Physical Sciences and Mathematics Commons^™

Positron Emission Tomography In Oncology And Environmental Science, Samantha Delaney

Dissertations, Theses, and Capstone Projects

The last half century has played witness to the onset of molecular imaging for the clinical assessment of physiological targets. While several medical imaging modalities allow for the visualization of the functional and anatomical properties of humans and living systems, few offer accurate quantitation and the ability to detect biochemical processes with low-administered drug mass doses. This limits how physicians and scientists may diagnose and treat medical issues, such as cancer, disease, and foreign agents.

A promising alternative to extant invasive procedures and suboptimal imaging modalities to assess the nature of a biological environment is the use of positron emission …

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 …

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, …

Artificial Intelligence In Neuroradiology: A Scoping Review Of Some Ethical Challenges, Pegah Khosravi, Mark Schweitzer

Publications and Research

Artificial intelligence (AI) has great potential to increase accuracy and efficiency in many aspects of neuroradiology. It provides substantial opportunities for insights into brain pathophysiology, developing models to determine treatment decisions, and improving current prognostication as well as diagnostic algorithms. Concurrently, the autonomous use of AI models introduces ethical challenges regarding the scope of informed consent, risks associated with data privacy and protection, potential database biases, as well as responsibility and liability that might potentially arise. In this manuscript, we will first provide a brief overview of AI methods used in neuroradiology and segue into key methodological and ethical challenges. …

The Compound Risk Of Heat And Covid-19 In New York City: Riskscapes, Physical And Social Factors, And Interventions, Janelle Knox-Hayes, Juan Camilo Osorio, Natasha Stamler, Maria Dombrov, Rose Winer, Mary Hannah Smith, Reginald Blake, Cynthia Rosenzweig

Publications and Research

Climate change is disrupting the fundamental conditions of human life and exacerbating existing inequity by placing further burdens on communities that are already vulnerable. Risk exposure varies by where people live and work. In this article, we examine the spatial overlap of the compound risks of COVID-19 and extreme heat in New York City. We assess the relationship between socio-demographic and natural, built and social environmental characteristics, and the spatial correspondence of COVID-19 daily case rates across three pandemic waves. We use these data to create a compound risk index combining heat, COVID-19, density and social vulnerability. Our findings demonstrate …

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 …

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 …

Toward Informatics-Enabled Preparedness For Natural Hazards To Minimize Health Impacts Of Climate Change, Jimmy Phuong, Naomi O. Riches, Luca Calzoni, Gora Datta, Deborah Duran, Asiyah Yu Lin, Ramesh P. Singh, Anthony E. Solomonides, Noreen Y. Whysel, Ramakanth Kavuluru

Publications and Research

Natural hazards (NHs) associated with climate change have been increasing in frequency and intensity. These acute events impact humans both directly and through their effects on social and environmental determinants of health. Rather than relying on a fully reactive incident response disposition, it is crucial to ramp up preparedness initiatives for worsening case scenarios. In this perspective, we review the landscape of NH effects for human health and explore the potential of health informatics to address associated challenges, specifically from a preparedness angle. We outline important components in a health informatics agenda for hazard preparedness involving hazard-disease associations, social determinants …

Student Self-Grading Form, Brett Whysel

Open Educational Resources

This is a word document that students use at the beginning, midpoint, and end of a semester to set relevant goals, measure progress towards goals, and self-grade. It is intended to build motivation, metacognition, and accountability. Instructors may use it on its own or to supplement other assessment tools, and improve the accuracy, validity, and fairness of final grades.

Biomimetic And Medical Applications Of Hollow Nanoscale Structures, Justin Fang

Dissertations, Theses, and Capstone Projects

Materials whose structure incorporates nanoscale void spaces have multiple possible uses, whether in a bulk form or as individual particles, due to the combination of high surface area ratios and nanoscale material properties. This thesis will explore a few of these possibilities, concentrating on potential biomimetic and biomedical applications, for two materials: metal- organic frameworks and superparamagnetic iron oxide nanocages.

Metal-organic frameworks consist of metal ions such as Cu2+ which have highly porous lattice structures allowing them to absorb and release guest molecules such as peptides like diphenylalanine; this stored chemical energy can be turned into kinetic energy and used …

The Significance Of Sonic Branding To Strategically Stimulate Consumer Behavior: Content Analysis Of Four Interviews From Jeanna Isham’S “Sound In Marketing” Podcast, Ina Beilina

Student Theses and Dissertations

Purpose:
Sonic branding is not just about composing jingles like McDonald’s “I’m Lovin’ It.” Sonic branding is an industry that strategically designs a cohesive auditory component of a brand’s corporate identity. This paper examines the psychological impact of music and sound on consumer behavior reviewing studies from the past 40 years and investigates the significance of stimulating auditory perception by infusing sound in consumer experience in the modern 2020s.

Design/methodology/approach:
Qualitative content analysis of audio media was used to test two hypotheses. Four archival oral interview recordings from Jeanna Isham’s podcast “Sound in Marketing” featuring the sonic branding experts …

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 …

Usability Of Health-Related Websites By Filipino-American Adults And Nursing Informatics Experts, Kathleen Begonia

Dissertations, Theses, and Capstone Projects

Filipino-Americans are an understudied minority group with high prevalence and mortality from chronic conditions, such as cardiovascular disease and diabetes. Facing barriers to care and lack of culturally appropriate health resources, they frequently use the internet to obtain health information. It is unknown whether they perceive health-related websites to be useful or easy to use because there are no published usability studies involving this population. Using the Technology Acceptance Model as a theoretical framework, this study investigated the difference between website design ratings by experts and the perceptions of Filipino-American users to determine if usability guidelines influenced the perceived ease …

Diagnosis Of Polypoidal Choroidal Vasculopathy From Fluorescein Angiography Using Deep Learning, Yu-Yeh Tsai, Wei-Yang Lin, 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 of PCV. …

Automatic Cephalometric Landmark Detection On X-Ray Images Using Object Detection, Cheng-Ho King, Yin-Lin Wang, Chia-Ling Tsai

Publications and Research

We propose a new deep convolutional cephalometric landmark detection framework for orthodontic treatment. Our proposed method consists of two major steps: landmark detection using a deep neural network for object detection, and landmark repair to ensure one instance per landmark class. For landmark detection, we modify the loss function of the backbone network YOLOv3 to eliminate the constrains on the bounding box and incorporate attention mechanism to improve the detection accuracy. For landmark repair, a triangle mesh is generated from the average face to eliminate superfluous instances, followed by estimation of missing landmarks from the detected ones using Laplacian Mesh. …

Biomedical Applications Of Lanthanide Nanomaterials, For Imaging, Sensing And Therapy, Qize Zhang, Stephen O'Brien, Jan Grimm

Publications and Research

The application of nanomaterials made of rare earth elements within biomedical sciences continues to make significant progress. The rare earth elements, also called the lanthanides, play an essential role in modern life through materials and electronics. As we learn more about their utility, function, and underlying physics, we can contemplate extending their applications to biomedicine. This particularly applies to diagnosis and radiation therapy due to their relatively unique features, such as an ultra-wide Stokes shift in the luminescence, variable magnetism and potentially tunable properties, due to the library of lanthanides available and their multivalent oxidation state chemistry. The ability to …

Behavioral Predictive Analytics Towards Personalization For Self-Management – A Use Case On Linking Health-Related Social Needs, Bon Sy, Michael Wassil, Helene Connelly, Alisha Hassan

Publications and Research

The objective of this research is to investigate the feasibility of applying behavioral predictive analytics to optimize patient engagement in diabetes self-management, and to gain insights on the potential of infusing a chatbot with NLP technology for discovering health-related social needs. In the U.S., less than 25% of patients actively engage in self-health management even though self-health management has been reported to associate with improved health outcomes and reduced healthcare costs. The proposed behavioral predictive analytics relies on manifold clustering to identify subpopulations segmented by behavior readiness characteristics that exhibit non-linear properties. For each subpopulation, an individualized auto-regression model and …

Treatment Selection Using Prototyping In Latent-Space With Application To Depression Treatment, Akiva Kleinerman, Ariel Rosenfeld, David Benrimoh, Robert Fratila, Caitrin Armstrong, Joseph Mehltretter, Eliyahu Shneider, Amit Yaniv-Rosenfeld, Jordan Karp, Charles F. Reynolds, Gustavo Turecki, Adam Kapelner

Publications and Research

Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not …

The Temperature-Dependent Conformational Ensemble Of Sars-Cov-2 Main Protease (Mpro), Ali Ebrahim, Blake T. Riley, Desigan Kumaran, Babak Andi, Martin R. Fuchs, Sean Mcsweeney, Daniel A. Keedy

Publications and Research

The COVID-19 pandemic, instigated by the SARS-CoV-2 coronavirus, continues to plague the globe. The SARS-CoV-2 main protease, or M^pro, is a promising target for development of novel antiviral therapeutics. Previous X-ray crystal structures of M^pro were obtained at cryogenic temperature or room temperature only. Here we report a series of high-resolution crystal structures of unliganded M^pro across multiple temperatures from cryogenic to physiological, and another at high humidity. We interrogate these datasets with parsimonious multiconformer models, multi-copy ensemble models, and isomorphous difference density maps. Our analysis reveals a temperature-dependent conformational landscape for M^pro, including …

Molecular Dynamics Simulations Of Self-Assemblies In Nature And Nanotechnology, Phu Khanh Tang

Dissertations, Theses, and Capstone Projects

Nature usually divides complex systems into smaller building blocks specializing in a few tasks since one entity cannot achieve everything. Therefore, self-assembly is a robust tool exploited by Nature to build hierarchical systems that accomplish unique functions. The cell membrane distinguishes itself as an example of Nature’s self-assembly, defining and protecting the cell. By mimicking Nature’s designs using synthetically designed self-assemblies, researchers with advanced nanotechnological comprehension can manipulate these synthetic self-assemblies to improve many aspects of modern medicine and materials science. Understanding the competing underlying molecular interactions in self-assembly is always of interest to the academic scientific community and industry. …

Enhanced Platinum (Ii) Drug Delivery For Anti-Cancer Therapy, Marek T. Wlodarczyk

Dissertations, Theses, and Capstone Projects

Over the years, anti-cancer therapies have improved the overall survival rate of patients. Nevertheless, the traditional free drug therapies still suffer from side effects and systemic toxicity, resulting in low drug dosages in the clinic. This often leads to suboptimal drug concentrations reaching cancer cells, contributing to treatment failure and drug resistance. Among available anti-cancer therapies, metallodrugs are of great interest. Platinum (II)-based agents are highly potent and are used to treat many cancers, including ovarian cancer (OC). Cisplatin (cis-diaminedichloroplatinum (II)) is the first Food and Drug Administration (FDA)-approved metallodrug for treatment of solid tumors, and its mechanism …

Decoding Clinical Biomarker Space Of Covid-19: Exploring Matrix Factorization-Based Feature Selection Methods, Farshad Saberi-Movahed, Mahyar Mohammadifard, Adel Mehrpooya, Mohammad Rezaei-Ravari, Kamal Berahmand, Mehrdad Rostami, Saeed Karami, Mohammad Najafzadeh, Davood Hajinezhad, Mina Jamshidi, Farshid Abedi, Mahtab Mohammadifard, Elnaz Farbod, Farinaz Safavi, Mohammadreza Dorvash, Shahrzad Vahedi, Mahdi Eftekhari, Farid Saberi-Movahed, Iman Tavassoly

Publications and Research

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 …

Pattern Of Use Of Electronic Health Record (Ehr) Among The Chronically Ill: A Health Information National Trend Survey (Hints) Analysis, Rose Calixte, Sumaiya Islam, Zainab Toteh Osakwe, Argelis Rivera, Marlene Camacho-Rivera

Publications and Research

Effective patient–provider communication is a cornerstone of patient-centered care. Patient portals provide an effective method for secure communication between patients or their proxies and their health care providers. With greater acceptability of patient portals in private practices, patients have a unique opportunity to manage their health care needs. However, studies have shown that less than 50% of patients reported accessing the electronic health record (EHR) in a 12-month period. We used HINTS 5 cycle 1 and cycle 2 to assess disparities among US residents 18 and older with any chronic condition regarding the use of EHR for secure direct messaging …

Covid-19 Impact On Radiology Students’ Distance Learning (Summer 2021), Mary Lee, Jason Chan, Cheryann Jackson-Holmes, Renzo Marmolejo, Zoya Vinokur

Publications and Research

The Radiological Technology students have adjusted from the urgent distance learning that was enacted in the Spring of 2020, to the hybrid distance learning that is currently in place. This hybrid distance learning is the same way the incoming class of radiological technology students were taught. Both cohorts of students were tracked over the year by online anonymous surveys. We wanted to know how students were adapting to distance learning, if their focus and motivation varied over the course of the year due to changing pandemic conditions. For the students that were working, what impact did it have on their …

Covid-19 Multi-Targeted Drug Repurposing Using Few-Shot Learning, Yang Liu, You Wu, Xiaoke Shen, Lei Xie

Publications and Research

The life-threatening disease COVID-19 has inspired significant efforts to discover novel therapeutic agents through repurposing of existing drugs. Although multi-targeted (polypharmacological) therapies are recognized as the most efficient approach to system diseases such as COVID-19, computational multi-targeted compound screening has been limited by the scarcity of high-quality experimental data and difficulties in extracting information from molecules. This study introduces MolGNN , a new deep learning model for molecular property prediction. MolGNN applies a graph neural network to computational learning of chemical molecule embedding. Comparing to state-of-the-art approaches heavily relying on labeled experimental data, our method achieves equivalent or superior prediction …

Design, Synthesis And Evaluation Of Molecules With Selective And Poly-Pharmacological Actions At D1r, D3r And Sigma Receptors, Pierpaolo Cordone

Dissertations, Theses, and Capstone Projects

The dopamine D3 receptor (D3R) is one of the most studied receptors involved in drug addiction. One of the most common strategies to treat substance use disorders is via D3R antagonism. The majority of the D3R antagonists synthesized so far have poor pharmacokinetic properties and/or lack selectivity toward D3R. In this thesis, the design, synthesis and biological evaluation of novel molecules that target the dopamine D1 receptor (D1R), D3R and the serendipitous discovery of molecules that target s receptors will be described.

Chapter 1 presents a survey of the fundamental pharmacology of D1R, D3R and s receptors and the therapeutic …

Intangible Cultural Heritage: A Benefit To Climate-Displaced And Host Communities, Gül Aktürk, Martha B. Lerski

Publications and Research

Climate change is borderless, and its impacts are not shared equally by all communities. It causes an imbalance between people by creating a more desirable living environment for some societies while erasing settlements and shelters of some others. Due to floods, sea level rise, destructive storms, drought, and slow-onset factors such as salinization of water and soil, people lose their lands, homes, and natural resources. Catastrophic events force people to move voluntarily or involuntarily. The relocation of communities is a debatable climate adaptation measure which requires utmost care with human rights, ethics, and psychological well-being of individuals upon the issues …

Interfacial Dynamics And Ionic Transport Of Radiologic Contrast Media In Carbohydrate Matrix: Utility And Limits Of X-Ray Imaging, Lin Mousa, Hayley Sanchez, Subhendra Sarkar, Zoya Vinokur

Publications and Research

Hello, our names are Lin Mousa and Hayley Sanchez, this semester we participated in a research project dedicated to analyzing the interactions of contrast media with the molecular components of fruits to compare how they would react with the human brain. This project involved the injection of fruits with varying contrasts and the imaging of the diffusion and interactions of the contrast within the fruits with X-rays. With setup technical parameters on the x-ray equipment images were taken with identical setups at an hourly rate for several days. The final results of this experiment indicated that contrasts such as Gadolinium …

Human Ace2‑Functionalized Gold “Virus‑Trap” Nanostructures For Accurate Capture Of Sars‑Cov‑2 And Single‑Virus Sers Detection, Yong Yang, Yusi Peng, Chenglong Lin, Li Long, Jingying Hu, Jun He, Hui Zeng, Zhengren Huang, Zhi-Yuan Li, Masaki Tanemura, Jianlin Shi, John R. Lombardi, Xiaoying Luo

Publications and Research

The current COVID-19 pandemic urges the extremely sensitive and prompt detection of SARS-CoV-2 virus. Here, we present a Human Angiotensin-converting-enzyme 2 (ACE2)-functionalized gold “virus traps” nanostructure as an extremely sensitive SERS biosensor, to selectively capture and rapidly detect S-protein expressed coronavirus, such as the current SARS-CoV-2 in the contaminated water, down to the single-virus level. Such a SERS sensor features extraordinary 106- fold virus enrichment originating from high-affinity of ACE2 with S protein as well as “virus-traps” composed of oblique gold nanoneedles, and 109- fold enhancement of Raman signals originating from multicomponent SERS effects. Furthermore, the identification standard of virus …

Protocol For A National Probability Survey Using Home Specimen Collection Methods To Assess Prevalence And Incidence Of Sars-Cov-2 Infection And Antibody Response, Aaron J. Siegler, Patrick S. Sullivan, Travis Sanchez, Ben Lopman, Mansour Fahimi, Charles Sailey, Martin Frankel, Richard Rothenberg, Colleen F. Kelley, Heather Bradley

Publications and Research

Purpose: The U.S. response to the SARS-CoV-2 epidemic has been hampered by early and ongoing delays in testing for infection; without data on where infections were occurring and the magnitude of the epidemic, early public health responses were not data-driven. Understanding the prevalence of SARSCoV- 2 infections and immune response is critical to developing and implementing effective public health responses. Most serological surveys have been limited to localities that opted to conduct them and/or were based on convenience samples. Moreover, results of antibody testing might be subject to high false positive rates in the setting of low prevalence of immune …