Physical Sciences and Mathematics Commons^™

Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando

Community & Environmental Health Faculty Publications

Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …

Development And Biological Evaluation Of Selective Small-Molecule Inhibitors Of The Human Cytochrome P450 1b1, Austin Hachey

Theses and Dissertations--Chemistry

The human cytochrome P450 1B1 (CYP1B1) is an emerging target for small- molecule therapeutics. Several solid tumors overexpress CYP1B1 to the degree that it has been referred to as a universal tumor antigen. Conversely, its expression is low in healthy tissues. CYP1B1 may drive tumorigenesis through promoting the formation of reactive toxins from environmental pollutants or from endogenous hormone substrates. Additionally, the expression of CYP1B1 in tumors is associated with resistance to several common chemotherapies and with poor prognoses in cancer patients. However, inhibiting CYP1B1 with small molecules has been demonstrated in cellular and murine model systems to reverse this …

Modeling Vascular Diffusion Of Oxygen In Breast Cancer, Tina Giorgadze

Senior Projects Spring 2023

Oxygen is a vital nutrient necessary for tumor cells to survive and proliferate. Oxygen is diffused from our blood vessels into the tissue, where it is consumed by our cells. This process can be modeled by partial differential equations with sinks and sources. This project focuses on adding an oxygen diffusion module to an existing 3D agent-based model of breast cancer developed in Dr. Norton’s lab. The mathematical diffusion module added to an existing agent-based model (ABM) includes deriving the 1-dimensional and multi-dimensional diffusion equations, implementing 2D and 3D oxygen diffusion models into the ABM, and numerically evaluating those equations …

The Effects Of Paclitaxel On Cellular Migration And The Cytoskeleton, Ashley Salguero-Gonzalez

Thinking Matters Symposium

In a clinical setting, some patients are exposed to an anti-cancer chemotherapy agent, paclitaxel. Cancerous cells undergo rapid, continuous cell division without control. Chemotherapy treatments try to slow and stop the uncontrollable cell division cycles and eliminate cancerous cells in the process. Paclitaxel serves as a treatment for some types of cancers, including lung, melanoma, bladder, and esophageal. Because it targets the cytoskeleton, paclitaxel can also influence cell migration. This project utilizes a cellular migration assay and an immunohistochemistry assay to analyze the effects of paclitaxel on the movement of cells and on the cytoskeleton of neuroglia rat cells with …

A Machine Learning Framework For Identifying Molecular Biomarkers From Transcriptomic Cancer Data, Md Abdullah Al Mamun

FIU Electronic Theses and Dissertations

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers.

However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical.

Traditional approaches for biomarker discovery calculate the fold change for each …

Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho

SMU Data Science Review

Breast cancer is prevalent among women in the United States. Breast cancer screening is standard but requires a radiologist to review screening images to make a diagnosis. Diagnosis through the traditional screening method of mammography currently has an accuracy of about 78% for women of all ages and demographics. A more recent and precise technique called Digital Breast Tomosynthesis (DBT) has shown to be more promising but is less well studied. A machine learning model trained on DBT images has the potential to increase the success of identifying breast cancer and reduce the time it takes to diagnose a patient, …

A New Mathematical Theory For The Dynamics Of Large Tumor Populations, A Potential Mechanism For Cancer Dormancy & Recurrence And Experimental Observation Of Melanoma Progression In Zebrafish, Adeyinka A. Lesi

Dissertations and Theses

Cancer, a family of over a hundred disease varieties, results in 600,000 deaths in the U.S. alone. Yet, improvements in imaging technology to detect disease earlier, pharmaceutical developments to shrink or eliminate tumors, and modeling of biological interactions to guide treatment have prevented millions of deaths. Cancer patients with initially similar disease can experience vastly different outcomes, including sustained recovery, refractory disease or, remarkably, recurrence years after apparently successful treatment. The current understanding of such recurrences is that they depend on the random occurrence of critical mutations. Clearly, these biological changes appear to be sufficient for recurrence, but are they …

Photodynamic Therapy Of Inorganic Complexes For The Treatment Of Cancer, Chloe B. Smith, Lindsay C. Days, Duaa R. Alajroush, Khadija Faye, Yara Khodour, Stephen J. Beebe, Alvin Holder

Chemistry & Biochemistry Faculty Publications

Photodynamic therapy (PDT) is a medicinal tool that uses a photosensitiser and a light source to treat several conditions, including cancer. PDT uses reactive oxygen species (ROS) such as cytotoxic singlet oxygen ¹O₂ to induce cell death in cancer cells. Chemotherapy has historically utilized the cytotoxic effects of many metals, especially transition-metal complexes. However, chemotherapy is a systemic treatment so all cells in a patient's body are exposed to the same cytotoxic effects. Transition metal complexes have also shown high cytotoxicity as PDT agents. PDT is a potential localized method for treating several cancer types by using inorganic …

The Resistive Barrier Discharge: A Brief Review Of The Device And Its Biomedical Applications, Mounir Laroussi

Electrical & Computer Engineering Faculty Publications

This paper reviews the principles behind the design and operation of the resistive barrier discharge, a low temperature plasma source that operates at atmospheric pressure. One of the advantages of this plasma source is that it can be operated using either DC or AC high voltages. Plasma generated by the resistive barrier discharge has been used to efficiently inactivate pathogenic microorganisms and to destroy cancer cells. These biomedical applications of low temperature plasma are of great interest because in recent times bacteria developed increased resistance to antibiotics and because present cancer therapies often are accompanied by serious side effects. Low …

Developing A New Water-Soluble Porphyrin As A Potential Photodynamic Cancer Therapy Agent, Catherine Shirley

Honors Theses

Photodynamic cancer therapy (PDT) is a type of treatment involving the use of light in conjunction with a photosensitive agent- a chemical or series of chemicals designed for activation when exposed to light. This research project investigated the synthesis and identification of the novel photosensitive agent, H2TPP-Pro-OH. To create the water-soluble porphyrin, (S)-(+)-prolinol was reacted with the tetra-carboxyl porphyrin, H2TPPC, to form the final H2TPP-Pro-OH product. This compound was then purified using syringe filtration and column chromatography, and subsequently characterized using infrared (IR), nuclear magnetic resonance (NMR), and Ultraviolet-visible (UV-vis) spectroscopies, as well as High Performance Liquid Chromatography (HPLC). Finally, …

Preparation And Cytotoxicity Of Novel Carbon Nano-Onion Materials, Cammie York

Honors Theses

The applications of carbon nanomaterials (CNM), including graphene and its derivatives such as carbon nanotubes (CNTs) in nanomedicine is well established. These nanomaterials have been widely used as theranostic delivery systems with the potential to deliver bioactive agents and simultaneously detect selectively diseased tissues. A rather underexplored CNM for biomedical imaging and theranostics delivery are carbon nano-onions (CNOs). CNOs are carbon-based nanomaterials that can potentially be used in cancer therapy when they are functionalized. Recent studies on cellular fate of different CNMs, including CNOs, have demonstrated that the surface composition is critical for the in vivo application of these CNM. …

9th Annual Postdoctoral Science Symposium, University Of Texas Md Anderson Cancer Center Postdoctoral Association

Annual Postdoctoral Science Symposium Abstracts

The mission of the Annual Postdoctoral Science Symposium (APSS) is to provide a platform for talented postdoctoral fellows throughout the Texas Medical Center to present their work to a wider audience. The MD Anderson Postdoctoral Association convened its inaugural Annual Postdoctoral Science Symposium (APSS) on August 4, 2011.

The APSS provides a professional venue for postdoctoral scientists to develop, clarify, and refine their research as a result of formal reviews and critiques of faculty and other postdoctoral scientists. Additionally, attendees discuss current research on a broad range of subjects while promoting academic interactions and enrichment and developing new collaborations.

Microarray Data Analysis And Classification Of Cancers, Grant Gates

Williams Honors College, Honors Research Projects

When it comes to cancer, there is no standardized approach for identifying new cancer classes nor is there a standardized approach for assigning cancer tumors to existing classes. These two ideas are known as class discovery and class prediction. For a cancer patient to receive proper treatment, it is important that the type of cancer be accurately identified. For my Senior Honors Project, I would like to use this opportunity to research a topic in bioinformatics. Bioinformatics incorporates a few different subjects into one including biology, computer science and statistics. An intricate method for class discovery and class prediction is …

Killing Breast Cancer One Porphyrin At A Time, Taylor C. Lymburner

Honors Theses

New treatments for cancer are continuously being developed and improved. One such treatment is Photodynamic Therapy, more commonly referred to as PDT. PDT is quickly becoming more popular due to its relative lack of side effects that are present in other treatments. In PDT, light-sensitive agents are required and are activated by light in the targeted cells. There are many types of PDT agents but the one focused on in this research is a four-pyrrole ring structure known as a porphyrin. The combination of H2 TPPC with 3- amino-oxetane-3-yl-methanol created the final product ofH2TPP-Oxo-MeOH. Once the porphyrin was formed, it …

Inactivation Of Myeloma Cancer Cells By Helium And Argon Plasma Jets: The Effect Comparison And The Key Reactive Species, Zeyu Chen, Qingjie Cui, Chen Chen, Dehui Xu, Dingxin Liu, H. L. Chen, Michael G. Kong

Bioelectrics Publications

In plasma cancer therapy, the inactivation of cancer cells under plasma treatment is closely related to the reactive oxygen and nitrogen species (RONS) induced by plasmas. Quantitative study on the plasma-induced RONS that related to cancer cells apoptosis is critical for advancing the research of plasma cancer therapy. In this paper, the effects of several reactive species on the inactivation of LP-1 myeloma cancer cells are comparatively studied with variable working gas composition, surrounding gas composition, and discharge power. The results show that helium plasma jet has a higher cell inactivation efficiency than argon plasma jet under the same discharge …

Evaluation And Adaptation Of Live-Cell Interferometry For Applications In Basic, Translational, And Clinical Research, Kevin A. Leslie

Theses and Dissertations

Cell mass is an important indicator of cell health and status. A diverse set of techniques have been developed to precisely measure the masses of single cells, with varying degrees of technical complexity and throughput. Here, the development of a non-invasive, label-free optical technique, termed Live-Cell Interferometry (LCI), is described. Several applications are presented, including an evaluation of LCI’s utility for assessing drug response heterogeneity in patient-derived melanoma lines and the measurement of CD3+ T cell kinetics during hematopoietic stem cell transplantation. The characterization of mast cells during degranulation, the measurement of viral reactivation kinetics in Kaposi’s Sarcoma, and drug …

Hypothesis Driven Single Nucleotide Polymorphism Search (Hydn-Snp-S), Rebecca J. Swett, Angela Elias, Jeffrey A. Miller, Gregory E. Dyson, G. AndréS Cisneros

Chemistry Faculty Research Publications

The advent of complete-genome genotyping across phenotype cohorts has provided a rich source of information for bioinformaticians. However the search for SNPs from this data is generally performed on a study-by-study case without any specific hypothesis of the location for SNPs that are predictive for the phenotype. We have designed a method whereby very large SNP lists (several gigabytes in size), combining several genotyping studies at once, can be sorted and traced back to their ultimate consequence in protein structure. Given a working hypothesis, researchers are able to easily search whole genome genotyping data for SNPs that link genetic locations …

Cancer Quasispecies And Stem-Like Adaptive Aneuploidy, Domenico Napoletani, M. Signore, Daniele C. Struppa

Mathematics, Physics, and Computer Science Faculty Articles and Research

In this paper we develop a theoretical frame to understand self-regulation of aneuploidy rate in cancer and stem cells. This is accomplished building upon quasispecies theory, by leaving its formal mathematical structure intact, but by drastically changing the meaning of its objects. In particular, we propose a novel definition of chromosomal master sequence, as a sequence of physically distinct whole or fragmented chromosomes, whose length is taken to be the sum of the copy numbers of each whole or fragmented chromosome. This fundamental change in the functional objects of quasispecies theory allows us to show that previously measured aneuploidy rates …