Diseases Commons^™

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, …

Modeling Of Cns Cancer With A Focus On The Immune Component, Daniel Zamler

Dissertations and Theses (Open Access)

The knowledge surrounding cancers of the central nervous system remains poorly developed, in particular with regard to the immune component. The works contained in this thesis look at craniopharyngioma, glioblastoma, and several forms of brain metastasis. While some attention is given to the tumor cells themselves, as well as the patient setting which these studies model, the immune component of disease progression and treatment plays a strong role in each and is the primary focus of the works contained.

Craniopharyngioma is a relatively rare tumor in adults. Although histologically benign, it can be locally aggressive and may require additional therapeutic …

Vampires And Other Diseases: Stochastic Infection Dynamics Of Small Populations, Marijn Jaarsma

Honors Projects in Science and Technology

Mathematical models are powerful tools often applied in the field of epidemiology. The type and shape of the model will differ between different types of diseases. In this study, stochastic dynamical models are applied to the entertaining example of vampires, and the results of this analysis are compared to real-life diseases with small populations of infected. The data used comes from pop-culture depictions of vampires in literature, television shows, movies, and fan pages associated with these depictions. The aim of this study is to serve as an educational tool for modeling diseases with small populations to predict and control the …

Three-Heartbeat Multilead Ecg Recognition Method For Arrhythmia Classification, Liang-Hung Wang, Yan-Ting Yu, Wei Liu, Lu Xu, Chao-Xin Xie, Tao Yang, I-Chun Kuo, Xin-Kang Wang, Jie Gao, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Electrocardiogram (ECG) is the primary basis for the diagnosis of cardiovascular diseases. However, the amount of ECG data of patients makes manual interpretation time-consuming and onerous. Therefore, the intelligent ECG recognition technology is an important means to decrease the shortage of medical resources. This study proposes a novel classification method for arrhythmia that uses for the very first time a three-heartbeat multi-lead (THML) ECG data in which each fragment contains three complete heartbeat processes of multiple ECG leads. The THML ECG data pre-processing method is formulated which makes use of the MIT-BIH arrhythmia database as training samples. Four arrhythmia classification …

Computer Simulations And Network-Based Profiling Of Binding And Allosteric Interactions Of Sars-Cov-2 Spike Variant Complexes And The Host Receptor: Dissecting The Mechanistic Effects Of The Delta And Omicron Mutations, Gennady M. Verkhivker, Steve Agajanian, Ryan Kassab, Keerthi Krishnan

Mathematics, Physics, and Computer Science Faculty Articles and Research

In this study, we combine all-atom MD simulations and comprehensive mutational scanning of S-RBD complexes with the angiotensin-converting enzyme 2 (ACE2) host receptor in the native form as well as the S-RBD Delta and Omicron variants to (a) examine the differences in the dynamic signatures of the S-RBD complexes and (b) identify the critical binding hotspots and sensitivity of the mutational positions. We also examined the differences in allosteric interactions and communications in the S-RBD complexes for the Delta and Omicron variants. Through the perturbation-based scanning of the allosteric propensities of the SARS-CoV-2 S-RBD residues and dynamics-based network centrality and …

A Proposed Treatment Of Mixed Connective Tissue Disease By Competitive Inhibition Of Autoantibodies, Thomas Russell

Senior Honors Theses

Mixed Connective Tissue Disease is an autoimmune disease characterized by Raynaud’s phenomenon and arthritis among other symptoms. It is primarily caused by antibodies that target the U1-RNP 70K peptide. The treatment proposed in this paper uses competitive inhibition to prevent the binding of the anti-U1-RNP 70K antibodies with the U1-RNP 70K peptide. A method for testing the designed treatment in silico is proposed using AutoDock Vina docking software.

Institute For Global Health And Development : Issue 3 - April - June 2022, Institute For Global Health And Development

IGHD Newsletters

• Research Highlights
• Reflections from IGHD’s Associate Faculty
• Key Publications
• Webinars and Dialogues
• Celebration
• Collaborate With U

Prognostic And Predictive Value Of Β-Blockers In The Eortc 1325/Keynote-054 Phase Iii Trial Of Pembrolizumab Versus Placebo In Resected High-Risk Stage Iii Melanoma, Oliver J. Kennedy, Michal Kicinski, Sara Valpione, Sara Gandini, Stefan Suciu, Christian U. Blank, Georgina V. Long, Victoria G. Atkinson, Stéphane Dalle, Andrew M. Haydon, Andrey Meshcheryakov, Adnan Khattak, Matteo S. Carlino, Shahneen Sandhu, James Larkin, Susana Puig, Paolo A. Ascierto, Piotr Rutkowski, Dirk Schadendorf, Rutger Koornstra, Leonel Hernandez-Aya, Anna M. Di Giacomo, Alfonsus J.M. Van Den Eertwegh, Jean Jacques Grob, Ralf Gutzmer, Rahima Jamal, Alexander C.J. Van Akkooi, Caroline Robert, Alexander M. M. Eggermont, Paul Lorigan, Mario Mandala

Research outputs 2022 to 2026

Background:

β-adrenergic receptors are upregulated in melanoma cells and contribute to an immunosuppressive, pro-tumorigenic microenvironment. This study investigated the prognostic and predictive value of β-adrenoreceptor blockade by β-blockers in the EORTC1325/KEYNOTE-054 randomised controlled trial.

Methods:

Patients with resected stage IIIA, IIIB or IIIC melanoma and regional lymphadenectomy received 200 mg of adjuvant pembrolizumab (n = 514) or placebo (n = 505) every three weeks for one year or until recurrence or unacceptable toxicity. At a median follow-up of 3 years, pembrolizumab prolonged recurrence-free survival (RFS) compared to placebo (hazard ratio (HR) 0.56, 95% confidence interval (CI) 0.47–0.68). β-blocker use was …

Succinic Semialdehyde Dehydrogenase Deficiency (Ssadhd): Qualitative Needs Assessment For Patients With A Rare Neurological Disorder, Cassandra Bovee, Kayla Woodring

Annual Research Symposium

No abstract provided.

A Quantitative Analysis Of The Efficacy Of Various Essential Oils Against The Sars Cov-2 Virus, Elizabeth Wagstaff, Chandrelyn Kraczek, Jack Brandon Lopez

Annual Research Symposium

A poster presentation and abstract for the Roseman Symposium. The project focuses on testing 3 essential oil blends and two disinfectants containing an essential oil blend against SARS CoV-2 in response to the COVID-19 pandemic. The project procedure involves plaque assays, disinfection, and neutralization techniques.

Dissecting Mutational Allosteric Effects In Alkaline Phosphatases Associated With Different Hypophosphatasia Phenotypes: An Integrative Computational Investigation, Fei Xiao, Ziyun Zhou, Xingyu Song, Mi Gan, Jie Long, Gennady M. Verkhivker, Guang Hu

Mathematics, Physics, and Computer Science Faculty Articles and Research

Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization and is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations in ALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach is proposed for the large-scale analysis of ALPL mutations-from nonpathogenic to severe HPPs. By using a pipeline of synergistic approaches including sequence-structure analysis, network modeling, elastic network models and atomistic simulations, we characterized allosteric signatures and effects of the ALPL mutations on protein dynamics and function. Statistical analysis of molecular features computed for the …

Allosteric Determinants Of The Sars-Cov-2 Spike Protein Binding With Nanobodies: Examining Mechanisms Of Mutational Escape And Sensitivity Of The Omicron Variant, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Structural and biochemical studies have recently revealed a range of rationally engineered nanobodies with efficient neutralizing capacity against the SARS-CoV-2 virus and resilience against mutational escape. In this study, we performed a comprehensive computational analysis of the SARS-CoV-2 spike trimer complexes with single nanobodies Nb6, VHH E, and complex with VHH E/VHH V nanobody combination. We combined coarse-grained and all-atom molecular simulations and collective dynamics analysis with binding free energy scanning, perturbation-response scanning, and network centrality analysis to examine mechanisms of nanobody-induced allosteric modulation and cooperativity in the SARS-CoV-2 spike trimer complexes with these nanobodies. By quantifying energetic and allosteric …

Adverse Events Reporting Of Clinical Trials In Exercise Oncology Research (Advance): Protocol For A Scoping Review, Hao Luo, Oliver Schumacher, Daniel A. Galvão, Robert U. Newton, Dennis R. Taaffe

Research outputs 2022 to 2026

Introduction: Adequate, transparent, and consistent reporting of adverse events (AEs) in exercise oncology trials is critical to assess the safety of exercise interventions for people following a cancer diagnosis. However, there is little understanding of how AEs are reported in exercise oncology trials. Thus, we propose to conduct a scoping review to summarise and evaluate current practice of reporting of AEs in published exercise oncology trials with further exploration of factors associated with inadequate reporting of AEs. The study findings will serve to inform the need for future research on standardisation of the definition, collection, and reporting of AEs for …

Diagnosis Of Polypoidal Choroidal Vasculopathy From Fluorescein Angiography Using Deep Learning, Yu-Yeh Tsai, Wei-Yang Ling, Shih-Jen Chen, Paisan Ruamviboonsuk, Cheng-Ho King, Chia-Ling Tsai

Publications and Research

Purpose: To differentiate polypoidal choroidal vasculopathy (PCV) from choroidal neovascularization (CNV) and to determine the extent of PCV from fluorescein angiography (FA) using attention-based deep learning networks.

Methods: We build two deep learning networks for diagnosis of PCV using FA, one for detection and one for segmentation. Attention-gated convolutional neural network (AG-CNN) differentiates PCV from other types of wet age-related macular degeneration. Gradient-weighted class activation map (Grad-CAM) is generated to highlight important regions in the image for making the prediction, which offers explainability of the network. Attention-gated recurrent neural network (AG-PCVNet) for spatiotemporal prediction is applied for segmentation …

Pparα And Pparγ Activation Is Associated With Pleural Mesothelioma Invasion But Therapeutic Inhibition Is Ineffective, M. Lizeth Orozco Morales, Catherine A. Rinaldi, Emma De Jong, Sally M. Lansley, Joel P. A. Gummer, Bence Olasz, Shabarinath Nambiar, Danika E. Hope, Thomas H. Casey, Y. C. Gary Lee, Connull Leslie, Gareth Nealon, David M. Shackleford, Andrew K. Powell, Marina Grimaldi, Patrick Balaguer, Rachael M. Zemek, Anthony Bosco, Matthew J. Piggott, Alice Vrielink, Richard A. Lake, W. Joost Lesterhuis

Research outputs 2022 to 2026

Mesothelioma is a cancer that typically originates in the pleura of the lungs. It rapidly invades the surrounding tissues, causing pain and shortness of breath. We compared cell lines injected either subcutaneously or intrapleurally and found that only the latter resulted in invasive and rapid growth. Pleural tumors displayed a transcriptional signature consistent with increased activity of nuclear receptors PPARα and PPARγ and with an increased abundance of endogenous PPAR-activating ligands. We found that chemical probe GW6471 is a potent, dual PPARα/γ antagonist with anti-invasive and anti-proliferative activity in vitro. However, administration of GW6471 at doses that provided sustained plasma …

Cardiovascular Applications Of Artificial Intelligence In Research, Diagnosis, And Disease Management, Viswanathan Rajagopalan, Houwei Cao

Center for No Boundary Thinking

Despite significant advancements in diagnosis and disease management, cardiovascular (CV) disorders remain the No. 1 killer both in the United States and across the world, and innovative and transformative technologies such as artificial intelligence (AI) are increasingly employed in CV medicine. In this chapter, the authors introduce different AI and machine learning (ML) tools including support vector machine (SVM), gradient boosting machine (GBM), and deep learning models (DL), and their applicability to advance CV diagnosis and disease classification, and risk prediction and patient management. The applications include, but are not limited to, electrocardiogram, imaging, genomics, and drug research in different …

A Multidisciplinary Collaboration Between Graphic Design And Physics Classes Responding To Covid-19, Szilvia Kadas, Eric M. Edlund

The SUNY Journal of the Scholarship of Engagement: JoSE

Students from graphic design and physics classes at SUNY Cortland collaborated during the spring semester of 2020 on a multidisciplinary project related to the COVID-19 pandemic. In these collaborations, the students’ individual contributions were part of a larger project that required a diverse skill set, through which students learned how different skills can complement their own disciplines. The graphic design and physics instructors applied a project-based learning philosophy applying the Common Problem Pedagogy (CPP) framework to construct student-teams composed of both disciplines. This project explored how coordinated social actions can allow the public to exercise control in uncertain times. Students …

Progress In Protein Structure Prediction: An Xai Perspective, Yosef E. Granillo, Badri Adhikari

Undergraduate Research Symposium

The full extent of the impact of deep learning models on structural biology will depend on their ability to provide novel biological insights. The field of structure prediction, where deep learning has produced miraculously accurate results, is at a critical stage of benefiting from the methods in interpretable deep learning. The exact mechanisms by which advanced computational models learn to interpret protein shapes and functions during their training remain largely unclear. These questions underscore the need for further research, as understanding these mechanisms is crucial for researchers to trust the predictions of these models. However, interpretable machine learning is ripening …

Cholera Transmission Dynamic Model With Environmental Impacts Of Plankton Reservoirs, Sweety Sarker

Electronic Theses and Dissertations, 2020-2023

Cholera is an acute disease that is a global threat to the world and can kill people within a few hours if left untreated. In the last 200 years, seven pandemics occurred, and, in some countries, it remains endemic. The World Health Organization (WHO) declared a global initiative to prevent cholera by 2030. Cholera dynamics are contributed by several environmental factors such as salinity level of water, water temperature, presence of plankton especially zooplankton such as cladocerans, rotifers, copepods, etc. Vibrio cholerae (V. cholerae) bacterium is the main reason behind the cholera disease and the growth of V. cholerae depends …

A Unified Health Information System Framework For Connecting Data, People, Devices, And Systems, Wu He, Justin Zuopeng Zhang, Huanmei Wu, Wenzhuo Li, Sachin Shetty

Information Technology & Decision Sciences Faculty Publications

The COVID-19 pandemic has heightened the necessity for pervasive data and system interoperability to manage healthcare information and knowledge. There is an urgent need to better understand the role of interoperability in improving the societal responses to the pandemic. This paper explores data and system interoperability, a very specific area that could contribute to fighting COVID-19. Specifically, the authors propose a unified health information system framework to connect data, systems, and devices to increase interoperability and manage healthcare information and knowledge. A blockchain-based solution is also provided as a recommendation for improving the data and system interoperability in healthcare.

An Odd-Protocol For Agent-Based Model For The Spread Of Covid-19 In Ireland, Elizabeth Hunter, John D. Kelleher

Reports

No abstract provided.

Quantification Of Infarct Core Signal Using Ct Imaging In Acute Ischemic Stroke, Uma Maria Lal-Trehan Estrada, Grant Meeks, Sergio Salazar-Marioni, Fabien Scalzo, Mudassir Farooqui, Juan Vivanco-Suarez, Santiago Ortega Gutierrez, Sunil A Sheth, Luca Giancardo

Faculty, Staff and Student Publications

In stroke care, the extent of irreversible brain injury, termed infarct core, plays a key role in determining eligibility for acute treatments, such as intravenous thrombolysis and endovascular reperfusion therapies. Many of the pivotal randomized clinical trials testing those therapies used MRI Diffusion-Weighted Imaging (DWI) or CT Perfusion (CTP) to define infarct core. Unfortunately, these modalities are not available 24/7 outside of large stroke centers. As such, there is a need for accurate infarct core determination using faster and more widely available imaging modalities including Non-Contrast CT (NCCT) and CT Angiography (CTA). Prior studies have suggested that CTA provides improved …

Causal Inference Of Genetic Variants And Genes In Amyotrophic Lateral Sclerosis, Siyu Pan, Xinxuan Liu, Tianzi Liu, Zhongming Zhao, Yulin Dai, Yin-Ying Wang, Peilin Jia, Fan Liu

Faculty, Staff and Student Publications

Amyotrophic lateral sclerosis (ALS) is a fatal progressive multisystem disorder with limited therapeutic options. Although genome-wide association studies (GWASs) have revealed multiple ALS susceptibility loci, the exact identities of causal variants, genes, cell types, tissues, and their functional roles in the development of ALS remain largely unknown. Here, we reported a comprehensive post-GWAS analysis of the recent large ALS GWAS (n = 80,610), including functional mapping and annotation (FUMA), transcriptome-wide association study (TWAS), colocalization (COLOC), and summary data-based Mendelian randomization analyses (SMR) in extensive multi-omics datasets. Gene property analysis highlighted inhibitory neuron 6, oligodendrocytes, and GABAergic neurons (Gad1/Gad2) as …

Fairly Predicting Graft Failure In Liver Transplant For Organ Assigning, Sirui Ding, Ruixiang Tang, Daochen Zha, Na Zou, Kai Zhang, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

Liver transplant is an essential therapy performed for severe liver diseases. The fact of scarce liver resources makes the organ assigning crucial. Model for End-stage Liver Disease (MELD) score is a widely adopted criterion when making organ distribution decisions. However, it ignores post-transplant outcomes and organ/donor features. These limitations motivate the emergence of machine learning (ML) models. Unfortunately, ML models could be unfair and trigger bias against certain groups of people. To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant. Specifically, knowledge distillation is employed to handle dense and sparse …

Application Of Artificial Intelligence And Machine Learning For Hiv Prevention Interventions, Yang Xiang, Jingcheng Du, Kayo Fujimoto, Fang Li, John Schneider, Cui Tao

Faculty, Staff and Student Publications

In 2019, the US Government announced its goal to end the HIV epidemic within 10 years, mirroring the initiatives set forth by UNAIDS. Public health prevention interventions are a crucial part of this ambitious goal. However, numerous challenges to this goal exist, including improving HIV awareness, increasing early HIV infection detection, ensuring rapid treatment, optimising resource distribution, and providing efficient prevention services for vulnerable populations. Artificial intelligence has had a pivotal role in revolutionising health care and has shown great potential in developing effective HIV prevention intervention strategies. Although artificial intelligence has been used in a few HIV prevention intervention …

A Nomogram For Predicting Upper Urinary Tract Damage Risk In Children With Neurogenic Bladder, Qi Li, Miao Cai, Qingsong Pu, Shengde Wu, Xing Liu, Tao Lin, Dawei He, Jianguo Wen, Guanghui Wei

Faculty, Staff and Student Publications

PURPOSE: To establish a predictive model for upper urinary tract damage (UUTD) in children with neurogenic bladder (NB) and verify its efficacy.

METHODS: A retrospective study was conducted that consisted of a training cohort with 167 NB patients and a validation cohort with 100 NB children. The clinical data of the two groups were compared first, and then univariate and multivariate logistic regression analyses were performed on the training cohort to identify predictors and develop the nomogram. The accuracy and clinical usefulness of the nomogram were verified by receiver operating characteristic (ROC) curve, calibration curve and decision curve analyses.

RESULTS: …

Artificial Intelligence In The Pediatric Echocardiography Laboratory: Automation, Physiology, And Outcomes, Minh B Nguyen, Olivier Villemain, Mark K Friedberg, Lasse Lovstakken, Craig G Rusin, Luc Mertens

Faculty, Staff and Students Publications

Artificial intelligence (AI) is frequently used in non-medical fields to assist with automation and decision-making. The potential for AI in pediatric cardiology, especially in the echocardiography laboratory, is very high. There are multiple tasks AI is designed to do that could improve the quality, interpretation, and clinical application of echocardiographic data at the level of the sonographer, echocardiographer, and clinician. In this state-of-the-art review, we highlight the pertinent literature on machine learning in echocardiography and discuss its applications in the pediatric echocardiography lab with a focus on automation of the pediatric echocardiogram and the use of echo data to better …

Smoking, Alcohol Consumption, And Depression In Association With Incidence Of Type 2 Diabetes Among Mexican Americans In Starr County, Texas, Gabriela Rubannelsonkumar

Honors Program Theses and Research Projects

Previous studies on conditions like obesity, hypertension, and type 2 diabetes mellitus (T2DM) have explored the correlations between them and various other human conditions, including aortic stiffness, left ventricular hypertrophy and sleep apnea, as they predict possibilities of developing certain diseases in Mexican Americans. This study aims to observe the correlation between lifestyle decisions that could relate to the onset of the depression in normal, prediabetic, and diabetic individuals. These include smoking habits and alcohol consumption. Many papers have previously conducted research on these lifestyle habits as they relate to obesity, hypertension, diabetes, however, have done so in a singular …

Visualizing Phytochemical-Protein Interaction Networks: Momordica Charantia And Cancer, Yumi L. Briones, Alexander T. Young, Fabian M. Dayrit, Armando Jerome De Jesus, Nina Rosario L. Rojas

Chemistry Faculty Publications

The in silico study of medicinal plants is a rapidly growing field. Techniques such as reverse screening and network pharmacology are used to study the complex cellular action of medicinal plants against disease. However, it is difficult to produce a meaningful visualization of phytochemical-protein interactions (PCPIs) in the cell. This study introduces a novel workflow combining various tools to visualize a PCPI network for a medicinal plant against a disease. The five steps are 1) phytochemical compilation, 2) reverse screening, 3) network building, 4) network visualization, and 5) evaluation. The output is a PCPI network that encodes multiple dimensions of …

Modeling, Analysis And Simulation Of Covid-19 Interaction Dynamics Between Local Community In Saudi Arabia And Visiting Sub-Population, Manal Badgaish, Padmanabhan Seshaiyer

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.