Knee osteoarthritis grading by resonant Raman and surface-enhanced Raman scattering (SERS) analysis of synovial fluid

In this preliminary study on synovial fluid (SF), knee osteoarthritis (OA) grading of n = 23 patients was accomplished by combining two methods: resonant Raman spectroscopy, and surface-enhanced Raman scattering (SERS) of native proteins acquired with iodide-modified silver nanoparticles and a laser...

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Bibliographic Details
Published inNanomedicine Vol. 20; p. 102012
Main Authors Bocsa, Corina D., Moisoiu, Vlad, Stefancu, Andrei, Leopold, Loredana F., Leopold, Nicolae, Fodor, Daniela
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.08.2019
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Summary:In this preliminary study on synovial fluid (SF), knee osteoarthritis (OA) grading of n = 23 patients was accomplished by combining two methods: resonant Raman spectroscopy, and surface-enhanced Raman scattering (SERS) of native proteins acquired with iodide-modified silver nanoparticles and a laser emitting at 633 nm. Based on principal component analysis–linear discriminant analysis (PCA–LDA), the SERS spectra of proteins enabled the classification of low-grade and high-grade OA groups with an accuracy of 91%. Resonant Raman spectra of SF, recorded with laser excitation at 532 nm, exhibited carotenoid-associated bands that were less intense in the case of high-grade knee OA patients. Based on the resonant Raman spectra, the grading of OA patients was accomplished with an accuracy of 74%. Concatenating SERS and Raman spectral information increased the classification accuracy between the two groups to 100%. These results demonstrate the potential of Raman and SERS as a point-of-care method for aiding OA grading. This study demonstrates that combined Raman and SERS spectra of synovial fluid enable an efficient grading of knee osteoarthritis based on the carotenoid and proteomic contents of synovial fluid. The turnaround time of the assay is around 10 min per sample, making it amenable to implementation in the point-of-care setting for aiding the diagnosis and grading of osteoarthritis. [Display omitted]
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ISSN:1549-9634
1549-9642
DOI:10.1016/j.nano.2019.04.015