Browsing by Author "Kim, Andrew"
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Item Investigating the role of interleukin 1 alpha during Listeria monocytogenes infection(2022) Kim, Andrew; Berg, Rance E.Purpose: Listeria monocytogenes (LM) causes listeriosis, one of the leading causes of death by foodborne illness in the United States. Although generally self-limiting in immunocompetent people, listeriosis can cause meningitis or sepsis in immunocompromised people and spontaneous abortion in pregnant women. Our interest in interleukin 1 alpha (IL-1α), a cytokine historically associated with inflammation and alarmin activity, stemmed from a previous study in our lab showing that mice produced IL-1α when infected with LM. Currently, the role of IL-1α during infection is largely unexplored. Elucidating the role of IL-1α during LM infection will determine if IL-1α can potentially be used as a therapeutic agent and will expand our understanding of this cytokine. Method: Enzyme-linked immunosorbent assay (ELISA) was used to measure IL-1α production by LM infected RAW 264.7 macrophages. Dose response and kinetic experiments were performed to optimize culture conditions. RAW 264.7 macrophages were then infected with LM and the impact of recombinant mouse IL-1α, recombinant interleukin 1 beta (IL-1β), recombinant interferon-gamma (IFN-γ), neutralization of IL-1α, or blockade of the interleukin 1 receptor (IL-1R1) on bacterial burden was determined. Macrophage viability was measured using trypan blue to determine if the culture conditions severely impacted cell viability and to correlate viability with cytokine production and specific treatments. The total LM burden in the cultures was quantified to determine the impact of specific treatments on the ability of macrophages to kill LM. Results: IL-1α production was significantly increased in LM infected RAW 264.7 macrophage cultures compared to uninfected control cultures. The production of IL-1α by RAW 264.7 macrophages increased, plateaued, and then decreased at 6, 12, 18, and 24 hours post LM infection. We determined that infecting 500,000 macrophages at a multiplicity of infection of 1 resulted in significant IL-1α production while maintaining adequate macrophage viability. These culture conditions were then used for our bacterial burden studies. Bacterial burden decreased in LM infected cultures with recombinant IL-1α at 18 hours compared to untreated cultures, but not significantly. Conclusion: Our data suggest that macrophages may contribute significantly to IL-1α production during LM infection. Furthermore, recombinant IL-1α may have the potential to activate macrophages, resulting in enhanced LM killing. The reduction in bacterial burden in macrophages treated with recombinant IL-1α was similar to the reduction in bacterial burden in IFN-γ treated macrophage cultures. Therefore, our data suggest that recombinant IL-1α may contribute to LM resistance. Future experiments include observation of bacterial burden after addition of recombinant IL-1β, neutralization of IL-1α and blockade of IL-1R1 in LM infected macrophage cultures. LM targets the liver, so we will also investigate the impact of recombinant IL-1α on LM infected Hepa 1-6 hepatocytes and cocultures of RAW 264.7 macrophages and Hepa 1-6 hepatocytes.Item Making Dementia Blood-based Biomarker Data More Interpretable Through Machine Learning(2024-03-21) Kim, AndrewBackground: Research and data have linked many possible factors that contribute to the cause and progression of Alzeheimer’s disease and dementia. These include traits such as age, gender, ethnicity, and specific blood-based biomarkers. There has been a great deal of work gathering this information, but comparatively less work has been done to consolidate and present it in an easily coherent and comprehensible form. This study aims to use and sort relevant data related to Alzheimer’s disease with machine learning and make it more interpretable through visualization. Methods: The data being analyzed was collected from n = 1705 Hispanic and Non-Hispanic participants with and without cognitive impairment (n = 1328 NC, n = 261 MCI, n = 116 AD) from the HABS-HD cohort. Associated factors measured and considered from each participant included: gender, Hispanic or Non-hispanic ethnicity, education level, and various blood biomarker levels (CRP, FABP3, IL-10, IL-6, Ab40, Ab42, Tau, NFL, PPY, sICAM-1, sVCAM-1, TNF-alpha, GLP-1, Glucagon, PYY, Insulin, HOMA-IR). The Decision Tree classifier tool was applied to the dataset incorporating the scikit-learn Python coding program and the use of multiple parameters in generating the decision tree. The dtreeviz method was also applied in order to provide further visualization to the data. Results: Decision trees were capable of being generated from the given data set of participants based on cognitive status and blood-based biomarkers for Alzheimer’s disease and dementia. Visualized versions of these decision trees were also capable of being successfully generated. The quality and parameters of the decision trees as well as the appearance of the visualization could also be modified. There appears to be some limitations in the Scikit-learn and dtreeviz package that could warrant further troubleshooting or acknowledgement. Conclusion: Based on the results, it appears that visualized decision trees are capable of being generated from a large set of data. Such visualized decision trees compared to the raw data tables or decision trees themselves are much simpler to interpret and recognize patterns. Such patterns could prove useful in determining future areas of study to focus on or affirm already completed studies.