677 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
Thyroid Immune Related Adverse Events Following Immune Checkpoint Inhibitor Treatment
The efficacy of immune checkpoint inhibitor (ICI) treatment rivals traditional anti-tumor therapies and in most cases results in significantly less drug related toxicity. However, ICI-use can result in a myriad of immune and inflammatory side effects termed immune related adverse events (irAEs). Thyroid irAEs are the most common endocrine toxicity related to ICI-treatment. In affected patients, permanent thyroid dysfunction can result, necessitating lifelong thyroid hormone replacement and long-term clinical follow-up is required. Prior to this candidature, clinical descriptions of thyroid irAEs were predominately based on small, heterogeneous patient cohorts and little was known about the underlying pathogenesis responsible for their development. This thesis produced a detailed phenotypic report of 1246 patients with melanoma undergoing ICI-treatment. We clearly showed that prevalence of thyroid irAEs was substantially higher than previously reported, and that most patients developed subclinical disease only. We demonstrated that overt thyrotoxicosis had distinct clinical features from other thyroid irAE presentations and was uniquely associated with improvements in survival. We prospectively measured anti-thyroid antibody levels and showed they were highly specific for identifying patients likely to experience a thyroid irAE. Our antibody work was complemented by preclinical studies including development of an animal model to test for autoantibodies against novel thyroid antigens, a genetic study to test for an association with FLT3 gene polymorphism and immunophenotyping peripheral blood to identify key immune cell populations involved in the pathogenesis of thyroid irAEs. In totality, the work in this thesis significantly advances understanding of thyroid irAEs and the mechanisms underpinning their development
International consensus statement on allergy and rhinology: Allergic rhinitis – 2023
Background
In the 5 years that have passed since the publication of the 2018 International Consensus Statement on Allergy and Rhinology: Allergic Rhinitis (ICAR-Allergic Rhinitis 2018), the literature has expanded substantially. The ICAR-Allergic Rhinitis 2023 update presents 144 individual topics on allergic rhinitis (AR), expanded by over 40 topics from the 2018 document. Originally presented topics from 2018 have also been reviewed and updated. The executive summary highlights key evidence-based findings and recommendation from the full document. Methods
ICAR-Allergic Rhinitis 2023 employed established evidence-based review with recommendation (EBRR) methodology to individually evaluate each topic. Stepwise iterative peer review and consensus was performed for each topic. The final document was then collated and includes the results of this work. Results
ICAR-Allergic Rhinitis 2023 includes 10 major content areas and 144 individual topics related to AR. For a substantial proportion of topics included, an aggregate grade of evidence is presented, which is determined by collating the levels of evidence for each available study identified in the literature. For topics in which a diagnostic or therapeutic intervention is considered, a recommendation summary is presented, which considers the aggregate grade of evidence, benefit, harm, and cost. Conclusion
The ICAR-Allergic Rhinitis 2023 update provides a comprehensive evaluation of AR and the currently available evidence. It is this evidence that contributes to our current knowledge base and recommendations for patient evaluation and treatment
A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data
The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines.
One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials.
In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications.
Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains
Effect of Treatment, Stage of Lung Cancer, and Socioeconomic Status on Life Expectancy Within Marginalized Communities
Lung cancer is one of the most common cancers in the United States, and it accounts for 25% of cancer deaths. About 70% of cancer cases are diagnosed during late stages, leading to poor outcomes. An estimated 60% of cancer cases involve underserved and disadvantaged communities. However, there are limited studies had addressed effects of treatment, stage of lung cancer, and socioeconomic status on life expectancy within marginalized communities. Research questions examined effect of treatment, stage of lung cancer, and socioeconomic status on life expectancy of lung cancer patients between 2009 and 2019. This study was grounded in the deductive approach theory that facilitates interpretation of causal relationships between variables and concepts. The study was also grounded in the socioecological model, which acknowledges that different contributing factors and determinants exist at different levels of the society and addressing them at all levels will facilitate more effective prevention and control. A quantitative method with a cross-sectional design was used to analyze data from a random sample of 86,998 lung cancer patients. The dataset was obtained from the Surveillance, Epidemiology, and End Results database from the National Cancer Institute. Multiple linear regression was used for descriptive and inferential statistical analyses. Results showed treatment, stage of lung cancer, and socioeconomic status had statistically significant effects on life expectancy of lung cancer patients. Positive social change implications include alleviation of burden of lung cancer by raising awareness, encouraging screening, and advocating to enact new government policies
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