Independent and combined effects of obesity and traumatic joint injury to the structure and composition of rat knee cartilage.

Osteoarthritis (OA) is a multifactorial joint disease characterized by articular cartilage degradation. Risk factors for OA include joint trauma, obesity, and inflammation, each of which can affect joint health independently, but their interaction and the associated consequences of such interaction were largely unexplored. Here, we studied compositional and structural alterations in knee joint cartilages of Sprague-Dawley rats exposed to two OA risk factors: joint injury and diet-induced obesity. Joint injury was imposed by surgical transection of anterior cruciate ligaments (ACLx), and obesity was induced by a high fat/high sucrose diet. Depth-dependent proteoglycan (PG) content and collagen structural network of cartilage were measured from histological sections collected previously in Collins et al.. (2015). We found that ACLx primarily affected the superficial cartilages. Compositionally, ACLx led to reduced PG content in lean animals, but increased PG content in obese rats. Structurally, ACLx caused disorganization of collagenous network in both lean and obese animals through increased collagen orientation in the superficial tissues and a change in the degree of fibrous alignment. However, the cartilage degradation attributed to joint injury and obesity was not necessarily additive when the two risk factors were present simultaneously, particularly for PG content and collagen orientation in the superficial tissues. Interestingly, sham surgeries caused a through-thickness disorganization of collagen network in lean and obese animals. We conclude that the interactions of multiple OA risk factors are complex and their combined effects cannot be understood by superposition principle. Further research is required to elucidate the interactive mechanism between OA subtypes.

Injury-related cell death and proteoglycan loss in articular cartilage: Numerical model combining necrosis, reactive oxygen species, and inflammatory cytokines.

Osteoarthritis (OA) is a common musculoskeletal disease that leads to deterioration of articular cartilage, joint pain, and decreased quality of life. When OA develops after a joint injury, it is designated as post-traumatic OA (PTOA). The etiology of PTOA remains poorly understood, but it is known that proteoglycan (PG) loss, cell dysfunction, and cell death in cartilage are among the first signs of the disease. These processes, influenced by biomechanical and inflammatory stimuli, disturb the normal cell-regulated balance between tissue synthesis and degeneration. Previous computational mechanobiological models have not explicitly incorporated the cell-mediated degradation mechanisms triggered by an injury that eventually can lead to tissue-level compositional changes. Here, we developed a 2-D mechanobiological finite element model to predict necrosis, apoptosis following excessive production of reactive oxygen species (ROS), and inflammatory cytokine (interleukin-1)-driven apoptosis in cartilage explant. The resulting PG loss over 30 days was simulated. Biomechanically triggered PG degeneration, associated with cell necrosis, excessive ROS production, and cell apoptosis, was predicted to be localized near a lesion, while interleukin-1 diffusion-driven PG degeneration was manifested more globally. Interestingly, the model also showed proteolytic activity and PG biosynthesis closer to the levels of healthy tissue when pro-inflammatory cytokines were rapidly inhibited or cleared from the culture medium, leading to partial recovery of PG content. The numerical predictions of cell death and PG loss were supported by previous experimental findings. Furthermore, the simulated ROS and inflammation mechanisms had longer-lasting effects (over 3 days) on the PG content than localized necrosis. The mechanobiological model presented here may serve as a numerical tool for assessing early cartilage degeneration mechanisms and the efficacy of interventions to mitigate PTOA progression.

Correction: A musculoskeletal finite element model of rat knee joint for evaluating cartilage biomechanics during gait.

[This corrects the article DOI: 10.1371/journal.pcbi.1009398.].

Morphology of proximal and distal human semitendinosus compartments and the effects of distal tendon harvesting for anterior cruciate ligament reconstruction.

The human semitendinosus muscle is characterized by a tendinous inscription separating proximal and distal neuromuscular compartments. As each compartment is innervated by separate nerve branches, potential exists for independent operation and control of compartments. However, the morphology and function of each compartment have not been thoroughly examined in an adult human population. Further, the distal semitendinosus tendon is typically harvested for use in anterior cruciate ligament reconstruction surgery, which induces long-term morphological changes to the semitendinosus muscle-tendon unit. It remains unknown if muscle morphological alterations following anterior cruciate ligament reconstruction are uniform between proximal and distal semitendinosus compartments. Here, we performed magnetic resonance imaging on 10 individuals who had undergone anterior cruciate ligament reconstruction involving an ipsilateral distal semitendinosus tendon graft 14 ± 4 months prior, extracting morphological parameters of the whole semitendinosus muscle and each individual compartment from both the (non-injured) contralateral and surgical legs. In the contralateral leg, volume and length of the proximal compartment were smaller than the distal compartment. No between-compartment differences in volume or length were found for anterior cruciate ligament reconstructed legs, likely due to greater shortening of the distal compared to the proximal compartment after anterior cruciate ligament reconstruction. The maximal anatomical cross-sectional area of both compartments was substantially smaller on the anterior cruciate ligament reconstructed leg but did not differ between compartments on either leg. The absolute and relative between-leg differences in proximal compartment morphology on the anterior cruciate ligament reconstructed leg were strongly correlated with the corresponding between-leg differences in distal compartment morphological parameters. Specifically, greater between-leg morphological differences in one compartment were highly correlated with large between-leg differences in the other compartment, and vice versa for smaller differences. These relationships indicate that despite the heterogeneity in compartment length and volume, compartment atrophy is not independent or random. Further, the tendinous inscription endpoints were generally positioned at the same proximodistal level as the compartment maximal anatomical cross-sectional areas, providing a wide area over which the tendinous inscription could mechanically interact with compartments. Overall, results suggest the two human semitendinosus compartments are not mechanically independent.

Dependence of crack shape in loaded articular cartilage on the collagenous structure.

Cartilage cracks disrupt tissue mechanics, alter cell mechanobiology, and often trigger tissue degeneration. Yet, some tissue cracks heal spontaneously. A primary factor determining the fate of tissue cracks is the compression-induced mechanics, specifically whether a crack opens or closes when loaded. Crack deformation is thought to be affected by tissue structure, which can be probed by quantitative polarized light microscopy (PLM). It is unclear how the PLM measures are related to deformed crack morphology. Here, we investigated the relationship between PLM-derived cartilage structure and mechanical behavior of tissue cracks by testing if PLM-derived structural measures correlated with crack morphology in mechanically indented cartilages. METHODS: Knee joint cartilages harvested from mature and immature animals were used for their distinct collagenous fibrous structure and composition. The cartilages were cut through thickness, indented over the cracked region, and processed histologically. Sample-specific birefringence was quantified as two-dimensional (2D) maps of azimuth and retardance, two measures related to local orientation and degree of alignment of the collagen fibers, respectively. The shape of mechanically indented tissue cracks, measured as depth-dependent crack opening, were compared with azimuth, retardance, or "PLM index," a new parameter derived by combining azimuth and retardance. RESULTS: Of the three parameters, only the PLM index consistently correlated with the crack shape in immature and mature tissues. CONCLUSION: In conclusion, we identified the relative roles of azimuth and retardance on the deformation of tissue cracks, with azimuth playing the dominant role. The applicability of the PLM index should be tested in future studies using naturally-occurring tissue cracks.

A musculoskeletal finite element model of rat knee joint for evaluating cartilage biomechanics during gait.

Abnormal loading of the knee due to injuries or obesity is thought to contribute to the development of osteoarthritis (OA). Small animal models have been used for studying OA progression mechanisms. However, numerical models to study cartilage responses under dynamic loading in preclinical animal models have not been developed. Here we present a musculoskeletal finite element model of a rat knee joint to evaluate cartilage biomechanical responses during a gait cycle. The rat knee joint geometries were obtained from a 3-D MRI dataset and the boundary conditions regarding loading in the joint were extracted from a musculoskeletal model of the rat hindlimb. The fibril-reinforced poroelastic (FRPE) properties of the rat cartilage were derived from data of mechanical indentation tests. Our numerical results showed the relevance of simulating anatomical and locomotion characteristics in the rat knee joint for estimating tissue responses such as contact pressures, stresses, strains, and fluid pressures. We found that the contact pressure and maximum principal strain were virtually constant in the medial compartment whereas they showed the highest values at the beginning of the gait cycle in the lateral compartment. Furthermore, we found that the maximum principal stress increased during the stance phase of gait, with the greatest values at midstance. We anticipate that our approach serves as a first step towards investigating the effects of gait abnormalities on the adaptation and degeneration of rat knee joint tissues and could be used to evaluate biomechanically-driven mechanisms of the progression of OA as a consequence of joint injury or obesity.

Multiscale In Silico Modeling of Cartilage Injuries.

Injurious loading of the joint can be accompanied by articular cartilage damage and trigger inflammation. However, it is not well-known which mechanism controls further cartilage degradation, ultimately leading to post-traumatic osteoarthritis. For personalized prognostics, there should also be a method that can predict tissue alterations following joint and cartilage injury. This chapter gives an overview of experimental and computational methods to characterize and predict cartilage degradation following joint injury. Two mechanisms for cartilage degradation are proposed. In (1) biomechanically driven cartilage degradation, it is assumed that excessive levels of strain or stress of the fibrillar or non-fibrillar matrix lead to proteoglycan loss or collagen damage and degradation. In (2) biochemically driven cartilage degradation, it is assumed that diffusion of inflammatory cytokines leads to degradation of the extracellular matrix. When implementing these two mechanisms in a computational in silico modeling workflow, supplemented by in vitro and in vivo experiments, it is shown that biomechanically driven cartilage degradation is concentrated on the damage environment, while inflammation via synovial fluid affects all free cartilage surfaces. It is also proposed how the presented in silico modeling methodology may be used in the future for personalized prognostics and treatment planning of patients with a joint injury.

A numerical framework for mechano-regulated tendon healing-Simulation of early regeneration of the Achilles tendon.

Mechano-regulation during tendon healing, i.e. the relationship between mechanical stimuli and cellular response, has received more attention recently. However, the basic mechanobiological mechanisms governing tendon healing after a rupture are still not well-understood. Literature has reported spatial and temporal variations in the healing of ruptured tendon tissue. In this study, we explored a computational modeling approach to describe tendon healing. In particular, a novel 3D mechano-regulatory framework was developed to investigate spatio-temporal evolution of collagen content and orientation, and temporal evolution of tendon stiffness during early tendon healing. Based on an extensive literature search, two possible relationships were proposed to connect levels of mechanical stimuli to collagen production. Since literature remains unclear on strain-dependent collagen production at high levels of strain, the two investigated production laws explored the presence or absence of collagen production upon non-physiologically high levels of strain (>15%). Implementation in a finite element framework, pointed to large spatial variations in strain magnitudes within the callus tissue, which resulted in predictions of distinct spatial distributions of collagen over time. The simulations showed that the magnitude of strain was highest in the tendon core along the central axis, and decreased towards the outer periphery. Consequently, decreased levels of collagen production for high levels of tensile strain were shown to accurately predict the experimentally observed delayed collagen production in the tendon core. In addition, our healing framework predicted evolution of collagen orientation towards alignment with the tendon axis and the overall predicted tendon stiffness agreed well with experimental data. In this study, we explored the capability of a numerical model to describe spatial and temporal variations in tendon healing and we identified that understanding mechano-regulated collagen production can play a key role in explaining heterogeneities observed during tendon healing.

Alterations in the Functional Knee Alignment Are Not an Effective Strategy to Modify the Mediolateral Distribution of Knee Forces During Closed Kinetic Chain Exercises.

Pain felt while performing rehabilitation exercises could be a reason for the low adherence of knee osteoarthritis patients to physical rehabilitation. Reducing compressive forces on the most affected knee regions may help to mitigate the pain. Knee frontal plane positioning with respect to pelvis and foot (functional knee alignment) has been shown to modify the mediolateral distribution of the tibiofemoral joint contact force in walking. Hence, different functional knee alignments could be potentially used to modify joint loading during rehabilitation exercises. The aim was to understand whether utilizing different alignments is an effective strategy to unload specific knee areas while performing rehabilitation exercises. Eight healthy volunteers performed 5 exercises with neutral, medial, and lateral knee alignment. A musculoskeletal model was modified for improved prediction of tibiofemoral contact forces and used to evaluate knee joint kinematics, moments, and contact forces. Functional knee alignment had only a small and inconsistent effect on the mediolateral distribution joint contact force. Moreover, the magnitude of tibiofemoral and patellofemoral contact forces, knee moments, and measured muscle activities was not significantly affected by the alignment. Our results suggest that altering the functional knee alignment is not an effective strategy to unload specific knee regions in physical rehabilitation.

Automated analysis of rabbit knee calcified cartilage morphology using micro-computed tomography and deep learning.

Structural dynamics of calcified cartilage (CC) are poorly understood. Conventionally, CC structure is analyzed using histological sections. Micro-computed tomography (µCT) allows for three-dimensional (3D) imaging of mineralized tissues; however, the segmentation between bone and mineralized cartilage is challenging. Here, we present state-of-the-art deep learning segmentation for µCT images to assess 3D CC morphology. The sample includes 16 knees from 12 New Zealand White rabbits dissected into osteochondral samples from six anatomical regions: lateral and medial femoral condyles, lateral and medial tibial plateaus, femoral groove, and patella (n = 96). The samples were imaged with µCT and processed for conventional histology. Manually segmented CC from the images was used to train segmentation models with different encoder-decoder architectures. The models with the greatest out-of-fold evaluation Dice score were selected. CC thickness was compared across 24 regions, co-registered between the imaging modalities using Pearson correlation and Bland-Altman analyses. Finally, the anatomical CC thickness variation was assessed via a Linear Mixed Model analysis. The best segmentation models yielded average Dice of 0.891 and 0.807 for histology and µCT segmentation, respectively. The correlation between the co-registered regions was strong (r = 0.897, bias = 21.9 µm, standard deviation = 21.5 µm). Finally, both methods could separate the CC thickness between the patella, femoral, and tibial regions (p < 0.001). As a conclusion, the proposed µCT analysis allows for ex vivo 3D assessment of CC morphology. We demonstrated the biomedical relevance of the method by quantifying CC thickness in different anatomical regions with a varying mean thickness. CC was thickest in the patella and thinnest in the tibial plateau. Our method is relatively straightforward to implement into standard µCT analysis pipelines, allowing the analysis of CC morphology. In future research, µCT imaging might be preferable to histology, especially when analyzing dynamic changes in cartilage mineralization. It could also provide further understanding of 3D morphological changes that may occur in mineralized cartilage, such as thickening of the subchondral plate in osteoarthritis and other joint diseases.

Mechanobiological model for simulation of injured cartilage degradation via pro-inflammatory cytokines and mechanical stimulus.

Post-traumatic osteoarthritis (PTOA) is associated with cartilage degradation, ultimately leading to disability and decrease of quality of life. Two key mechanisms have been suggested to occur in PTOA: tissue inflammation and abnormal biomechanical loading. Both mechanisms have been suggested to result in loss of cartilage proteoglycans, the source of tissue fixed charge density (FCD). In order to predict the simultaneous effect of these degrading mechanisms on FCD content, a computational model has been developed. We simulated spatial and temporal changes of FCD content in injured cartilage using a novel finite element model that incorporates (1) diffusion of the pro-inflammatory cytokine interleukin-1 into tissue, and (2) the effect of excessive levels of shear strain near chondral defects during physiologically relevant loading. Cytokine-induced biochemical cartilage explant degradation occurs near the sides, top, and lesion, consistent with the literature. In turn, biomechanically-driven FCD loss is predicted near the lesion, in accordance with experimental findings: regions near lesions showed significantly more FCD depletion compared to regions away from lesions (p<0.01). Combined biochemical and biomechanical degradation is found near the free surfaces and especially near the lesion, and the corresponding bulk FCD loss agrees with experiments. We suggest that the presence of lesions plays a role in cytokine diffusion-driven degradation, and also predisposes cartilage for further biomechanical degradation. Models considering both these cartilage degradation pathways concomitantly are promising in silico tools for predicting disease progression, recognizing lesions at high risk, simulating treatments, and ultimately optimizing treatments to postpone the development of PTOA.

Clinical Contrast-Enhanced Computed Tomography With Semi-Automatic Segmentation Provides Feasible Input for Computational Models of the Knee Joint.

Computational models can provide information on joint function and risk of tissue failure related to progression of osteoarthritis (OA). Currently, the joint geometries utilized in modeling are primarily obtained via manual segmentation, which is time-consuming and hence impractical for direct clinical application. The aim of this study was to evaluate the applicability of a previously developed semi-automatic method for segmenting tibial and femoral cartilage to serve as input geometry for finite element (FE) models. Knee joints from seven volunteers were first imaged using a clinical computed tomography (CT) with contrast enhancement and then segmented with semi-automatic and manual methods. In both segmentations, knee joint models with fibril-reinforced poroviscoelastic (FRPVE) properties were generated and the mechanical responses of articular cartilage were computed during physiologically relevant loading. The mean differences in the absolute values of maximum principal stress, maximum principal strain, and fibril strain between the models generated from semi-automatic and manual segmentations were <1 MPa, <0.72% and <0.40%, respectively. Furthermore, contact areas, contact forces, average pore pressures, and average maximum principal strains were not statistically different between the models (p >0.05). This semi-automatic method speeded up the segmentation process by over 90% and there were only negligible differences in the results provided by the models utilizing either manual or semi-automatic segmentations. Thus, the presented CT imaging-based segmentation method represents a novel tool for application in FE modeling in the clinic when a physician needs to evaluate knee joint function.

Anterior cruciate ligament transection alters the n-3/n-6 fatty acid balance in the lapine infrapatellar fat pad.

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.

Health-related quality of life in relation to symptomatic and radiographic definitions of knee osteoarthritis: data from Osteoarthritis Initiative (OAI) 4-year follow-up study.

BACKGROUND: The purpose was to quantify the decrement in health utility (referred as disutility) associated with knee osteoarthritis (OA) and different symptomatic and radiographic uni- and bilateral definitions of knee OA in a repeated measures design of persons with knee OA or at increased risk of developing knee OA. METHODS: Data were obtained from the Osteoarthritis Initiative database. SF-12 health-related quality of life was converted into SF-6D utilities, and were then handled as the health utility loss by subtracting 1.000 from the utility score, yielding a negative value (disutility). Symptomatic OA was defined by radiographic findings (Kellgren-Lawrence, K-L, grade ≥ 2) and frequent knee pain in the same knee. Radiographic OA was defined by five different definitions (K-L ≥ 2 unilaterally / bilaterally, or the highest / mean / combination of K-L grades of both knees). Repeated measures generalized estimating equation (GEE) models were used to investigate disutility in relation to these different definitions. RESULTS: Utility decreased with worsening of symptomatic or radiographic status of knee OA. The participants with bilateral and unilateral symptomatic knee OA had 0.03 (p < 0.001) and 0.02 (p < 0.001) points lower utility scores, respectively, compared with the reference group. The radiographic K-L grade 4 defined as the mean or the highest grade of both knees was related to a decrease of 0.04 (p < 0.001) and 0.03 (p < 0.001) points in utility scores, respectively, compared to the reference group. CONCLUSIONS: Knee OA is associated with diminished health-related quality of life. Health utility can be quantified in relation to both symptomatic and radiographic uni- and bilateral definitions of knee OA, and these definitions are associated with differing disutilities. The performance of symptomatic definition was better, indicating that pain experience is an important factor in knee OA related quality of life.

Evaluation of the Effect of Bariatric Surgery-Induced Weight Loss on Knee Gait and Cartilage Degeneration.

The objective of the study was to investigate the effects of bariatric surgery-induced weight loss on knee gait and cartilage degeneration in osteoarthritis (OA) by combining magnetic resonance imaging (MRI), gait analysis, finite element (FE) modeling, and cartilage degeneration algorithm. Gait analyses were performed for obese subjects before and one-year after the bariatric surgery. FE models were created before and after weight loss for those subjects who did not have severe tibio-femoral knee cartilage loss. Knee cartilage degenerations were predicted using an adaptive cartilage degeneration algorithm which is based on cumulative overloading of cartilage, leading to iteratively altered cartilage properties during OA. The average weight loss was 25.7±11.0 kg corresponding to a 9.2±3.9 kg/m2 decrease in body mass index (BMI). External knee rotation moment increased, and minimum knee flexion angle decreased significantly (p < 0.05) after weight loss. Moreover, weight loss decreased maximum cartilage degeneration by 5±23% and 13±11% on the medial and lateral tibial cartilage surfaces, respectively. Average degenerated volumes in the medial and lateral tibial cartilage decreased by 3±31% and 7±32%, respectively, after weight loss. However, increased degeneration levels could also be observed due to altered knee kinetics. The present results suggest that moderate weight loss changes knee kinetics and kinematics and can slow-down cartilage degeneration for certain patients. Simulation results also suggest that prediction of cartilage degeneration is subject-specific and highly depend on the altered gait loading, not just the patient's weight.

Optical coherence tomography enables accurate measurement of equine cartilage thickness for determination of speed of sound.

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.

New Concept to Restore Normal Cell Responses in Osteoarthritic Knee Joint Cartilage.

Prediction of osteoarthritis progression does not exist. Cartilage "health" and degeneration during osteoarthritis depend on the signals perceived by chondrocytes. We hypothesize that biomechanical responses of chondrocytes in osteoarthritic cartilage can be restored close to their normal state. We propose an approach to evaluate quantitatively these responses in human joints and demonstrate how they can return close to normal levels.

Cell-tissue interactions in osteoarthritic human hip joint articular cartilage.

Volume and morphology of chondrocytes in osteoarthritic human hip joint articular cartilage were characterized, and their relationship to tissue structure and function was determined. Human osteochondral articular cartilage samples (n=16) were obtained from the femoral heads of nine patients undergoing total hip arthroplasty due to osteoarthritis (OA). Superficial chondrocytes (N=65) were imaged in situ with a confocal laser scanning microscope at 37 °C. This was followed by the determination of the mechanical properties of the tissue samples, depth-wise characterization of cell morphology (height, width; N=385) as well as structure and composition of the tissues using light microscopy, digital densitometry, Fourier transform infrared microspectroscopy and polarized light microscopy. Significant correlations were found between the cell volume and the orientation angle associated with the collagen fibers (r=0.320, p=0.009) as well as between the cell volume and the initial dynamic modulus of the tissue (r=-0.305, p=0.013). Furthermore, the depth-dependent chondrocyte aspect ratio (height/width) correlated significantly with the orientation angle of the collagen fibers and with the tissue's proteoglycan content (r=0.261 and r=0.228, respectively, p<0.001). Our findings suggest that the orientation angle of the collagen fibers primarily controls chondrocyte volume and shape in osteoarthritic human hip joint articular cartilage.

Importance of material properties and porosity of bone on mechanical response of articular cartilage in human knee joint--a two-dimensional finite element study.

Mechanical behavior of bone is determined by the structure and intrinsic, local material properties of the tissue. However, previously presented knee joint models for evaluation of stresses and strains in joints generally consider bones as rigid bodies or linearly elastic solid materials. The aim of this study was to estimate how different structural and mechanical properties of bone affect the mechanical response of articular cartilage within a knee joint. Based on a cadaver knee joint, a two-dimensional (2D) finite element (FE) model of a knee joint including bone, cartilage, and meniscus geometries was constructed. Six different computational models with varying properties for cortical, trabecular, and subchondral bone were created, while the biphasic fibril-reinforced properties of cartilage and menisci were kept unaltered. The simplest model included rigid bones, while the most complex model included specific mechanical properties for different bone structures and anatomically accurate trabecular structure. Models with different porosities of trabecular bone were also constructed. All models were exposed to axial loading of 1.9 times body weight within 0.2 s (mimicking typical maximum knee joint forces during gait) while free varus-valgus rotation was allowed and all other rotations and translations were fixed. As compared to results obtained with the rigid bone model, stresses, strains, and pore pressures observed in cartilage decreased depending on the implemented properties of trabecular bone. Greatest changes in these parameters (up to -51% in maximum principal stresses) were observed when the lowest modulus for trabecular bone (measured at the structural level) was used. By increasing the trabecular bone porosity, stresses and strains were reduced substantially in the lateral tibial cartilage, while they remained relatively constant in the medial tibial plateau. The present results highlight the importance of long bones, in particular, their mechanical properties and porosity, in altering and redistributing forces transmitted through the knee joint.

Deep learning based detection of osteophytes in radiographs and magnetic resonance imagings of the knee using 2D and 3D morphology.

In this study, we investigated the discriminative capacity of knee morphology in automatic detection of osteophytes defined by the Osteoarthritis Research Society International atlas, using X-ray and magnetic resonance imaging (MRI) data. For the X-ray analysis, we developed a deep learning (DL) based model to segment femur and tibia. In case of MRIs, we utilized previously validated segmentations of femur, tibia, corresponding cartilage tissues, and menisci. Osteophyte detection was performed using DL models in four compartments: medial femur (FM), lateral femur (FL), medial tibia (TM), and lateral tibia (TL). To analyze the confounding effects of soft tissues, we investigated their morphology in combination with bones, including bones+cartilage, bones+menisci, and all the tissues. From X-ray-based 2D morphology, the models yielded balanced accuracy of 0.73, 0.69, 0.74, and 0.74 for FM, FL, TM, TL, respectively. Using 3D bone morphology from MRI, balanced accuracy was 0.80, 0.77, 0.71, and 0.76, respectively. The performance was higher than in 2D for all the compartments except for TM, with significant improvements observed for femoral compartments. Adding menisci or cartilage morphology consistently improved balanced accuracy in TM, with the greatest improvement seen for small osteophyte. Otherwise, the models performed similarly to bones-only. Our experiments demonstrated that MRI-based models show higher detection capability than X-ray based models for identifying knee osteophytes. This study highlighted the feasibility of automated osteophyte detection from X-ray and MRI data and suggested further need for development of osteophyte assessment criteria in addition to OARSI, particularly, for early osteophytic changes.