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The study of Alzheimer's disease as the most common cause of dementia is difficult in terms of finding the cause, monitoring the pathogenesis, and early diagnosis and effective treatment. Rapid and accurate detection of AD biomarkers in the brain is vital to gain key insights into AD disease research and ease the development of early diagnosis devices. We built a website that promotes rapid screening of AD biomarkers by using graphene-assisted Raman spectroscopy and machine learning analysis in AD transgenic animal brains. To classify AD and non-AD spectra, we used Raman spectra on mouse brain slices with and without AD, as well as machine learning to classify AD and non-AD spectra. Further, we developed a spectral feature importance chart that shows the importance of each Raman wavenumber in classifying AD and non-AD spectra.
Source link: https://doi.org/10.1101/2021.06.03.446929
We conducted an epigenome-wide association study using DNAm profiles in entorhinal cortex from 149 AD patients and control brains and merged these with two previously published EC data by meta-analysis. For six of the 12 important CpGs, integrating DNAm levels with RNA sequencing-based mRNA expression data obtained in the same individuals showed significant DNAm-mRNA correlations for six of the 12 significant CpGs. Lastly, we discovered a significant correlation between AD patients with increased epigenetic age progression in AD patients vs. controls by calculating epigenetic age acceleration using two recently introduced "epigenetic clock" estimators.
Source link: https://doi.org/10.1101/2021.07.02.450878
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