Alzheimerâ??s disease (AD) is the most common form of dementia worldwide and is\ncharacterized by progressive cognitive decline. Along with being incurable and lethal, AD is difficult\nto diagnose with high levels of accuracy. Blood serum from Alzheimerâ??s disease (AD) patients was\nanalyzed by surface-enhanced Raman spectroscopy (SERS) coupled with multivariate statistical\nanalysis. The obtained spectra were compared with spectra from healthy controls (HC) to develop\na simple test for AD detection. Serum spectra from AD patients were further compared to spectra from\npatients with other neurodegenerative dementias (OD). Colloidal silver nanoparticles (AgNPs) were\nused as the SERS-active substrates. Classification experiments involving serum SERS spectra using\nartificial neural networks (ANNs) achieved a diagnostic sensitivity around 96% for differentiating AD\nsamples from HC samples in a binary model and 98% for differentiating AD, HC, and OD samples\nin a tertiary model. The results from this proof-of-concept study demonstrate the great potential of\nSERS blood serum analysis to be developed further into a novel clinical assay for the effective and\naccurate diagnosis of AD.
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