells. We obtained blood and CSF samples from eight relapsing-remitting patients in the process of diagnosis. We stimulated cells from the blood with autologous EBV-infected lymphoblastoid cell lines (LCL), EBV, varicella zoster virus, influenza, and candida and sorted the responding cells with flow cytometry after 6 d. We sequenced the …

Qiditangshen Granules Alleviates Diabetic Nephropathy Podocyte Injury: A Network Pharmacology Study And Experimental Validation In Vivo And Vitro, Fei Gao, Ying Zhou, Borui Yu, Huidi Xie, Yang Shi, Xianhui Zhang, Hongfang Liu

Faculty, Staff and Student Publications

BACKGROUND: QiDiTangShen granules (QDTS), a traditional Chinese medicine (TCM) compound prescription, have remarkable efficacy in diabetic nephropathy (DN) patients, and their pharmacological mechanism needs further exploration.

METHODS: According to the active ingredients and targets of the QDTS in the TCMSP database, the network pharmacology of QDTS was investigated. The potential active ingredients were chosen based on the oral bioavailability and the drug similarity index. At the same time, targets for DN-related disease were obtained from GeneCards, OMIM, PharmGKB, TTD, and DrugBank. The TCM-component-target network and the protein-protein interaction (PPI) network were constructed with the Cytoscape and STRING platforms, respectively, and …

The Influence Of Green Building Design On Tourists' Intention To Stay In Eco-Friendly Hotels: A Study On Perceptions And Preferences, Enas Al Sayed Salama Dr., Gehad Nasr Abdelsalam Dr.

Journal of Engineering Research

Green building design involves integrating sustainable and environmentally friendly practices into the construction and operation of buildings, including hotels, with the goal of minimizing the adverse impact on the environment. This paper delves into the pivotal role of green building design and eco-service provision within the context of green hotels. The aim is to investigate the interconnections among tourists' environmental concerns, their perceived significance of green building design, and their intention to choose green hotels.

To enhance the understanding of tourist behavior and its implications for tourism in Egypt, a questionnaire was administered in Sharm El Sheikh, a prominent Egyptian …