Near infrared spectroscopy for rapid determination of Mankin score components: a potential tool for quantitative characterization of articular cartilage at surgery

Infrared Spectroscopy Can Differentiate Between Cartilage Injury Models: Implication for Assessment of Cartilage Integrity

In order to improve the ability of clinical diagnosis to differentiate articular cartilage (AC) injury of different origins, this study explores the sensitivity of mid-infrared (MIR) spectroscopy for detecting structural, compositional, and functional changes in AC resulting from two injury types. Three grooves (two in parallel in the palmar-dorsal direction and one in the mediolateral direction) were made via arthrotomy in the AC of the radial facet of the third carpal bone (middle carpal joint) and of the intermediate carpal bone (the radiocarpal joint) of nine healthy adult female Shetland ponies (age = 6.8 ± 2.6 years; range 4-13 years) using blunt and sharp tools. The defects were randomly assigned to each of the two joints. Ponies underwent a 3-week box rest followed by 8 weeks of treadmill training and 26 weeks of free pasture exercise before being euthanized for osteochondral sample collection. The osteochondral samples underwent biomechanical indentation testing, followed by MIR spectroscopic assessment. Digital densitometry was conducted afterward to estimate the tissue's proteoglycan (PG) content. Subsequently, machine learning models were developed to classify the samples to estimate their biomechanical properties and PG content based on the MIR spectra according to injury type. Results show that MIR is able to discriminate healthy from injured AC (91%) and between injury types (88%). The method can also estimate AC properties with relatively low error (thickness = 12.7% mm, equilibrium modulus = 10.7% MPa, instantaneous modulus = 11.8% MPa). These findings demonstrate the potential of MIR spectroscopy as a tool for assessment of AC integrity changes that result from injury.

Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.

The Level of Histone Deacetylase 4 is Associated with Aging Cartilage Degeneration and Chondrocyte Hypertrophy

Purpose

To determine the role of histone deacetylase 4 (HDAC4)-controlled chondrocyte hypertrophy in the onset and development of age-related osteoarthritis (OA).

Methods

Morphological analysis of human knee cartilages was performed to observe structural changes during cartilage degeneration. HDAC4 expression was deleted in adult aggrecan (Acan)-CreERT2; HDAC4fl/fl transgenic mice. The onset and development of age-related OA were investigated in transgenic and control mice using hematoxylin and eosin (H&E) and Safranin O staining. Furthermore, the progression of ACLT-induced OA following adenovirus-mediated HDAC4 overexpression was explored in rats. The expression levels of genes related to hypertrophy, cartilage matrix and its digestion, and chondrocyte proliferation were investigated using qPCR. Immunohistochemistry (IHC) was used to explore the mechanisms underlying HDAC4-controlled age-related changes in OA progression.

Results

In human cartilage, we performed morphological analysis and IHC, the results showed that hypertrophy-related structural changes are related to HDAC4 expression. Age-related OA was detected early (OARSI scores 2.7 at 8-month-old) following HDAC4 deletion in 2-month-old mice. Furthermore, qPCR and IHC results showed changes in hypertrophy-related genes Col10a1, Runx2 and Sox9 in chondrocytes, particularly in the expression of Runt-related transcription factor 2 (Runx2, 13.29±0.99 fold). The expression of the main cartilage matrix-related genes Col2a1 and Acan decreased, that of cartilage matrix digestion-related gene MMP-13 increased, while that of chondrocyte proliferation-related genes PTHrP, Ihh and Gli1 changed. In contrast, rat cartilage’s qPCR and IHC results showed opposite outcomes after HDAC4 overexpression.

Conclusion

Based on the results above, we concluded that HDAC4 expression regulates the onset and development of age-related OA by controlling chondrocyte hypertrophy. These results may help in the development of early diagnosis and treatment of age-related OA.

Resolving the Near-Infrared Spectrum of Articular Cartilage

OBJECTIVE

Spectroscopic techniques, such as near-infrared (NIR) spectroscopy, are gaining significant research interest for characterizing connective tissues, particularly articular cartilage, because there is still a largely unmet need for rapid, accurate and objective methods for assessing tissue integrity in real-time during arthroscopic surgery. This study aims to identify the NIR spectral range that is optimal for characterizing cartilage integrity by (a) identifying the contribution of its major constituents (collagen and proteoglycans) to its overall spectrum using proxy constituent models and (b) determining constituent-specific spectral contributions that can be used for assessment of cartilage in its physiological state.

DESIGN

The NIR spectra of cartilage matrix constituent models were measured and compared with specific molecular components of organic compounds in the NIR spectral range in order to identify their bands and molecular assignments. To verify the identified bands, spectra of the model compounds were compared with those of native cartilage. Since water obscures some bands in the NIR range, spectral measurements of the native cartilage were conducted under conditions of decreasing water content to amplify features of the solid matrix components. The identified spectral bands were then compared and examined in the resulting spectra of the intact cartilage samples.

RESULTS

As water was progressively eliminated from cartilage, the specific contribution of the different matrix components was observed to correspond with those identified from the proxy cartilage component models.

CONCLUSION

Spectral peaks in the regions 5500 to 6250 cm-1 and 8100 to 8600 cm-1 were identified to be effective for characterizing cartilage proteoglycan and collagen contents, respectively.

Characterization of connective tissues using near-infrared spectroscopy and imaging

Near-infrared (NIR) spectroscopy is a powerful analytical method for rapid, non-destructive and label-free assessment of biological materials. Compared to mid-infrared spectroscopy, NIR spectroscopy excels in penetration depth, allowing intact biological tissue assessment, albeit at the cost of reduced molecular specificity. Furthermore, it is relatively safe compared to Raman spectroscopy, with no risk of laser-induced photothermal damage. A typical NIR spectroscopy workflow for biological tissue characterization involves sample preparation, spectral acquisition, pre-processing and analysis. The resulting spectrum embeds intrinsic information on the tissue's biomolecular, structural and functional properties. Here we demonstrate the analytical power of NIR spectroscopy for exploratory and diagnostic applications by providing instructions for acquiring NIR spectra, maps and images in biological tissues. By adapting and extending this protocol from the demonstrated application in connective tissues to other biological tissues, we expect that a typical NIR spectroscopic study can be performed by a non-specialist user to characterize biological tissues in basic research or clinical settings. We also describe how to use this protocol for exploratory study on connective tissues, including differentiating among ligament types, non-destructively monitoring changes in matrix formation during engineered cartilage development, mapping articular cartilage proteoglycan content across bovine patella and spectral imaging across the depth-wise zones of articular cartilage and subchondral bone. Depending on acquisition mode and experiment objectives, a typical exploratory study can be completed within 6 h, including sample preparation and data analysis.

Quantification of Myocardial Blood Flow by Machine Learning Analysis of Modified Dual Bolus MRI Examination

Contrast-enhanced magnetic resonance imaging (MRI) is a promising method for estimating myocardial blood flow (MBF). However, it is often affected by noise from imaging artefacts, such as dark rim artefact obscuring relevant features. Machine learning enables extracting important features from such noisy data and is increasingly applied in areas where traditional approaches are limited. In this study, we investigate the capacity of machine learning, particularly support vector machines (SVM) and random forests (RF), for estimating MBF from tissue impulse response signal in an animal model. Domestic pigs (n = 5) were subjected to contrast enhanced first pass MRI (MRI-FP) and the impulse response at different regions of the myocardium (n = 24/pig) were evaluated at rest (n = 120) and stress (n = 96). Reference MBF was then measured using positron emission tomography (PET). Since the impulse response may include artefacts, classification models based on SVM and RF were developed to discriminate noisy signal. In addition, regression models based on SVM, RF and linear regression (for comparison) were developed for estimating MBF from the impulse response at rest and stress. The classification and regression models were trained on data from 4 pigs (n = 168) and tested on 1 pig (n = 48). Models based on SVM and RF outperformed linear regression, with higher correlation (R_SVM² = 0.81, R_RF² = 0.74, R_{linear_regression}² = 0.60; ρ_SVM = 0.76, ρ_RF = 0.76, ρ_{linear_regression} = 0.71) and lower error (RMSE_SVM = 0.67 mL/g/min, RMSE_RF = 0.77 mL/g/min, RMSE_{linear_regression} = 0.96 mL/g/min) for predicting MBF from MRI impulse response signal. Classifier based on SVM was optimal for detecting impulse response signals with artefacts (accuracy = 92%). Modified dual bolus MRI signal, combined with machine learning, has potential for accurately estimating MBF at rest and stress states, even from signals with dark rim artefacts. This could provide a protocol for reliable and easy estimation of MBF, although further research is needed to clinically validate the approach.

Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy

Introduction

Assessment of cartilage integrity during arthroscopy is limited by the subjective visual nature of the technique. To address this shortcoming in diagnostic evaluation of articular cartilage, near infrared spectroscopy (NIRS) has been proposed. In this study, we evaluated the capacity of NIRS, combined with machine learning techniques, to classify cartilage integrity.

Methods

Rabbit (n = 14) knee joints with artificial injury, induced via unilateral anterior cruciate ligament transection (ACLT), and the corresponding contra-lateral (CL) joints, including joints from separate non-operated control (CNTRL) animals (n = 8), were used. After sacrifice, NIR spectra (1000–2500 nm) were acquired from different anatomical locations of the joints (n_TOTAL = 313: n_CNTRL = 111, n_CL = 97, n_ACLT = 105). Machine and deep learning methods (support vector machines–SVM, logistic regression–LR, and deep neural networks–DNN) were then used to develop models for classifying the samples based solely on their NIR spectra.

Results

The results show that the model based on SVM is optimal of distinguishing between ACLT and CNTRL samples (ROC_AUC = 0.93, kappa = 0.86), LR is capable of distinguishing between CL and CNTRL samples (ROC_AUC = 0.91, kappa = 0.81), while DNN is optimal for discriminating between the different classes (multi-class classification, kappa = 0.48).

Conclusion

We show that NIR spectroscopy, when combined with machine learning techniques, is capable of holistic assessment of cartilage integrity, with potential for accurately distinguishing between healthy and diseased cartilage.

Electronic supplementary material

The online version of this article (10.1007/s12195-020-00612-5) contains supplementary material, which is available to authorized users.

Dataset on equine cartilage near infrared spectra, composition, and functional properties

Near infrared (NIR) spectroscopy is a well-established technique that is widely employed in agriculture, chemometrics, and pharmaceutical engineering. Recently, the technique has shown potential in clinical orthopaedic applications, for example, assisting in the diagnosis of various knee-related diseases (e.g., osteoarthritis) and their pathologies. NIR spectroscopy (NIRS) could be especially useful for determining the integrity and condition of articular cartilage, as the current arthroscopic diagnostics is subjective and unreliable. In this work, we present an extensive dataset of NIRS measurements for evaluating the condition, mechanical properties, structure, and composition of equine articular cartilage. The dataset contains NIRS measurements from 869 different locations across the articular surfaces of five equine fetlock joints. A comprehensive library of reference values for each measurement location is also provided, including results from a mechanical indentation testing, digital densitometry imaging, polarized light microscopy, and Fourier transform infrared spectroscopy. The published data can either be used as a model of human cartilage or to advance equine veterinary research.

Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy

OBJECTIVE

To investigate the feasibility of near-infrared (NIR) spectroscopy (NIRS) for evaluation of human articular cartilage biomechanical properties during arthroscopy.

DESIGN

A novel arthroscopic NIRS probe designed in our research group was utilized by an experienced orthopedic surgeon to measure NIR spectra from articular cartilage of human cadaver knee joints (ex vivo, n = 18) at several measurement locations during an arthroscopic surgery. Osteochondral samples (n = 265) were extracted from the measurement sites for reference analysis. NIR spectra were remeasured in a controlled laboratory environment (in vitro), after which the corresponding cartilage thickness and biomechanical properties were determined. Hybrid multivariate regression models based on principal component analysis and linear mixed effects modeling (PCA-LME) were utilized to relate cartilage in vitro spectra and biomechanical properties, as well as to account for the spatial dependency. Additionally, a k-nearest neighbors (kNN) classifier was employed to reject outlying ex vivo NIR spectra resulting from a non-optimal probe-cartilage contact. Model performance was evaluated for both in vitro and ex vivo NIR spectra via Spearman's rank correlation (ρ) and the ratio of performance to interquartile range (RPIQ).

RESULTS

Regression models accurately predicted cartilage thickness and biomechanical properties from in vitro NIR spectra (Model: 0.77 ≤ ρ ≤ 0.87, 2.03 ≤ RPIQ ≤ 3.0; Validation: 0.74 ≤ ρ ≤ 0.84, 1.87 ≤ RPIQ ≤ 2.90). When predicting cartilage properties from ex vivo NIR spectra (0.33 ≤ ρ ≤ 0.57 and 1.02 ≤ RPIQ ≤ 2.14), a kNN classifier enhanced the accuracy of predictions (0.52 ≤ ρ ≤ 0.87 and 1.06 ≤ RPIQ ≤ 1.88).

CONCLUSION

Arthroscopic NIRS could substantially enhance identification of damaged cartilage by enabling quantitative evaluation of cartilage biomechanical properties. The results demonstrate the capacity of NIRS in clinical applications.

Characterizing human subchondral bone properties using near-infrared (NIR) spectroscopy

Degenerative joint conditions are often characterized by changes in articular cartilage and subchondral bone properties. These changes are often associated with subchondral plate thickness and trabecular bone morphology. Thus, evaluating subchondral bone integrity could provide essential insights for diagnosis of joint pathologies. This study investigates the potential of optical spectroscopy for characterizing human subchondral bone properties. Osteochondral samples (n = 50) were extracted from human cadaver knees (n = 13) at four anatomical locations and subjected to NIR spectroscopy. The samples were then imaged using micro-computed tomography to determine subchondral bone morphometric properties, including: plate thickness (Sb.Th), trabecular thickness (Tb.Th), volume fraction (BV/TV), and structure model index (SMI). The relationship between the subchondral bone properties and spectral data in the 1^st (650–950 nm), 2^nd (1100–1350 nm) and 3^rd (1600–1870 nm) optical windows were investigated using partial least squares (PLS) regression multivariate technique. Significant correlations (p < 0.0001) and relatively low prediction errors were obtained between spectral data in the 1^st optical window and Sb.Th (R² = 92.3%, error = 7.1%), Tb.Th (R² = 88.4%, error = 6.7%), BV/TV (R² = 83%, error = 9.8%) and SMI (R² = 79.7%, error = 10.8%). Thus, NIR spectroscopy in the 1^st tissue optical window is capable of characterizing and estimating subchondral bone properties, and can potentially be adapted during arthroscopy.

Near-Infrared Spectroscopy Predicts Compositional and Mechanical Properties of Hyaluronic Acid-Based Engineered Cartilage Constructs

Hyaluronic acid (HA) has been widely used for cartilage tissue engineering applications. However, the optimal time point to harvest HA-based engineered constructs for cartilage repair is still under investigation. In this study, we investigated the ability of a nondestructive modality, near-infrared spectroscopic (NIR) analysis, to predict compositional and mechanical properties of HA-based engineered cartilage constructs. NIR spectral data were collected from control, unseeded constructs, and twice per week by fiber optic from constructs seeded with chondrocytes during their development over an 8-week period. Constructs were harvested at 2, 4, 6, and 8 weeks, collagen and sulfated glycosaminoglycan content measured using biochemical assays, and the mechanical properties of the constructs evaluated using unconfined compression tests. NIR absorbances associated with the scaffold material, water, and engineered cartilage matrix, were identified. The NIR-determined matrix absorbance plateaued after 4 weeks of culture, which was in agreement with the biochemical assay results. Similarly, the mechanical properties of the constructs also plateaued at 4 weeks. A multivariate partial least square model based on NIR spectral input was developed to predict the moduli of the constructs, which resulted in a prediction error of 10% and R value of 0.88 for predicted versus actual values of dynamic modulus. Furthermore, the maximum increase in moduli was calculated from the first derivative of the curve fit of NIR-predicted and actual moduli values over time, and both occurred at ∼2 weeks. Collectively, these data suggest that NIR spectral data analysis could be an alternative to destructive biochemical and mechanical methods for evaluation of HA-based engineered cartilage construct properties.

Optical spectroscopic characterization of human meniscus biomechanical properties

This study investigates the capacity of optical spectroscopy in the visible (VIS) and near-infrared (NIR) spectral ranges for estimating the biomechanical properties of human meniscus. Seventy-two samples obtained from the anterior, central, and posterior locations of the medial and lateral menisci of 12 human cadaver joints were used. The samples were subjected to mechanical indentation, then traditional biomechanical parameters (equilibrium and dynamic moduli) were calculated. In addition, strain-dependent fibril network modulus and permeability strain-dependency coefficient were determined via finite-element modeling. Subsequently, absorption spectra were acquired from each location in the VIS (400 to 750 nm) and NIR (750 to 1100 nm) spectral ranges. Partial least squares regression, combined with spectral preprocessing and transformation, was then used to investigate the relationship between the biomechanical properties and spectral response. The NIR spectral region was observed to be optimal for model development (83.0%≤R2≤90.8%). The percentage error of the models are: Eeq (7.1%), Edyn (9.6%), Eϵ (8.4%), and Mk (8.9%). Thus, we conclude that optical spectroscopy in the NIR range is a potential method for rapid and nondestructive evaluation of human meniscus functional integrity and health in real time during arthroscopic surgery.

Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy

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%, 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.

Characterization of Articular Cartilage Recovery and Its Correlation with Optical Response in the Near-Infrared Spectral Range

OBJECTIVES

In this study, we examine the capacity of a new parameter, based on the recovery response of articular cartilage, to distinguish between healthy and damaged tissues. We also investigate whether or not this new parameter correlates with the near-infrared (NIR) optical response of articular cartilage.

DESIGN

Normal and artificially degenerated (proteoglycan-depleted) bovine cartilage samples were nondestructively probed using NIR spectroscopy. Subsequently they were subjected to a load and unloading protocol, and the recovery response was logged during unloading. The recovery parameter, elastic rebound ( E_R), is based on the strain energy released as the samples underwent instantaneous elastic recovery.

RESULTS

Our results reveal positive relationship between the rebound parameter and cartilage proteoglycan content (normal samples: 2.20 ± 0.10 N mm; proteoglycan-depleted samples: 0.50 ± 0.04 N mm for 1 hour of enzymatic treatment and 0.13 ± 0.02 N mm for 4 hours of enzymatic treatment). In addition, multivariate analysis using partial least squares regression was employed to investigate the relationship between E_R and NIR spectral data. The results reveal significantly high correlation ( R²_cal = 98.35% and R²_val = 79.87%; P < 0.0001), with relatively low error (14%), between the recovery and optical response of cartilage in the combined NIR regions 5,450 to 6,100 cm^-1 and 7,500 to 12,500 cm^-1.

CONCLUSION

We conclude that E_R can indicate the mechanical condition and state of health of articular cartilage. The correlation of E_R with cartilage optical response in the NIR range could facilitate real-time evaluation of the tissue's integrity during arthroscopic surgery and could also provide an important tool for cartilage assessment in tissue engineering and regeneration research.

Near Infrared Spectroscopic Mapping of Functional Properties of Equine Articular Cartilage

Mechanical properties of articular cartilage are vital for normal joint function, which can be severely compromised by injuries. Quantitative characterization of cartilage injuries, and evaluation of cartilage stiffness and thickness by means of conventional arthroscopy is poorly reproducible or impossible. In this study, we demonstrate the potential of near infrared (NIR) spectroscopy for predicting and mapping the functional properties of equine articular cartilage at and around lesion sites. Lesion and non-lesion areas of interests (AI, N = 44) of equine joints (N = 5) were divided into grids and NIR spectra were acquired from all grid points (N = 869). Partial least squares (PLS) regression was used to investigate the correlation between the absorbance spectra and thickness, equilibrium modulus, dynamic modulus, and instantaneous modulus at the grid points of 41 AIs. Subsequently, the developed PLS models were validated with spectral data from the grid points of 3 independent AIs. Significant correlations were obtained between spectral data and cartilage thickness (R ² = 70.3%, p < 0.0001), equilibrium modulus (R ² = 67.8%, p < 0.0001), dynamic modulus (R ² = 68.9%, p < 0.0001) and instantaneous modulus (R ² = 41.8%, p < 0.0001). Relatively low errors were observed in the predicted thickness (5.9%) and instantaneous modulus (9.0%) maps. Thus, if well implemented, NIR spectroscopy could enable arthroscopic evaluation and mapping of cartilage functional properties at and around lesion sites.

Nondestructive Techniques to Evaluate the Characteristics and Development of Engineered Cartilage

In this review, methods for evaluating the properties of tissue engineered (TE) cartilage are described. Many of these have been developed for evaluating properties of native and osteoarthritic articular cartilage. However, with the increasing interest in engineering cartilage, specialized methods are needed for nondestructive evaluation of tissue while it is developing and after it is implanted. Such methods are needed, in part, due to the large inter- and intra-donor variability in the performance of the cellular component of the tissue, which remains a barrier to delivering reliable TE cartilage for implantation. Using conventional destructive tests, such variability makes it near-impossible to predict the timing and outcome of the tissue engineering process at the level of a specific piece of engineered tissue and also makes it difficult to assess the impact of changing tissue engineering regimens. While it is clear that the true test of engineered cartilage is its performance after it is implanted, correlation of pre and post implantation properties determined non-destructively in vitro and/or in vivo with performance should lead to predictive methods to improve quality-control and to minimize the chances of implanting inferior tissue.

Optical spectroscopic determination of human meniscus composition

This study investigates the correlation between the composition of human meniscus and its absorption spectrum in the visible (VIS) and near infrared (NIR) spectral range. Meniscus samples (n = 24) were obtained from nonarthritic knees of human cadavers with no history of joint diseases. Specimens (n = 72) were obtained from three distinct sections of the meniscus, namely; anterior, center, posterior. Absorption spectra were acquired from each specimen in the VIS and NIR spectral range (400-1,100 nm). Following spectroscopic probing, the specimens were subjected to biochemical analyses to determine the matrix composition, that is water, hydroxyproline, and uronic acid contents. Multivariate analytical techniques, including principal component analysis (PCA) and partial least squares (PLS) regression, were then used to investigate the correlation between the matrix composition and it spectral response. Our results indicate that the optical absorption of meniscus matrix is related to its composition, and this relationship is optimal in the NIR spectral range (750-1,100 nm). High correlations (R(2) (uronic)  = 86.9%, R(2) (water)  = 83.8%, R(2) (hydroxyproline)  = 81.7%, p < 0.0001) were obtained between the spectral predicted and measured meniscus composition, thus suggesting that spectral data in the NIR range can be utilized for estimating the matrix composition of human meniscus. In conclusion, optical spectroscopy, particularly in the NIR spectral range, is a potential method for evaluating the composition of human meniscus. This presents a promising technique for rapid and nondestructive evaluation of meniscus integrity in real-time during arthroscopic surgery.

Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal

OBJECTIVE

The aim was to investigate the applicability of multivariate analysis of optical coherence tomography (OCT) information for determining structural integrity, composition and mechanical properties of articular cartilage.

DESIGN

Equine osteochondral samples (N = 65) were imaged with OCT, and their total attenuation and backscattering coefficients (μt and μb) were measured. Subsequently, the Mankin score, optical density (OD) describing the fixed charge density, light absorbance in amide I region (Aamide), collagen orientation, permeability, fibril network modulus (Ef) and non-fibrillar matrix modulus (Em) of the samples were determined. Partial least squares (PLS) regression model was calculated to predict tissue properties from the OCT signals of the samples.

RESULTS

Significant correlations between the measured and predicted mean collagen orientation (R(2) = 0.75, P < 0.0001), permeability (R(2) = 0.74, P < 0.0001), mean OD (R(2) = 0.73, P < 0.0001), Mankin scores (R(2) = 0.70, P < 0.0001), Em (R(2) = 0.50, P < 0.0001), Ef (R(2) = 0.42, P < 0.0001), and Aamide (R(2) = 0.43, P < 0.0001) were obtained. Significant correlation was also found between μb and Ef (ρ = 0.280, P = 0.03), but not between μt and any of the determined properties of articular cartilage (P > 0.05).

CONCLUSION

Multivariate analysis of OCT signal provided good estimates for tissue structure, composition and mechanical properties. This technique may significantly enhance OCT evaluation of articular cartilage integrity, and could be applied, for example, in delineation of degenerated areas around cartilage injuries during arthroscopic repair surgery.

Optical absorption spectra of human articular cartilage correlate with biomechanical properties, histological score and biochemical composition

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  <  0.0001), biochemical (65.1%  ⩽  R(2)  ⩽  81.0%, p  < 0.0001), and histological scores ([Formula: see text] = 83.3%, p  < 0.0001) properties. Significant correlation was also obtained with the non-calcified cartilage thickness ([Formula: see text] = 83.2%, p  <  0.0001).We conclude that optical absorption of human cartilage in the VIS and NIR spectral range correlates with the overall tissue properties, thus providing knowledge that could facilitate development of systems for rapid assessment of tissue integrity.

Near-Infrared (NIR) Spectroscopy of Synthetic Hydroxyapatites and Human Dental Tissues

Near-infrared spectroscopy (NIR) was used to analyze synthetic hydroxyapatite calcined at various temperatures, synthetic carbonated hydroxyapatite, and human hard dental tissues (enamel and dentin). The NIR bands of those materials in the combination, first-overtone, and second-overtone spectral regions were assigned and evaluated for structural characterization. They were attributed to adsorbed and structural water, structural hydroxyl (OH) groups and surface P-OH groups. The NIR spectral features were quantitatively discussed in view of proton solid-state magic-angle spinning nuclear magnetic resonance ((1)H MAS NMR) results. We conclude that the NIR spectra of apatites are useful in the structural characterization of synthetic and biogenic apatites.

Monitoring the Progression of Spontaneous Articular Cartilage Healing with Infrared Spectroscopy

OBJECTIVE

Evaluation of early compositional changes in healing articular cartilage is critical for understanding tissue repair and for therapeutic decision-making. Fourier transform infrared imaging spectroscopy (FT-IRIS) can be used to assess the molecular composition of harvested repair tissue. Furthermore, use of an infrared fiber-optic probe (IFOP) has the potential for translation to a clinical setting to provide molecular information in situ. In the current study, we determined the feasibility of IFOP assessment of cartilage repair tissue in a rabbit model, and assessed correlations with gold-standard histology.

DESIGN

Bilateral osteochondral defects were generated in mature white New Zealand rabbits, and IFOP data obtained from defect and adjacent regions at 2, 4, 6, 8, 12, and 16 weeks postsurgery. Tissues were assessed histologically using the modified O'Driscoll score, by FT-IRIS, and by partial least squares (PLS) modeling of IFOP spectra.

RESULTS

The FT-IRIS parameters of collagen content, proteoglycan content, and collagen index correlated significantly with modified O'Driscoll score (P = 0.05, 0.002, and 0.02, respectively), indicative of their sensitivity to tissue healing. Repair tissue IFOP spectra were distinguished from normal tissue IFOP spectra in all samples by PLS analysis. However, the PLS model for prediction of histological score had a high prediction error, which was attributed to the spectral information being acquired from the tissue surface only.

CONCLUSION

The strong correlations between FT-IRIS data and histological score support further development of the IFOP technique for clinical applications, although further studies to optimize data collection from the full sample depths are required.

Spatial mapping of proteoglycan content in articular cartilage using near-infrared (NIR) spectroscopy

Diagnosis of articular cartilage pathology in the early disease stages using current clinical diagnostic imaging modalities is challenging, particularly because there is often no visible change in the tissue surface and matrix content, such as proteoglycans (PG). In this study, we propose the use of near infrared (NIR) spectroscopy to spatially map PG content in articular cartilage. The relationship between NIR spectra and reference data (PG content) obtained from histology of normal and artificially induced PG-depleted cartilage samples was investigated using principal component (PC) and partial least squares (PLS) regression analyses. Significant correlation was obtained between both data (R(2) = 91.40%, p<0.0001). The resulting correlation was used to predict PG content from spectra acquired from whole joint sample, this was then employed to spatially map this component of cartilage across the intact sample. We conclude that NIR spectroscopy is a feasible tool for evaluating cartilage contents and mapping their distribution across mammalian joint.