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The lateral resolution of infrared spectroscopy has been inadequate for accurate biochemical characterization of the cell microenvironment, a region regulating biochemical and biomechanical signals to cells. In this study, we demonstrate the capacity of a high-resolution Fourier transform infrared microspectroscopy (HR-FTIR-MS) to characterize the collagen content of this region. Specifically, we focus on the collagen content in the cartilage cell (chondrocyte) microenvironment of healthy and osteoarthritic (OA) cartilage. Human tibial cartilage samples (N = 28) were harvested from 7 cadaveric donors and graded for OA severity (healthy, early OA, advanced OA). HR-FTIR-MS was used to analyze the collagen content of the chondrocyte microenvironment of five distinct zones across the tissue depth. HR-FTIR-MS successfully showed collagen content distribution across chondrocytes and their environment. In zones 2 and 3 (10 - 50% of the tissue thickness), we observed that collagen content was smaller (P < 0.05) in early OA compared to the healthy tissue in the vicinity of cells (pericellular region). The collagen content loss was extended to the extracellular matrix in advanced OA tissue. No significant differences in the collagen content of the chondrocyte microenvironment were observed between the groups in the most superficial (0–10%) and deep zones (50–100%). HR-FTIR-MS revealed collagen loss in the early OA cartilage pericellular region before detectable changes in the extracellular matrix in advanced OA. HR-FTIR-MS-based compositional assessment enables a better understanding of OA-related changes in tissues. This technique can be used to identify new disease mechanisms enabling better intervention strategies. Statement of significance: Osteoarthritis (OA) is the most common degenerative joint disease causing pain and disability. While significant progress has been made in OA research, OA pathogenesis is still poorly understood and current OA treatments are mainly palliative. This study demonstrates that high-resolution FTIR microspectroscopy (HR-FTIR-MS) can characterize OA-induced compositional changes in the cell microenvironment (pericellular matrix) during the early disease stages before tissue changes in the extracellular matrix become apparent. This technique may further enable the identification of new OA mechanisms and improve our current understanding of OA pathogenesis, thus, enabling the development of better treatment methods.

Objective Joint injuries may lead to degeneration of cartilage tissue and initiate development of posttraumatic osteoarthritis. Arthroscopic surgeries can be used to treat joint injuries, but arthroscopic evaluation of articular cartilage quality is subjective. Fourier transform infrared spectroscopy combined with fiber optics and attenuated total reflectance crystal could be used for the assessment of tissue quality during arthroscopy. We hypothesize that fiber-optic mid-infrared spectroscopy can detect enzymatically and mechanically induced damage similar to changes occurring during progression of osteoarthritis. Design Bovine patellar cartilage plugs were extracted and degraded enzymatically and mechanically. Adjacent untreated samples were utilized as controls. Enzymatic degradation was done using collagenase and trypsin enzymes. Mechanical damage was induced by (1) dropping a weight impactor on the cartilage plugs and (2) abrading the cartilage surface with a rotating sandpaper. Fiber-optic mid-infrared spectroscopic measurements were conducted before and after treatments, and spectral changes were assessed with random forest, partial least squares discriminant analysis, and support vector machine classifiers. Results All models had excellent classification performance for detecting the different enzymatic and mechanical damage on cartilage matrix. Random forest models achieved accuracies between 90.3% and 77.8%, while partial least squares model accuracies ranged from 95.8% to 84.7%, and support vector machine accuracies from 91.7% to 80.6%. Conclusions The results suggest that fiber-optic Fourier transform infrared spectroscopy attenuated total reflectance spectroscopy is a viable way to detect minor and major degeneration of articular cartilage. Objective measures provided by fiber-optic spectroscopic methods could improve arthroscopic evaluation of cartilage damage.

Raman spectroscopy is promising in vivo tool in various biomedical applications; moreover, in recent years, its use for characterizing articular cartilage degeneration has been developing. It has also shown potential for scoring the severity of cartilage lesions, which could be useful in determining the optimal treatment strategy during cartilage repair surgery. However, the effect of different cartilage injury types on Raman spectra is unknown. This study aims to investigate the potential of Raman spectroscopy for detecting changes in cartilage due to different injury types. Artificial injuries were induced in cartilage samples using established mechanical and enzymatic approaches to mimic trauma‐induced and natural degeneration. Mechanical damage was induced using surface abrasion (ABR, n = 12) or impact loading (IMP, n = 12), while enzymatic damage was induced using three different treatments: 30 min trypsin digestion (T30, n = 12), 90 min collagenase digestion (C90, n = 12), and 24 h collagenase digestion (C24, n = 12). Raman spectra were obtained from all specimens, and partial least squares discriminant analysis (PLS‐DA) was used to distinguish cartilage injury types from their respective controls. PLS‐DA cross‐validation accuracies were higher for C24 (88%) and IMP (79%) than for C90 (67%), T30 (63%), and ABR (58%) groups. This study indicates that Raman spectroscopy, combined with multivariate analysis, can discern different cartilage injury types. This knowledge could be useful in clinical decision‐making, for example, selecting the optimal treatment remedy during cartilage repair surgery.

Background: As nocturnal hypoxemia and heart rate variability are associated with excessive daytime sleepiness (EDS) related to OSA, we hypothesize that the power spectral densities (PSD) of nocturnal pulse oximetry signals could be utilized in the assessment of EDS. Thus, we aimed to investigate if PSDs contain features that are related to EDS and whether a convolutional neural network (CNN) could detect patients with EDS using self-learned PSD features. Methods: A total of 915 OSA patients who had undergone polysomnography with multiple sleep latency test on the following day were investigated. PSDs for nocturnal blood oxygen saturation (SpO), heart rate (HR), and photoplethysmogram (PPG), as well as power in the 15–35 mHz band in SpO (P) and HR (P), were computed. Differences in PSD features were investigated between EDS groups. Additionally, a CNN classifier was developed for identifying severe EDS patients based on spectral data. Results: SpO power content increased significantly (p < 0.002) with increasing severity of EDS. Furthermore, a significant (p < 0.001) increase in HR-PSD was found in severe EDS (mean sleep latency < 5 min). Elevated odds of having severe EDS was found in P (OR = 1.19–1.29) and P (OR = 1.81–1.83). Despite these significant spectral differences, the CNN classifier reached only moderate sensitivity (49.5%) alongside high specificity (80.4%) in identifying patients with severe EDS. Conclusions: We conclude that PSDs of nocturnal pulse oximetry signals contain features significantly associated with OSA-related EDS. However, CNN-based identification of patients with EDS is challenging via pulse oximetry.

The scourge of malaria infection continues to strike hardest against pregnant women and children in Africa and South East Asia. For global elimination, testing methods that are ultrasensitive to low-level ring-staged parasitemia are urgently required. In this study, we used a novel approach for diagnosis of malaria infection by combining both electronic ultraviolet-visible (UV/vis) spectroscopy and near infrared (NIR) spectroscopy to detect and quantify low-level (1-0.000001%) ring-staged malaria-infected whole blood under physiological conditions uisng Multiclass classification using logistic regression, which showed that the best results were achieved using the extended wavelength range, providing an accuracy of 100% for most parasitemia classes. Likewise, partial least-squares regression (PLS-R) analysis showed a higher quantification sensitivity (R2 = 0.898) for the extended spectral region compared to UV/vis and NIR (R2 = 0.806 and 0.556, respectively). For quantifying different-stage blood parasites, the extended wavelength range was able to detect and quantify all thePlasmodium falciparum accurately compared to testing each spectral component separately. These results demonstrate the potential of a combined UV/vis-NIR spectroscopy to accurately diagnose malaria-infected patients without the need for elaborate sample preparation associated with the existing mid-IR approaches.

Articular cartilage (AC) is a soft connective tissue that covers the ends of articulating bones. Joint injuries may lead to degeneration of cartilage tissue and initiate development of post-traumatic osteoarthritis (OA). Arthroscopic surgeries can be used to treat joint injuries, but arthroscopic evaluation of cartilage quality is subjective. Therefore, new methods are needed for objective assessment of cartilage degeneration. Fourier transform infrared (FTIR) spectroscopy can be used to assess tissue composition based on the fundamental molecular vibrations. When combined with fiber optics and attenuated total reflectance (ATR) crystal, the measurements can be done flexibly without any sample processing. We hypothesize that Fourier transform infrared attenuated total reflection (FTIR-ATR) spectroscopy can detect enzymatically and mechanically induced changes similar to changes occurring during progression of OA. Fresh bovine patellar cartilage plugs (n = 60) were extracted and degraded enzymatically and mechanically. Adjacent untreated control samples (n = 60) were utilized as controls. Enzymatic degradation was implemented by 90-min and 24-hour collagenase as well as 30-min trypsin treatments. Mechanical damage was induced by: 1) dropping a weight impactor on the cartilage plugs, and 2) abrading the cartilage surface with a rotating sandpaper. Fiber optic FTIR-ATR spectroscopic measurements were conducted for control and degraded samples, and spectral changes were assessed with random forest (RF), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM) classifiers. RF (accuracy 93.1 % to 79.2 %), PLS-DA (accuracy 95.8% to 81.9%), and SVM (accuracy 91.7% to 80.6%) all had excellent classification performance for detecting the different enzymatic and mechanical damage on cartilage matrix. The results suggest that fiber optic FTIR-ATR spectroscopy is a viable way to detect minor degeneration of AC.

Articular cartilage is a connective tissue that enables smooth movements between bones in articulating joints. Cartilage consists of extracellular matrix (ECM) and chondrocytes – the cells responsible for synthesis of the ECM. The ECM consists of type II collagen, proteoglycans, water, and some other minor components. Cartilage is prone to degenerative joint conditions, such as osteoarthritis, due to its weak repair capacity resulting from a lack of vascular, neural, and lymphatic networks. Osteoarthritis causes erosion of the cartilage matrix and therefore inhibits its function, resulting in joint pain, loss of mobility, and significant global socioeconomic burden. Currently, surgical treatment of cartilage pathologies is carried out during arthroscopy with variable outcomes. This variability occurs due to the subjective nature of arthroscopy, which relies on manual palpation and visual evaluation of the tissue surface. Diffuse optical spectroscopy in the near-infrared spectral region probes tissue structure and composition via a relationship with its optical properties (the absorption and reduced scattering coefficients). Due to its avascular nature, healthy cartilage is translucent. It thus has low absorption in the near-infrared region, providing the necessary conditions for light to traverse deep into the tissue. This research reports, for the first time, cartilage absorption and reduced scattering coefficients in the near-infrared spectral range and assess their capacity for characterizing the depth-wise profile of cartilage proteoglycan content. The results revealed that cartilage optical properties are strong predictors of its proteoglycan content. The best performance was observed with the prediction of the proteoglycan content by the absorption coefficient.

Combination of FTIR spectroscopy with fiber optics provides a powerful diagnostic tool for diagnosing of human diseases, including osteoarthritis. To detect cartilage degradation, an arthroscopic probe based on polycrystalline fibers was developed and evaluated on equine cartilage specimen. The hook shape allows reaching a significant portion of the articular surface; the flat tip ensures avoidance of tissue destruction. Efficient QCL-coupling and stable transmission of PIR fibers under bending allows the assembling of effective thin arthroscopy probes and customized multispectral systems for medical diagnostic applications. The presented work was performed within the MIRACLE project (Grant Agreement No 780598, Horizon 2020).

Background Histological evaluation of articular cartilage, such as using the Mankin scoring system, is the gold standard for characterization of tissue integrity. This scoring system takes into account several parameters indicative of the tissue's health; however, the collagen integrity, which is a primary indicator of cartilage health is not taken into consideration. Thus, there is need to enhance histological grading of articular cartilage by incorporating explicit scoring of collagen degeneration into the Modified Mankin grading system. This paper explores a new histological grading parameter for collagen network degradation and how this information can be used to augment a widely used grading scheme like the Modified Mankin grading system. Methods Intact and degenerated human cartilage were examined histologically and then subjected to second harmonic generation imaging, leading to qualitative and quantitative description of collagen disruption emanating from the surface to subsurface layers of the tissue. This data was then incorporated into the Modified Mankin grading system. Findings Second harmonic generation image analysis reveals a relationship between changes in collagen architecture and histologically observed tissue disruption in degenerated articular cartilage. Interpretation Histological tissue disruption in degenerated human articular cartilage is directly related to the reorganization of collagen fibrils in the form of intense fibril aggregation, either as a result of degeneration or aging. This method of mapping disrupted tissue regions to quantitative collagen fibril damage can be coded into cartilage grading systems and could inform clinical practice and scientific research.

Objective We investigate the potential of a prototype multimodality arthroscope, combining ultrasound, optical coherence tomography (OCT) and arthroscopic indentation device, for assessing cartilage lesions, and compare the reliability of this approach with conventional arthroscopic scoring ex vivo. Design Areas of interest (AIs, N = 43) were selected from equine fetlock joints (N = 5). Blind-coded AIs were independently scored by two equine surgeons employing International Cartilage Repair Society (ICRS) scoring system via conventional arthroscope and multimodality arthroscope, in which high-frequency ultrasound and OCT catheters were attached to an arthroscopic indentation device. In addition, cartilage stiffness was measured with the indentation device, and lesions in OCT images scored using custom-made automated software. Measurements and scorings were performed twice in two separate rounds. Finally, the scores were compared to histological ICRS scores. Results OCT and arthroscopic examinations showed the highest average agreements (55.2%) between the scoring by surgeons and histology scores, whereas ultrasound had the lowest (50.6%). Average intraobserver agreements of surgeons and interobserver agreements between rounds were, respectively, for conventional arthroscope (68.6%, 69.8%), ultrasound (68.6%, 68.6%), OCT (65.1%, 61.7%) and automated software (65.1%, 59.3%). Conclusions OCT imaging supplemented with the automated software provided the most reliable lesion scoring. However, limited penetration depth of light limits the clinical potential of OCT in assessing human cartilage thickness; thus, the combination of OCT and ultrasound could be optimal for reliable diagnostics. Present findings suggest imaging and quantitatively analyzing the entire articular surface to eliminate surgeon-related variation in the selection of the most severe lesion to be scored., final draft, peerReviewed

This review describes recent developments and applications of near-infrared (NIR) spectroscopy for characterization of articular cartilage integrity. It summarizes the research findings in this area and presents some spectral ranges and peaks associated with the different properties and components of articular cartilage. We further describe recent adaptations of NIR spectroscopy for clinical evaluation of articular cartilage injury and degeneration. Critical to accurate decision-making during repair surgery is having clear knowledge of lesion severity and spread, and how to grade the quality of surrounding cartilage. Thus, in this review, we detail efforts aimed at quantification and classification of cartilage pathology using NIR spectroscopy. Finally, we present open questions and challenges with a view to guiding future directions in NIR spectroscopy research on articular cartilage.

The severity of obstructive sleep apnea (OSA) is classified using apnea-hypopnea index (AHI). Accurate determination of AHI currently requires manual analysis and complicated registration setup making it expensive and labor intensive. Partially for these reasons, OSA is a heavily underdiagnosed disease as only 7% of women and 18% of men suffering from OSA have diagnosis. To resolve these issues, we introduce an artificial neural network (ANN) that estimates AHI and oxygen desaturation index (ODI) using only the blood oxygen saturation signal (SpO2), recorded during ambulatory polygraphy, as an input. Therefore, hypopneas associated only with an arousal were not considered in this study. SpO2 signals from 1692 patients were used for training and 99 for validation. Two test sets were used consisting of 198 and 1959 patients. In the primary test set, the median absolute errors of ANN estimated AHI and ODI were 0.78 events/hour and 0.68 events/hour respectively. Based on the ANN estimated AHI and ODI, 90.9% and 94.4% of the test patients were classified into the correct OSA severity category. In conclusion, AHI and ODI can be reliably determined using neural network analysis of SpO2 signal. The developed method may enable a more affordable screening of OSA.

This study investigates the relationship between the optical response of human articular cartilage in the visible (VIS) and near infrared (NIR) spectral range and its matrix properties.Full-thickness osteochondral cores (dia. = 16 mm, n = 50) were extracted from human cadaver knees (N = 13) at four anatomical locations and divided into quadrants. Absorption spectra were acquired in the spectral range 400-1100 nm from one quadrant. Reference biomechanical, biochemical composition, histological, and cartilage thickness measurements were obtained from two other quadrants. A multivariate statistical technique based on partial least squares (PLS) regression was then employed to investigate the correlation between the absorption spectra and tissue properties.Our results demonstrate that cartilage optical response correlates with its function, composition and morphology, as indicated by the significant relationship between spectral predicted and measured biomechanical (79.0% ⩽ R(2) ⩽ 80.3%, p

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 3) cartilage degeneration. In addition, the spectral data contains information that enables estimation of the tissue’s Mankin score (error = 12.6%, R2 = 86.2%) and GAG content (error = 7.6%, R2 = 95%). We conclude that NIR spectroscopy is a viable tool for assessing cartilage degeneration post-injury, such as, post-traumatic osteoarthritis., published version, peerReviewed

The conventional mechanical properties of articular cartilage, such as compressive stiffness, have been shown to have limited capacity to distinguish visually normal from degraded cartilage samples. In this study, a new mechanical indentation framework for assessing functional properties of articular cartilage during loading/unloading, i.e. deformation and recovery, was established. The capacity of a ring-shaped indenter integrated with an ultrasound transducer to distinguish mechanically intact from proteoglycan-depleted tissue was investigated. To achieve this, normal and enzymatically degraded bovine osteochondral samples were subjected to loading/unloading while the response of the tissue at the middle was captured by ultrasound at the same time. The enzymatic degradation model was characterized by amount of proteoglycan content, glycosaminoglycan release and proteomic analysis. The mechanical response of a wider continuum of articular cartilage in the loaded area and its surrounding region was captured in this framework leading to investigate two parameters, L and TS, related to the surrounding tissue of the loaded area for functional assessment of cartilage. L is the distance between the ultrasound transducer and articular cartilage surface and TS is the transient strain of articular cartilage during loading and unloading. Classification Analysis based on Principal Component Analysis was used to investigate the capacity of the new parameters to assess the functionality of the tissue. Multivariate statistics based on Partial Least Squares regression was employed to identify the correlation between the response of the tissue in the indented area and its surrounding cartilage. The results of this study indicate that L during loading (deformation) can differentiate normal and mildly proteoglycan-depleted samples from severely depleted samples and L during unloading (recovery) can distinguish between normal and proteoglycan-depleted tissue. However, TS during deformation and recovery is unable to discriminate normal cartilage samples from proteoglycan-depleted tissue. The results also demonstrate a strong correlation between mechanical properties of the loaded area with the response of its surrounding cartilage during recovery. It is therefore concluded that L in this newly established framework can discriminate between normal and proteoglycan-depleted cartilage samples. However, more samples will be needed to verify the demarcation between samples degraded for varying amount of time.

Conventional arthroscopic evaluation of articular cartilage is subjective and poorly reproducible. Therefore, implementation of quantitative diagnostic techniques, such as near infrared spectroscopy (NIRS) and optical coherence tomography (OCT), is essential. Locations (n = 44) with various cartilage conditions were selected from mature equine fetlock joints (n = 5). These locations and their surroundings were measured with NIRS and OCT (n = 530). As a reference, cartilage proteoglycan (PG) and collagen contents, and collagen network organization were determined using quantitative microscopy. Additionally, lesion severity visualized in OCT images was graded with an automatic algorithm according to International Cartilage Research Society (ICRS) scoring system. Artificial neural network with variable selection was then employed to predict cartilage composition in the superficial and deep zones from NIRS data, and the performance of two models, generalized (including all samples) and condition-specific models (based on ICRS-grades), was compared. Spectral data correlated significantly (p, published version, peerReviewed

Objective Accurate arthroscopic evaluation of cartilage lesions could significantly improve the outcome of repair surgery. In this study, we investigated for the first time the potential of intra-articular ultrasound as an arthroscopic tool for grading cartilage defects in the human shoulder joint in vivo and compared the outcome to results from arthroscopic evaluation and magnetic resonance imaging findings. Design A total of 26 sites from 9 patients undergoing routine shoulder arthroscopy were quantitatively evaluated with a clinical intravascular (40MHz) ultrasound imaging system, using the regular arthroscopy portals. Reflection coefficient ( R), integrated reflection coefficient ( IRC), apparent integrated backscattering ( AIB), and ultrasound roughness index ( URI) were calculated, and high-resolution ultrasound images were obtained per site. Each site was visually graded according to the International Cartilage Repair Society (ICRS) system. “Ultrasound scores” corresponding to the ICRS system were determined from the ultrasound images. Magnetic resonance imaging was conducted and cartilage integrity at each site was classified into 5 grades (0 = normal, 4 = severely abnormal) by a radiologist. Results R and IRC were lower at sites with damaged cartilage surface ( P = 0.033 and P = 0.043, respectively) and correlated with arthroscopic ICRS grades ( rs = −0.444, P = 0.023 and rs = −0.426, P = 0.03, respectively). Arthroscopic ICRS grades and ultrasound scores were significantly correlated (rs = 0.472, P = 0.015), but no significant correlation was found between magnetic resonance imaging data and other parameters. Conclusion The results suggest that ultrasound arthroscopy could facilitate quantitative clinical appraisal of articular cartilage integrity in the shoulder joint and provide information on cartilage lesion depth and severity for quantitative diagnostics in surgery.

PURPOSE The purpose of this study was to demonstrate the potential of near infrared (NIR) spectroscopy for characterizing the health and degenerative state of articular cartilage based on the components of the Mankin score. METHODS Three models of osteoarthritic degeneration induced in laboratory rats by anterior cruciate ligament (ACL) transection, meniscectomy (MSX), and intra-articular injection of monoiodoacetate (1 mg) (MIA) were used in this study. Degeneration was induced in the right knee joint; each model group consisted of 12 rats (N = 36). After 8 weeks, the animals were euthanized and knee joints were collected. A custom-made diffuse reflectance NIR probe of 5-mm diameter was placed on the tibial and femoral surfaces, and spectral data were acquired from each specimen in the wave number range of 4,000 to 12,500 cm(-1). After spectral data acquisition, the specimens were fixed and safranin O staining (SOS) was performed to assess disease severity based on the Mankin scoring system. Using multivariate statistical analysis, with spectral preprocessing and wavelength selection technique, the spectral data were then correlated to the structural integrity (SI), cellularity (CEL), and matrix staining (SOS) components of the Mankin score for all the samples tested. RESULTS ACL models showed mild cartilage degeneration, MSX models had moderate degeneration, and MIA models showed severe cartilage degenerative changes both morphologically and histologically. Our results reveal significant linear correlations between the NIR absorption spectra and SI (R(2) = 94.78%), CEL (R(2) = 88.03%), and SOS (R(2) = 96.39%) parameters of all samples in the models. In addition, clustering of the samples according to their level of degeneration, with respect to the Mankin components, was also observed. CONCLUSIONS NIR spectroscopic probing of articular cartilage can potentially provide critical information about the health of articular cartilage matrix in early and advanced stages of osteoarthritis (OA). CLINICAL RELEVANCE This rapid nondestructive method can facilitate clinical appraisal of articular cartilage integrity during arthroscopic surgery.

The determination of the characteristics of articular cartilage such as thickness, stiffness and swelling, especially in the form that can facilitate real-time decisions and diagnostics is still a matter for research and development. This paper correlates near infrared spectroscopy with mechanically measured cartilage thickness to establish a fast, non-destructive, repeatable and precise protocol for determining this tissue property. Statistical correlation was conducted between the thickness of bovine cartilage specimens (n = 97) and regions of their near infrared spectra. Nine regions were established along the full absorption spectrum of each sample and were correlated with the thickness using partial least squares (PLS) regression multivariate analysis. The coefficient of determination (R2) varied between 53 and 93%, with the most predictive region (R2 = 93.1%, p < 0.0001) for cartilage thickness lying in the region (wavenumber) 5350–8850 cm−1. Our results demonstrate that the thickness of articular cartilage can be measured spectroscopically using NIR light. This protocol is potentially beneficial to clinical practice and surgical procedures in the treatment of joint disease such as osteoarthritis.

Determining the properties and integrity of subchondral bone in the developmental stages of osteoarthritis, especially in a form that can facilitate real-time characterization for diagnostic and decision-making purposes, is still a matter for research and development. This paper presents relationships between near infrared absorption spectra and properties of subchondral bone obtained from 3 models of osteoarthritic degeneration induced in laboratory rats via: (i) menisectomy (MSX); (ii) anterior cruciate ligament transaction (ACL); and (iii) intra-articular injection of mono-ido-acetate (1 mg) (MIA), in the right knee joint, with 12 rats per model group (N = 36). After 8 weeks, the animals were sacrificed and knee joints were collected. A custom-made diffuse reflectance NIR probe of diameter 5 mm was placed on the tibial surface and spectral data were acquired from each specimen in the wavenumber range 4000–12 500 cm− 1. After spectral acquisition, micro computed tomography (micro-CT) was performed on the samples and subchondral bone parameters namely: bone volume (BV) and bone mineral density (BMD) were extracted from the micro-CT data. Statistical correlation was then conducted between these parameters and regions of the near infrared spectra using multivariate techniques including principal component analysis (PCA), discriminant analysis (DA), and partial least squares (PLS) regression. Statistically significant linear correlations were found between the near infrared absorption spectra and subchondral bone BMD (R2 = 98.84%) and BV (R2 = 97.87%). In conclusion, near infrared spectroscopic probing can be used to detect, qualify and quantify changes in the composition of the subchondral bone, and could potentially assist in distinguishing healthy from OA bone as demonstrated with our laboratory rat models.

Summary Objective The aim of this study was to demonstrate the potential of near-infrared (NIR) spectroscopy for categorizing cartilage degeneration induced in animal models. Method Three models of osteoarthritic degeneration were induced in laboratory rats via one of the following methods: (1) menisectomy (MSX); (2) anterior cruciate ligament transection (ACLT); and (3) intra-articular injection of mono-ido-acetate (1 mg) (MIA), in the right knee joint, with 12 rats per model group. After 8 weeks, the animals were sacrificed and tibial knee joints were collected. A custom-made near-infrared (NIR) probe of diameter 5 mm was placed on the cartilage surface and spectral data were acquired from each specimen in the wave number range 4,000–12,500 cm −1 . Following spectral data acquisition, the specimens were fixed and Safranin-O staining was performed to assess disease severity based on the Mankin scoring system. Using multivariate statistical analysis based on principal component analysis and partial least squares regression, the spectral data were then related to the Mankin scores of the samples tested. Results Mild to severe degenerative cartilage changes were observed in the subject animals. The ACLT models showed mild cartilage degeneration, MSX models moderate, and MIA severe cartilage degenerative changes both morphologically and histologically. Our result demonstrates that NIR spectroscopic information is capable of separating the cartilage samples into different groups relative to the severity of degeneration, with NIR correlating significantly with their Mankin score ( R 2 = 88.85%). Conclusion We conclude that NIR is a viable tool for evaluating articular cartilage health and physical properties such as change in thickness with degeneration.

This study aimed to quantify the long‐term progression of blunt and sharp cartilage defects and their effect on joint homeostasis and function of the equine carpus. In nine adult Shetland ponies, the cartilage in the radiocarpal and middle carpal joint of one front limb was grooved (blunt or sharp randomized). The ponies were subjected to an 8‐week exercise protocol and euthanized at 39 weeks. Structural and compositional alterations in joint tissues were evaluated in vivo using serial radiographs, synovial biopsies, and synovial fluid samples. Joint function was monitored by quantitative gait analysis. Macroscopic, microscopic, and biomechanical evaluation of the cartilage and assessment of subchondral bone parameters were performed ex vivo. Grooved cartilage showed higher OARSI microscopy scores than the contra‐lateral sham‐operated controls (p

This study evaluates the feasibility of near infrared (NIR) spectroscopy to distinguish between different cartilage injury types associated with post-traumatic osteoarthritis and idiopathic osteoarthritis (OA) induced by mechanical and enzymatic damages. Bovine osteochondral samples (n = 72) were subjected to mechanical (n = 24) and enzymatic (n = 36) damage; NIR spectral measurements were acquired from each sample before and after damage, and from a separate control group (n = 12). Biomechanical measurements were then conducted to determine the functional integrity of the samples. NIR spectral variations resulting from different damage types were investigated and the samples classified using partial least squares discriminant analysis (PLS-DA). Partial least squares regression (PLSR) was then employed to investigate the relationship between the NIR spectra and biomechanical properties of the samples. Results of the study demonstrate that substantial spectral changes occur in the region of 1700–2200 nm due to tissue damages, while differences between enzymatically and mechanically induced damages can be observed mainly in the region of 1780–1810 nm. We conclude that NIR spectroscopy, combined with multivariate analysis, is capable of discriminating between cartilage injuries that mimic idiopathic OA and traumatic injuries based on specific spectral features. This information could be useful in determining the optimal treatment strategy during cartilage repair in arthroscopy.