Prognostic potential and pathological validation of a diagnostic application using Raman spectroscopy in the characterization of degenerative changes in the cartilage of the humeral head

Ryuji Asaoka, Hiroshi Kiyomatsu, Hiromasa Miura, Akihiro Jono, Tomofumi Kinoshita, Masaki Takao, Takashi Katagiri, Yusuke Oshima*

*この論文の責任著者

研究成果: ジャーナルへの寄稿学術論文査読

8 被引用数 (Scopus)

抄録

Significance: Raman spectroscopy is a well-established analytical method in the fields of chemistry, industry, biology, pharmaceutics, and medicine. Previous studies have investigated optical imaging and Raman spectroscopy for osteoarthritis (OA) diagnosis in weight-bearing joints such as hip and knee joints. However, to realize early diagnosis or a curable treatment, it is still challenging to understand the correlations with intrinsic factors or patients' background. Aim: To elucidate the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage. Approach: Osteoarthritic cartilage specimens excised from the humeral heads of 14 patients who underwent shoulder arthroplasty were assessed by a confocal Raman microscope and histological staining. The Raman spectroscopic dataset of degenerative cartilage was further analyzed by principal component analysis and hierarchical cluster analysis. Results: Multivariate association of the Raman spectral data generated three major clusters. The first cluster of patients shows a relatively high Raman intensity of collagen. The second cluster displays relatively low Raman intensities of proteoglycans (PGs) and glycosaminoglycans (GAGs), whereas the third cluster shows relatively high Raman intensities of PGs and GAGs. The reduced PGs and GAGs are typical changes in OA cartilage, which have been confirmed by safranin-O staining. In contrast, the increased Raman intensities of collagen, PGs, and GAGs may reflect the instability of the cartilage matrix structure in OA patients. Conclusions: The results obtained confirm the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage. Unsupervised machine learning methods successfully yielded a clinically meaningful classification between the shoulder OA patients. This approach not only has potential to confirm severity of cartilage defects but also to determine the origin of an individual's OA by evaluating the cartilage quality.

本文言語英語
論文番号115002
ジャーナルJournal of Biomedical Optics
27
11
DOI
出版ステータス出版済み - 2022/11/01

ASJC Scopus 主題領域

  • 電子材料、光学材料、および磁性材料
  • 生体材料
  • 原子分子物理学および光学
  • 生体医工学

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