A new method for diagnosing biochemical abnormalities of anterior cruciate ligament (ACL) in human knees: A Raman spectroscopic study
A new method for diagnosing biochemical abnormalities of anterior cruciate ligament (ACL) in human knees: A Raman spectroscopic study
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DOI：
10.1016/j.actbio.2019.09.016
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发表时间：
2019-11-01
影响因子：
9.7
通讯作者：
Yamamoto, Kengo
中科院分区：
文献类型：
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作者：
Matsunaga, Ryo;Takahashi, Yasuhito;Yamamoto, Kengo
前交叉韧带（ACL）对膝关节的稳定性和运动学起着至关重要的作用。微结构的不规则性，如细胞变化和细胞外基质（ECM）的解体，改变了韧带的机械性能，导致膝关节功能的显著不稳定和骨关节炎（OA）的进展。到目前为止，ACL局部异常的识别通常依赖于侵入性分析技术，如组织学或生化测定。使用磁共振成像（MRI）的非侵入性诊断仍然限于识别部分/完全破裂和粘液变性的存在/不存在。在这项研究中，激光显微拉曼光谱与近红外激发（785 nm）应用于人类ACL，以建立光学算法，非破坏性地诊断在分子水平上的退化状态。从44个离体ACL样本中获得拉曼光谱，随后根据组织病理学评分系统将其分类为早期（亚临床）和晚期（临床）组织降解水平。发现不同变性组之间的拉曼峰强度存在显著差异，其被分配给细胞中的核酸、胶原和磷脂的振动模式。进行线性判别分析（LDA）以确定拉曼强度和强度比的分布的截止值，这使得能够最好地区分早期和晚期退化组织。源自I-1101/I-1749、[I-1002/I-1516 vs. I-1101/I-1749]和[I-1002/I-1749 vs. I-1101/I-1749]的拉曼强度算法产生了100%的最大诊断灵敏度、80%的特异性和91%的准确性，用于区分变性严重程度。将激光显微拉曼光谱应用于人体前交叉韧带（ACL），以建立在分子水平上无损诊断组织变性的光学算法。据我们所知，这是第一个报告的拉曼诊断人类ACL。进行线性判别分析（LDA），以确定拉曼强度和强度比的截止值，这使得能够最好地区分ACL退变的早期（亚临床）和晚期（临床）水平。I-1101/I-1749、[I-1002/I-1516 vs. I-1101/I-1749]和[I-1002/I-1749 vs. I-1101/I-1749]的强度比产生的最大诊断灵敏度为100%，特异性为80%，区分ACL变性的准确性为91%。目前的研究结果可能有助于扩大非侵入性识别组织变性的临床诊断可能性。(C)2019 Acta Materialia Inc.由爱思唯尔有限公司出版。保留所有权利。
Anterior cruciate ligament (ACL) plays an essential role in knee joint stability and kinematics. The microstructural irregularities such as cellular changes and disorganization of the extracellular matrix (ECM) alter the mechanical properties of the ligament, leading to a significant knee functional instability and progression of osteoarthritis (OA). So far, the identification of the local abnormality in ACL has routinely relied on invasive analytical techniques such as histology or biochemical assays. The non-invasive diagnosis using magnetic resonance imaging (MRI) is still limited to identifying the presence/absence of partial/complete ruptures and mucoid degeneration. In this study, laser micro-Raman spectroscopy with near-infrared excitation (785 nm) was applied to human ACL in order to establish optical algorithms for non-destructively diagnosing a degeneration state at molecular level. Raman spectra were obtained from 44 ex-vivo ACL specimens, and these were subsequently classified as an early (subclinical) and advanced (clinical) level of tissue degradation based on the histopathological scoring system. The significant differences in Raman peak intensities were found between the different degeneration groups, which were assigned to the vibrational modes of nucleic acids in cells, collagens, and phospholipids. Linear discriminant analysis (LDA) was performed to identify cut-off values for the distributions of Raman intensity and intensity ratios, which enable to best discriminate between the early and advanced degenerated tissues. Raman intensity algorithms derived from I-1101/I-1749, [I-1002/I-1516 vs. I-1101/I-1749], and [I-1002/I-1749 vs. I-1101/I-1749], yielded a maximum diagnostic sensitivity of 100%, specificity of 80%, and accuracy of 91% for discriminating the degeneration severity.Statement of SignificanceIn this study, laser micro-Raman spectroscopy was applied to human anterior cruciate ligament (ACL) to establish optical algorithms for non-destructively diagnosing the tissue degeneration at molecular level. To our knowledge, this is the first report on Raman diagnosis for human ACL. Linear discriminant analysis (LDA) was performed to identify cut-off values for Raman intensity and intensity ratios, which enable to best discriminate between an early (subclinical) and advanced (clinical) level of ACL degeneration. The intensity ratios of I-1101/I-1749, [I-1002/I-1516 vs. I-1101/I-1749], and [I-1002/I-1749 vs. I-1101/I-1749] yielded a maximum diagnostic sensitivity of 100%, specificity of 80%, and accuracy of 91% for discriminating the ACL degeneration. The present findings might contribute to expanding clinical diagnostic possibilities for non-invasively identifying tissue degeneration. (C) 2019 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.