Proteoglycan concentrations in healthy and diseased articular cartilage by Fourier transform infrared imaging and principal component regression

Yin, Jianhua; Xia, Yang

doi:10.1016/j.saa.2014.05.092

Cited by 25 publications

(33 citation statements)

References 38 publications

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“…Furthermore, the differences in the proteoglycan content between healthy and osteoarthritic canine tibial AC were investigated using the earlier model. Proteoglycan loss in the meniscus-covered area of cartilage was found to be less than that of the meniscus-uncovered area (43). In another study, partial least squares (PLS) regression models were built to predict proteoglycan and collagen contents in bovine cartilage by calibrating the models against the optical density of safranin O or uronic acid content and hydroxyproline content for proteoglycans and collagen, respectively (44,45).…”

Section: 448 CMmentioning

confidence: 99%

Vibrational spectroscopy of articular cartilage

Rieppo

Töyräs

Saarakkala

2016

Applied Spectroscopy Reviews

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Articular cartilage is a connective tissue that is located at the ends of long bones. Type II collagen, proteoglycans, water, and chondrocytes are the main constituents of articular cartilage. Osteoarthritis, the most common joint disease in the world, causes degenerative changes in articular cartilage tissue. Fourier transform infrared, Raman, and near infrared spectroscopic techniques offer versatile tools to assess biochemical composition and quality of articular cartilage. These vibrational spectroscopic techniques can be used to broaden our understanding about the compositional changes during osteoarthritis, and they also hold promise in disease diagnostics. In this article, the current literature of articular cartilage spectroscopic studies is reviewed.

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Section: 448 CMmentioning

confidence: 99%

Vibrational spectroscopy of articular cartilage

Rieppo

Töyräs

Saarakkala

2016

Applied Spectroscopy Reviews

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“…FTIRI was performed on a PerkinElmer Spotlight-300 infrared imaging system [8,14]. The specimen sections were mounted on a movable mechanical stage for FTIRI.…”

Section: Ftiri Experimentsmentioning

confidence: 99%

“…The most obvious concentration change in cartilage matrix is the loss of PG in SZ and TZ at the early stage of OA [14]. Therefore, the spectra extracted from SZ are optimal for the training set.…”

Section: Ftiri Experimentsmentioning

confidence: 99%

“…) [9,14]. In addition, the 1338 cm −1 band did not exist in pure PG spectrum so that it could be used to qualitatively estimate the collagen [8].…”

Section: Spectrum and Image Analysismentioning

confidence: 99%

“…The major difference between the OA and healthy sections was the decrease of PG concentration in the corresponding areas [14,17], which could lead to the spectral change so as to be used as the main parameter in PCA-FDA. This confirmed the OA features, which may be caused by the deformation of joint motion and the cartilage injury after ACLT [8,18].…”

Section: Oa Featuresmentioning

confidence: 99%

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Discrimination of healthy and osteoarthritic articular cartilage by Fourier transform infrared imaging and Fisher’s discriminant analysis

Mao

Yin

Zhang

et al. 2016

Biomed. Opt. Express

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Fourier transform infrared spectroscopic imaging (FTIRI) technique can be used to obtain the quantitative information of content and spatial distribution of principal components in cartilage by combining with chemometrics methods. In this study, FTIRI combining with principal component analysis (PCA) and Fisher's discriminant analysis (FDA) was applied to identify the healthy and osteoarthritic (OA) articular cartilage samples. Ten 10-μm thick sections of canine cartilages were imaged at 6.25μm/pixel in FTIRI. The infrared spectra extracted from the FTIR images were imported into SPSS software for PCA and FDA. Based on the PCA result of 2 principal components, the healthy and OA cartilage samples were effectively discriminated by the FDA with high accuracy of 94% for the initial samples (training set) and cross validation, as well as 86.67% for the prediction group. The study showed that cartilage degeneration became gradually weak with the increase of the depth. FTIRI combined with chemometrics may become an effective method for distinguishing healthy and OA cartilages in future. Madariaga, "Classification and identification of organic binding media in artworks by means of Fourier transform infrared spectroscopy and principal component analysis," Anal. Bioanal. Chem. 399(10), 3601-3611 (2011). 12. E. Ostrovsky, U. Zelig, I. Gusakova, S. Ariad, S. Mordechai, I. Nisky, and J. Kapilushnik, "Detection of cancer using advanced computerized analysis of infrared spectra of peripheral blood," IEEE Trans. Biomed. Eng. 60(2), 343-353 (2013

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Development of diagnostic models for canine osteoarthritis based on serum and joint fluid mid‐infrared spectral data using five different discrimination and classification methods

Hou¹

2016

Journal of Chemometrics

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Osteoarthritis (OA) is an insidious joint disease that gradually leads to cartilage loss and the morphological impairment of other joint tissues. Therefore, early diagnosis and timely therapeutic intervention are of importance. Although there are a few diagnostic techniques used in clinics, these methods have various drawbacks. Infrared spectroscopy has emerged as an important analytical technique with wide applications in a variety of areas including clinical diagnosis. Research has shown that the presence of OA is associated with biochemical changes that are presumed to be reflected in serum or joint fluid. Hence, OA may be detected provided that serum or joint fluid is measured by infrared spectroscopy and appropriate data analysis methods are used to extract the diagnostic information from the infrared spectra. In this work, 5 discrimination and classification methods ([1] principal component analysis coupled with linear discriminant analysis, [2] principal component analysis coupled with multiple logistic regression, [3] partial least squares discriminant analysis, [4] regularized linear discriminant analysis, and [5] support vector machine) were used to build OA diagnostic models based on midinfrared spectra of serum and joint fluid. Useful diagnostic models were developed, indicating that infrared spectroscopy coupled with multivariate data analysis methods is very promising as a simple and accurate approach for OA diagnosis. The results also showed that models built from the 5 methods were different, as were the models' predictive performances. Therefore, choice of appropriate data analysis methods in model development should be taken into account.

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Proteoglycan concentrations in healthy and diseased articular cartilage by Fourier transform infrared imaging and principal component regression

Cited by 25 publications

References 38 publications

Vibrational spectroscopy of articular cartilage

Vibrational spectroscopy of articular cartilage

Discrimination of healthy and osteoarthritic articular cartilage by Fourier transform infrared imaging and Fisher’s discriminant analysis

Development of diagnostic models for canine osteoarthritis based on serum and joint fluid mid‐infrared spectral data using five different discrimination and classification methods

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