Prof. Masaki Takao Profile

Prof. Masaki Takao

at Ehime Univ

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Publications (2)

Proceedings Article | 15 March 2023 Poster + Paper

Pathological analysis of degenerative changes in humeral osteoarthritis using Raman spectroscopy

Ryuji Asaoka, Hiroshi Kiyomatsu, Akihiro Jono, Masaki Takao, Takashi Katagiri, Yusuke Oshima

Proceedings Volume 12363, 1236309 (2023) https://doi.org/10.1117/12.2650088

KEYWORDS: Raman spectroscopy, Cartilage, Principal component analysis, Collagen, Tissues, Head, Resection, Matrices, Surgery, Statistical analysis

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SPIE Journal Paper | 10 November 2022 Open Access

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

JBO, Vol. 27, Issue 11, 115002, (November 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.11.115002

KEYWORDS: Raman spectroscopy, Cartilage, Collagen, Diagnostics, Spectroscopy, Head, Principal component analysis, Tissues, Proteins, Statistical analysis

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SignificanceRaman 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.AimTo elucidate the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage.ApproachOsteoarthritic 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.ResultsMultivariate 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.ConclusionsThe 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.

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