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Objective: Neuroimaging investigations are critical to provide a more direct assessment of brain disturbances associated with osteoarthritis (OA)-related pain, and to better understand its pathophysiology to develop new treatment strategies. This viewpoint aims to summarize the importance of the brain in OA pain. Method: A European working group on pain in osteoarthritis GO-PAIN (Going Inside Osteoarthritis-related Pain Phenotyping) has been created to work on a global assessment of the OA-related pain. Relevant scientific literature was evaluated, summarized and discussed to expose advances in functional brain alterations related-to OA pain. Results: Findings of neuroimaging studies are highly heterogenous and based on small sample size, but some key brain alterations associated with OA pain can be identified across experiments. A systematic literature review conducted by Hall and colleagues (2023) found lower activity, connectivity, and grey matter volume in the right anterior insula in patients with OA than in healthy controls. Other works also pointed out that activity of specific brain regions could serve as a potential surrogate biomarker, but several limitations and confounding factors needs to be addressed. Conclusions: Brain functional imaging provides opportunities to accurately address an OA-related pain endophenotype. To encompass limitations and fill the gaps from the previous studies, we propose a blueprint for the next 5 years and stimulate ideas for others working in the field.

Objective: To evaluate the performance of a deep learning (DL) model in an external dataset to assess radiographic knee osteoarthritis using Kellgren-Lawrence (KL) grades against versatile human readers. Materials and methods: Two-hundred-eight knee anteroposterior conventional radiographs (CRs) were included in this retrospective study. Four readers (three radiologists, one orthopedic surgeon) assessed the KL grades and consensus grade was derived as the mean of these. The DL model was trained using all the CRs from Multicenter Osteoarthritis Study (MOST) and validated on Osteoarthritis Initiative (OAI) dataset and then tested on our external dataset. To assess the agreement between the graders, Cohen's quadratic kappa (k) with 95 ​% confidence intervals were used. Diagnostic performance was measured using confusion matrices and receiver operating characteristic (ROC) analyses. Results: The multiclass (KL grades from 0 to 4) diagnostic performance of the DL model was multifaceted: sensitivities were between 0.372 and 1.000, specificities 0.691–0.974, PPVs 0.227–0.879, NPVs 0.622–1.000, and AUCs 0.786–0.983. The overall balanced accuracy was 0.693, AUC 0.886, and kappa 0.820. If only dichotomous KL grading (i.e. KL0-1 vs. KL2-4) was utilized, superior metrics were seen with an overall balanced accuracy of 0.902 and AUC of 0.967. A substantial agreement between each reader and DL model was found: the inter-rater agreement was 0.737 [0.685–0.790] for the radiology resident, 0.761 [0.707–0.816] for the musculoskeletal radiology fellow, 0.802 [0.761–0.843] for the senior musculoskeletal radiologist, and 0.818 [0.775–0.860] for the orthopedic surgeon. Conclusion: In an external dataset, our DL model can grade knee osteoarthritis with diagnostic accuracy comparable to highly experienced human readers.

Abstract Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with machine learning-based partial least squares discriminant analysis (PLS-DA) was applied to study if severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could be detected from nasopharyngeal swab samples originally collected for polymerase chain reaction (PCR) analysis. Our retrospective study included 558 positive and 558 negative samples collected from Northern Finland. Overall, we found moderate diagnostic performance for ATR-FTIR when PCR analysis was used as the gold standard: the average area under the receiver operating characteristics curve (AUROC) was 0.67–0.68 (min. 0.65, max. 0.69) with 20, 10 and 5 k-fold cross validations. Mean accuracy, sensitivity and specificity was 0.62–0.63 (min. 0.60, max. 0.65), 0.61 (min. 0.58, max. 0.65) and 0.64 (min. 0.59, max. 0.67) with 20, 10 and 5 k-fold cross validations. As a conclusion, our study with relatively large sample set clearly indicate that measured ATR-FTIR spectrum contains specific information for SARS-CoV-2 infection (P

Objective: To investigate the value of ultrasonographic data in predicting total knee replacement (TKR). Design: Data from the Musculoskeletal Pain in Ullensaker study (MUST) was linked to the Norwegian Arthroplasty Register to form a 5–7 year prospective cohort study of 630 persons (69% women, mean (SD) age 64 (8.7) years). We examined the predictive power of ultrasound (US) features, i.e. osteophytes, meniscal extrusion, synovitis in the suprapatellar recess, femoral cartilage thickness, and quality for future knee osteoarthritis (OA) surgery. We investigated 4 main settings for multivariate predictive modeling: 1) clinical predictors (age, sex, body mass index, knee injury, familial OA and workload), 2) radiographic data (assessed by the Kellgren Lawrence grade, KL) with clinical predictors, 3) US features and clinical predictors. Finally, we also considered an ensemble of models 2) and 3) and used it as our fifth model. All models were compared using the Average Precision (AP) and the Area Under Receiver Operating Characteristic Curve (AUC) metrics. Results: Clinical predictors yielded AP of 0.11 (95% confidence interval [CI] 0.05–0.23) and AUC of 0.69 (0.58–0.79). Clinical predictors with KL grade yielded AP of 0.20 (0.12–0.33) and AUC of 0.81 (0.67–0.90). The clinical variables with ultrasound yielded AP of 0.17 (0.08–0.30) and AUC of 0.79 (0.69–0.86). Conclusion: Ultrasonographic examination of the knee may provide added value to basic clinical and demographic descriptors when predicting TKR. While it does not achieve the same predictive performance as radiography, it can provide additional value to the radiographic examination.

Abstract Background Trabecular bone texture analysis (TBTA) has been identified as an imaging biomarker that provides information on trabecular bone changes due to knee osteoarthritis (KOA). Consequently, it is important to conduct a comprehensive review that would permit a better understanding of this unfamiliar image analysis technique in the area of KOA research. We examined how TBTA, conducted on knee radiographs, is associated to (i) KOA incidence and progression, (ii) total knee arthroplasty, and (iii) KOA treatment responses. The primary aims of this study are twofold: to provide (i) a narrative review of the studies conducted on radiographic KOA using TBTA, and (ii) a viewpoint on future research priorities. Method Literature searches were performed in the PubMed electronic database. Studies published between June 1991 and March 2020 and related to traditional and fractal image analysis of trabecular bone texture (TBT) on knee radiographs were identified. Results The search resulted in 219 papers. After title and abstract scanning, 39 studies were found eligible and then classified in accordance to six criteria: cross-sectional evaluation of osteoarthritis and non-osteoarthritis knees, understanding of bone microarchitecture, prediction of KOA progression, KOA incidence, and total knee arthroplasty and association with treatment response. Numerous studies have reported the relevance of TBTA as a potential bioimaging marker in the prediction of KOA incidence and progression. However, only a few studies have focused on the association of TBTA with both OA treatment responses and the prediction of knee joint replacement. Conclusion Clear evidence of biological plausibility for TBTA in KOA is already established. The review confirms the consistent association between TBT and important KOA endpoints such as KOA radiographic incidence and progression. TBTA could provide markers for enrichment of clinical trials enhancing the screening of KOA progressors. Major advances were made towards a fully automated assessment of KOA.

Abstract Background The infrapatellar fat pad (IFP) of the knee joint has received lots of attention recently due to its emerging role in the pathogenesis of osteoarthritis (OA), where it displays an inflammatory phenotype. The aim of the present study was to examine the infrapatellar fatty acid (FA) composition in a rabbit (Oryctolagus cuniculus) model of early OA created by anterior cruciate ligament transection (ACLT). Methods OA was induced randomly in the left or right knee joint of skeletally mature New Zealand White rabbits by ACLT, while the contralateral knee was left intact. A separate group of unoperated rabbits served as controls. The IFP of the ACLT, contralateral, and control knees were harvested following euthanasia 2 or 8 weeks post-ACLT and their FA composition was determined with gas chromatography–mass spectrometry. Results The n-3/n-6 polyunsaturated FA (PUFA) ratio shifted in a pro-inflammatory direction after ACLT, already observed 2 weeks after the operation (0.20 ± 0.008 vs. 0.18 ± 0.009). At 8 weeks, the FA profile of the ACLT group was characterized with increased percentages of 20:4n-6 (0.44 ± 0.064 vs. 0.98 ± 0.339 mol-%) and 22:6n-3 (0.03 ± 0.014 vs. 0.07 ± 0.015 mol-%) and with decreased monounsaturated FA (MUFA) sums (37.19 ± 1.586 vs. 33.20 ± 1.068 mol-%) and n-3/n-6 PUFA ratios (0.20 ± 0.008 vs. 0.17 ± 0.008). The FA signature of the contralateral knees resembled that of the unoperated controls in most aspects, but had increased proportions of total n-3 PUFA and reduced MUFA sums. Conclusions These findings provide novel information on the effects of early OA on the infrapatellar FA profile in the rabbit ACLT model. The reduction in the n-3/n-6 PUFA ratio of the IFP is in concordance with the inflammation and cartilage degradation in early OA and could contribute to disease pathogenesis.

Summary: Background: Quantitative magnetic resonance imaging (MRI), e.g. relaxation parameter mapping, may be sensitive to structural and compositional tissue changes, and could potentially be used to non-invasively detect and monitor early meniscus degeneration related to knee osteoarthritis. Objective: To investigate MR relaxation times as potential biomarkers for meniscus degeneration through comparisons with histopathology. Methods: We measured MR relaxation parameters in the posterior horn of 40 menisci (medial and lateral) at a wide range of degenerative stages. T1, T2 and T2∗ were mapped using standard and ultrashort echo time sequences at 9.4 T and compared to gold standard histology using Pauli's histopathological scoring system, including assessment of surface integrity, collagen organization, cellularity and Safranin-O staining. Results: All three relaxation times increased with total Pauli score (mean difference per score (95% CI) for T2∗: 0.62 (0.37, 0.86), T2: 0.83 (0.53, 1.1) and T1: 24.7 (16.5, 32.8) ms/score). Clear associations were seen with scores of surface integrity (mean difference per score for T2∗: 3.0 (1.8, 4.2), T2: 4.0 (2.5, 5.5) and T1: 116 (75.6, 156) ms/score) and collagen organization (mean difference between highest and lowest score for T2∗: 5.3 (1.6, 8.9), T2: 6.1 (1.7, 11) and T1: 204 (75.9, 332) ms). The results were less clear for the remaining histopathological measures. Conclusions: MR relaxation times T1, T2 and T2∗ of ex vivo human menisci are associated with histologically verified degenerative processes, in particular related to surface integrity and collagen organization. If confirmed in vivo, MR relaxation times may thus be potential biomarkers for meniscus degeneration.

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm−1, followed by peak normalization at 850 cm−1 and preprocessing by MSC.

Abstract Economic costs of osteoarthritis (OA) are considerable. However, there are no clinical tools to predict the progression of OA or guide patients to a correct treatment for preventing OA. We tested the ability of our cartilage degeneration algorithm to predict the subject-specific development of OA and separate groups with different OA levels. The algorithm was able to predict OA progression similarly with the experimental follow-up data and separate subjects with radiographical OA (Kellgren-Lawrence (KL) grade 2 and 3) from healthy subjects (KL0). Maximum degeneration and degenerated volumes within cartilage were significantly higher (p

Abstract Research is ongoing to develop drug therapies to manage osteoarthritis (OA) and articular cartilage (AC) injuries. However, means to deliver drug to localized AC lesions are highly limited and not clinically available. This study investigates the capability of laser ultrasound (laser-induced plasma sound source) to deliver agents (methylene blue, MB, in PBS) into bovine AC. Treatment samples (n = 10) were immersed in MB solution simultaneously with LU exposure, while adjacent control 1 tissue (n = 10) was pre-treated with LU followed by immersion in MB and adjacent control 2 tissue (n = 10) was only immersed in MB. AC exposed (n = 22) or not exposed (n = 27) to LU were characterized for anomalies in structure, composition, viability or RNA expression. Optically detected MB content was significantly (p

Recent research in the field of osteoarthritis (OA) has focused on understanding the underlying molecular and clinical phenotypes of the disease. This narrative review article focuses on recent advances in our understanding of the phenotypes of OA and proposes that the disease represents a diversity of clinical phenotypes that are underpinned by a number of molecular mechanisms, which may be shared by several phenotypes and targeted more specifically for therapeutic purposes. The clinical phenotypes of OA supposedly have different underlying etiologies and pathogenic pathways and they progress at different rates. Large OA population cohorts consist of a majority of patients whose disease progresses slowly and a minority of individuals whose disease may progress faster. The ability to identify the people with relatively rapidly progressing OA can transform clinical trials and enhance their efficiency. The identification, characterization, and classification of molecular phenotypes of rapidly progressing OA, which represent patients who may benefit most from intervention, could potentially serve as the basis for precision medicine for this disabling condition. Imaging and biochemical markers (biomarkers) are important diagnostic and research tools that can assist with this challenge.

Background and purpose — Arthroscopic estimation of articular cartilage thickness is important for scoring of lesion severity, and measurement of cartilage speed of sound (SOS)—a sensitive index of changes in cartilage composition. We investigated the accuracy of optical coherence tomography (OCT) in measurements of cartilage thickness and determined SOS by combining OCT thickness and ultrasound (US) time-of-flight (TOF) measurements. Material and methods — Cartilage thickness measurements from OCT and microscopy images of 94 equine osteochondral samples were compared. Then, SOS in cartilage was determined using simultaneous OCT thickness and US TOF measurements. SOS was then compared with the compositional, structural, and mechanical properties of cartilage. Results — Measurements of non-calcified cartilage thickness using OCT and microscopy were significantly correlated (ρ = 0.92; p < 0.001). With calcified cartilage included, the correlation was ρ = 0.85 (p < 0.001). The mean cartilage SOS (1,636 m/s) was in agreement with the literature. However, SOS and the other properties of cartilage lacked any statistically significant correlation. Interpretation — OCT can give an accurate measurement of articular cartilage thickness. Although SOS measurements lacked accuracy in thin equine cartilage, the concept of SOS measurement using OCT appears promising.

A low-energy acetabular fracture, as a result of falling from standing height, is common among elderly patients and the number of cases is increasing rapidly in developed countries. Several biomechanical factors contribute to the incidence, severity, and type of acetabular fractures, such as body configuration at the impact moment or bone and soft-tissue quality. The current parametric study developed a comprehensive finite element model of the pelvic girdle and simple representation of the whole body and investigated the effects of impact velocity, conventional indoor/outdoor flooring material, and trochanteric soft-tissue stiffness on an acetabular fracture. Our results show that whereas the impact velocity has a substantial influence on the incidence and type of acetabular fracture, the effects of conventional flooring materials and trochanteric soft-tissue quality are not remarkable. It seems that other factors such as the quality of bone (healthy vs. osteoporotic), the thickness of trochanteric soft-tissue, and body configuration at the impact are more critical in the occurrence and type of the acetabular fracture. These results can be valuable in the prevention of acetabular fractures and the design of protective measures such as hip pads or novel flooring materials.

Knee osteoarthritis (OA) is the most common musculoskeletal disease in the world. In primary healthcare, knee OA is diagnosed using clinical examination and radiographic assessment. Osteoarthritis Research Society International (OARSI) atlas of OA radiographic features allows performing independent assessment of knee osteophytes, joint space narrowing and other knee features. This provides a fine-grained OA severity assessment of the knee, compared to the gold standard and most commonly used Kellgren–Lawrence (KL) composite score. In this study, we developed an automatic method to predict KL and OARSI grades from knee radiographs. Our method is based on Deep Learning and leverages an ensemble of residual networks with 50 layers. We used transfer learning from ImageNet with a fine-tuning on the Osteoarthritis Initiative (OAI) dataset. An independent testing of our model was performed on the Multicenter Osteoarthritis Study (MOST) dataset. Our method yielded Cohen’s kappa coefficients of 0.82 for KL-grade and 0.79, 0.84, 0.94, 0.83, 0.84 and 0.90 for femoral osteophytes, tibial osteophytes and joint space narrowing for lateral and medial compartments, respectively. Furthermore, our method yielded area under the ROC curve of 0.98 and average precision of 0.98 for detecting the presence of radiographic OA, which is better than the current state-of-the-art.

BACKGROUND:Articular cartilage (AC) is mainly composed of water, type II collagen, proteoglycans (PGs) and chondrocytes. The amount of PGs in AC is routinely quantified with digital densitometry (DD) from Safranin O-stained sections, but it is unclear whether similar method could be used for collagens. OBJECTIVE:The aim of this study was to clarify whether collagens can be quantified from histological AC sections using DD. MATERIAL AND METHODS:Sixteen human AC samples were stained with Masson's trichrome or Picrosirius red. Optical densities of histological stains were compared to two commonly used collagen parameters (amide I and collagen CH2 side chain peak at 1338cm-1) measured using Fourier Transform Infrared (FTIR) spectroscopic imaging. RESULTS:Optical density of Modified Masson's trichrome staining, which included enzymatic removal of PGs before staining, correlated significantly with FTIR-derived collagen parameters at almost all depths of cartilage. The other studied staining protocols displayed significant correlations with the reference parameters at only few depth layers. CONCLUSIONS:Based on our findings, modified Masson's trichrome staining protocol is suitable for quantification of AC collagen content. Enzymatic removal of PGs prior to staining is critical as us allows better staining of the collagen. Further optimization of staining protocols may improve the results in the future studies.

Localized delivery of drugs into articular cartilage (AC) may facilitate the development of novel therapies to treat osteoarthritis (OA). We investigated the potential of spark-gap-generated sound to deliver a drug surrogate, i.e., methylene blue (MB), into AC. In vitro experiments exposed bovine AC samples to either simultaneous sonication and immersion in MB (Treatment 1; n = 10), immersion in MB after sonication (Control 1; n = 10), solely immersion in MB (Control 2; n = 10), or neither sonication nor immersion in MB (Control 3; n = 10). The sonication protocol consisted of 1,000 spark-gap -generated pulses. Delivery of MB into AC was estimated from optical absorbance in transmission light microscopy. Optical absorbance was significantly greater in the treatment group up to 900 μm depth from AC surface as compared to all controls. Field emission scanning electron microscopy (FESEM), histological analysis, and digital densitometry (DD) of sonicated (n = 6) and non-sonicated (n = 6) samples showed no evidence of sonication-induced changes in proteoglycan content or collagen structure. Consequently, spark-gap -generated sound may offer a solution for localized drug delivery into AC in a non-destructive fashion. Further research on this method may contribute to OA drug therapies.

One of the characteristics of early osteoarthritis (OA) is the loss of fixed charged density (FCD) of glycosaminoglycans in the superficial zone of articular cartilage. However, possible local changes in the FCD content of the pericellular matrix (PCM) are not fully understood. Hence, our aim was to investigate the effect of unilateral anterior cruciate ligament transection (ACLT) in rabbit knees on estimated FCD in the PCM compared to that in the ECM, and relate these results with cell morphology.Articular cartilage samples were collected from ACLT, contralateral and intact control knee joints from lateral and medial femoral condyles and tibial plateaus, and from the femoral groove and patella. Histological samples were prepared and stained with Safranin-O to estimate the FCD content around the chondrocytes in the PCM and the ECM with digital densitometry.As a result of ACLT, the greatest decreases in the FCD content of the PCM were observed in the superficial zone of the lateral femoral condyle (p = 0.02), medial tibial plateau (p = 0.002) and patellar (p < 0.001) cartilage. The normalized FCD content of the PCM compared to the surrounding ECM was increased most in the femoral condyles (p < 0.01) and medial tibial plateau (p = 0.02) cartilage. The high normalized FCD content of the PCM in the superficial zone of lateral femoral condyle cartilage was consistent with the round cell morphology in that location.In conclusion, we suggest that certain sites in the knee joint, particularly the lateral femoral condyle cartilage, experience less FCD loss in the PCM than in the ECM in early post-traumatic OA, which could lead to altered cell shape.

Arthritis induces joint erosions and skeletal bone fragility.The main goal of this work was to analyze the early arthritis induced events at bone architecture and mechanical properties at tissue level.Eighty-eight Wistar rats were randomly housed in experimental groups, as follows: adjuvant induced arthritis (AIA) (N = 47) and a control healthy group (N = 41). Rats were monitored during 22 days for the inflammatory score, ankle perimeter and body weight and sacrificed at different time points (11 and 22 days post disease induction). Bone samples were collected for histology, micro computed tomography (micro-CT), 3-point bending and nanoindentation. Blood samples were also collected for bone turnover markers and systemic cytokine quantification.At bone tissue level, measured by nanoindentation, there was a reduction of hardness in the arthritic group, associated with an increase of the ratio of bone concentric to parallel lamellae and of the area of the osteocyte lacuna. In addition, increased bone turnover and changes in the microstructure and mechanical properties were observed in arthritic animals, since the early phase of arthritis, when compared with healthy controls.We have shown in an AIA rat model that arthritis induces very early changes at bone turnover, structural degradation and mechanical weakness. Bone tissue level is also affected since the early phase of arthritis, characterized by decreased tissue hardness associated with changes in bone lamella organization and osteocyte lacuna surface. These observations highlight the pertinence of immediate control of inflammation in the initial stages of arthritis.

Contrast-enhanced micro-computed tomography (CEμCT) with cationic and anionic contrast agents reveals glycosaminoglycan (GAG) content and distribution in articular cartilage (AC). The advantage of using cationic stains (e.g., CA4+) compared to anionic stains (e.g., Hexabrix®), is that it distributes proportionally with GAGs, while anionic stain distribution in AC is inversely proportional to the GAG content. To date, studies using cationic stains have been conducted with sufficient resolution to study its distributions on the macro-scale, but with insufficient resolution to study its distributions on the micro-scale. Therefore, it is not known whether the cationic contrast agents accumulate in extra/pericellular matrix and if they interact with chondrocytes. The insufficient resolution has also prevented to answer the question whether CA4+ accumulation in chondrons could lead to an erroneous quantification of GAG distribution with low-resolution μCT setups. In this study, we use high-resolution μCT to investigate whether CA4+ accumulates in chondrocytes, and further, to determine whether it affects the low-resolution ex vivo μCT studies of CA4+ stained human AC with varying degree of osteoarthritis. Human osteochondral samples were immersed in three different concentrations of CA4+ (3 mgI/ml, 6 mgI/ml, and 24 mgI/ml) and imaged with high-resolution μCT at several timepoints. Different uptake diffusion profiles of CA4+ were observed between the segmented chondrons and the rest of the tissue. While the X-ray -detected CA4+ concentration in chondrons was greater than in the rest of the AC, its contribution to the uptake into the whole tissue was negligible and in line with macro-scale GAG content detected from histology. The efficient uptake of CA4+ into chondrons and surrounding territorial matrix can be explained by the micro-scale distribution of GAG content. CA4+ uptake in chondrons occurred regardless of the progression stage of osteoarthritis in the samples and the relative difference between the interterritorial matrix and segmented chondron area was less than 4%. To conclude, our results suggest that GAG quantification with CEμCT is not affected by the chondron uptake of CA4+. This further confirms the use of CA4+ for macro-scale assessment of GAG throughout the AC, and highlight the capability of studying chondron properties in 3D at the micro scale.

OBJECTIVE:Osteoarthritis (OA) has often regarded as a disease of articular cartilage only. New evidence has shifted the paradigm towards a system biology approach, where also the surrounding tissue, especially bone is studied more vigorously. However, the histological features of subchondral bone are only poorly characterized in current histological grading scales of OA. The aim of this study is to specifically characterize histological changes occurring in subchondral bone at different stages of OA and propose a simple grading system for them. DESIGN:20 patients undergoing total knee replacement surgery were randomly selected for the study and series of osteochondral samples were harvested from the tibial plateaus for histological analysis. Cartilage degeneration was assessed using the standardized OARSI grading system, while a novel four-stage grading system was developed to illustrate the changes in subchondral bone. Subchondral bone histology was further quantitatively analyzed by measuring the thickness of uncalcified and calcified cartilage as well as subchondral bone plate. Furthermore, internal structure of calcified cartilage-bone interface was characterized utilizing local binary patterns (LBP) based method. RESULTS:The histological appearance of subchondral bone changed drastically in correlation with the OARSI grading of cartilage degeneration. As the cartilage layer thickness decreases the subchondral plate thickness and disorientation, as measured with LBP, increases. Calcified cartilage thickness was highest in samples with moderate OA. CONCLUSION:The proposed grading system for subchondral bone has significant relationship with the corresponding OARSI grading for cartilage. Our results suggest that subchondral bone remodeling is a fundamental factor already in early stages of cartilage degeneration.

Contrast-enhanced micro-computed tomography (CEμCT) with phosphotungstic acid (PTA) has shown potential for detecting collagen distribution of articular cartilage. However, the selectivity of the PTA staining to articular cartilage constituents remains to be elucidated. The aim of this study was to investigate the dependence of PTA for the collagen content in bovine articular cartilage. Adjacent bovine articular cartilage samples were treated with chondroitinase ABC and collagenase to degrade the proteoglycan and the collagen constituents in articular cartilage, respectively. Enzymatically degraded samples were compared to the untreated samples using CEμCT and reference methods, such as Fourier-transform infrared imaging. Decrease in the X-ray attenuation of PTA in articular cartilage and collagen content was observed in cartilage depth of 0-13% and deeper in tissue after collagen degradation. Increase in the X-ray attenuation of PTA was observed in the cartilage depth of 13-39% after proteoglycan degradation. The X-ray attenuation of PTA-labelled articular cartilage in CEμCT is associated mainly with collagen content but the proteoglycans have a minor effect on the X-ray attenuation of the PTA-labelled articular cartilage. In conclusion, the PTA labeling provides a feasible CEμCT method for 3D characterization of articular cartilage.

Objectives: The purpose of the present study was to evaluate bone healing in rabbit critical-sized calvarial defects using two different synthetic scaffold materials, solid biodegradable bioactive glass and tricalcium phosphate granules alongside solid and particulated autogenous bone grafts. Material and Methods: Bilateral full thickness critical-sized calvarial defects were created in 15 New Zealand white adult male rabbits. Ten defects were filled with solid scaffolds made of bioactive glass or with porous tricalcium phosphate granules. The healing of the biomaterial-filled defects was compared at the 6 week time point to the healing of autologous bone grafted defects filled with a solid cranial bone block in 5 defects and with particulated bone combined with fibrin glue in 10 defects. In 5 animals one defect was left unfilled as a negative control. Micro-computed tomography (micro-CT) was used to analyze healing of the defects. Results: Micro-CT analysis revealed that defects filled with tricalcium phosphate granules showed new bone formation in the order of 3.89 (SD 1.17)% whereas defects treated with solid bioactive glass scaffolds showed 0.21 (SD 0.16)%, new bone formation. In the empty negative control defects there was an average new bone formation of 21.8 (SD 23.7)%. Conclusions: According to findings in this study, tricalcium phosphate granules have osteogenic potential superior to bioactive glass, though both particulated bone with fibrin glue and solid bone block were superior defect filling materials.

A potential of quantitative noninvasive knee ultrasonography (US) for detecting changes in femoral subchondral bone related to knee osteoarthritis (OA) was investigated. Thirty-nine patients referred to a knee arthroscopy underwent dynamic noninvasive US examination of the knee joint. The subchondral bone was semiautomatically segmented from representative US images of femoral medial and lateral condyles and intercondylar notch area. Subsequently, the normalized mean gray-level intensity profile, starting from the cartilage-bone interface and extending to the subchondral bone depth of ~1.7 mm, was calculated. The obtained profile was divided into 5 depth levels and the mean of each level, as well as the slope of the profile within the first two levels, was calculated. The US quantitative data were compared with the arthroscopic Noyes’ grading and radiographic Kellgren-Lawrence (K-L) grading. Qualitatively, an increase in relative subchondral bone US gray-level values was observed as OA progressed. Statistically significant correlations were observed between normalized US mean intensity or intensity slope especially in subchondral bone depth level 2 and K-L grading (r=0.600, P

Fourier Transform Infrared (FT-IR) spectroscopic imaging has been earlier applied for the spatial estimation of the collagen and the proteoglycan (PG) contents of articular cartilage (AC). However, earlier studies have been limited to the use of univariate analysis techniques. Current analysis methods lack the needed specificity for collagen and PGs. The aim of the present study was to evaluate the suitability of partial least squares regression (PLSR) and principal component regression (PCR) methods for the analysis of the PG content of AC. Multivariate regression models were compared with earlier used univariate methods and tested with a sample material consisting of healthy and enzymatically degraded steer AC. Chondroitinase ABC enzyme was used to increase the variation in PG content levels as compared to intact AC. Digital densitometric measurements of Safranin O-stained sections provided the reference for PG content. The results showed that multivariate regression models predict PG content of AC significantly better than earlier used absorbance spectrum (i.e. the area of carbohydrate region with or without amide I normalization) or second derivative spectrum univariate parameters. Increased molecular specificity favours the use of multivariate regression models, but they require more knowledge of chemometric analysis and extended laboratory resources for gathering reference data for establishing the models. When true molecular specificity is required, the multivariate models should be used.