Li W, Chen G, Chen M, Shen K, Wu C, Shen W, Zhang F. PCA-WRKNN-assisted label-free SERS serum analysis platform enabling non-invasive diagnosis of Alzheimer's disease.
SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023;
302:123088. [PMID:
37392535 DOI:
10.1016/j.saa.2023.123088]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/03/2023]
Abstract
Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative brain disorder with significant economic and societal impacts, whereas early AD diagnosis remains a considerable challenge. Here, a robust and convenient surface-enhanced Raman scattering (SERS) analysis platform was fabricated on a microarray chip to dissect the variation in serum composition for AD diagnosis, eliminating the invasive cerebrospinal fluid (CSF)-based and costly instrument-dependent diagnostic methods. AuNOs array prepared by self-assembly at liquid-liquid interface enabled the acquirement of SERS spectra with excellent reproducibility. Moreover, a finite-difference time-domain (FDTD) simulation suggested the significant plasmon hybridization generated by AuNOs aggregation, resulting in high signal-to-noise ratio SERS spectra. We established an AD mice model with Aβ1-40 induction followed by recording the serum SERS spectra at different stages. A multivariate analysis method of principal component analysis (PCA)-weighted representation-based k-nearest neighbor (WRKNN) was applied for the characteristics extraction to improve the classification performance, with an accuracy of over 95 %, an AUC of over 90 %, a sensitivity of over 80 %, and a specificity of over 96.7 %. The results of this study demonstrate the potential of SERS application as a diagnostic screening method, following further validation and optimization, which may open up new exciting opportunities for future biomedical applications.
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