Office of Undergraduate Research

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Emphasizing equitable and inclusive access to research opportunities, the University of Maryland's Office of Undergraduate Research (OUR) empowers undergraduates and faculty to engage and succeed in inquiry, creative activity, and scholarship. This collection includes materials shared by undergraduate researchers during OUR events. It also encompasses materials from Undergraduate Research Day 2020, Undergraduate Research Day 2021, and Undergraduate Research Day 2022, which were organized by the Maryland Center for Undergraduate Research.

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Author: search.filters.author.Buckmon, LoganSubject: search.filters.subject.Acute Myeloid Leukemia

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    Ligation LAMP Assay Targeting miRNA-155 Biomarker for Diagnosing Acute Myeloid Leukemia
    (2024-12-06) Berdia, Jason; Buckmon, Logan; Vitievsky, Mark; Hebbar, Soma; Spirito, Catherine; Mcdonald, Cyan; Adane, Yedidya
    Ligation LAMP Assay Targeting miRNA-155 Biomarker for Diagnosing Acute Myeloid Leukemia Mark Vitievsky, Logan Buckmon, Soma Hebbar, Jason Berdia Acute Myeloid Leukemia (AML) is a severely dangerous form of blood cancer with high mortality rates, in part due to limitations in current diagnostic methods, which are invasive, expensive, and time-consuming. This study uses a ligation LAMP (loop-mediated isothermal amplification) assay as a possible new diagnostic tool which targets the overexpression of the miRNA-155 biomarker, which is known for its role in immune suppression in AML. The Ligation-LAMP assay offers a rapid, cost-effective, and minimally invasive alternative to the traditional methods of diagnosis, such as bone marrow biopsies or blood counts. This project evaluates the sensitivity and specificity of the Ligation LAMP assay using both fluorescent and colorimetric standards to detect varying concentrations of miRNA-155. Early findings show that higher levels of miRNA-155 correlate to faster fluorescence and colorimetric changes, in turn validating the assay’s potential for earlier AML detection. Future enhancements include the implementation of machine learning algorithms to hone in on the diagnostic thresholds, testing patient samples to address variability, and improving assay reliability. This project represents a step forward in the development of accessible and relatively inexpensive diagnostic tools for AML, providing the possibility of earlier diagnosis and improved patient livelihoods.