Medicine and Health Sciences Commons^™

Development, Validation, And Diagnostic Performance Of A Novel Radiomic Model For Predicting Prostate Cancer Recurrence, Linda M. Huynh

Theses & Dissertations

Multi-parametric magnetic resonance imaging (MP-MRI)-derived radiomics have been shown to capture sub-visual patterns for the quantitative characterization of prostate cancer (PC) phenotypes. The present dissertation seeks to develop, evaluate, and compare the performance of an MRI-derived radiomic model for the prediction of PC recurrence following definitive treatment with radical prostatectomy (RP).

MP-MRI was obtained from 339 patients who had a minimum of 2 years follow-up following RP at three institutions. The prostate was manually delineated as the region of interest and 924 radiomic features were extracted. All features were evaluated for stability via intraclass correlation coefficient (ICC) and image normalization …

Advances In Antimicrobial Peptide Discovery Via Machine Learning And Delivery Via Nanotechnology, Alexa Sowers, Guangshun Wang, Malcolm Xing, Bingyun Li

Journal Articles: Pathology and Microbiology

Antimicrobial peptides (AMPs) have been investigated for their potential use as an alternative to antibiotics due to the increased demand for new antimicrobial agents. AMPs, widely found in nature and obtained from microorganisms, have a broad range of antimicrobial protection, allowing them to be applied in the treatment of infections caused by various pathogenic microorganisms. Since these peptides are primarily cationic, they prefer anionic bacterial membranes due to electrostatic interactions. However, the applications of AMPs are currently limited owing to their hemolytic activity, poor bioavailability, degradation from proteolytic enzymes, and high-cost production. To overcome these limitations, nanotechnology has been used …

The Antimicrobial Peptide Database Is 20 Years Old: Recent Developments And Future Directions, Guangshun Wang

Journal Articles: Pathology and Microbiology

In 2023, the Antimicrobial Peptide Database (currently available at https://aps.unmc.edu) is 20-years-old. The timeline for the APD expansion in peptide entries, classification methods, search functions, post-translational modifications, binding targets, and mechanisms of action of antimicrobial peptides (AMPs) has been summarized in our previous Protein Science paper. This article highlights new database additions and findings. To facilitate antimicrobial development to combat drug-resistant pathogens, the APD has been re-annotating the data for antibacterial activity (active, inactive, and uncertain), toxicity (hemolytic and nonhemolytic AMPs), and salt tolerance (salt sensitive and insensitive). Comparison of the respective desired and undesired AMP groups produces new knowledge …

The Future Of Zoonotic Risk Prediction, Colin J. Carlson, Maxwell J. Farrell, Zoe Grange, Barbara A. Han, Nardus Mollentze, Alexandra L. Phelan, Angela L. Rasmussen, Gregory F. Albery, Bernard Bett, David Brett-Major, Lily E. Cohen, Tad Dallas, Evan A. Eskew, Anna C. Fagre, Kristian M. Forbes, Rory Gibb, Sam Halabi, Charlotte C. Hammer, Rebecca Katz, Jason Kindrachuk, Renata L. Muylaert, Felicia B. Nutter, Joseph Ogola, Kevin J. Olival, Michelle Rourke, Sadie J. Ryan, Noam Ross, Stephanie N. Seifert, Tarja Sironen, Claire J. Standley, Kishana Taylor, Marietjie Venter, Paul W. Webala

Journal Articles: Epidemiology

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open …

Fluorescent Sensor Arrays Can Predict And Quantify The Composition Of Multicomponent Bacterial Samples, Denis Svechkarev, Marat Sadykov, Lucas J. Houser, Kenneth W. Bayles, Aaron M. Mohs

Journal Articles: Pathology and Microbiology

Fast and reliable identification of infectious disease agents is among the most important challenges for the healthcare system. The discrimination of individual components of mixed infections represents a particularly difficult task. In the current study we further expand the functionality of a ratiometric sensor array technology based on small-molecule environmentally-sensitive organic dyes, which can be successfully applied for the analysis of mixed bacterial samples. Using pattern recognition methods and data from pure bacterial species, we demonstrate that this approach can be used to quantify the composition of mixtures, as well as to predict their components with the accuracy of ~80% …

Inference Of Cancer-Specific Gene Regulatory Networks Using Soft Computing Rules., Xiaosheng Wang, Osamu Gotoh

Journal Articles: Genetics, Cell Biology & Anatomy

Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One …

A Robust Gene Selection Method For Microarray-Based Cancer Classification., Xiaosheng Wang, Osamu Gotoh

Journal Articles: Genetics, Cell Biology & Anatomy

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification …