Medicine and Health Sciences Commons^™

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

Validating Osteological Correlates For The Hepatic Piston In The American Alligator (Alligator Mississippiensis), Clinton A. Grand Pre, William Thielicke, Raul E. Diaz, Brandon P. Hedrick, Ruth M. Elsey, Emma R. Schachner

School of Graduate Studies Faculty Publications

Unlike the majority of sauropsids, which breathe primarily through costal and abdominal muscle contractions, extant crocodilians have evolved the hepatic piston pump, a unique additional ventilatory mechanism powered by the diaphragmaticus muscle. This muscle originates from the bony pelvis, wrapping around the abdominal viscera, extending cranially to the liver. The liver then attaches to the caudal margin of the lungs, resulting in a sub-fusiform morphology for the entire ‘‘pulmo-hepatic-diaphragmatic’’ structure. When the diaphragmaticus muscle contracts during inspiration, the liver is pulled caudally, lowering pressure in the thoracolumbar cavity, and inflating the lungs. It has been established that the hepatic piston …

Beyond The Basics: Unraveling The Complexity Of Coronary Artery Calcification, Satwat Hashmi, Pashmina Wiqar Shah, Zouhair Aherrahrou, Elena Aikawa, Rédouane Aherrahrou

Department of Biological & Biomedical Sciences

Coronary artery calcification (CAC) is mainly associated with coronary atherosclerosis, which is an indicator of coronary artery disease (CAD). CAC refers to the accumulation of calcium phosphate deposits, classified as micro- or macrocalcifications, that lead to the hardening and narrowing of the coronary arteries. CAC is a strong predictor of future cardiovascular events, such as myocardial infarction and sudden death. Our narrative review focuses on the pathophysiology of CAC, exploring its link to plaque vulnerability, genetic factors, and how race and sex can affect the condition. We also examined the connection between the gut microbiome and CAC, and the impact …

Interpretable Mechanistic And Machine Learning Models For Pre-Dicting Cardiac Remodeling From Biochemical And Biomechanical Features, Anamul Haque

All Dissertations

Biochemical and biomechanical signals drive cardiac remodeling, resulting in altered heart physiology and the precursor for several cardiac diseases, the leading cause of death for most racial groups in the USA. Reversing cardiac remodeling requires medication and device-assisted treatment such as Cardiac Resynchronization Therapy (CRT), but current interventions produce highly variable responses from patient to patient. Mechanistic modeling and Machine learning (ML) approaches have the functionality to aid diagnosis and therapy selection using various input features. Moreover, 'Interpretable' machine learning methods have helped make machine learning models fairer and more suited for clinical application. The overarching objective of this doctoral …

Research Data Management Readiness At Uganda Cancer Institute, Edward Mukiibi, Joyce Bukirwa

Library Philosophy and Practice (e-journal)

The study explored research data management readiness at the Uganda Cancer Institute. Its objectives were to; establish the state of research data and the institutional readiness for research data management practices. The case study applied a survey method using a questionnaire modified from the Data Asset Framework and the Community Capability Model Framework. The respondents were 60 staff members at different professional levels purposively selected. The findings show massive data generated from clinical trials, and routine cancer clinics at the institute. The business processes are mainly manual except for the funded research projects which are hybrid. The existing data sets …

Predicting Multiple Sclerosis Severity With Multimodal Deep Neural Networks, Kai Zhang, John A Lincoln, Xiaoqian Jiang, Elmer V Bernstam, Shayan Shams

Journal Articles

Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) create opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning …

Molecular Diagnostics - Biomarker Based Diagnosis Of Human Papillomavirus (Hpv), Lilly Hivner

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

Research on how HPV-16 E6 identifies cervical cancer more often than others.

Operationalizing The Datagauge Framework In A Health Information Exchange Utilizing Hepatitis C Data, Edward Yao

Translational Projects (Open Access)

This project aims to implement the DataGauge framework in a health information exchange (HIE) setting as a proof of concept. The modified DataGauge framework, described by Diaz-Garelli et al. (2019), is utilized to test its functionality and applicability with any dataset. The specific objective of the project is to determine the number of hepatitis C-positive tests within the HIE. The implementation involved multiple iterations following the DataGauge framework's steps for data extraction and analysis. Five iterations were conducted, resulting in both successful and failed queries based on the validity of the data standards. The findings revealed that the HIE, in …

Advancing Precision Medicine: Unveiling Disease Trajectories, Decoding Biomarkers, And Tailoring Individual Treatments, Yanfei Wang

Dissertations & Theses (Open Access)

Chronic diseases are not only prevalent but also exert a considerable strain on the healthcare system, individuals, and communities. Nearly half of all Americans suffer from at least one chronic disease, which is still growing. The development of machine learning has brought new directions to chronic disease analysis. Many data scientists have devoted themselves to understanding how a disease progresses over time, which can lead to better patient management, identification of disease stages, and targeted interventions. However, due to the slow progression of chronic disease, symptoms are barely noticed until the disease is advanced, challenging early detection. Meanwhile, chronic diseases …

Pca-Clf: A Classifier Of Prostate Cancer Patients Into Patients With Indolent And Aggressive Tumors Using Machine Learning, Yashwanth Karthik Kumar Mamidi, Tarun Karthik Kumar Mamidi, Md Wasi Ul Kabir, Jiande Wu, Md Tamjidul Hoque, Chindo Hicks

School of Medicine Faculty Publications

A critical unmet medical need in prostate cancer (PCa) clinical management centers around distinguishing indolent from aggressive tumors. Traditionally, Gleason grading has been utilized for this purpose. However, tumor classification using Gleason Grade 7 is often ambiguous, as the clinical behavior of these tumors follows a variable clinical course. This study aimed to investigate the application of machine learning techniques (ML) to classify patients into indolent and aggressive PCas. We used gene expression data from The Cancer Genome Atlas and compared gene expression levels between indolent and aggressive tumors to identify features for developing and validating a range of ML …

Self-Supervised Deep Clustering Of Single-Cell Rna-Seq Data To Hierarchically Detect Rare Cell Populations., Tianyuan Lei, Ruoyu Chen, Shaoqiang Zhang, Yong Chen

Faculty Scholarship for the College of Science & Mathematics

Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. …

Sctiger: A Deep-Learning Method For Inferring Gene Regulatory Networks From Single-Cell Gene Expression Data, Madison Dautle

Theses and Dissertations

Inferring gene regulatory networks (GRNs) from single-cell RNA-sequencing (scRNA-seq) data is an important computational question to reveal fundamental regulatory mechanisms. Although many computational methods have been designed to predict GRNs, none work on condition specific GRNs by directly using paired datasets of case versus control experiments, common in diverse biological research projects. We present a novel deep-learning based method, scTIGER, for GRN detection by using the co-dynamics of gene expression. scTIGER also employs cell type-based pseudotiming, an attention-based convolutional neural network method, and permutation-based significance testing to infer GRNs from gene modules. We first applied scTIGER to scRNA-seq datasets of …

A Quantitative Visualization Tool For The Assessment Of Mammographic Risky Dense Tissue Types, Margaret R. Mccarthy

Electronic Theses and Dissertations

Breast cancer is the second most occurring cancer type and is ranked fifth in terms of mortality. X-ray mammography is the most common methodology of breast imaging and can show radiographic signs of cancer, such as masses and calcifcations. From these mammograms, radiologists can also assess breast density, which is a known cancer risk factor. However, since not all dense tissue is cancer-prone, we hypothesize that dense tissue can be segregated into healthy vs. risky subtypes. We propose that risky dense tissue is associated with tissue microenvironment disorganization, which can be quantified via a computational characterization of the whole breast …

Mutation-Induced Changes In The Stability, B-Cell Epitope, And Antigenicity Of The Sars-Cov-2 Variant Spike Protein: A Comparative Computational Stud, Nira Meirita Wijayanti, Muhammad Hermawan Widyananda, Lailil Muflikhah, Nashi Widodo

Karbala International Journal of Modern Science

The spike (S) protein is a major antigenicity site that targets neutralizing antibodies and drugs. The growing number of S protein mutations has become a severe problem for developing effective vaccines. Here, we investigated four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that were the most infectious and widespread during the COVID-19 pandemic to determine the trends and patterns of mutation-induced changes in the stability, B-cell epitope, and antigenicity of the SARS-CoV-2 S protein. The data showed that the Beta and Gamma variants had three mutations on the receptor-binding domain (RBD), which is the specific site on the S …

Genome-Scale Methylation Analysis In Blood And Tumor Identifies Immune Profile, Age Acceleration, And Dna Methylation Alterations Associated With Bladder Cancer Outcomes, Ji-Qing Chen

Dartmouth College Ph.D Dissertations

Bladder cancer patients receive frequent screening due to the high tumor recurrence rate (more than 60%). Nowadays, the conventional monitoring method relies on cystoscopy which is highly invasive and increases patient morbidity and burden to the health care system with frequent follow-up. As a result, it is urgent to explore novel markers related to the outcomes of bladder cancer. Immune profiles have been associated with cancer outcomes and may have the potential to be biomarkers for outcomes management. However, little work has been conducted to investigate the associations of immune cell profiles with bladder cancer outcomes. Here, I utilized the …

Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney

Theses and Dissertations

Federated learning is a framework in machine learning that allows for training a model while maintaining data privacy. Moreover, it allows clients with their own data to collaborate in order to build a stronger, shared model. Federated learning is of particular interest to healthcare data, since it is of the utmost importance to respect patient privacy while still building useful diagnostic tools. However, healthcare data can be complicated — data format might differ across providers, leading to unexpected inputs and incompatibility between different providers. For example, electrocardiograms might differ in sampling rate or number of leads used, meaning that a …

Evidence Assisted Learning For Clinical Decision Support Systems, Bhanu Pratap Singh Rawat

Doctoral Dissertations

Clinical decision support systems (CDSS) provide intelligently filtered knowledge and patient-specific and population information to the clinicians, nursing staff and healthcare professionals. CDSS can significantly improve the quality, safety, efficiency and effectiveness of health care. Over the last decade, American hospitals have adopted electronic health records (EHRs) widely resulting in a massive collection of clinical notes such as admission notes, physician notes, nursing notes and discharge summaries. For the past couple of decades, most of the work in CDSS has been focused on developing knowledge-based systems using structured data such as medications and ICD codes. In contrast, the EHR notes …

An Ontology-Based Approach For Harmonization And Cross-Cohort Query Of Alzheimer’S Disease Data Resources, Xubing Hao, Xiaojin Li, Guo-Qiang Zhang, Cui Tao, Paul E Schulz, Licong Cui

Journal Articles

BACKGROUND: In the United States, the National Alzheimer's Coordinating Center (NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) are two major data sharing resources for Alzheimer's Disease (AD) research. NACC and ADNI strive to make their data more FAIR (findable, interoperable, accessible and reusable) for the broader research community. However, there is limited work harmonizing and supporting cross-cohort interoperability of the two resources.

METHOD: In this paper, we leverage an ontology-based approach to harmonize data elements in the two resources and develop a web-based query system to search patient cohorts across the two resources. We first mapped data elements across …

Non-Invasive Arterial Blood Pressure Measurement And Spo2 Estimation Using Ppg Signal: A Deep Learning Framework, Yan Chu, Kaichen Tang, Yu-Chun Hsu, Tongtong Huang, Dulin Wang, Wentao Li, Sean I Savitz, Xiaoqian Jiang, Shayan Shams

Journal Articles

BACKGROUND: Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection.

METHOD: Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers' interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct …

Interpreting P Values In 2023, Jennifer K. Homa-Bonell

Journal of Patient-Centered Research and Reviews

If recent experiences shared among the biostatistician community are indicative of a sea change in research, then a most-welcome culture shift in dialogue surrounding the proper use and interpretation of the P value, which measures statistical probability, is underway. This editorial strives to offer guidance for researchers who would like to incorporate more comprehensive reporting in their research, namely, a broader discussion that goes beyond looking at the P value by itself and includes effect size estimates, confidence intervals, and clinical implications when interpreting quantitative results. Another evolving development in clinical research is the preferred language when referring …

Delineating The Cellular Mechanisms Of Endoplasmic Reticulum-Retained Endoglin Mutants Causing Hereditary Hemorrhagic Telangiectasia Type 1, Nesrin Mohammed Gariballa

Dissertations

Endoglin, also known as cluster of differentiation 105 (CD105), is an auxiliary receptor in the TGFβ signaling pathway. It is predominantly expressed in endothelial cells as a component of the heterotetrameric receptor dimers comprising type I, type II receptors and the binding ligands. Mutations in ENG, the gene encoding endoglin, have been associated with Hereditary Hemorrhagic Telangiectasia type 1 (HHT1), a rare autosomal dominant inherited disorder affecting about 1 in 5000-8000 individuals, which is generally characterized by vascular malformations. Secretory and many endomembrane proteins synthesized in the Endoplasmic Reticulum (ER) are subjected to a highly stringent protein folding and assembly …

Towards Generalizable Machine Learning Models For Computer-Aided Diagnosis In Medicine, Yiyang Wang

College of Computing and Digital Media Dissertations

Hidden stratification represents a phenomenon in which a training dataset contains unlabeled (hidden) subsets of cases that may affect machine learning model performance. Machine learning models that ignore the hidden stratification phenomenon--despite promising overall performance measured as accuracy and sensitivity--often fail at predicting the low prevalence cases, but those cases remain important. In the medical domain, patients with diseases are often less common than healthy patients, and a misdiagnosis of a patient with a disease can have significant clinical impacts. Therefore, to build a robust and trustworthy CAD system and a reliable treatment effect prediction model, we cannot only pursue …

Feature Selection From Clinical Surveys Using Semantic Textual Similarity, Benjamin Warner

McKelvey School of Engineering Theses & Dissertations

Survey data collected from human subjects can contain a high number of features while having a comparatively low quantity of examples. Machine learning models that attempt to predict outcomes from survey data under these conditions can overfit and result in poor generalizability. One remedy to this issue is feature selection, which attempts to select an optimal subset of features to learn upon. A relatively unexplored source of information in the feature selection process is the usage of textual names of features, which may be semantically indicative of which features are relevant to a target outcome. The relationships between feature names …

Cardiovascular Disease Prediction Modelling: A Machine Learning Approach, Usmaan Al-Shehab, Maduka Gunasinghe, Yousuf Elkhoga, Nimay Patel, Juliana Yang

Rowan-Virtua Research Day

The objective of this project is to utilize the UCI Heart Disease dataset to identify physiological biomarkers that are highly correlated with heart disease incidence. A predictive model can then be developed using these biomarkers to estimate the likelihood of someone having or developing a heart-related condition. This study compares the efficacy of predicting cardiovascular disease as an outcome using three machine learning algorithms: Support Vector Machine, Gaussian Naive Bayes, and logistic regression. Support Vector Machine works by creating hyperplanes between data points to conduct classification. Gaussian Naive Bayes works by using the conditional probabilities of events to classify the …

Classification Of Normal Versus Pneumonia From Chest X-Ray Using And Ai Model, Tassadit Lounes

Publications and Research

Hypothesis: Deep learning (DL) algorithms, in particular convolutional neural networks (CNNs), have recently been used to address a number of medical-imaging problems, such as pneumonia detection using chest X-ray, and determining the aggressiveness prostate cancer using magnetic resonance images (MRI). They have become the technique of choice in computer vision and they are the most successful type of model for image analysis.

The Developing Effects Of Potassium Ferricyanide On Tetrahymena, Katelyn Coronell

Whittier Scholars Program

Potassium Cyanide is a highly toxic chemical asphyxiant that interferes with the body's ability to use oxygen, typically by directly affecting the body by ingestion, inhalation, skin contact, or eye contact(CDC, 2011). Due to its high toxicity, the main effect that leads to the downfall of the organism begins with the cessation of aerobic metabolism; it does this by cyanide binding to the ferric ions and inhibiting cytochrome oxidase within the mitochondria (Zhang, 2015). There are no physical dangers the substance causes. Although, there are many chemical dangers. If used at temperatures higher than 70℉ The substance may produce toxic …

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline For Oropharyngeal Cancer Radiotherapy Treatment Guidance, Kareem Wahid

Dissertations & Theses (Open Access)

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information …

Evaluation Of Medical Decision Errors During The Transition Period To Telemedicine, Marius Moroianu, Roxana- Elena Bogdan-Goroftei, Teodor Salmen, Cristina Ioana Bica, Valeria-Anca Pietrosel, Razvan Hainarosie, Anca Pantea Stoian

Journal of Mind and Medical Sciences

The context of the Coronavirus pandemic has fundamentally changed the way we approach medical services. Beyond setting up new technological possibilities, it has propelled telemedicine to become a reality, bringing undeniable practical benefits. The questions that arise are both justified and worrying: can digitalization replace a direct interpersonal relationship that involves a physical examination, while preserving the quality of the medical act and the degree of patient satisfaction? Isn't there a risk that the digitization of the medical record will cancel out the deep human character of classical medicine that has evolved since the time of Hippocrates? Should the implementation …

Meta-Narrative Review Of Possible Impacts Of Genetic Screening On Treatment Of Breast Cancer, Toqa Al Alawi, Sheza Khan, Ivey Knebel, Steven Luong, Vilma Sanchez, Kamilah Walker-Charles

Research Methods Poster Session 2023

Objective: To examine the impacts of genetic screening on the treatment of breast cancer, in relation to differences, outcomes and decisions in treatment plans or surgery in patients that performed genetic screening versus those that did not.

Background: Genetic screening technology has become commercially available, yet standard preventative care for breast cancer has no genetic screening involved. Genetic screening in breast cancer treatment is performed, but its usage is not standardized.

Methods: Findings were synthesized using the meta-narrative review style to examine articles retrieved from searches of digital databases PubMed and the M.D. Anderson Scholarly Library.

Discussion: Articles were selected …

Mapping Next Generation Sequence Data With Bwa (Burrows-Wheel Aligner) On Galaxy Software, Rabeh Z. Omar

Honors College Theses

Advancement of next-generation sequencing technologies introduces a vast amount of data which has become a challenge for researchers to organize and sequence data sets. BWA (Burrows-Wheeler Aligner) is one of the widely used software for aligning and mapping sequencing data against a reference genome. In my thesis, I present a comprehensive guide for analyzing genome sequences using BWA. I discuss the various steps involved in the process, including gathering the data, preparing the reference genome, aligning the sequences, and processing the data to visualize the results.