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使用近红外(NIR)光谱监测骨关节炎的进展。

Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy.

机构信息

Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.

School of Chemistry, Physics and Mechanical Engineering, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia.

出版信息

Sci Rep. 2017 Sep 13;7(1):11463. doi: 10.1038/s41598-017-11844-3.

Abstract

We demonstrate in this study the potential of near infrared (NIR) spectroscopy as a tool for monitoring progression of cartilage degeneration in an animal model. Osteoarthritic degeneration was artificially induced in one joint in laboratory rats, and the animals were sacrificed at four time points: 1, 2, 4, and 6 weeks (3 animals/week). NIR spectra were acquired from both (injured and intact) knees. Subsequently, the joint samples were subjected to histological evaluation and glycosaminoglycan (GAG) content analysis, to assess disease severity based on the Mankin scoring system and to determine proteoglycan loss, respectively. Multivariate spectral techniques were then employed for classification (principal component analysis and support vector machines) and prediction (partial least squares regression) of the samples' Mankin scores and GAG content from their NIR spectra. Our results demonstrate that NIR spectroscopy is sensitive to degenerative changes in articular cartilage, and is capable of distinguishing between mild (weeks 1&2; Mankin <=2) and advanced (weeks 4&6; Mankin =>3) cartilage degeneration. In addition, the spectral data contains information that enables estimation of the tissue's Mankin score (error = 12.6%, R = 86.2%) and GAG content (error = 7.6%, R = 95%). We conclude that NIR spectroscopy is a viable tool for assessing cartilage degeneration post-injury, such as, post-traumatic osteoarthritis.

摘要

在本研究中,我们展示了近红外(NIR)光谱作为监测动物模型中软骨退变进展的工具的潜力。在实验大鼠的一个关节中人为诱导骨关节炎退变,并且在四个时间点(每周 3 只动物)对动物进行安乐死:1、2、4 和 6 周。从(受伤和未受伤)膝关节采集 NIR 光谱。随后,对关节样本进行组织学评估和糖胺聚糖(GAG)含量分析,分别根据 Mankin 评分系统评估疾病严重程度和确定蛋白聚糖损失。然后,采用多元光谱技术(主成分分析和支持向量机)对样本的 Mankin 评分和 GAG 含量进行分类(偏最小二乘回归)和预测(偏最小二乘回归),并从 NIR 光谱中进行预测。我们的结果表明,NIR 光谱对关节软骨的退行性变化敏感,能够区分轻度(1&2 周;Mankin <=2)和晚期(4&6 周;Mankin =>3)软骨退变。此外,光谱数据包含可用于估计组织 Mankin 评分(误差=12.6%,R=86.2%)和 GAG 含量(误差=7.6%,R=95%)的信息。我们得出结论,NIR 光谱是评估损伤后软骨退变(例如创伤后骨关节炎)的一种可行工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfbc/5597588/47fdb0b74411/41598_2017_11844_Fig1_HTML.jpg

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