Early Diagnosis Of Osteoarthritis Research Articles

Key roles of the superficial zone in articular cartilage physiology, pathology, and regeneration.

The superficial zone (SFZ) of articular cartilage is an important interface that isolates deeper zones from the microenvironment of the articular cavity and is directly exposed to various biological and mechanical stimuli. The SFZ is not only a crucial structure for maintaining the normal physiological function of articular cartilage but also the earliest site of osteoarthritis (OA) cartilage degeneration and a major site of cartilage progenitor cells, suggesting that the SFZ might represent a key target for the early diagnosis and treatment of OA. However, to date, SFZ research has not received sufficient attention, accounting for only about 0.58% of cartilage tissue research. The structure, biological composition, function, and related mechanisms of the SFZ in the physiological and pathological processes of articular cartilage remain unclear. This article reviews the key role of the SFZ in articular cartilage physiology and pathology and focuses on the characteristics of SFZ in articular cartilage degeneration and regeneration in OA, aiming to provide researchers with a systematic understanding of the current research status of the SFZ of articular cartilage, hoping that scholars will give more attention to the SFZ of articular cartilage in the future.

Correlation of Pronated Foot Posture and Vertical Jump Measurement in Adolescent Recreational Basketball Players - An Observational Study

Background: Osteoarthritis (OA); a whole joint disorder is a most common and progressive disease that worsens with age. Synovitis and synovial effusion can play important role in early OA diagnosis and progression. Limited Background: Jumping is a significant move commonly performed by basketball players, with an average of 44 jumps completed by each player during a competitive game. The influence of foot pronation on the mechanics of the lower extremities in basketball players' movements, particularly during repeated jumps and various landing techniques, is a significant area under investigation. Methodology: After receiving ethical approval, a total of 250 individuals underwent screening. Out of these, 85 participants were selected for the study on specific criteria and convenience sampling. Prior to the commencement of the study, written consent was obtained from the parents of the participants, along with their assent. The participants were then categorized into different groups based on their navicular drop, with Group 1 (consisting of 42 individuals) exhibiting a pronated foot posture, and Group 2 (comprising 43 individuals) displaying a neutral foot posture. Subsequently, the Sargent jump test was conducted on all participants, and the data obtained was analyzed for both groups. Results: Data from basketball players were examined using SPSS 16.0. After conducting an unpaired t-test (p=0.15, Df= 83, t= 1.43) and a Pearson correlation test (r=0.12 and r=0.08), it was found that there was no statistically significant correlation between pronated foot posture and vertical jump performance. Conclusion: This research study has determined that there is no connection between a pronated foot position and the vertical jump height among young recreational basketball players. Keywords: Pronated foot, Navicular drop, Vertical jump, Adolescent, Basketball players.

Label-Free and Ultrasensitive Detection of Cartilage Acidic Protein 1 in Osteoarthritis Using a Single-Walled Carbon Nanotube Field-Effect Transistor Biosensor.

Osteoarthritis (OA), a prevalent degenerative joint disease, significantly affects the well-being of afflicted individuals and compromises the standard functionality of human joints. The emerging biomarker, Cartilage acidic protein 1 (CRTAC1), intricately associates with OA initiation and serves as a prognostic indicator for the trajectory toward joint replacement. However, existing diagnostic methods for CRTAC1 are hampered by the limited abundance, thus restricting the precision and specificity. Herein, a novel approach utilizing a single-walled carbon nanotube field-effect transistor (SWCNTs FET) biosensor is reported for the direct label-free detection of CRTAC1. High-purity semiconducting carbon nanotube films, functionalized with antibodies of CRTAC1, provide excellent electrical and sensing properties. The SWCNTs FET biosensor exhibits high sensitivity, notable reproducibility, and a wide linear detection range (1 fg/mL to 100 ng/mL) for CRTAC1 with a theoretical limit of detection (LOD) of 0.2 fg/mL. Moreover, the SWCNTs FET biosensor is capable of directly detecting human serum samples, showing excellent sensing performance in differentiating clinical samples from OA patients and healthy populations. Comparative analysis with traditional enzyme-linked immunosorbent assay (ELISA) reveals that the proposed biosensor demonstrates faster detection speeds, higher sensitivity/accuracy, and lower errors, indicating high potential for the early OA diagnosis. Furthermore, the SWCNTs FET biosensor has good scalability for the combined diagnosis and measurement of multiple disease markers, thereby significantly expanding the application of SWCNTs FETs in biosensing and clinical diagnostics.

Mendelian randomization based on genome-wide association studies and expression quantitative trait loci, predicting gene targets for the complexity of osteoarthritis as well as the clinical prognosis of the condition.

Osteoarthritis (OA) entails a prevalent chronic ailment, marked by the widespread involvement of entire joints. Prolonged low-grade synovial inflammation serves as the key instigator for a cascade of pathological alterations in the joints. The study seeks to explore potential therapeutic targets for OA and investigate the associated mechanistic pathways. Summary-level data for OA were downloaded from the genome-wide association studies (GWAS) database, expression quantitative trait loci (eQTL) data were acquired from the eQTLGen consortium, and synovial chip data for OA were obtained from the GEO database. Following the integration of data and subsequent Mendelian randomization analysis, differential analysis, and weighted gene co-expression network analysis (WGCNA) analysis, core genes that exhibit a significant causal relationship with OA traits were pinpointed. Subsequently, by employing three machine learning algorithms, additional identification of gene targets for the complexity of OA was achieved. Additionally, corresponding ROC curves and nomogram models were established for the assessment of clinical prognosis in patients. Finally, western blotting analysis and ELISA methodology were employed for the initial validation of marker genes and their linked pathways. Twenty-two core genes with a significant causal relationship to OA traits were obtained. Through the application of distinct machine learning algorithms, MAT2A and RBM6 emerged as diagnostic marker genes. ROC curves and nomogram models were utilized for evaluating both the effectiveness of the two identified marker genes associated with OA in diagnosis. MAT2A governs the synthesis of SAM within synovial cells, thereby thwarting synovial fibrosis induced by the TGF-β1-activated Smad3/4 signaling pathway. The first evidence that MAT2A and RBM6 serve as robust diagnostic for OA is presented in this study. MAT2A, through its involvement in regulating the synthesis of SAM, inhibits the activation of the TGF-β1-induced Smad3/4 signaling pathway, thereby effectively averting the possibility of synovial fibrosis. Concurrently, the development of a prognostic risk model facilitates early OA diagnosis, functional recovery evaluation, and offers direction for further therapy.

Identification and Construction of a Disulfidptosis-Mediated Diagnostic Model and Associated Immune Microenvironment of Osteoarthritis from the Perspective of PPPM.

Osteoarthritis (OA) is a major cause of human disability. Despite receiving treatment, patients with the middle and late stage of OA have poor survival outcomes. Therefore, within the framework of predictive, preventive, and personalized medicine (PPPM/3PM), early personalized diagnosis of OA is particularly prominent. PPPM aims to accurately identify disease by integrating multiple omic techniques; however, the efficiency of currently available methods and biomarkers in predicting and diagnosing OA should be improved. Disulfidptosis, a novel programmed cell death mechanism and appeared in particular metabolic status, plays a mysterious characteristic in the occurrence and development of OA, which warrants further investigation. In this study, we integrated three public datasets from the Gene Expression Omnibus (GEO) database, including 26 OA samples and 20 normal samples. Via a series of bioinformatic analysis and machine learning, we identified the diagnostic biomarkers and several subtypes of OA. Moreover, the expression of these biomarkers were verified in our in-house cohort and the single cell dataset. Three significant regulators of disulfidptosis (NCKAP1, OXSM, and SLC3A2) were identified through differential expression analysis and machine learning. And a nomogram constructed based on these three regulators exhibited ideal efficiency in predicting early- and late-stage OA. Furthermore, based on the expression of three regulators, we identified two disulfidptosis-related subtypes of OA with different infiltration of immune cells and personalized expression level of immune checkpoints. Notably, the expression of the three regulators was demonstrated in a single-cell RNA profile and verified in the synovial tissue in our in-house cohort including 6 OA patients and 6 normal people. Finally, an efficient disulfidptosis-mediated diagnostic model was constructed for OA, with the AUC value of 97.6923% in the training set and 93.3333% and 100% in two validation sets. Overall, with regard to PPPM, this study provided novel insights into the role of disulfidptosis regulators in the personalized diagnosis and treatment of OA.

Identification of diagnostic biomarkers for osteoarthritis through bioinformatics and machine learning

Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by cartilage degradation, inflammatory arthritis, and joint dysfunction. Currently, there is a lack of effective early diagnostic methods and treatment strategies for OA. Bioinformatics and biomarker research provide new possibilities for early detection and personalized therapy of OA. In this study, we investigated the molecular mechanisms of OA and important signaling pathways involved in disease progression through bioinformatics analysis. Firstly, using the limma package, we analyzed the differentially expressed genes (DEGs) between normal healthy samples and OA cartilage tissue samples. These DEGs were found to be primarily involved in biological processes such as extracellular matrix (ECM) binding, immune receptor activity, and cytokine activity, as well as signaling pathways including cytokine receptors, ECM-receptor interaction, and PI3K-Akt. Gene set enrichment analysis revealed that in the OA group, signaling pathways such as AMPK, B cell receptor, IL-17, and PPAR were downregulated, while calcium signaling pathway, cell adhesion molecules, ECM-receptor interaction, TGF-β signaling pathway, and Wnt signaling pathway were upregulated. Additionally, we constructed a co-expression module network using WGCNA and identified key modules associated with OA, from which we selected 7 most predictive OA characteristic genes. Among them, ANTXR1, KCNS3, SGCD, and LIN7A were correlated with the level of immune cell infiltration. This study elucidates the mechanisms underlying the development of OA and identifies potential diagnostic markers and therapeutic targets, providing important insights for early diagnosis and treatment of OA.

Over-expression of microRNA-145 Elevating Autophagy Activities via Downregulating FRS2 Expression.

Osteoarthritis (OA) is one of the most common chronic and progressive joint diseases characterized by cartilage degeneration and chondrocyte death. In this study, we aimed to identify the modulation effect of miR-145 on chondrocytes' autophagy during the development of OA. Osteoarthritis (OA) is one of the most prevalent types of chronic and progressive joint disorder with the symptoms of joint pain and stiffness, and it leads to disability at the end stage. In recent years, microRNA-145 (miR-145) has been found to activate autophagy in various cell types, including mesenchymal stem cells, cardiomyocytes, and osteosarcoma cells. However, it is unknown whether miR-145 regulates the progression of OA by influencing chondrocyte autophagy. Before investigating the regulatory effect of miR-145 on the autophagic activity of chondrocytes, the expression of miR-145 in human joint samples was analyzed. The targeting relationship between miR-145 and FRS2 was detected by dual luciferase assay. The effect of FRS2 and miR-145 on the autophagic activity of chondrocytes was observed by bidirectional expression of FRS2 and miR-145. The miR-145 expression and LC3-II/LC3-I ratio were significantly decreased and the SQSTM1 expression was increased in OA patients. The miR-145 overexpression in C20A4 cells increased LC3-II/LC3-I ratio, decreased SQSTM1 expression, and was positively correlated with autophagic activity. Under oxidative stress, miR-145 overexpression significantly improved chondrocyte viability through autophagy stimulation. FRS2 is a potential target of miR-145 via a binding sequence within its 3' UTR. FRS2 acts as the downstream mediator of miR-145 to suppress autophagy through activating PI3K/Akt/mTOR pathways. The miR-145 acts as a protective factor against chondrocytes by regulating miRFRS2- autophagy axis. The decrease of miR-145 in articular synovial fluid may turn out to be an important marker for early diagnosis of OA, and modulation of miR-145 may represent a promising therapeutic strategy for OA.

Plasma-derived extracellular vesicle surface markers CD45, CD326 and CD56 correlate with the stage of osteoarthritis: a primary study of a novel and promising diagnostic tool of the disease

Recently, there is a growing interest in the research based on extracellular vesicles (EVs) which represent paracrine factors secreted by almost all cell types. Both, normal and pathological cells are able to release various types of EVs with different physiological properties, functions and compositions. EVs play an important role in intercellular communication, mechanism and tissue repair. Moreover, EVs could help not only in the treatment of diseases but also in their diagnostics. This work focused on the evaluation of the potential of EVs being used as biomarkers for the diagnosis of osteoarthritis (OA) based on a comparison of the composition of EVs separated from platelet-poor plasma (PPP) of healthy donors and OA patients at different stages of OA. OA is established as a complex syndrome with extensive impact on multiple tissues within the synovial joint. It is a chronic disease of musculoskeletal system that mainly affects the elderly. Depending on the use of the Kellgren–Lawrence classification system, there are four grades of OA which have a negative impact on patients' quality of life. It is very difficult to detect OA in its early stages, so it is necessary to find a new diagnostic method for its timely detection. PPP samples were prepared from whole blood. PPP-EVs were separated from 3 groups of donors—healthy control, early stage OA, end-stage OA, and their content was compared and correlated. EVs from PPP were separated by size exclusion chromatography and characterized in terms of their size, yield and purity by NTA, western blotting, ELISA and flow cytometry. Detection of surface markers expression in EVs was performed using MACSPlex approach. Inflammatory and growth factors in EVs were analysed using MAGPix technology. Our study confirmed significant differences between EVs surface markers of patients and healthy controls correlating with the age of donor (CD63, CD31 and ROR1) and stage of OA (CD45, CD326 and CD56), respectively. Circulating EVs have been under extensive investigation for their capability to predict OA pathology diagnosis as potential targets for biomarker discovery. Taken together, obtained results indicated that PPP-EVs surface markers could be used as potential biomarkers in the early diagnosis of OA.

Gaussian mixture model in clustering acoustic emission signals for characterizing osteoarthritic knees

High-frequency acoustic waves produced by the sudden release of energy within a material caused by mechanical loading, deformation, corrosion, or other forms of damage are detected by Acoustic Emission (AE). It is a non-destructive testing technique utilized to monitor the structural health of materials and components and to detect and locate defects or damage that could lead to failure. Researchers in the biomedical field have lately focused on AE due to its potential for early detection, tracking, and managing various health issues, such as identifying and evaluating joint disorders, tissue damage, etc. However, due to the variance in AE signals produced from complex structures like knee joints, a general statistical method frequently finds it difficult to differentiate between distinct types of AE signals from different kinds of damage. In such instances, machine learning (ML) and deep learning (DL) can be beneficial for differentiating these signals. In this study, the application of Gaussian mixture model (GMM) clustering with principal component analysis (PCA) for dimension reduction was proposed to detect knee osteoarthritis (OA) from AE data. Four groups of participants, Group A (age 20–39), Group B (age 40–59), and Group C (age 60+), along with diagnosed OA as Group D, were included in this study. Avoiding overlapping AE signal parameters from different groups of participants is a significant challenge. GMM was applied to identify and eliminate the coinciding data points to address this issue. Moreover, the impact of this approach on clustering was also investigated in this research, and by utilizing GMM and PCA, compact clustering of the AE data potentially ensured the efficiency and effectiveness of early diagnosis of OA using the AE Technique (AET).

MRI overestimates articular cartilage thickness and volume compared to synchrotron radiation phase-contrast imaging.

Accurate evaluation of morphological changes in articular cartilage are necessary for early detection of osteoarthritis (OA). 3T magnetic resonance imaging (MRI) has highly sensitive contrast resolution and is widely used clinically to detect OA. However, synchrotron radiation phase-contrast imaging computed tomography (SR-PCI) can also provide contrast to tissue interfaces that do not have sufficient absorption differences, with the added benefit of very high spatial resolution. Here, MRI was compared with SR-PCI for quantitative evaluation of human articular cartilage. Medial tibial condyles were harvested from non-OA donors and from OA patients receiving knee replacement surgery. Both imaging methods revealed that average cartilage thickness and cartilage volume were significantly reduced in the OA group, compared to the non-OA group. When comparing modalities, the superior resolution of SR-PCI enabled more precise mapping of the cartilage surface relative to MRI. As a result, MRI showed significantly higher average cartilage thickness and cartilage volume, compared to SR-PCI. These data highlight the potential for high-resolution imaging of articular cartilage using SR-PCI as a solution for early OA diagnosis. Recognizing current limitations of using a synchrotron for clinical imaging, we discuss its nascent utility for preclinical models, particularly longitudinal studies of live animal models of OA.

Exploring the Feasibility of Circulating miRNAs as Diagnostic and Prognostic Biomarkers in Osteoarthritis: Challenges and Opportunities.

Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by progressive cartilage degradation and joint inflammation. As the most common aging-related joint disease, OA is marked by inadequate extracellular matrix synthesis and the breakdown of articular cartilage. However, traditional diagnostic methods for OA, relying on clinical assessments and radiographic imaging, often need to catch up in detecting early-stage disease or i accurately predicting its progression. Consequently, there is a growing interest in identifying reliable biomarkers that can facilitate early diagnosis and prognosis of OA. MicroRNAs (miRNAs) have emerged as potential candidates due to their involvement in various cellular processes, including cartilage homeostasis and inflammation. This review explores the feasibility of circulating miRNAs as diagnostic and prognostic biomarkers in OA, focusing on knee OA while shedding light on the challenges and opportunities associated with their implementation in clinical practice.

Integrated analysis reveals potential significance of FKBP5 in the prognosis and immunity of osteoarthritis and pan-cancer

BackgroundThe high disability rate of osteoarthritis (OA), a joint disease with an insidious onset and widespread effects, places a heavy financial burden on patients, families, and society. Traditional diagnostic approaches, including radiology and physical examination, cannot achieve early-stage screening of OA and thus, miss early intervention for patients. Therefore, the need of biomarkers for the early diagnosis of OA is crucial. ResultsA total of 390 differentially expressed genes (DEGs) were identified from the training set, and 1077 key module genes were found by constructing a weighted gene co-expression network, and 161 key genes were obtained as a result. Four diagnostic marker genes highly associated with OA were screened for key genes using machine learning algorithms, and the resulting nomogram model showed excellent predictive power and clinical value. After further background studies, immune infiltration and functional enrichment analysis, we found that FKBP5 may play an important role in the prognosis and immune infiltration of multiple cancers, and this hypothesis was verified by pan-cancer analysis. ConclusionsWe screened four diagnostic marker genes (FKBP5, EPYC, KLF9 and PDZRN4) that are highly associated with OA. And this led to a diagnostic model, which was assessed to have good predictive power and clinical value. FKBP5 may be a potential intervention target for human diseases such as osteoarthritis and tumors. How to citeXiao Y, Wang Y, Xu X, et al. Integrated analysis reveals potential significance of FKBP5 in the prognosis and immunity of osteoarthritis and pan-cancer. Electron J Biotechnol 2023;65. https://doi.org/10.1016/j.ejbt.2023.05.002.

Early osteoarthritis diagnosis based on near-infrared spectroscopy combined with aquaphotomics

Osteoarthritis (OA) is the most common joint disease and the leading cause of disability in elderly individuals. Despite rapid advances in imaging techniques, early OA diagnosis remains a clinical challenge. In the present study, the feasibility of early OA diagnosis was explored via near-infrared spectroscopy (NIRS) combined with aquaphotomics. Synovial fluid samples from 65 cases of OA categorized as mild, moderate, and severe according to theKellgrenandLawrence classification criteria were analyzed via NIRS. The 1st overtone of water (1300–1600 nm) was considered as the research object for an aquaphotomics model, and aquagrams of the mild, moderate, and severe OA cases were generated using 12 water absorption patterns for early OA diagnosis.The aquaphotomics results exhibited clear differences in the region of 1300–1500 nm, and the number of hydrogen bonds of different water species (1412,1424, 1482, and 1496 nm) evidently correlated with OA occurrence and development. With OA progression, the absorption intensity of water molecules without hydrogen bonds (1412 nm/1424 nm) became stronger, while the absorption intensity of water molecules with four hydrogen bonds (1482 nm/1496 nm) decreased.These results together reveal that the established accurate and rapid early OA diagnosis model based on NIRS combined with aquaphotomics is effective and feasible, and that the number of hydrogen bonds can be used as a biomarker for early OA diagnosis.

A Machine Learning based Approach for Detection of Osteoarthritis using Thermal Images

Osteoarthritis (OA) of the knee is a common disorder that contributes to physical decline and activity limitation. Early OA diagnosis and treatment can stop the disease's progression. The assessment of a physician's visual examination is impartial, subject to different interpretations, and highly dependent on their level of experience. Therefore, a system that employs machine learning techniques to automatically determine the degree of knee OA has been proposed in this study. At first, a specifically created one stage YOLOv2 network is employed to estimate the size of the kneecap according to the distribution of knee joints in low contrast thermal images. To be more specific, the knee Kellgren-Lawrence (KL) grading assignment is ordinal; therefore, a harsher penalty is provided for misrepresentation with a larger gap between the anticipated and actual KL grade. A machine learning architecture is then constructed and extensive tests are performed to demonstrate how texture properties affect diagnostic performance. Thermal images are used to determine if they might be used to distinguish between radiographs of diseased and healthy knees. The outcomes of machine learning features and manually extracted features are compared. Finally, a stacked model that combines second-level machine learning with predictions of patellar texture and clinical characteristics is provided.

Circulating miR-146b and miR-27b are efficient biomarkers for early diagnosis of Equidae osteoarthritis

One of the most orthopedic problems seen in the equine is osteoarthritis (OA). The present study tracks some biochemical, epigenetic, and transcriptomic factors along different stages of monoiodoacetate (MIA) induced OA in donkeys in serum and synovial fluid. The aim of the study was the detection of sensitive noninvasive early biomarkers. OA was induced by a single intra-articular injection of 25 mg of MIA into the left radiocarpal joint of nine donkeys. Serum and synovial samples were taken at zero-day and different intervals for assessment of total GAGs and CS levels as well as miR-146b, miR-27b, TRAF-6, and COL10A1 gene expression. The results showed that the total GAGs and CS levels increased in different stages of OA. The level of expression of both miR-146b and miR-27b were upregulated as OA progressed and then downregulated at late stages. TRAF-6 gene was upregulated at the late stage while synovial fluid COL10A1 was over-expressed at the early stage of OA and then decreased at the late stages (P < 0.05). In conclusion, both miR-146b and miR-27b together with COL10A1 could be used as promising noninvasive biomarkers for the very early diagnosis of OA.

Challenges of transforming evidence-based management of osteoarthritis into clinical practice in rural central Western India. Perceptions of an educational program.

Osteoarthritis (OA) is a major threat to public health worldwide and is predicted to increase. Existing interventions to implement clinical practice guidelines (CPGs) seem to be used mainly in the Western world. We conducted a structured educational program on the evidence-based management of OA (BOA) for Indian physical therapists (PT). Our study aimed to describe Indian PTs' knowledge, attitudes and confidence on evidence-based management of OA, and their perceptions of a course on this subject. The 2-day course included didactic parts and practical skills training. Thirty-five PTs participated and answered a questionnaire. Fourteen of them participated in focus group interviews. Questionnaire data are presented as medians and full ranges. Manifest content analysis was used to analyze interview data that are presented as catagories illustrated by interview quotes. The formal ethics permission was granted. 74% of PTs agreed that radiography determines the type of treatment required, and 69% agreed that a prescription for exercise is enough to ensure adherence. PTs agreed (mean 5 on 6-point scale) that exercises increasing pain should be advised against. Confidence in guiding the physical activity was generally high (≥5 on 6-point scales). Five categories reflected participants' perceptions of the course content: Shift in management focus, Need for cultural adaptation, Importance of social support, Development of organization and collaboration, and Feelings of hesitation. Our results indicate that in order to facilitate the implementation of CPGs, PT curricula may consider the inclusion of knowledge on CPGs, focus more on students' own reflections on transforming theory into practice, and incorporate training of basic skills required for implementation of self-management, body awareness, and neuromuscular fitness. If given access and mandates, PTs may play a major role in the early diagnosis and treatment of OA and thus contribute to the prevention of an epidemic of OA in India.

A discriminative shape-texture convolutional neural network for early diagnosis of knee osteoarthritis from X-ray images.

Knee Osteoarthritis (OA) is one of the most common causes of physical disability worldwide associated with a significant personal and socioeconomic burden. Deep Learning approaches based on Convolutional Neural Networks (CNNs) achieved remarkable improvements in knee OA detection. Despite this success, the problem of early knee OA diagnosis from plain radiographs remains a challenging task. This is due to the high similarity between the X-ray images of OA and non-OA subjects and the disappearance of texture information regarding bone microarchitecture changes in the top layers during the learning process of the CNN models. To address these issues, we propose a Discriminative Shape-Texture Convolutional Neural Network (DST-CNN), which automatically diagnoses early knee OA from X-ray images. The proposed model incorporates a discriminative loss to improve class separability and deal with high inter-class similarities. In addition, a new Gram Matrix Descriptor (GMD) block is embedded in the CNN architecture to compute texture features from several intermediate layers and combine them with the shape features in the top layers. We show that merging texture features with deep ones leads to better prediction of the early stages of OA. Comprehensive experimental results on two large public databases, Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis Study (MOST) demonstrate the potential of the proposed network. Ablation studies and visualizations are provided for a detailed understanding of our proposed approach.

The application of exosomes in the early diagnosis and treatment of osteoarthritis.

With the increase in human lifespan and the aggravation of global aging, the incidence of osteoarthritis (OA) is increasing annually. To better manage and control the progression of OA, prompt diagnosis and treatment for early-stage OA are important. However, a sensitive diagnostic modality and therapy for early OA have not been well developed. The exosome is a class of extracellular vesicles containing bioactive substances, that can be delivered directly from original cells to neighboring cells to modulate cellular activities through intercellular communication. In recent years, exosomes have been considered important in the early diagnosis and treatment of OA. Synovial fluid exosome and its encapsulated substances, e.g., microRNA, lncRNA, and proteins, can not only distinguish OA stages but also prevent the progression of OA by directly targeting cartilage or indirectly modulating the immune microenvironment in the joints. In this mini-review, we include recent studies on the diagnostic and therapeutic modalities of exosomes and hope to provide a new direction for the early diagnosis and treatment of OA disease in the future.

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.

Effect of Online Simulation-Based Education on Nurses' Knowledge and Skills Toward Electrocardiogram Interpretation

Background: Osteoarthritis (OA); a whole joint disorder is a most common and progressive disease that worsens with age. Synovitis and synovial effusion can play important role in early OA diagnosis and progression. Limited pharmacological interventions affecting synovial effusion are studied clinically in Indian population. Aim: This was a pilot, observational, single arm, open label study in real world set-up designed to evaluate the efficacy and safety of Mobileye® capsules containing sodium hyaluronate along with collagen and polysaccharides derived from rooster comb extract in subjects suffering from synovial effusion and knee pain of osteoarthritis. Materials and Method: The study was conducted on total 30 subjects at Outpatient Department (OPD) at three different sites under the supervision of medical doctor. All subjects consumed Mobileye® capsule orally twice daily for 3 months. Synovial effusion, VAS score, WOMAC score and safety parameters were assessed as scheduled. Results: Treatment with Mobileye® capsule showed significant and progressive improvement in all study related parameters. Baseline score vs. day 90 score (mean + SD) for synovial effusion, WOMAC pain score, WOMAC stiffness score, WOMAC physical function score, WOMAC total score and VAS score were 8.52+2.960 mm vs. 2.58+0.903 mm (p&lt;0.0001), 15.13+3.10 vs. 4.13+2.4 (p&lt;0.0001), 6.63+1.098 vs. 1.60+1.192 (p&lt;0.0001), 54.33+8.39 vs. 14.23+8.67 (p&lt;0.0001), 76.13+ 11 vs. 20.33+ 10.86 and 8.13 + 1.28 vs. 1.93+1.17 (p&lt;0.0001) respectively. No significant adverse events were reported. Conclusion: Oral intake of Mobileye® capsule is safe and effective intervention in reducing synovial effusion, pain, stiffness and in improving joint mobility. Keywords: Electrocardiogram interpretation, nurses' knowledge and skills, online simulation education.