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Full-Text Articles in Medicine and Health Sciences
Distribution Of Microglial Phenotypes As A Function Of Age And Alzheimer's Disease Neuropathology In The Brains Of People With Down Syndrome, Alessandra C. Martini, Alex M. Helman, Katie L. Mccarty, Ira T. Lott, Eric Doran, Frederick A. Schmitt, Elizabeth Head
Distribution Of Microglial Phenotypes As A Function Of Age And Alzheimer's Disease Neuropathology In The Brains Of People With Down Syndrome, Alessandra C. Martini, Alex M. Helman, Katie L. Mccarty, Ira T. Lott, Eric Doran, Frederick A. Schmitt, Elizabeth Head
Sanders-Brown Center on Aging Faculty Publications
Introduction: Microglial cells play an important role in the development of Alzheimer's disease (AD). People with Down syndrome (DS) inevitably develop AD neuropathology (DSAD) by 40 years of age. We characterized the distribution of different microglial phenotypes in the brains of people with DS and DSAD.
Methods: Autopsy tissue from the posterior cingulate cortex (PCC) from people with DS, DSAD, and neurotypical controls was immunostained with the microglial marker Iba1 to assess five microglia morphological types.
Results: Individuals with DS have more hypertrophic microglial cells in their white matter. In the gray matter, individuals with DSAD had significantly fewer ramified …
Hierarchical Clustering Analyses Of Plasma Proteins In Subjects With Cardiovascular Risk Factors Identify Informative Subsets Based On Differential Levels Of Angiogenic And Inflammatory Biomarkers, Zachary Winder, Tiffany L. Sudduth, David W. Fardo, Qiang Cheng, Larry B. Goldstein, Peter T. Nelson, Frederick A. Schmitt, Gregory A. Jicha, Donna M. Wilcock
Hierarchical Clustering Analyses Of Plasma Proteins In Subjects With Cardiovascular Risk Factors Identify Informative Subsets Based On Differential Levels Of Angiogenic And Inflammatory Biomarkers, Zachary Winder, Tiffany L. Sudduth, David W. Fardo, Qiang Cheng, Larry B. Goldstein, Peter T. Nelson, Frederick A. Schmitt, Gregory A. Jicha, Donna M. Wilcock
Sanders-Brown Center on Aging Faculty Publications
Agglomerative hierarchical clustering analysis (HCA) is a commonly used unsupervised machine learning approach for identifying informative natural clusters of observations. HCA is performed by calculating a pairwise dissimilarity matrix and then clustering similar observations until all observations are grouped within a cluster. Verifying the empirical clusters produced by HCA is complex and not well studied in biomedical applications. Here, we demonstrate the comparability of a novel HCA technique with one that was used in previous biomedical applications while applying both techniques to plasma angiogenic (FGF, FLT, PIGF, Tie-2, VEGF, VEGF-D) and inflammatory (MMP1, MMP3, MMP9, IL8, TNFα) protein data to …