Early Detection Of Osteoarthritis Research Articles

Utilising Discriminant Function Analysis (DFA) for Classifying Osteoarthritis (OA) Patients and Volunteers Based on Biomarker Concentration.

Osteoarthritis (OA) is a degenerative joint disease characterised by the breakdown of cartilage, causing pain, stiffness, and limited movement. Early diagnosis is crucial for effective management but remains challenging due to non-specific early symptoms. This study explores the application of Discriminant Function Analysis (DFA) to classify OA patients and healthy volunteers based on biomarker concentrations of Interleukin-6 (IL-6), Tumour necrosis factor-alpha (TNF-α), and Myeloperoxidase (MPO). DFA was employed to analyse biomarker data from 86 participants (58 patients, 28 volunteers) to evaluate the discriminatory power of these biomarkers in predicting OA. Significant differences were observed in MPO and TNF-α levels between groups, while IL-6 did not show a significant distinction. The iterative classification process improved model assumptions and classification accuracy, achieving a pre-classification accuracy of 71.8%, which adjusted to 57.1% post-classification. The results highlight DFA's potential in OA diagnosis, suggesting its utility in managing complex data and aiding personalised treatment strategies. The study underscores the need for larger sample sizes and additional biomarkers to enhance diagnostic robustness and provides a foundation for integrating DFA into clinical practice for early OA detection.

Shining Light on Osteoarthritis: Spatially Offset Raman Spectroscopy as a Window into Cartilage Health.

Articular cartilage is a complex tissue, and early detection of osteoarthritis (OA) is crucial for effective treatment. However, current imaging modalities lack molecular specificity and primarily detect late-stage changes. In this study, we propose the use of spatially offset Raman spectroscopy (SORS) for noninvasive, depth-dependent, and molecular-specific diagnostics of articular cartilage. We demonstrate the potential of SORS to penetrate deep layers of cartilage, providing a comprehensive understanding of disease progression. Our SORS measurements were characterized and validated through mechanical and histological techniques, revealing strong correlations between spectroscopic measurements and both Young's modulus and depth of cartilage damage. By longitudinally monitoring enzymatically degraded condyles, we further developed a depth-dependent damage-tracking method. Our analysis revealed distinct components related to sample depth and glycosaminoglycan (GAG) changes, offering a comprehensive picture of cartilage health. Collectively, these findings highlight the potential of SORS as a valuable tool for enhancing OA management and improving patient outcomes.

Holistic vibrational spectromics assessment of human cartilage for osteoarthritis diagnosis.

Osteoarthritis (OA) is the most common degenerative joint disease, presented as wearing down of articular cartilage and resulting in pain and limited mobility for 1 in 10 adults in the UK [Osteoarthr. Cartil.28(6), 792 (2020)10.1016/j.joca.2020.03.004]. There is an unmet need for patient friendly paradigms for clinical assessment that do not use ionizing radiation (CT), exogenous contrast enhancing dyes (MRI), and biopsy. Hence, techniques that use non-destructive, near- and shortwave infrared light (NIR, SWIR) may be ideal for providing label-free, deep tissue interrogation. This study demonstrates multimodal "spectromics", low-level abstraction data fusion of non-destructive NIR Raman scattering spectroscopy and NIR-SWIR absorption spectroscopy, providing an enhanced, interpretable "fingerprint" for diagnosis of OA in human cartilage. This is proposed as method level innovation applicable to both arthro- or endoscopic (minimally invasive) or potential exoscopic (non-invasive) optical approaches. Samples were excised from femoral heads post hip arthroplasty from OA patients (n = 13) and age-matched control (osteoporosis) patients (n = 14). Under multivariate statistical analysis and supervised machine learning, tissue was classified to high precision: 100% segregation of tissue classes (using 10 principal components), and a classification accuracy of 95% (control) and 80% (OA), using the combined vibrational data. There was a marked performance improvement (5 to 6-fold for multivariate analysis) using the spectromics fingerprint compared to results obtained from solely Raman or NIR-SWIR data. Furthermore, clinically relevant tissue components were identified through discriminatory spectral features - spectromics biomarkers - allowing interpretable feedback from the enhanced fingerprint. In summary, spectromics provides comprehensive information for early OA detection and disease stratification, imperative for effective intervention in treating the degenerative onset disease for an aging demographic. This novel and elegant approach for data fusion is compatible with various NIR-SWIR optical devices that will allow deep non-destructive penetration.

CDK: A novel high-performance transfer feature technique for early detection of osteoarthritis

Knee osteoarthritis (OA) is a prevalent condition causing significant disability, particularly among the elderly, necessitating advancements in diagnostic methodologies to facilitate early detection and treatment. Traditional OA diagnosis, relying on radiography and physical exams, faces limitations in accuracy and objectivity. This underscores the need for more advanced diagnostic methods, such as machine learning (ML) and deep learning (DL), to improve OA detection and classification. This research introduces a novel ensemble learning approach for image data feature extraction which ingeniously combines the strengths of 2 advanced (ML) models with a (DL) method to substantially improve the accuracy of OA detection from radiographic images. This innovative strategy aims to address the limitations of traditional diagnostic tools by leveraging the enhanced sensitivity and specificity of combined ML and DL models. The methodology deployed in this study encompasses the application of 10 ML models to a comprehensive publicly available Kaggle dataset with a total of 3615 samples of knee X-ray images. Through rigorous k-fold cross-validation and meticulous hyperparameter optimization, we also included evaluation metrics like accuracy, receiver operating characteristic, precision, recall, and F1-score to assess our models' performance effectively. The proposed novel CDK (convolutional neural network, decision tree, K-nearest classifier) ensemble approach for feature extraction is designed to synergize the predictive capabilities of individual models, thereby significantly improving the detection accuracy of OA indicators within radiographic images. We applied several ML and DL approaches to the newly created feature set to evaluate performance. The CDK ensemble model outperformed state-of-the-art studies with a high-performance score of 99.72% accuracy. This remarkable achievement underscores the model's exceptional capability in the early detection of OA, highlighting its superiority in comparison to existing methods.

Shear wave ultrasound elastography for estimating cartilage stiffness: implications for early detection of osteoarthritis

Current osteoarthritis (OA) diagnosis relies on radiographic abnormalities found in later stages of the disease, posing a challenge to the treatment efficacy. Therefore, earlier detection of OA is essential for improving therapeutic outcomes. The aim of this study was to investigate the feasibility of shear wave ultrasound elastography (SWUE) to detect changes in cartilage mechanical properties under OA conditions ex-vivo. Bovine osteochondral units were harvested from femoral condyles and subjected to either trypsin degradation, cartilage surface roughness defect using varying degrees of sandpaper, or subchondral bone degeneration using formic acid (FA) injection. Shear waves were generated using a mechanical shaker, while a high-frequency ultrasound system operating at 18 MHz was employed to detect wave propagation along the samples. The elasticity of cartilage was estimated by the shear wave speed (SWS) through the auto-correlation method. Our results show that the estimated SWS of cartilage after 24, 48, and 72 hours of trypsin incubation significantly decreased by 37%, 43%, and 59%, respectively, compared to the control group. Surface roughness treatment using 150-grit sandpaper significantly decreased the SWS by 35% compared to the control. Samples treated with 7% FA showed a significant increase in SWS by 62%, 89%, and 53% compared to control, 1% FA, and 3% FA, respectively. Our findings demonstrate the feasibility of SWUE to differentiate the elastic properties of cartilage under different OA conditions. This study presents the potential of a noninvasive, nonionizing tool for early detection of OA, representing a significant step toward its clinical implementation.Graphical

MONITORING IN VITRO AND EX VIVO INFLAMMATION USING SINGLE-WALLED CARBON NANOTUBES (SWCNTS) SENSOR TECHNOLOGY

Osteoarthritis (OA) is a degenerative joint disease affecting millions worldwide. Early detection of OA and monitoring its progression is essential for effective treatment and for preventing irreversible damage. Although sensors have emerged as a promising tool for monitoring analytes in patients, their application for monitoring the state of pathology is currently restricted to specific fields (such as diabetes). In this study, we present the development of an optical sensor system for real-time monitoring of inflammation based on the measurement of nitric oxide (NO), a molecule highly produced in tissues during inflammation.Single-walled carbon nanotubes (SWCNT) were functionalized with a single-stranded DNA (ssDNA) wrapping designed using an artificial intelligence approach and tested using S-nitroso-N-acetyl penicillamine (SNAP) as a standard released-NO marker. An optical SWIR reader with LED excitation at 650 nm, 730 nm and detecting emission above 1000 nm was developed to read the fluorescence signal from the SWCNTs. Finally, the SWCNT was embedded in GelMa to prove the feasibility of monitoring the release of NO in bovine chondrocyte and osteochondral inflamed cultures (1–10 ng/ml IL1β) monitored over 48 hours. The stability of the inflammation model and NO release was indirectly validated using the Griess and DAF-FM methods. A microfabricated sensor tag was developed to explore the possibility of using ssDNA-SWCNT in an ex vivo anatomic set-up for surgical feasibility, the limit of detection, and the stability under dynamic flexion.The SWCNT sensor was sensitive to NO in both in silico and in vitro conditions during the inflammatory response from chondrocyte and osteochondral plug cultures. The fluorescence signal decreased in the inflamed group compared to control, indicating increased NO concentration. The micro-tag was suitable and stable in joints showing a readable signal at a depth of up to 6 mm under the skin.The ssDNA-SWCNT technology showed the possibility of monitoring inflammation continuously in an in vitro set-up and good stability inside the joint. However, further studies in vivo are needed to prove the possibility of monitoring disease progression and treatment efficacy in vivo.Acknowledgments: The project was co-financed by Innosuisse (grant nr. 56034.1 IP-LS)

Umjetna inteligencija u oslikavanju koljena magnetnom rezonancijom

Introduction: Technological progress leads to an increasing use of radiological imaging, and an increase in the number of imaging results in an increased workload for radiologists. The driver of the application of AI in radiology is considered to be the reduction of the workload of radiologists and the need for faster and more accurate diagnosis. Aim: The aim of this paper is to bring the reader closer to the implementation of AI in radiology, especially in the MRI modality, and how deep learning algorithms improve image reconstruction. Discussion: Numerous studies have confirmed the importance of implementing machine learning, a subset of artificial intelligence, in the radiology system. In this review paper, numerous researches on the application of deep learning in magnetic resonance imaging are highlighted, and the emphasis is on models for automatic segmentation. Automatic segmentation has shown excellent results in the early detection of osteoarthritis, then in anterior cruciate ligament and meniscus tears, the most common knee injuries, and more recently, the deep learning model has excelled in automatic bone age estimation. Automatic segmentation has achieved, above all, high accuracy and precision, objectivity and time saving. Conclusion: Previous research has already highlighted the significant advantage of using machine learning in radiology and the exceptional compatibility between the work of radiologists and machine learning, which achieves precise and quick diagnoses. All this is a great incentive for further research, and technological progress will certainly speed up its integration into clinical practice.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) reveals potential lipid markers between infrapatellar fat pad biopsies of osteoarthritis and cartilage defect patients

The incidence of osteoarthritis (OA) has been expected to increase due to an aging population, as well as an increased incidence of intra-articular (osteo-) chondral damage. Lipids have already been shown to be involved in the inflammatory process of OA. This study aims at revealing region-specific lipid profiles of the infrapatellar fat pad (IPFP) of OA or cartilage defect patients by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), which could be used as biomarkers for early OA detection. A higher presence of phospholipids was found in OA patients compared with cartilage defect patients. In addition, a higher abundance of ether-linked phosphatidylethanolamines (PE O-s) containing arachidonic acid was specifically found in OA patients compared with cartilage defect patients. These lipids were mainly found in the connective tissue of the IPFP. Specific lipid species were associated to OA patients compared with cartilage defect patients. PE O-s have been suggested as possible biomarkers for OA. As these were found more abundantly in the connective tissue, the IPFP’s intra-tissue heterogeneity might play an important role in biomarker discovery, implying that the amount of fibrous tissue is associated with OA.Graphical

Pharmatherapeutic Treatment of Osteoarthrosis-Does the Pill against Already Exist? A Narrative Review.

The aim of this narrative review is to summarize the current pharmacotherapeutic treatment options for osteoarthritis (OA). Is therapy still mainly symptomatic or does the pill against arthrosis already exist? Causal and non-causal, as well as future therapeutic approaches, are discussed. Various surgical and non-surgical treatment options are available that can help manage symptoms, slow down progression, and improve quality of life. To date, however, therapy is still mainly symptomatic, often using painkilling and anti-inflammatory drugs until the final stage, which is usually joint replacement. These "symptomatic pills against" have side effects and do not alter the progression of OA, which is caused by an imbalance between degenerative and regenerative processes. Next to resolving mechanical issues, the goal must be to gain a better understanding of the cellular and molecular basis of OA. Recently, there has been a lot of interest in cartilage-regenerative medicine and in the current style of treating rheumatoid arthritis, where drug therapy ("the pill against") has been established to slow down or even stop the progression of rheumatoid arthritis and has banned the vast majority of former almost regular severe joint destructions. However, the "causal pill against" OA does not exist so far. First, the early detection of osteoarthritis by means of biomarkers and imaging should therefore gain more focus. Second, future therapeutic approaches have to identify innovative therapeutic approaches influencing inflammatory and metabolic processes. Several pharmacologic, genetic, and even epigenetic attempts are promising, but none have clinically improved causal therapy so far, unfortunately.

Cationic Carrier Mediated Delivery of Anionic Contrast Agents in Low Doses Enable Enhanced Computed Tomography Imaging of Cartilage for Early Osteoarthritis Diagnosis.

Cartilage tissue exhibits early degenerative changes with onset of osteoarthritis (OA). Early diagnosis is critical as there is only a narrow time window during which therapeutic intervention can reverse disease progression. Computed tomography (CT) has been considered for cartilage imaging as a tool for early OA diagnosis by introducing radio-opaque contrast agents like ioxaglate (IOX) into the joint. IOX, however, is anionic and thus repelled by negatively charged cartilage glycosaminoglycans (GAGs) that hinders its intra-tissue penetration and partitioning, resulting in poor CT attenuation. This is further complicated by its short intra-tissue residence time owing to rapid clearance from joints, which necessitates high doses causing toxicity concerns. Here we engineer optimally charged cationic contrast agents based on cartilage negative fixed charge density by conjugating cartilage targeting a cationic peptide carrier (CPC) and multi-arm avidin nanoconstruct (mAv) to IOX, such that they can penetrate through the full thickness of cartilage within 6 h using electrostatic interactions and elicit similar CT signal with about 40× lower dose compared to anionic IOX. Their partitioning and distribution correlate strongly with spatial GAG distribution within healthy and early- to late-stage arthritic bovine cartilage tissues at 50-100× lower doses than other cationic contrast agents used in the current literature. The use of contrast agents at low concentrations also allowed for delineation of cartilage from subchondral bone as well as other soft tissues in rat tibial joints. These contrast agents are safe to use at current doses, making CT a viable imaging modality for early detection of OA and staging of its severity.

OSTHERSIT- Dataset for Osteoarthritis Analysis using Thermal Images

Osteoarthritis (OA) is a disease categorized under arthritis and has impact on bone and cartilage dilapidation. Still now, OA is visualized only with X-ray and MRI scanning process. When a person exposed to X-rays will develop cancer later and it also causes tissue effects like cataracts, skin reddening and hair loss.To overcome this issue,a thermal camera(FLIR420bx) is used to capture the Knee OA from the subjects.The thermal imaging system is a non-contact,non-invasive passive imaging system that is the heat emmitted, or given off, by the human body.The medical evaluation of thermal images is validated and ground truth is received from skilled physicians which is time consuming and prone to error. Considering this fact, recently more image based diagnosis system is evolving for the early detection of OA. The success of this diagnosis system mostly relies on large number of OA/Non OA images to be trained. The novel dataset, titled as ‘Osthersit’, is a small seed thrown to afford OA thermal images of knee bones. It also consist OA affected images in thermograph modality. To have wider opening in many OA related ambiguity problems ,Osthersit dataset is made available to the researchers who work in the medical databases and can be retrieve from this web address: http://www.sethu.ac.in/osthersit/.

Regenerative medicine for early osteoarthritis.

The concept of early osteoarthritis (OA) is based on the expectation that if found and treated in the early stage, the progression of the disease might be arrested before affected joints are irreversibly destroyed. This notion of early OA detection can also bear meaning for regenerative medicine (RM) which is purposed to cure a disease by regenerating the damaged tissue. RM can be a category of disease-modifying osteoarthritis drugs (DMOADs) and provide an attractive treatment for OA, restoring structural damage incurred during the disease by repopulating cells and reconstituting. While cell therapy including the use of stem cells is conflated with RM, it may also comprise gene therapy, exosomes, and other cell or cell-free-derived products. Considering that not all early OA will become advanced OA and that RM has a characteristic of personalized medicine, it would be very important to foretell, even roughly, which patients will progress rapidly and who will favorably respond to regenerative treatment. Subclassification and comprehensive endotyping or phenotyping (E/P) can be very helpful in detecting the population who would benefit from RM as well as rapid progressors who need closer monitoring.

Portable, High-Sensitive, and Metglas Alloy 2826 MB-Based Magnetoelastic Biosensor to Detect the Osteoarthritis Marker MMP-3

Osteoarthritis (OA) is one of the most common joint disorders affecting human health. Early detection of OA is essential for effective treatment of the disease. There is a growing demand of techniques for highly sensitive detection of trace OA markers. Here, the magnetoelastic (ME) biosensor based on Metglas alloy 2826 MB is presented to detect matrix metalloproteinase-3 (MMP-3) of the OA biomarker, through the measurement of resonant frequency changes with excellent magnetostrictive effects. The detection system consists of an ME chip immobilized with MMP-3 antibody and an electromagnetic coil. Since the ME biosensor is ultrasensitive to mass changes, the frequency shift caused by mass changes induced by antibody–antigen-specific binding can be used to reflect the amount of MMP-3. The ME biosensor has been applied to detect the actual joint fluid sample of OA patients, demonstrating its feasibility. The results show that this assay has a linear response to the MMP-3 concentrations ranging from 30.7 to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2000\,\,{\mathrm {ng}} \cdot\,{\mathrm {mL}}^{-}1$ </tex-math></inline-formula> with a detection limit of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$30.7\,\,{\mathrm {ng}} \cdot\,{\mathrm {mL}}^{-}1$ </tex-math></inline-formula> , covering the range of clinical tests required for the diagnosis of OA. The ME biosensor provides a novel and viable method for the diagnosis of early OA.

Identification of serum micro-RNAs of early knee osteoarthritis in a cohort of Egyptian patients

ABSTRACT Background Osteoarthritis (OA), is a prevalent low-grade inflammatory synovial joint disease characterized by progressive cartilage degradation and alteration of the entire joint. Knee OA (KOA) is the commonest form of OA. Osteoarthritis is the 4th leading cause of disability worldwide with no cure and therefore identifying persons with early OA of the knee (KOA), is important to retard progression of the disease process. Several proinflammatory cytokines and microRNAs (miRNAs) are involved in OA pathogenesis. Objective The aim of the work was to investigate the serum miRNA-136 (miR-136) and miR-155-5p expression profile, IL-8 and IL-17 levels in subjects with early KOA and to further evaluate and compare the expression profile of these miRNAs, and level of serum IL-8 and IL-17 in subjects with early and severe KOA. Methods Serum miR-136 and miR-155-5p expression profile in 40 patients with early KOA were compared to 40 age and sex matched healthy controls and 10 patients with severe KOA. Clinical, laboratory, and disease parameters were assessed. Serum and synovial fluid miRNAs were assessed by real-time polymerase chain reaction. Levels of IL-17 and IL-8 levels were determined by the enzyme linked immunosorbent assay test. Results miR-155-5p in serum was significantly higher in early KOA patients compared to healthy controls and to patients with severe OA. Synovial miRNA-155-5p expression levels were significantly higher than serum miRNA-155-5p in severe KOA patients compared to early OA patients. When we compared the expression levels of miRNA-136 between cases and healthy controls, there was no statistical significant difference, p=0.413. Serum IL-8 levels were significantly higher in early OA patients compared to healthy controls. Conclusions Serum miR-155-5p and IL-8 levels are potential useful biomarkers for the early detection of OA and in particular, early KOA.

The articular cartilage surface is impaired by a loss of thick collagen fibers and formation of type I collagen in early osteoarthritis

Osteoarthritis (OA) is a joint disease affecting millions of patients worldwide. During OA onset and progression, the articular cartilage is destroyed, but the underlying complex mechanisms remain unclear. Here, we uncover changes in the thickness of collagen fibers and their composition at the onset of OA. For articular cartilage explants from knee joints of OA patients, we find that type I collagen-rich fibrocartilage-like tissue was formed in macroscopically intact cartilage, distant from OA lesions. Importantly, the number of thick fibers (>100 nm) has decreased early in the disease, followed by complete absence of thick fibers in advanced OA. We have obtained these results by a combination of high-resolution atomic force microscopy imaging under near-native conditions, immunofluorescence, scanning electron microscopy and a fluorescence-based classification of the superficial chondrocyte spatial organization. Taken together, our data suggests that the loss of tissue functionality in early OA cartilage is caused by a reduction of thick type II collagen fibers, likely due to the formation of type I collagen-rich fibrocartilage, followed by the development of focal defects in later OA stages. We anticipate that such an integrative characterization will be very beneficial for an in-depth understanding of other native biological tissues and the development of sustainable biomaterials. Statement of significanceIn early osteoarthritis (OA) the cartilage appears macroscopically intact. However, this study demonstrates that the collagen network already changes in early OA by collagen fiber thinning and the formation of fibrocartilage-like tissue. Both nanoscopic deficiencies already occur in macroscopically intact regions of the human knee joint and are likely connected to processes that result in a weakened extracellular matrix. This study enhances the understanding of earliest progressive cartilage degeneration in the absence of external damage. The results suggest a determination of the mean collagen fiber thickness as a new target for the detection of early OA and a regulation of type I collagen synthesis as a new path for OA treatment.

Quantitative MRI T2 relaxometry of knee joint in early detection of osteoarthritis

Background: Magnetic resonance imaging (MRI) T2 is an advance modality for the early diagnosis of osteoarthritis. This study was performed to determine the MRI T2 relaxometry value of knee joint in early detection of osteoarthritis among suspected cases.Patients and methods: This observational study was conducted at Department of Radiology, Jinnah Postgraduate Medical Centre (JPMC), Karachi, Pakistan from 20th September 2020 to 28th February 2021. All patients aged 20-60 years of either gender suspected of knee osteoarthritis were consecutively enrolled. Osteoarthritis was confirmed based on Kellgren &amp; Lawrence (KL) radiographic grading of 2-5. MRI T2 relaxometry was performed in all patients.Results: Of 102 patients, there were 67 (65.7%) males and 35 (34.3%) females. Mean age was 43.72 ±14.01 years. KL grading showed that KL grade 0 observed in 29 (28.4%), grade I in 13 (12.7%), grade II in 25 (24.5%), grade III in 30 (29.4%), and grade IV in 5 (4.9%) patients. The frequency of osteoarthritis was found in 60 (58.8%) patients. Mean MRI T2 value was found to be 94.12 ±16.32. Mean MRI T2 value was found significantly higher in patients with KL grade IV (109.89 ±5.38) followed by KL grade III (107.35 ±3.24), KL grade II (97.72 ±14.65), KL grade I (89.54 ±13.69), and KL grade 0 (76.65 ±10.56). (p-value&lt;0.001) The findings of ROC curve showed that AUC was found to be 0.911 (0.85-0.97) (p-value&lt;0.001).Conclusion: MRI T2 relaxometry is highly recommended for the prediction of osteoarthritis in suspected cases.

Mechanical metrics may show improved ability to predict osteoarthritis compared to T1rho mapping

Changes in cartilage structure and composition are commonly observed during the progression of osteoarthritis (OA). Importantly, quantitative magnetic resonance imaging (MRI) methods, such as T1rho relaxation imaging, can noninvasively provide in vivo metrics that reflect changes in cartilage composition and therefore have the potential for use in early OA detection. Changes in cartilage mechanical properties are also hallmarks of OA cartilage; thus, measurement of cartilage mechanical properties may also be beneficial for earlier OA detection. However, the relative predictive ability of compositional versus mechanical properties in detecting OA has yet to be determined. Therefore, we developed logistic regression models predicting OA status in an ex vivo environment using several mechanical and compositional metrics to assess which metrics most effectively predict OA status. Specifically, in this study the compositional metric analyzed was the T1rho relaxation time, while the mechanical metrics analyzed were the stiffness and recovery (defined as a measure of how quickly cartilage returns to its original shape after loading) of the cartilage. Cartilage recovery had the best predictive ability of OA status both alone and in a multivariate model including the T1rho relaxation time. These findings highlight the potential of cartilage recovery as a non-invasive marker of in vivo cartilage health and motivate future investigation of this metric clinically.

Detection of osteoarthritis in dogs by metabolic, pro-inflammatory and degenerative synovial fluid biomarkers and traditional radiographic screening: A pilot study

Secondary osteoarthritis (OA) is a slow progressive, common disorder of synovial joints in dogs. It is characterized by a loss of balance between the synthesis and degeneration of articular cartilage components. Its diagnosis is currently based on the presence of clear radiographic changes, which only occur in the later stages of the disease. Hence, early diagnosis of OA remains a major problem. Therefore, interest in synovial fluid (SF) biomarkers has emerged. Besides pro-inflammatory and degenerative markers, i.e. tumor necrosis factor alpha (TNF-alpha), interleukin-1beta (IL-1beta), tenascin-c (TN-C) and matrix metalloproteinase-2 (MMP-2), metabolic parameters, i.e. pH, glucose and lactate, can potentially be used to detect OA.The current study demonstrated statistically significant differences in the SF levels of pH, glucose and lactate between OA-affected and normal joints. In addition, the in-house validated immuno-assays for TNF-alpha, IL-1beta, TN-C and MMP-2 allowed to demonstrate also statistically significant differences in the SF concentrations for all these biomarkers - except TNF-alpha - between OA-affected and normal joints. However, no correlation was found between any of these biomarkers and the currently used radiographic scoring system for OA in dogs.Future research is warranted to explore the potential of these biomarkers in the early detection of OA and in the severity characterization of this disease.

Quantitative 3D Ultrashort Echo Time Magnetization Transfer Imaging for Evaluation of Knee Cartilage Degeneration In Vivo.

Recent studies suggest that macromolecular fraction (MMF) derived from three-dimensional ultrashort echo time magnetization transfer (UTE-MT) imaging is insensitive to the magic angle effect. However, its clinical use in osteoarthritis (OA) remains to be investigated. To investigate the feasibility of 3D UTE-MT-derived MMF in differentiating normal from degenerated cartilage. Prospective. Sixty-two participants (54.8 ± 16.7 years, 30 females) with and without OA, plus two healthy volunteers (mean age 35.0 years) for reproducibility test. 3 T/UTE-MT sequence. A 3D UTE-MT sequence was employed to calculate MMF based on a two-pool model. Kellgren-Lawrence (KL) grade and Whole-Organ Magnetic Resonance Imaging Score (WORMS) were evaluated by three experienced musculoskeletal radiologists. KL grade was condensed into three groups: KL0, KL1-2, and KL3-4. WORMS was regrouped based on extent of lesion (extent group) and depth of lesion (depth group), respectively. The performance of MMF at evaluating the degeneration of cartilage was assessed via Spearman's correlation coefficient and the area under the curve (AUC) calculated according to the receiver-operating characteristic curve. After normality check, one-way analysis of variance was used to evaluate the performance. Tukey-Kramer test was performed for post hoc testing. MMF showed significant negative correlations with KL grade (r=-0.53, P < 0.05) and WORMS (r=-0.49, P < 0.05). Significantly lower MMFs were found in subjects with greater KL grade (11.8 ± 0.8% for KL0; 10.9 ± 0.9% for KL1-2; 10.6 ± 1.1% for KL3-4; P < 0.05) and in cartilage with greater extent (12.1 ± 1.6% for normal cartilage; 10.9 ± 1.6% for regional lesions; 9.6 ± 1.7% for diffuse lesions; P < 0.05) and depth (12.1 ± 1.6% for normal cartilage; 10.6 ± 1.6% for partial-thickness lesions; 8.8 ± 1.7% for full-thickness lesions; P < 0.05) of lesions. AUC values of MMF for doubtful-minimal OA (KL1-2) and mild cartilage degradation (WORMS1-2) were 0.8 and 0.7, respectively. This study highlights the clinical potential of MMF in the detection of early OA. 2 TECHNICAL EFFICACY STAGE: 2.

Early detection of osteoarthritis in the rat with an antibody specific to type II collagen modified by reactive oxygen species

BackgroundOsteoarthritis (OA) is a disease of the whole joint, with articular cartilage breakdown as a major characteristic. Inflammatory mediators, proteases, and oxidants produced by chondrocytes are known to be responsible for driving cartilage degradation. Nevertheless, the early pathogenic events are still unclear. To investigate this, we employed an antibody that is specific to oxidative post-translationally modified collagen type II (anti-oxPTM-CII) to detect early cartilage pathogenic changes in two rat models of OA.MethodsThe animals underwent surgery for destabilization of the medial meniscus (DMM) and were sacrificed after 3, 5, 7, 14, and 28 days. Alternatively, anterior cruciate ligament transection with partial meniscectomy (ACLT+pMx) was performed and animals were sacrificed after 1, 3, 5, 7, and 14 days. Joints were stained with toluidine blue and saffron du Gatinais for histological scoring, anti-oxPTM-CII, and anti-collagen type X antibodies (anti-CX).ResultsWe observed positive oxPTM-CII staining as early as 1 or 3 days after ACLT+pMx or DMM surgeries, respectively, before overt cartilage lesions were visible. oxPTM-CII was located mostly in the deep zone of the medial tibial cartilage, in the pericellular and territorial matrix of hypertrophic chondrocytes, and co-localized with CX staining. Staining was weak or absent for the lateral compartment or the contralateral knees except at later time points.ConclusionThe results demonstrate that oxidant production and chondrocyte hypertrophy occur very early in the onset of OA, possibly initiating the pathogenic events of OA. We propose to use anti-oxPTM-CII as an early biomarker for OA ahead of radiographic changes.