Search

Osteoarthritis (OA) is a complex disease of cartilage characterised by joint pain, functional limitation, and reduced quality of life with affected joint movement leading to pain and limited mobility. Current methods to diagnose OA are predominantly limited to X-ray, MRI and invasive joint fluid analysis, all of which lack chemical or molecular specificity and are limited to detection of the disease at later stages. A rapid minimally invasive and non-destructive approach to disease diagnosis is a critical unmet need. Label-free techniques such as Raman Spectroscopy (RS), Coherent anti-Stokes Raman scattering (CARS), Second Harmonic Generation (SHG) and Two Photon Fluorescence (TPF) are increasingly being used to characterise cartilage tissue. However, current studies are based on whole tissue analysis and do not consider the different and structurally distinct layers in cartilage. In this work, we use Raman spectroscopy to obtain signatures from the superficial (top) and deep (bottom) layer of healthy and osteoarthritic cartilage samples from 64 patients (19 control and 45 OA). Spectra were acquired both in the 'fingerprint' region from 700 to 1720 cm− 1 and high-frequency stretching region from 2500 to 3300 cm− 1. Principal component and linear discriminant analysis was used to identify the peaks that contributed significantly to classification accuracy of the different samples. The most pronounced differences were observed at the proline (855 cm− 1 and 921 cm− 1) and hydroxyproline (877 cm− 1 and 938 cm− 1), sulphated glycosaminoglycan (sGAG) (1064 cm− 1 and 1380 cm− 1) frequencies for both control and OA as well as the 1245 cm− 1 and 1272 cm− 1, 1320 cm− 1 and 1345 cm− 1, 1451 cm− 1 collagen modes were altered in OA samples, consistent with expected collagen structural changes. Classification accuracy based on Raman fingerprint spectral analysis of superficial and deep layer cartilage for controls was found to be 97% and 93% on using individual/all spectra and, 100% and 95% on using mean spectra per patient, respectively. OA diseased cartilage was classified with an accuracy of 88% and 84% for individual/all spectra, and 96% and 95% for mean spectra per patient based on analysis of the superficial and the deep layers, respectively. Raman spectra from the C-H stretching region (2500–3300 cm− 1) resulted in high classification accuracy for identification of different layers and OA diseased cartilage but low accuracy for controls. Differential changes in superficial and deep layer cartilage signatures were observed with age (under 60 and over 60 years), in contrast, less significant differences were observed with gender. Prominent chemical changes in the different layers of cartilage were preliminarily imaged using CARS, SHG and TPF. Cell clustering was observed in OA together with differences in pericellular matrix and collagen structure in the superficial and the deep layers correlating with the Raman spectral analysis. The current study demonstrates the potential of Raman Spectroscopy and multimodal imaging to interrogate cartilage tissue and provides insight into the chemical and structural composition of its different layers with significant implications for OA diagnosis for an increasing aging demographic. [ABSTRACT FROM AUTHOR]

Exosomes extracted from synovial fluid (SF-exo) reflect the status of their originating cells. The proteomic profiles of SF-exo are important for the diagnosis of osteoarthritis (OA). To delineate the proteomic differences between SF-exo from OA patients and healthy individuals, a quantitative proteomic study based on iTRAQ technology was performed. In this study, a total of 439 proteins were identified, with 20 proteins exhibiting increased expression in the OA patient group, while 5 showed decreased expression levels. Bioinformatic analysis showed these differentially expressed proteins (DEPs) were involved in a variety of immune-related processes, including complement activation and antigen binding. For further screening, we downloaded a publicly available dataset of synovial fluid (PXD023708) and compared it with our dataset. The comparative Results identified that 5 DEPs overlapped in two datasets, and protein-protein interaction revealed that C3, C4B and APOM were key members of a tightly interactive network. Through receiver operating characteristic (ROC) curve analysis and enzyme-linked immunosorbent assay (ELISA), we confirmed 5 DEPs (C3, C4B, APOM, MMP3, DPYSL2) as potential diagnostic biomarkers for OA. And Pearson correlation analysis confirmed that most of these biomarkers had no significant linear correlation with age. Overall, our study provides the first comprehensive description of the proteomic landscape of SF-exo in OA and identifies several potential biomarkers. These findings are expected to provide valuable insights into the diagnosis and treatment of OA. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a common cause of disability among the elderly, profoundly affecting quality of life. This study aims to leverage bioinformatics and machine learning to develop an artificial neural network (ANN) model for diagnosing OA, providing new avenues for early diagnosis and treatment. Methods: From the Gene Expression Omnibus (GEO) database, we first obtained OA synovial tissue microarray datasets. Differentially expressed genes (DEGs) associated with OA were identified through utilization of the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, protein-protein interaction (PPI) network analysis and machine learning were employed to identify the most relevant potential feature genes of OA, and ANN diagnostic model and receiver operating characteristic (ROC) curve were constructed to evaluate the diagnostic performance of the model. In addition, the expression levels of the feature genes were verified using real-time quantitative polymerase chain reaction (qRT-PCR). Finally, immune cell infiltration analysis was performed using CIBERSORT algorithm to explore the correlation between feature genes and immune cells. Results: The Limma package and WGCNA identified a total of 72 DEGs related to OA, of which 12 were up-regulated and 60 were down-regulated. Then, the PPI network analysis identified 21 hub genes, and three machine learning algorithms finally screened four feature genes (BTG2, CALML4, DUSP5, and GADD45B). The ANN diagnostic model was constructed based on these four feature genes. The AUC of the training set was 0.942, and the AUC of the validation set was 0.850. In addition, the qRT-PCR validation results demonstrated a significant downregulation of BTG2, DUSP5, and GADD45 mRNA expression levels in OA samples compared to normal samples, while CALML4 mRNA expression level exhibited an upregulation. Immune cell infiltration analysis revealed B cells memory, T cells gamma delta, B cells naive, Plasma cells, T cells CD4 memory resting, and NK cells The abnormal infiltration of activated cells may be related to the progression of OA. Conclusions: BTG2, CALML4, DUSP5, and GADD45B were identified as potential feature genes for OA, and an ANN diagnostic model with good diagnostic performance was developed, providing a new perspective for the early diagnosis and personalized treatment of OA. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to investigate the effect of LINC00472 in osteoarthritis (OA) and its molecular mechanism. Patients and methods: This prospective study was conducted with 110 patients (59 females, 51 males; mean age: 58.6±10.3 years; range, 37 to 79 year) with OA and 101 healthy controls (58 females, 43 males; mean age: 60.6±10.3 years; range, 35 to 78 years) between June 2020 and November 2022. First, we measured LINC00472 levels in OA patients using RT-qPCR (real-time quantitative reverse transcription polymerase chain reaction). Afterward, we treated human chondrocytes with interleukin (IL)-1ß, which aimed to construct an OA cellular model to explore the function of LINC00472 in OA. Messenger RNA levels were detected by RT-qPCR. Apoptosis was measured by flow cytometry. Cell viability was measured by CCK-8 (cell counting kit-8) assay. Enzyme-linked immunosorbent assay was used to detect inflammatory factor levels. Finally, we verified the targeting of miR-361-5p with LINC00472 and MECP2 by luciferase assay and RNA immunoprecipitation. Results: In OA patients and OA cells, LINC00472 and MECP2 levels were increased, and miR-361-5p levels were decreased. LINC00472 levels were negatively correlated with miR-361-5p levels and positively correlated with MECP2 levels. In human chondrocytes, LINC00472 knockdown inhibited apoptosis, cellular inflammation, and extracellular matrix degradation. However, miR-361-5p inhibitor reversed these effects. In addition, LINC00472 knockdown downregulated MECP2 levels, and miR-361-5p inhibitor reversed the effect. Conclusion: LINC00472 is involved in chondrocyte apoptosis, extracellular matrix degradation, and cellular inflammation in OA through the miR-361-5p/MECP2 axis. LINC00472 may regulate OA development by increasing MECP2 expression through sponged miR-361-5p and may be a new target for OA diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a degenerative musculoskeletal disease that significantly impacts the quality of life. Currently, no validated biomarkers for early detection of OA are defined. The possibility of discovering OA biomarkers is the focus of this study. Methods: Human primary OA synovial cells (SVs), isolated from discarded joint tissue of patients with Kellgren & Lawrence score (KL) < 3, (mild/moderate) and KL ≥ 3 (severe), were characterized by FACS analysis. Through qRT-PCR and ELISA assays the inflammation, fibrosis status and the different miRNAs expression has been investigated. The role of miR-203a-3p and its precursors were evaluated through gain and loss of function study, IL-1β synoviocytes treatments and methylation analysis of miR203a promoter. The qRT-PCR analysis of miR203a-3p and pre-miR203a on plasma (isolated 24 h before surgery, 3 days and 1 month after surgery) and synovial fluid (recovered during the surgery) were done to support our in vitro data. Results: MiR203a-3p expression is inversely correlated with the aggressiveness of OA, modulating the expression of epithelial to mesenchymal transition (EMT) and pro-inflammatory factors, as well as regulating the expression of secreted protein acidic and rich in cysteine (SPARC) mRNA. Methylation analysis of the miR203a promoter and SVs IL-1β treatment's highlighted the impact of inflammation on miR203a-3p and pre-miR203a expression; as confirmed by both miRNAs detection in biological fluids derived from patients with severe OA. Conclusion: Our preliminary results suggest that miR-203a-3p might be a potential candidate for staging OA progression and a new protective/predictive biomarker for synovial OA degeneration. Further studies are needed to validate its potential impact on OA. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is the most common arthritis globally. Besides cartilage loss, all surrounding structures of the joint, including the synovium, capsule, ligament, bone, nerve, and muscle, are affected by this disease, resulting in joint failure. The traditional diagnosis of OA depends on clinical symptoms, physical examination, and radiographic findings. Ultrasonography (US), computed tomography, and magnetic resonance imaging (MRI) are imaging modalities commonly used for assessing OA. Current Concepts: US findings of OA indicate synovitis, hyaline and fibrous cartilage damage, ligament and tendon changes, and bony abnormalities. US is clinically useful because it correlates with the clinical pain index and radiographic and MRI findings, differentiates the various causes of OA pain, helps diagnose early disease, predicts prognosis, and increases the accuracy and safety of intra-articular injections in OA joints. Discussion and Conclusion: Although US has some limitations as a tool for examining whole joint structures, its use is predicted to increase in the future because it is beneficial and easily accessible for assessing OA. To produce accurate US results, the examiner should have deep anatomical knowledge and skilled practice techniques, and a clinical diagnosis should be performed based on clinical symptoms and physical examination. [ABSTRACT FROM AUTHOR]

The use of three-dimensional (3D) gait analysis to image femorotibial translation can aid in the diagnosis of pathology and provide additional insight into the severity of KOA (knee osteoarthritis). Femorotibial translation is of particular importance in patients undergoing UKA (unicompartmental knee arthroplasty), as the absence or elongation of ligamentous structures results in changes in the kinematic alignment. The aim of the study was to evaluate the parameters of femorotibial translation in patients with MOA (medial unicompartmental OA). An artificial model was employed to develop a method for calculating femorotibial translation in vitro. In a prospective cohort study, gait data using three-dimensional gait analysis were collected from 11 patients (68.73 ± 9.22 years) with severe OA scheduled for UKA and 29 unmatched healthy participants (22.07 ± 2.23 years). The discrete variables characterising femorotibial translation were compared for statistical significance (p < 0.05) using the Student's t-test and the Mann–Whitney U-test. The results of the study validated an artificial model to mimic femorotibial translation. The comparison of patients scheduled for UKA and a healthy unmatched control group showed no statistically significant differences concerning femorotibial translation in all three planes (p > 0.05). However, the PROMs (patient-reported outcome measures), spatiotemporal, and kinematic parameters showed statistically significant differences between the groups (p < 0.001). The data presented here demonstrate typical changes in PROMs as well as spatiotemporal and kinematic outcomes for MOA as seen in knee OA. The results of the clinical gait analyses demonstrate individualised femorotibial translation. The extent of individual femorotibial translation may prove to be an important parameter for altered joint kinematics in patients with MOA, especially prior to UKA implantation. [ABSTRACT FROM AUTHOR]

Background: Administrative healthcare databases, such as Medicare, are increasingly used to identify groups at risk of a crash. However, they only contain information on crash-related injuries, not all crashes. If the driver characteristics associated with crash and crash-related injury differ, conflating the two may result in ineffective or imprecise policy interventions. Methods: We linked 10 years (2008–2017) of Medicare claims to New Jersey police crash reports to compare the demographics, clinical diagnoses, and prescription drug dispensings for crash-involved drivers ≥ 68 years with a police-reported crash to those with a claim for a crash-related injury. We calculated standardized mean differences to compare characteristics between groups. Results: Crash-involved drivers with a Medicare claim for an injury were more likely than those with a police-reported crash to be female (62.4% vs. 51.8%, standardized mean difference [SMD] = 0.30), had more clinical diagnoses including Alzheimer's disease and related dementias (13.0% vs. 9.2%, SMD = 0.20) and rheumatoid arthritis/osteoarthritis (69.5% vs 61.4%, SMD = 0.20), and a higher rate of dispensing for opioids (33.8% vs 27.6%, SMD = 0.18) and antiepileptics (12.9% vs 9.6%, SMD = 0.14) prior to the crash. Despite documented inconsistencies in coding practices, findings were robust when restricted to claims indicating the injured party was the driver or was left unspecified. Conclusions: To identify effective mechanisms for reducing morbidity and mortality from crashes, researchers should consider augmenting administrative datasets with information from police crash reports, and vice versa. When those data are not available, we caution researchers and policymakers against the tendency to conflate crash and crash-related injury when interpreting their findings. [ABSTRACT FROM AUTHOR]

Introduction: Osteoarthritis (OA) is one of the leading causes of disability and has a significant socioeconomic impact on public healthcare. Given the chronic nature of OA and the lack of effective treatments, patients often seek information from ttie media Objectives: To describe and evaluate the quality of information on ttie diagnosis and treatment of OA published in Spanish generalist media, as well as to know the type of information source consulted for the publications evaluated Methodology: A search was conducted in five Spanish generalist newspapers (El País, El Mundo, La Vanguardia, 20minutos, and ABC) from 2018 to 2022, including their digital versions A comparative analysis of ttie quality of recommendations was carried out using ttie clinical guideline of the National Institute for Health and Care Excellence (NICE) Results: A total of 67 articles were included. 94.1% of the articles ttiat defined OA did so exclusively in terms of the etiology of joint cartilage degeneration The most frequent treatment recommendation (47.6%) was pharmacological treatment. 52.2% of the sources used were based on specialist healthcare professionals Conclusion: The analysis of articles published in the press revealed a prevailing trend of presenting an outdated, mechanistic definition of OA focused on cartilage wear. The recommendations presented a low level of agreement with scientific evidence [ABSTRACT FROM AUTHOR]

An electrochemical sensor sensitive to coenzyme A (CoA) was designed using a CoA-responsive polyallylamine–manganese oxide–polymer dot nanogel coated on the electrode surface to detect various genetic models of osteoarthritis (OA). The CoA-responsive nanogel sensor responded to the abundance of CoA in OA, causing the breakage of MnO2 in the nanogel, thereby changing the electroconductivity and fluorescence of the sensor. The CoA-responsive nanogel sensor was capable of detecting CoA depending on the treatment time and distinguishing the response towards different OA genetic models that contained different levels of CoA (wild type/WT, NudT7 knockout/N7KO, and Acot12 knockout/A12KO). The WT, N7KO, and A12KO had distinct resistances, which further increased as the incubation time were changed from 12 h (R12h = 2.11, 2.40, and 2.68 MΩ, respectively) to 24 h (R24h = 2.27, 2.59, and 2.92 MΩ, respectively) compared to the sensor without treatment (Rcontrol = 1.63 MΩ). To simplify its application, the nanogel sensor was combined with a wireless monitoring device to allow the sensing data to be directly transmitted to a smartphone. Furthermore, OA-indicated anabolic (Acan) and catabolic (Adamts5) factor transcription levels in chondrocytes provided evidence regarding CoA and nanogel interactions. Thus, this sensor offers potential usage in simple and sensitive OA diagnostics. [ABSTRACT FROM AUTHOR]

Objective: Femoroacetabular impingement is a dysfunction of the hip joint with the potential to cause early hip osteoarthritis. The aim of this study was to examine the Alpha Angle and Femoral Neck-Head Offset measurements of patients who underwent pelvic Computed Tomography examination for any indication with radiologists. Material and Method: The results of 1,782 right and left hip joints of 891 individuals aged 18-45 years who underwent pelvic computed tomography examination (with or without contrast) for various reasons were evaluated retrospectively. Alpha Angle and Femoral Neck-Head Offset measurements were performed on computed tomography sections. In this study, an Alpha Angle of 55° or more and a Femoral Neck-Head Offset distance of less than 8 mm were considered abnormal. Results: A total of 891 individuals, 758 (85.1%) males and 133 (14.9%) females, were included in this study. The mean age of the individuals was 24.65 ± 6.01 years. The mean right Alpha Angle was 45.42 ± 4.4 (min 38.00, max 62.00), the mean left Alpha Angle was 46.65 ± 5.1 (min 38.00, max 72.10), the mean right Femoral Neck-Head Offset was 10.21 ± 0.02 (min 6.2, max 11.4), and the mean left Femoral Neck-Head Offset was 10.57 ± 0.01 (min 5.6, max 12.0). The total number of individuals with Alpha Angle = 55° in both hip joints was 37, 33 of whom were male and 4 were female. There were no individuals with Femoral Neck-Head Offset < 8 mm in both hip joints. Conclusion: Imaging features known to be associated with femoroacetabular impingement are seen in asymptomatic adult hip joints, particularly in males. If anthropometric measurements are outside normal limits in asymptomatic young male and female individuals, clinical correlation of the findings of these individuals should be recommended. [ABSTRACT FROM AUTHOR]

Objectives: The study aimed to investigate the diagnostic values of different musculoskeletal ultrasound (MSUS) signs, serum uric acid (SUA), and their combined detection for gouty arthritis (GA). Patients and methods: In this retrospective study, 70 patients (62 males, 8 females; mean age: 46.1±14.1 years; range, 25 to 86 years) diagnosed with GA (the GA group) between August 2022 and March 2023 and 70 patients (54 females, 16 males; mean age: 49.0±14.1 years; range, 21 to 75 years) diagnosed with rheumatoid arthritis and osteoarthritis during the same period (the non-GA group) were included. The positive rate of MSUS signs and SUA in both groups was recorded to compare the differences. The correlations of MSUS signs and SUA with GA were analyzed using Spearman's rank correlation analysis. The diagnostic values of different MSUS signs, SUA, and their combined detection for GA were analyzed using a receiver operating characteristic, the area under the curve (AUC), sensitivity, specificity, and the Youden index. Results: The positive rate of the double contour (DC) sign (chi-squared [Χ²]=102.935, p<0.001), hyperechoic spots (Χ²=56.395, p<0.001), bone erosions (Χ²=10.080, p<0.001), and SUA (Χ²=41.117, p<0.001) were higher in the GA group than in the non-GA group. The positive rate of the DC sign (rs=0.829, p=0.001), hyperechoic spots (rs=0.631, p<0.001), bone erosion (rs=0.268, p=0.001), and SUA (rs=0.542, p<0.001) were positively correlated with GA. Among the single-indicator measures, the DC sign exhibited the highest diagnostic value (AUC=0.907, sensitivity=81.4%, specificity=100%, p<0.001). Among the combined-indicator measures, the DC sign combined with SUA exhibited the highest diagnostic value (AUC=0.929, sensitivity=91.4%, specificity=94.3%, p<0.001), higher than DC sign detection alone. Conclusion: The DC sign combined with SUA yielded a high diagnostic value and can thus provide a reliable basis for effectively and efficiently diagnosing GA. [ABSTRACT FROM AUTHOR]

Objective: Automated machine learning (autoML) platforms allow health care professionals to play an active role in the development of machine learning (ML) algorithms according to scientific or clinical needs. The aim of this study was to develop and evaluate such a model for automated detection and grading of distal hand osteoarthritis (OA). Methods: A total of 13,690 hand radiographs from 2,863 patients within the Swiss Cohort of Quality Management (SCQM) and an external control data set of 346 non‐SCQM patients were collected and scored for distal interphalangeal OA (DIP‐OA) using the modified Kellgren/Lawrence (K/L) score. Giotto (Learn to Forecast [L2F]) was used as an autoML platform for training two convolutional neural networks for DIP joint extraction and subsequent classification according to the K/L scores. A total of 48,892 DIP joints were extracted and then used to train the classification model. Heatmaps were generated independently of the platform. User experience of a web application as a provisional user interface was investigated by rheumatologists and radiologists. Results: The sensitivity and specificity of this model for detecting DIP‐OA were 79% and 86%, respectively. The accuracy for grading the correct K/L score was 75%, with a κ score of 0.76. The accuracy per DIP‐OA class differed, with 86% for no OA (defined as K/L scores 0 and 1), 71% for a K/L score of 2, 46% for a K/L score of 3, and 67% for a K/L score of 4. Similar values were obtained in an independent external test set. Qualitative and quantitative user experience testing of the web application revealed a moderate to high demand for automated DIP‐OA scoring among rheumatologists. Conversely, radiologists expressed a low demand, except for the use of heatmaps. Conclusion: AutoML platforms are an opportunity to develop clinical end‐to‐end ML algorithms. Here, automated radiographic DIP‐OA detection is both feasible and usable, whereas grading among individual K/L scores (eg, for clinical trials) remains challenging. [ABSTRACT FROM AUTHOR]

Objective: The study aims to evaluate the accuracy of an MRI-based artificial intelligence (AI) segmentation cartilage model by comparing it to the natural tibial plateau cartilage. Methods: This study included 33 patients (41 knees) with severe knee osteoarthritis scheduled to undergo total knee arthroplasty (TKA). All patients had a thin-section MRI before TKA. Our study is mainly divided into two parts: (i) In order to evaluate the MRI-based AI segmentation cartilage model's 2D accuracy, the natural tibial plateau was used as gold standard. The MRI-based AI segmentation cartilage model and the natural tibial plateau were represented in binary visualization (black and white) simulated photographed images by the application of Simulation Photography Technology. Both simulated photographed images were compared to evaluate the 2D Dice similarity coefficients (DSC). (ii) In order to evaluate the MRI-based AI segmentation cartilage model's 3D accuracy. Hand-crafted cartilage model based on knee CT was established. We used these hand-crafted CT-based knee cartilage model as gold standard to evaluate 2D and 3D consistency of between the MRI-based AI segmentation cartilage model and hand-crafted CT-based cartilage model. 3D registration technology was used for both models. Correlations between the MRI-based AI knee cartilage model and CT-based knee cartilage model were also assessed with the Pearson correlation coefficient. Results: The AI segmentation cartilage model produced reasonably high two-dimensional DSC. The average 2D DSC between MRI-based AI cartilage model and the tibial plateau cartilage is 0.83. The average 2D DSC between the AI segmentation cartilage model and the CT-based cartilage model is 0.82. As for 3D consistency, the average 3D DSC between MRI-based AI cartilage model and CT-based cartilage model is 0.52. However, the quantification of cartilage segmentation with the AI and CT-based models showed excellent correlation (r = 0.725; P values < 0.05). Conclusion: Our study demonstrated that our MRI-based AI cartilage model can reliably extract morphologic features such as cartilage shape and defect location of the tibial plateau cartilage. This approach could potentially benefit clinical practices such as diagnosing osteoarthritis. However, in terms of cartilage thickness and three-dimensional accuracy, MRI-based AI cartilage model underestimate the actual cartilage volume. The previous AI verification methods may not be completely accurate and should be verified with natural cartilage images. Combining multiple verification methods will improve the accuracy of the AI model. [ABSTRACT FROM AUTHOR]

Objective: Diagnosing musculoskeletal infections in children is challenging. In recent years, with the advancement of ultrasound technology, high-resolution ultrasound has unique advantages for musculoskeletal children. The aim of this work is to summarize the ultrasonographic and clinical characteristics of children with pyogenic arthritis and osteomyelitis. This study provides a simpler and more effective diagnostic basis for clinical treatment. Methods: Fifty children with osteomyelitis or arthritis were diagnosed via ultrasound, and the results of the ultrasound diagnosis were compared with those of magnetic resonance imaging and surgery. Clinical and ultrasound characteristics were also analyzed. Results: Out of 50 patients, 46 were confirmed to have suppurative infection by surgical and microbiological examination. Among these 46 patients, 26 were diagnosed with osteomyelitis and 20 had arthritis. The manifestations of osteomyelitis were subperiosteal abscess (15 patients), bone destruction (17 patients), bone marrow abscess (9 patients), and adjacent joint abscess (13 patients). Osteomyelitis mostly affects the long bones of the limbs, femur and humerus (10 and 9 patients, respectively), followed by the ulna, radius, tibia and fibula (one patient each). The manifestations of arthritis were joint pus (20 patients) and joint capsule thickening (20 patients), and hip dislocation (8 patients). All the patients had arthritis involving the hip joint. Conclusion: Subperiosteal abscess, bone destruction, and joint abscess with dislocation are ultrasonographic features of pyogenic osteoarthritis. The findings of this work can improve the early diagnosis and differentiation of pyogenic osteoarthritis and provide a reliable basis for treatment. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a chronic and low-grade inflammatory disease associated with metabolism disorder and multiple cell death types in the synovial tissues. Sulfur metabolism has not been studied in OA. Methods: First, we calculated the single sample gene set enrichment analysis score of sulfur metabolism-associated annotations (i.e., cysteine metabolism process, regulation of sulfur metabolism process, and disulfidptosis) between healthy and synovial samples from patients with OA. Sulfur metabolism-related differentially expressed genes (DEGs) were analyzed in OA. Least absolute shrinkage and selection operator COX regression were used to identify the sulfur metabolism-associated gene signature for diagnosing OA. Correlation and immune cell deconvolution analyses were used to explore the correlated functions and cell specificity of the signature gene, TM9SF2. TM9SF2's effect on the phagocytosis of macrophages M2 was analyzed by coculturing macrophages with IgG-coated beads or apoptotic Jurkat cells. Results: A diagnostic six gene signature (i.e., MTHFD1, PDK4, TM9SF2, POU4F1, HOXA2, NCKAP1) was identified based on the ten DEGs, validated using GSE12021 and GSE1919 datasets. TM9SF2 was upregulated in the synovial tissues of OA at both mRNA and protein levels. The relationship between TM9SF2 and several functional annotations, such as antigen processing and presentation, lysosome, phagosome, Fcγ-mediated phagocytosis, and tyrosine metabolism, was identified. TM9SF2 and macrophages M2 were significantly correlated. After silencing TM9SF2 in THP-1-derived macrophages M2, a significantly reduced phagocytosis and attenuated activation of PLC-γ1 were observed. Conclusion: A sulfur metabolism-associated six-gene signature for OA diagnosis was constructed and upregulation of the phagocytosis-associated gene, TM9SF2, was identified. The findings are expected to deepen our understanding of the molecular mechanism underlying OA development and be used as potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a common degenerative disease involving articular cartilage, in which ferroptosis of chondrocytes plays an important role. Baicalin (BAI) exerts regulatory effects in a wide range of orthopedic diseases including OA, but its effect on ferroptosis of chondrocytes (CHs) is still unclear. The purpose of this study was to determine the effect of BAI on ferroptosis in human OA chondrocytes (OACs), and to explore its possible mechanism. Methods: CHs were treated with IL-1β (10 ng/mL) to simulate inflammation in vitro. Immunofluorescence, quantitative RT-PCR, Western blotting and cell viability assay were performed to evaluate the impacts of BAI on Fe2+ level, mitochondrial dysfunction, ferroptosis-related proteins, oxidative stress and cytotoxicity in CHs. Additionally, siRNA was made use of to knock out nuclear factor E2-related factor 2 (Nrf2) to analyze the role played by Nrf2 in BAI-induced CH ferroptosis. Results: BAI eliminated IL-1β-induced Fe2+ accumulation, changes in mitochondrial membrane potential and ferroptosis-related protein GPX4, SLC7A11, P53 and ACSL4 levels, as well as reactive oxygen species (ROS), lipid peroxidation (LPO) and malondialdehyde (MDA) accumulation in CHs. Besides, BAI reversed IL-1β-induced decrease of Collagen II and increase of MMP13 in CHs. Meanwhile, BAI attenuated IL-1β-induced CH toxicity and promoted Nrf2 antioxidant system activation. When Nrf2 was knocked down by siRNA, the effects of BAI on IL-1β-induced ferroptosis-related proteins and antioxidant stress in CHs were significantly weakened. Conclusions: This study demonstrates that IL-1β can induce CH ferroptosis. BAI is able to inhibit IL-1β-induced CH ferroptosis and ECM degradation, and the specific mechanism may be that it can inhibit IL-1β-induced CH ferroptosis by activating Nrf2 antioxidant system to attenuate the accumulation of intracellular ROS and lipid ROS. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a degenerative disease. It is common in middle-aged and elderly people and is one of the main causes of disability. Currently, the etiology of OA is unclear, and no specific biomarkers for the diagnosis of OA have been identified. Therefore, finding a highly sensitive biomarker is essential for a proper diagnosis. TRNA-derived fragments (tRFs) and tRNA-derived stress-induced RNAs (tiRNAs) are newly discovered classes of noncoding RNAs. tRF has been proven in several studies to have significant associations with tumor diagnosis, making it a promising biomarker in cancer research. However, the diagnostic utility of tRF in OA patients and the correlation between OA progression and trf differential expression have yet to be elaborated. The purpose of this research was to identify tRFs with differential expression in OA to assess their potential as OA biomarkers. To determine the tRF-5022B expression level in this research, real-time fluorescence quantitative PCR has been employed. Agarose gel electrophoresis, Sanger sequencing, and other investigations have been employed for evaluating tRF-5022B's molecular properties. Receiver operating characteristic curve analysis has been utilized for assessing the diagnostic effectiveness of the tRF-5022B. The findings demonstrated that tRF-5022B expression was considerably lower in OA serum. The Kellgren–Lawrence grading scale was shown to correspond with serum expression levels. The ROC curve confirmed that tRF-5022B serum expression levels might differentiate OA cases from healthy individuals and RA patients. According to the aforementioned findings, tRF-5022B may be employed as a novel biomarker for OA diagnosis due to its excellent diagnostic value. [ABSTRACT FROM AUTHOR]

Objectives To explore clustering of comorbidities among patients with a new diagnosis of OA and estimate the 10-year mortality risk for each identified cluster. Methods This is a population-based cohort study of individuals with first incident diagnosis of OA of the hip, knee, ankle/foot, wrist/hand or 'unspecified' site between 2006 and 2020, using SIDIAP (a primary care database representative of Catalonia, Spain). At the time of OA diagnosis, conditions associated with OA in the literature that were found in ≥1% of the individuals (n  = 35) were fitted into two cluster algorithms, k -means and latent class analysis. Models were assessed using a range of internal and external evaluation procedures. Mortality risk of the obtained clusters was assessed by survival analysis using Cox proportional hazards. Results We identified 633 330 patients with a diagnosis of OA. Our proposed best solution used latent class analysis to identify four clusters: 'low-morbidity' (relatively low number of comorbidities), 'back/neck pain plus mental health', 'metabolic syndrome' and 'multimorbidity' (higher prevalence of all studied comorbidities). Compared with the 'low-morbidity' cluster, the 'multimorbidity' cluster had the highest risk of 10-year mortality (adjusted hazard ratio [HR]: 2.19 [95% CI: 2.15, 2.23]), followed by the 'metabolic syndrome' cluster (adjusted HR: 1.24 [95% CI: 1.22, 1.27]) and the 'back/neck pain plus mental health' cluster (adjusted HR: 1.12 [95% CI: 1.09, 1.15]). Conclusion Patients with a new diagnosis of OA can be clustered into groups based on their comorbidity profile, with significant differences in 10-year mortality risk. Further research is required to understand the interplay between OA and particular comorbidity groups, and the clinical significance of such results. [ABSTRACT FROM AUTHOR]

X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost. However, the manual categorization of knee joint disorders is time-consuming, requires an expert person, and is costly. This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm. Two pre-trained deep learning models (Efficientnet-b0 and Densenet201) have been employed for the training and feature extraction. Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images. In the next step, fusion is performed using a canonical correlation approach and obtained a feature vector that hasmore information than the original feature vector. After that, an improved whale optimization algorithm is developed for dimensionality reduction. The selected features are finally passed to the machine learning algorithms such as Fine-Tuned support vector machine (SVM) and neural networks for classification purposes. The experiments of the proposed framework have been conducted on the publicly available dataset and obtained the maximum accuracy of 90.1%. Also, the system is explained using Explainable Artificial Intelligence (XAI) technique called occlusion, and results are compared with recent research. Based on the results compared with recent techniques, it is shown that the proposed method's accuracy significantly improved. [ABSTRACT FROM AUTHOR]

Osteoarthritis of the knee is a common problem in the elderly, leading to severe morbidity. Total Knee Arthroplasty (TKA) is a widely validated surgery to provide a remarkable extent of knee function and simultaneously alleviates pain for knee osteoarthritis (OA). It is clearly understood that precision of the rotational alignment and accuracy of the technique in the placement of the femoral component is a prerequisite for excellent and successful outcomes of TKA. Advanced technology has now allowed surgeons to understand patient-specific variabilities in anatomical reference landmarks and the relationship of component positioning in relation to the reference landmarks to achieve accurate gap balancing with minimal soft tissue release.14 Robotic Arm Assisted-Total Knee Arthroplasty (RAA-TKA) is a semi-automated system that enables us in replicating the same. Using this technology, the bony resections, component positions, probable component sizing and gap balancing can be tentatively planned preoperatively with CT Scan Analysis and executed intraoperatively. Hence this study was undertaken to estimate the relationship between femoral component placement to normal rotational landmarks such as the Posterior Condylar Axis (PCA) and to quantitatively evaluate coronal and sagittal plane correction obtained. Also, we aimed to use the data to detect any anatomical variations in the study population and evaluate the accuracy of predicted component sizing, including gender-based evaluation. A Prospective Observational Study of 1073 knees of patients of either sex above 50 years of age with Kellgren Lawrence Grade 4 Osteoarthritis of the knee which were confirmed with X-Ray undergoing RAA-TKA using MAKO Robotic System using Stryker Triathlon (Cruciate Substituting) CS Knee was conducted during the period between 2022 and 2023 in two South Indian hospitals specializing in joint replacement surgeries. We found a statistically significant difference between the native Posterior Condylar Axis (PCA) (4.82 ± 2.15°) and final femoral component external rotation (3.24 ± 1.29°) with a p-value of <0.001 at 95% confidence interval. The accuracy of component size prediction was 99.8%. Also, analysis in our study has shown the most common implant sizes to be 4 in males and 2 in females. We also found no statistically significant difference based on age, size, laterality, or primary varus deformity. RAA-TKA provides patient-specific alignment/restricted kinematic alignment which might further enhance the outcome for the patient. Reliable deformity correction in coronal and sagittal planes can be achieved. Accurate flexion and extension gap balancing can be done through component placement and with minimal soft tissue dissection. Irrespective of all the advantages noted in RAA-TKA, further follow-up and long-term outcome studies are required to properly gauge and analyze this new technology. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is the most common chronic joint disease and how ferroptosis contributes to OA has garnered much attention recently. Bioinformatics promoted the discovery of ferroptosis-related biomarkers for OA. But since OA is a whole-joint disease, sensitive biomarkers for OA are still limited. We herein focused on subchondral bone, a joint component often-ignored by existing bioinformatic reports, to identify ferroptosis-related diagnostic biomarkers for OA. Method: Microarray datasets GSE51588 and GSE55457 were downloaded from Gene Expression Omnibus database. Ferroptosis-related differential expression genes (Ferr-DEGs) between OA and normal samples were identified and their functional enrichment was analyzed. Common genes for OA diagnosis were selected from Ferr-DEGs using the combination of SVM-RFE, LASSO regression, and RandomForest machine learning algorithms. Common genes' diagnostic value was verified by receiver operating characteristic (ROC) curve and their association with immune infiltration was analyzed by CIBERSORT. Finally, candidate gene's expression was verified in chondrocytes from OA patients and in an in vitro IL-1β-induced OA model, by RT-PCR. Results: Two ferroptosis-related genes, LPCAT3 and PGD, were identified as OA diagnostic biomarkers and confirmed by ROC diagnostic test. The association of LPCAT3 and PGD with the infiltration of several types of immune cells was identified. The decreased expression of LPCAT3 and PGD was both confirmed in OA chondrocytes and IL-1β-induced OA condition. Conclusions: We identified ferroptosis-related genes LPCAT3 and PGD as potential diagnostic biomarkers for OA, which may offer insight into the role of ferroptosis in OA and provides useful information for the diagnosis and treatment of OA. [ABSTRACT FROM AUTHOR]

Background: N6-methyladenosine (m6A) is a universal RNA modification pattern regulated by multiple m6A regulators. In osteoarthritis (OA), m6A regulators influence disease progression by regulating cartilage degradation. However, the function of m6A regulators in synovial tissue remains unclear. In this work, we investigated the biological significance of m6A regulators in osteoarthritic synovitis. Methods: Datasets were acquired from Gene Expression Omnibus. Differential analysis of merged data identified the differentially expressed m6A regulators. Machine learning models were used to evaluate genetic importance. To predict disease risk, a nomogram was constructed based on above m6A regulators. Cluster analysis divided the OA sample into different subgroups. Immune infiltration revealed the immune m6A regulators, which were validated using clinical samples. Eventually, a competing endogenous RNA (ceRNA) network was constructed. Results: We acquired five differentially expressed m6A regulators and a random forest model. The nomogram accurately predicted disease risk. We identified 122 differentially expressed genes between two m6A subgroups. The analysis of immune infiltration showed that YTHDF2 was an immune-related m6A regulator closely related with macrophages. In clinical samples, the protein and mRNA contents of YTHDF2 were consistent with the results of bioinformatic analysis. The ceRNA network based on YTHDF2 revealed 75 lncRNA nodes and 19 miRNA nodes. Conclusion: YTHDF2 has a high diagnostic value in the synovitis of OA and significantly influences the immune status of patients. Hence, YTHDF2, a critical m6A regulator, may provide a biomarker for diagnosis and immune therapy of osteoarthritic synovitis. [ABSTRACT FROM AUTHOR]

Knee osteoarthritis (OA) is a chronic degenerative joint disease affecting millions worldwide, particularly those over 60. It is a significant cause of disability and can impact an individual’s quality of life. The condition occurs when the cartilage in the knee joint wears away over time, leading to bone-onbone contact, which can result in pain, stiffness, swelling, and decreased range of motion. Deep neural networks, especially convolutional neural networks (CNN), are powerful tools in medical applications such as diagnosis and detection. This research proposes a CNN model to classify knee osteoarthritis into five categories using x-ray images. These classes are labeled: Minimal, Healthy, Moderate, Doubtful, and Severe. Furthermore, the proposed CNN model has been compared with two pre-trained transfer learning models: Xception and InceptionResNet V2. These models were evaluated based on precision, recall, F1 score, and accuracy. The results showed that although all three models performed very well, the proposed model outperformed both transfer learning models with 98% accuracy. It also achieved the highest values for other parameters such as precision, recall, and F1 score. The proposed model has several potential applications in clinical practice, such as assisting doctors in accurately classifying knee osteoarthritis severity levels by analyzing single X-ray images. [ABSTRACT FROM AUTHOR]

Degenerative osteoarthritis of the knee (KOA) affects the knee compartments and worsens over 10–15 years. Knee osteoarthritis is the major cause of activity restrictions and impairment in older persons. Clinicians' expertise affects visual examination interpretation. Hence, achieving early detection requires fast, accurate, and affordable methods. Deep learning (DL) convolutional neural networks (CNN) are the most accurate knee osteoarthritis diagnosis approach. CNNs require a significant amount of training data. Knee X-rays can be analyzed by models that use deep learning to extract the features and reduce number of training cycles. This study suggests the usage of DL system that is based on a trained network on fiveclass knee X-rays with VGG16, SoftMax (Normal, Doubtful, Mild, Moderate, Severe). Two deep CNNs are used to grade knee OA instantly using the Kellgren-Lawrence (KL) methodology. The experimental analysis makes use of two sets of 1650 different knee X-ray images. Each set consists of 514 normal, 477 doubtful, 232 mild, 221 moderate, and 206 severe cases of osteoarthritis of the knee. The suggested model for knee osteoarthritis (OA) identification and severity prediction using knee X-ray radiographs has a classification accuracy of more than 95%, with training and validation accuracy of 95% and 87%, respectively. [ABSTRACT FROM AUTHOR]

Purpose: The management of infrapatellar fat pad (IPFP) during total knee arthroplasty (TKA) is the subject of ongoing debate. There is conflicting evidence whether IPFP should be preserved or resected during TKA to improve clinical outcomes. This study sought to establish if there is a benefit of one over another in terms of clinical function & patient satisfaction. Material & Methods: Total of 67 patients (96 knees), 11 males & 56 females with a mean age of 66.29 years, who underwent TKA between 2016 and 2020 were included in the study. The planning for IPFP preservation (IPFP-P) on right side (50 knees) & IPFP resection (IPFP-R) on left side (46 knees) was done prospectively. We prospectively evaluated clinical outcomes post-TKA, comparing the right knee to the left knee in terms of IPFP-P vs IPFP-R using the oxford knee scoring (OKS) system. The SF-12 was used to evaluate and compare patient satisfaction between the two groups. Results: The mean OKS in the IPFP-P group was 42.86 ± 2.63 & in the IPFP-R group was 44.22 ± 2.40. The OKS differed significantly between the two groups (p < 0.05). Patient with IPFP-R group had significantly better OKS. The mean Sf-12 (physical & mental component) in the IPFP-P was 51.05 ± 4.15 & 59.29 ± 2.53 & in IPFP-R was 51.23 ± 4.74 & 59.24 ± 2.78, showing no significant differences. Conclusion: Our study shows that IPFP-R gave marginally better patient-reported functional outcome scores (OKS), but there was no difference in patient satisfaction (SF-12 scores) between the two groups. [ABSTRACT FROM AUTHOR]

Physical function decline due to aging or disease can be assessed with quantitative motion analysis, but this currently requires expensive laboratory equipment. We introduce a self-guided quantitative motion analysis of the widely used five-repetition sit-to-stand test using a smartphone. Across 35 US states, 405 participants recorded a video performing the test in their homes. We found that the quantitative movement parameters extracted from the smartphone videos were related to a diagnosis of osteoarthritis, physical and mental health, body mass index, age, and ethnicity and race. Our findings demonstrate that at-home movement analysis goes beyond established clinical metrics to provide objective and inexpensive digital outcome metrics for nationwide studies. [ABSTRACT FROM AUTHOR]

Background: We hypothesized the risk of prosthetic instability would be similar in patients treated for femoral neck fracture (FNF) comparatively to patients treated for osteoarthritis (OA) using a dual-mobility cup—total hip arthroplasty (DMC-THA). Methods: One hundred and nine patients (109 hips), with a mean age of 69.6 ± 3 years (65–75 years), underwent DMC-THA using a posterolateral approach for a FNF (study group). Patients were matched for age and sex with 109 OA patients treated by the same procedure (control). Results: Postoperatively, there were four dislocations of the large articulation in the study group, and none in the control (p = 0.12). Periprosthetic joint infection (PJI) was the most common surgical complication and was more frequent in the study group. Postoperatively, functional results were at least good in > 90% patients in both groups. At the last follow-up, patients in the study group had recovered autonomy and physical activity comparable to those of the preoperative period. Conclusion: We found no increased risk of dislocation after DMC-THA using a posterolateral approach for FNF as compared to the same procedure for OA. In this study, function and independence were restored postoperatively in the vast majority of patients. DMC-THA appears as a viable therapeutic option in autonomous patients with a displaced FNF. [ABSTRACT FROM AUTHOR]

Total knee arthroplasty is an effective way to manage osteoarthritis patients surgically. However, patients may encounter post-surgical complications, such as quadriceps rupture in rare instances, in addition to surgical complications. In our clinical practice, we encountered a 67-year-old Saudi male patient with a rare bilateral quadriceps rupture two weeks post-total knee arthroplasty. The cause of the bilateral rupture was a history of falls in both knees. The patient was reported to our clinic with clinical features like pain in the knee joint, immobility, and bilateral swelling in the knees. The X-ray did not show any periprosthetic fracture, but an ultrasound of the anterior thigh revealed a complete cut of the quadriceps tendon on both sides. The repair of the bilateral quadriceps tendon was done by direct repair using the Kessler technique and was reinforced with fiber tape. Following knee immobilization for six weeks, the patient began intensive physical therapy management to decrease pain, enhance muscle strength, and increase range of motion. After rehabilitation, the patient regained a complete range of motion in the knee and improved functionality, and he could walk independently without crutches. [ABSTRACT FROM AUTHOR]

Background: The incidence of osteoarthritis (OA) is high in patients suffering from 25-hydroxyvitamin D3 (25(OH)D) deficiency. The goal of this study is to examine the association between saliva and serum 25(OH)D and knee OA. Methods: Serum and saliva 25(OH)D levels of 30 patients with knee OA and 30 matched healthy people in a control group were measured by ELISA. Knee pain was assessed by Western Ontario and McMaster Universities Arthritis Index (WOMAC). Data were analyzed through student's t-test, Pearson correlation test and receiver operating characteristic (ROC). Results: The mean serum and saliva 25(OH)D levels were lower in knee OA group than the healthy group. WOMAC negatively correlated with serum (r = -0.37; P = 0.02) and with unstimulated (r = -0.30; P = 0.04) saliva 25(OH)D. The unstimulated saliva 25(OH)D cutoff value was 27.8 pg/m1 regarding the diagnosis of knee OA. Conclusion: Serum 25(OH)D levels were positively associated with saliva 25(OH)D, and 25(OH)D level in saliva, as in serum, was low in knee OA. [ABSTRACT FROM AUTHOR]

Though osteoarthritis (OA) affects millions of people worldwide, many fail to access recommended early, person-centered OA care, particularly women who are disproportionately impacted by OA. A prior review identified few strategies to improve equitable access to early diagnosis and management for multiple disadvantaged groups. We aimed to update that review with literature published in 2010 or later on strategies to improve OA care for disadvantaged groups including women. We identified only 11 eligible studies, of which only 2 (18%) focused on women only. Other disadvantaged groups targeted in the largely US-based studies included patients who are Black, Spanish-speaking, rural, and adults aged 60 years and older. All studies evaluated interventions targeted to patients; 4 (36%) assessed video decision aids, and 7 (63.6%) assessed in-person, video, or telephone self-management education. Interventions were often multifaceted (n = 9, 82%), and most studies (n = 8, 73%) achieved positive outcomes in at least some outcomes measured. No studies evaluated clinician- or system-level strategies. Few studies (n = 5, 45%) described how they tailored strategies to disadvantaged groups or how they addressed person-centered care concepts apart from enabling self-management. Future research is needed to develop, implement, evaluate, and scale-up multilevel strategies to enhance equitable, person-centered OA care for disadvantaged groups including women., (© 2023 The Authors. Arthritis Care & Research published by Wiley Periodicals LLC on behalf of American College of Rheumatology.)

Background: Osteoarthritis (OA) is a chronic joint condition that causes pain, limited mobility, and reduced quality of life, posing a threat to healthy aging. Early detection is crucial for improving prognosis. Recent research has focused on the role of ubiquitination-related genes (URGs) in early OA prediction. This study aims to integrate URG expression data with machine learning (ML) to identify biomarkers that improve diagnosis and prognosis in the early stages of OA., Methods: OA single-cell RNA sequencing datasets were collected from the GEO database. Single-cell analysis was performed to investigate the composition and relationships of chondrocytes in OA. The potential intercellular communication mechanisms were explored using the CellChat R package. URGs were retrieved from GeneCards, and ubiquitination scores were calculated using the AUCell package. Gene module analysis based on co-expression network analysis was conducted to identify core genes. Additionally, ML analysis was performed to identify core URGs and construct a diagnostic model. We employed XGBoost, a gradient-boosting ML algorithm, to identify core URGs and construct a diagnostic model. The model's performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. In addition, we explored the relationship between core URGs and immune processes. The ChEA3 database was utilized to predict the transcription factors regulated by core ubiquitination-related genes. The expression of select URGs was validated using qRT-PCR and immunohistochemistry (IHC)., Results: We identified WDR74 and TNFRSF12A as pivotal ubiquitination-related genes associated with OA, exhibiting considerable differential expression. The diagnostic model constructed with URGs exhibited remarkable accuracy, with area under the curve (AUC) values consistently exceeding 0.9. The expression levels of WDR74 and TNFRSF12A were significantly higher in the IL-1β-induced group in an in vitro qPCR experiment. The IHC validation on human knee joint specimens confirmed the upregulation of WDR74 and TNFRSF12A in OA tissues, corroborating their potential as diagnostic biomarkers., Conclusions: WDR74 and TNFRSF12A as principal biomarkers highlighted their attractiveness as therapeutic targets. The identification of core biomarkers might facilitate early intervention options, potentially modifying the illness trajectory and enhancing patient outcomes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2025 Chen, Lin, Shi, Miao, Xue, Liu, Wang and Zhang.)

Background: Osteoarthritis (OA), characterized by progressive degeneration of cartilage and reactive proliferation of subchondral bone, stands as a prevalent condition in orthopedic clinics. However, the precise mechanisms underlying OA pathogenesis remain inadequately explored., Methods: In this study, Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) machine learning techniques were employed to identify hub genes. Based on these hub genes, an Artificial Neural Network (ANN) diagnostic model was constructed. The Drug Signatures Database (DSigDB) was utilized to screen small-molecule drugs targeting these hub genes, and molecular docking analyses and molecular dynamics simulations were employed to explore and validate the binding interactions between proteins and small-molecule drugs. Expression changes of the hub genes under inflammatory conditions were validated through in vitro experiments, including RT-qPCR and Western blotting, and the therapeutic effects of the identified small-molecule drug on chondrocytes under inflammatory conditions were further verified in vitro. Lastly, Mendelian randomization analysis was conducted to examine the causal association between progesterone levels and various OA phenotypes., Results: In this study, we identified three hub genes: interleukin 1 receptor-associated kinase 3 (IRAK3), integrin subunit beta-like 1 (ITGBL1), and Ras homolog family member U (RHOU). An Artificial Neural Network (ANN) diagnostic model constructed based on these hub genes demonstrated excellent performance in both training and validation phases. Screening with the Drug Signatures Database (DSigDB) identified progesterone as a small-molecule drug targeting these key proteins. Molecular docking analysis using AutoDock Vina revealed that progesterone exhibited binding energies of ≤ -7 kcal/mol with each of the key proteins, indicating strong binding affinity. Furthermore, molecular dynamics simulations validated the stability and strength of these interactions. RT-qPCR and Western blotting confirmed the downregulation of the hub genes in IL-1β-treated chondrocytes. Western blotting also demonstrated the potential therapeutic effects of progesterone on IL-1β-treated chondrocytes. Finally, Mendelian randomization analysis established a significant association between progesterone levels and multiple OA phenotypes., Conclusion: In our study, IRAK3, ITGBL1, and RHOU were identified as potential novel diagnostic and therapeutic targets for OA. Progesterone was preliminarily validated as a small-molecule drug with potential effects on OA. Further research is crucial to elucidate the pathogenesis of OA and the specific therapeutic mechanisms involved., Competing Interests: Declarations. Ethics approval and consent to participate: The protocols for animal experiments were approved by the Ethics Committee of Zhongshan Hospital [2024–015] and were in accordance with the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals. Consent for publication: Not applicable. Competing interests: Ziyu Weng, Chenzhong Wang, Bo Liu, Yi Yang, Yueqi Zhang and Chi Zhang declare no potential conflicts of interest., (© 2025. The Author(s).)

Background: We aimed to update the 2018 systematic literature review on the efficacy and safety of treatments for hand osteoarthritis (OA), which was based on 126 studies., Methods: We performed a systematic literature search on randomised controlled trials from June 2017 up to 31 December 2023. Risk of bias was assessed using the RoB2 tool. Meta-analyses of previous and new studies regarding the efficacy for pain, function, grip strength and OMERACT/OARSI responders were performed. Certainty of evidence was judged using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) tool., Results: Sixty-five new studies were included. For non-pharmacological interventions, there was low-certainty evidence for a small long-term effect of hand exercises and a moderate long-term effect of thumb orthoses for pain, and moderate-certainty evidence that assistive devices had a moderate long-term effect on function. Concerning pharmacological interventions, there was low-certainty evidence for a moderate short-term effect of oral non-steroidal anti-inflammatory drugs (NSAIDs) on pain, high- and moderate-certainty evidence for a small short-term effect of topical NSAIDs and oral glucocorticoids on function, respectively, and low-certainty evidence that oral glucocorticoids had a small short-term effect on function. Further, there was low-certainty evidence that methotrexate had a small long-term effect on pain. The heterogeneity of studies did not allow for any meta-analyses on surgery., Conclusion: The results largely support current treatment recommendations. However, there is a lack of interventions that efficiently improve grip strength, and the evidence for most current treatments is still limited. To better understand action mechanism of different treatments, future trials should include hand OA subtyping and be powered for subgroup analyses., Competing Interests: Competing interests: This study had no competing financial interests. Interests disclosed in the international Committee of Medical Journal Editors (ICMJE) conflict of interest forms are as follows: IKH has received consulting fees from Novartis and GSK and is a member of the OARSI executive committee. MK has received grants from IMI APPROACH and the Dutch Arthritis Society, royalties/licences from Wolters Kluwer and Springer Verlag, and consulting fees from AbbVie, Pfizer, Kiniksa Flexion, Galapagos, CHDR, Novartis and UCB, and she is a member of the OARSI board, the EULAR council and President of the Dutch Society for Rheumatology., (© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Background: Osteoarthritis (OA) is a common degenerative joint disease that significantly impacts the quality of life, especially among older adults. Testosterone, a critical hormone for musculoskeletal health, has been suggested to influence OA pathogenesis. However, the relationship between low testosterone levels and OA risk remains underexplored in large, representative populations. This study aimed to investigate the association between low testosterone levels and OA risk using data from the National Health and Nutrition Examination Survey (NHANES, 2011-2016)., Methods: This cross-sectional analysis included 4,548 participants from NHANES, a nationally representative U.S., Dataset: Testosterone levels were categorized as low or normal, with low testosterone defined as < 300 ng/dL for men and population-based cutoffs for women. The presence of OA was determined through self-reported physician diagnosis. Multivariable logistic regression models were used to examine the association between testosterone levels and OA risk, adjusting for demographic, socioeconomic, lifestyle, and clinical factors. Restricted cubic spline (RCS) analysis was conducted to evaluate non-linear relationships. Subgroup analyses were performed to assess consistency across key demographic and clinical strata., Results: Among the 4,548 participants, 812 (17.9%) were diagnosed with OA. Participants with OA were older, more likely to be female, and exhibited higher rates of obesity and hyperlipidemia. In fully adjusted models, low testosterone levels were significantly associated with increased OA risk (OR, 1.22; 95% CI, 1.02-1.46; P = 0.028). RCS analysis indicated a non-linear relationship, with a steep increase in OA risk at lower testosterone levels, suggesting a threshold effect. Subgroup analyses demonstrated consistent associations across demographic and clinical groups without significant interactions., Conclusion: Low testosterone levels are independently associated with an increased risk of OA in the U.S., Population: These findings underscore the potential role of hormonal health in OA pathogenesis and highlight the need for longitudinal studies to clarify causal pathways. The observed non-linear relationship suggests that maintaining optimal testosterone levels may be important for joint health, and testosterone replacement therapy could be explored as a preventative strategy for individuals with testosterone deficiency., Competing Interests: Declarations. Ethics approval and informed consent statement: All protocols were approved by the NCHS Ethics Review Board and conducted in accordance with the Declaration of Helsinki, with all NHANES participants providing written informed consent. The Institutional Review Board at our institution classified this analysis as exempt, as the dataset employed was entirely de-identified. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Clinical trial number: Not applicable., (© 2025. The Author(s).)

Developing a new diagnostic prediction model for osteoarthritis (OA) to assess the likelihood of individuals developing OA is crucial for the timely identification of potential populations of OA. This allows for further diagnosis and intervention, which is significant for improving patient prognosis. Based on the NHANES for the periods of 2011-2012, 2013-2014, and 2015-2016, the study involved 11,366 participants, of whom 1,434 reported a diagnosis of OA. LASSO regression, XGBoost algorithm, and RF algorithm were used to identify significant indicators, and a OA prediction nomogram was developed. The nomogram was evaluated by measuring the AUC, calibration curve, and DCA curve of training and validation sets. In this study, we identified 5 predictors from 19 variables, including age, gender, hypertension, BMI and caffeine intake, and developed an OA nomogram. In both the training and validation cohorts, the OA nomogram exhibited good diagnostic predictive performance (with AUCs of 0.804 and 0.814, respectively), good consistency and stability in calibration curve and high net benefit in DCA. The nomogram based on 5 variables demonstrates a high accuracy in predicting the diagnosis of OA, indicating that it is a convenient tool for clinicians to identify potential populations of OA., Competing Interests: Declarations. Ethics approval and consent to participate: The NHANES survey protocol was approved by the NCHS Research Ethics Review Committee (Protocol #2011-17), and all study participants provided informed written consent. All studies were conducted in accordance with the Declaration of Helsinki and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist for evaluation., (© 2025. The Author(s).)

Background: Inflammation is increasingly recognised as a treatment target in hand osteoarthritis, and therefore correct measurement of local inflammation is essential. This study aimed to assess ultrasound scoring of synovitis and the additional value of the Global OMERACT/EULAR Ultrasound Synovitis Score (GLOESS) in hand osteoarthritis., Methods: Data from the randomised, double-blinded Hand Osteoarthritis Prednisolone Efficacy (HOPE) trial were used. The HOPE trial included patients with painful, inflammatory hand OA, treated with prednisolone or placebo (1:1). Ultrasound was performed in 30 hand joints at weeks 0, 6 and 14. Effusion, synovial thickening and Doppler signal were measured, the GLOESS was calculated from the latter two. Joint tenderness on palpation was assessed semiquantitatively (0-3), soft swelling as present/absent. Changes in ultrasound scores, and their association with change in joint tenderness or soft swelling, were investigated using generalised estimating equations. Effect sizes were calculated., Results: Of 92 included patients 79% were women, with mean (SD) age 63.9 (8.8) and body mass index 27.2 (4.6). Synovial thickening was the most prevalent. All ultrasound scores were strongly associated with joint tenderness and soft swelling cross-sectionally. There was no association of change in ultrasound scores with change in tenderness, but there was with change in soft tissue swelling. Synovial thickening and the GLOESS responded to treatment (effect size -0.39 (-0.72 to -0.07), -0.39 (-0.71 to -0.07), respectively)., Discussion: Various ultrasound scores were associated with joint tenderness and soft swelling. The GLOESS and synovial thickening were both responsive to treatment, but GLOESS was not superior to synovial thickening alone., Competing Interests: Competing interests: MK reports the following, all outside the current study. Grant from IMI-APPROACH. Royalties or licences from Wolters Kluwer and Springer Verlag, paid to the institution. Fees for consulting/advisory boards by Pfizer, UCB, CHDR, GSK, Novartis and Peptinov, all paid to the institution. Payment or honoraria for lectures or presentations from Novartis, paid to the institution. Roles on the OARSI board (member), EULAR council (member advocacy committee EULAR) and presidency of the Dutch Society for Rheumatology. For the current study, MK reports funding from the Dutch Arthritis Society, paid to the institution. The other authors report no competing interests., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: In recent years, the increasing incidence of osteoarthritis (OA) has attracted widespread public attention; however, the available effective treatments are limited. As a result, new therapeutic approaches, including stem cell and exosome therapies, have been proposed and are gradually gaining popularity. Because exosomes are immunocompatible, there is thought to be more potential for their use in clinical settings. This study summarizes the efficacy of exosomes in the treatment of OA., Methods: In total, we conducted a comprehensive search of the PubMed, Web of Science, and Embase databases using medical subject headings terms to identify studies published from their inception until November 2023 that investigated the use of stem cell-derived exosomes in treating OA. We focused on specific outcomes including osteophyte score, chondrocyte count, pain level, qPCR and histological assessments such as the OARSI (Osteoarthritis research society international) score to measure cartilage degeneration. For data extraction, we used GetData Graph Digitizer to retrieve values from graphs, and the meta-analysis was conducted using RevMan 5.3 software. We chose mean difference (MD) as the primary effect measure since all included studies reported the same outcomes. Ultimately, 20 articles met the inclusion criteria and were included in the meta-analysis., Results: We evaluated 20 studies comprising a total of 400 subjects. Compared with control groups, the exosome-treated groups showed significantly improved histological outcomes, as measured by the OARSI score (n = 400; MD = -3.54; 95% CI = [-4.30, -2.79]; P < 0.00001; I 2  = 98%). This indicates a marked reduction in cartilage degeneration and OA severity in the exosome-treated groups. Notably, exosome therapy was more effective when administered during the early stages of OA. Additionally, a once-weekly dosing schedule yielded better results compared to more frequent administrations. Of the three exosome isolation methods assessed, kit-based extraction demonstrated a trend toward superior therapeutic efficacy., Conclusions: Exosome treatment improved OA compared to placebo treatment., Competing Interests: Declarations. Ethical approval and consent to participate: Not applicable. Consent for publication: All authors gave their consent to publish this manuscript. Competing interests: The authors declare that they have no conflict of interest., (© 2024. The Author(s).)

Introduction-Aim: Postpartum septic osteoarthritis is a rare but serious condition often misdiagnosed due to overlap with common postpartum symptoms like pelvic pain and joint stiffness. This case series aims to describe the clinical, bacteriological, and radiological characteristics of postpartum septic osteoarthritis, as well as the treatment approaches and patient outcomes. Methods: A retrospective case series was conducted at Farhat Hached University Hospital, from 2006 to 2022, involving patients with confirmed postpartum septic osteoarthritis. Clinical data, laboratory findings, imaging results, treatments, and outcomes were analyzed Results: The mean age of the seven patients was 31.4 years. All patients presented with joint pain, with fever and functional impairment where each was observed in 85.7% of cases. The average diagnostic delay was 17 days. MRI findings revealed bone marrow edema in all patients, Abscesses in 28.5% of cases, periarticular soft tissue edema, and intra-articular effusions in 71.4% of cases. The pubic symphysis was the most commonly affected site (57.1%), followed by the sacroiliac joints (28.6%). Only one pathogen, Streptococcus pyogenes, was isolated. Nearly all patients received dual antibiotic therapy for an average duration of 4 to 6 weeks. Satisfactory clinical progress without sequelae was noted and maintained after an average follow-up of 5 to 6 months. .Conclusion: Our results revealed that postpartum septic osteoarthritis requires prompt diagnosis and early treatment to prevent severe complications. MRI plays a critical role in early detection, while timely antibiotic therapy leads to favorable long-term outcomes.

Background: Osteoarthritis (OA) is a prevalent joint disorder characterized by degeneration and inflammation. Understanding its molecular mechanisms is crucial for diagnosis and treatment., Methods: We employed bioinformatics analyses to study OA using gene expression data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis were conducted. Enrichment analyses were performed to elucidate the biological significance of identified genes. Additionally, signature genes were identified using LASSO regression analysis, and a diagnostic nomogram was developed. qRT-PCR was conducted to confirm the expression levels of signature genes., Results: We identified 200 differentially expressed genes (DEGs) and a lightgreen module strongly correlated with OA. Within this module, 97 core genes were identified. Fifteen core lipopolysaccharide-related genes (LRGs) were found, enriched in immune and inflammatory pathways. Three hub genes (CCL3, ZFP36, and CCN1) emerged as potential biomarkers for OA diagnosis, with a nomogram showing high predictive accuracy, and validated by using clinical samples. Gene set enrichment analysis (GSEA) revealed distinct signaling pathways associated with the signature genes. Immunological analysis indicated altered immune profiles in OA, with the signature genes influencing immune cell infiltration and immune response pathways., Conclusion: Our study provides insights into OA pathogenesis and identifies potential diagnostic biomarkers. The developed nomogram shows promise for accurate OA diagnosis. Furthermore, the signature genes play crucial roles in modulating the immune microenvironment in OA, suggesting their therapeutic potential., Clinical Trial Number: Not applicable., Competing Interests: Declarations. Ethics approval and consent to participate: This study was approved by the Ethical Committee of Heilongjiang University of Traditional Chinese Medicine. All procedures were conducted in accordance with the Declaration of Helsinki. Prior to participation, patients provided their written informed consent. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Osteoarthritis (OA) is a degenerative disease closely associated with Anoikis. The objective of this work was to discover novel transcriptome-based anoikis-related biomarkers and pathways for OA progression.The microarray datasets GSE114007 and GSE89408 were downloaded using the Gene Expression Omnibus (GEO) database. A collection of genes linked to anoikis has been collected from the GeneCards database. The intersection genes of the differential anoikis-related genes (DEARGs) were identified using a Venn diagram. Infiltration analyses were used to identify and study the differentially expressed genes (DEGs). Anoikis clustering was used to identify the DEGs. By using gene clustering, two OA subgroups were formed using the DEGs. GSE152805 was used to analyse OA cartilage on a single cell level. 10 DEARGs were identified by lasso analysis, and two Anoikis subtypes were constructed. MEgreen module was found in disease WGCNA analysis, and MEturquoise module was most significant in gene clusters WGCNA. The XGB, SVM, RF, and GLM models identified five hub genes (CDH2, SHCBP1, SCG2, C10orf10, P FKFB3), and the diagnostic model built using these five genes performed well in the training and validation cohorts. analysing single-cell RNA sequencing data from GSE152805, including 25,852 cells of 6 OA cartilage.

This first of a 2-part series of articles recounts the key points presented in a collaborative symposium sponsored jointly by the Arthritis Foundation and the American Orthopaedic Foot & Ankle Society with the intent to survey the state of scientific knowledge related to incidence, diagnosis, pathologic mechanisms, and injection treatment options for osteoarthritis (OA) of the foot and ankle. A meeting was held virtually on December 3, 2021. A group of experts were invited to present brief synopses of the current state of knowledge and research in this area. Part 1 overviews areas of epidemiology and pathophysiology, current approaches in imaging, diagnostic and therapeutic injections, and genetics. Opportunities for future research are discussed. The OA scientific community, including funding agencies, academia, industry, and regulatory agencies, must recognize the needs of patients that suffer from arthritis of foot and ankle. The foot and ankle contain a myriad of interrelated joints and tissues that together provide a critical functionality. When this functionality is compromised by OA, significant disability results, yet the foot and ankle are generally understudied by the research community. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis, a common degenerative disease of articular cartilage, is characterized by degeneration of articular cartilage, changes in subchondral bone structure, and formation of osteophytes, with main clinical manifestations including increasingly serious swelling, pain, stiffness, deformity, and mobility deficits of the knee joints. With the advent of the big data era, the processing of mass data has evolved into a hot topic and gained a solid foundation from the steadily developed and improved machine learning algorithms. Aiming to provide a reference for the diagnosis and treatment of osteoarthritis, this paper using machine learning identifies the key feature genes of osteoarthritis and explores its relationship with immune infiltration, thereby revealing its pathogenesis at the molecular level. Methods: From the GEO database, GSE55235 and GSE55457 data were derived as training sets and GSE98918 data as a validation set. Differential gene expressions of the training sets were analyzed, and the LASSO regression model and support vector machine model were established by applying machine learning algorithms. Moreover, their intersection genes were regarded as feature genes, the receiver operator characteristic (ROC) curve was drawn, and the results were verified using the validation set. In addition, the expression spectrum of osteoarthritis was analyzed by immunocyte infiltration and the co-expression correlation between feature genes and immunocytes was construed. Conclusion: EPYC and KLF9 can be viewed as feature genes for osteoarthritis. The silencing of EPYC and the overexpression of KLF9 are associated with the occurrence of osteoarthritis and immunocyte infiltration. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is thought to be the most prevalent chronic joint disease. The incidence of OA is rising because of the ageing population and the epidemic of obesity. This research was designed for the identification of novel diagnostic biomarkers for OA and analyzing the possible association between critical genes and infiltrated immune cells. 10 OA samples from patients with spinal OA and 10 normal samples were collected. GSE55235 and GSE55457 datasets including human OA and normal samples were downloaded from the GEO datasets. Differentially expressed genes (DEGs) were identified between 20 OA and 20 controls. SVM-RFE analysis and LASSO regression model were carried out to screen possible markers. The compositional patterns of the 22 types of immune cell fraction in OA were determined by the use of CIBERSORT. The expression level of the biomarkers in OA was examined by the use of RT-PCR. In this study, an overall 44 DEGs were identified: 18 genes were remarkably upregulated and 26 genes were distinctly downregulated. KEGG pathway analyses revealed that pathways were significantly enriched including IL-17 signal path, rheumatoid arthritis, TNF signal path, and lipid and atherosclerosis. Based on the results of machine learning, we identified APOLD1 and EPYC as critical diagnostic genes in OA, which were further confirmed using ROC assays. Immune cell infiltration analysis revealed that APOLD1 was correlated with mastocytes stimulated, NK cells resting, T cells CD4 memory resting, DCs stimulated, T cells gamma delta, macrophages M0, NK cells stimulated, and mastocytes resting. Moreover, we found that EPYC was correlated with mastocytes stimulated, NK cells resting, T cells CD4 memory resting, DCs stimulated, T cells gamma delta, macrophages M0, NK cells stimulated, and mastocytes resting. Overall, our findings might provide some novel clue for the exploration of novel markers for OA diagnosis. The critical genes and their associations with immune infiltration may offer new insight into understanding OA developments. [ABSTRACT FROM AUTHOR]

Background: In Sweden, core treatment for osteoarthritis is offered through a Supported Osteoarthritis Self-Management Programme (SOASP), combining education and exercise to provide patients with coping strategies in self-managing the disease. The aim was to study enablement and empowerment among patients with osteoarthritis in the hip and/or knee participating in a SOASP. An additional aim was to study the relation between the Swedish version of the Patient Enablement Instrument (PEI) and the Swedish Rheumatic Disease Empowerment Scale (SWE-RES-23).Methods: Patients with osteoarthritis participating in a SOASP in primary health care were recruited consecutively from 2016 to 2018. The PEI (score range 0-12) was used to measure enablement and the SWE-RES-23 (score range 1-5) to measure empowerment. The instruments were answered before (SWE-RES-23) and after the SOASP (PEI, SWE-RES-23). A patient partner was incorporated in the study. Descriptive statistics, the Wilcoxon's signed rank test, effect size (r), and the Spearman's rho (rs) were used in the analysis.Results: In total, 143 patients were included in the study, 111 (78%) were women (mean age 66, SD 9.3 years). At baseline the reported median value for the SWE-RES-23 (n = 142) was 3.6 (IQR 3.3-4.0). After the educational part of the SOASP, the reported median value was 6 (IQR 3-6.5) for the PEI (n = 109) and 3.8 (IQR 3.6-4.1) for the SWE-RES-23 (n = 108). At three months follow-up (n = 116), the reported median value was 6 (IQR 4-7) for the PEI and 3.9 (IQR 3.6-4.2) for the SWE-RES-23. The SWE-RES-23 score increased between baseline and three months (p ≤ 0.000). The analysis showed a positive correlation between PEI and SWE-RES-23 after the educational part of the SOASP (rs = 0.493, p < 0.00, n = 108) and at follow-up at three months (rs = 0.507, p < 0.00, n = 116).Conclusions: Patients reported moderate to high enablement and empowerment and an increase in empowerment after participating in a SOASP, which might indicate that the SOASP is useful to enable and empower patients at least in the short term. Since our results showed that the PEI and the SWE-RES-23 are only partly related both instruments can be of use in evaluating interventions such as the SOASP.Trial Registration: ClinicalTrials.gov. NCT02974036 . First registration 28/11/2016, retrospectively registered. [ABSTRACT FROM AUTHOR]

In clinical practice, osteoarthritis (OA) is frequently misdiagnosed as rheumatoid arthritis (RA) and osteonecrosis (ON), leading to wrong treatment and disease progression. Circular RNA- (circRNA-) 016901 affects the recovery of irradiation-induced injury in the bone, while its role in OA is unclear. This study is aimed at exploring the role of circRNA-016901 in improving the diagnostic accuracy of OA. The present study included patients with OA (n = 80), patients with RA (n = 80), patients with ON (n = 80), and healthy controls (HCs, n = 80) to collect plasma samples before and after treatment. RT-qPCR was performed to detect RNA accumulation of circRNA-016901 in plasma samples from all participants. The role of plasma expression of circRNA-016901 in predicting OA was studied with ROC curve analysis. Association between plasma expression of circRNA-016901 and patients' clinical features was analyzed with the chi-squared test. Compared to HCs, increased accumulation of circRNA-016901 was only observed in the OA group, but not in the RA and ON groups before treatment. OA patients were effectively separated from the RA, ON, and HC groups using plasma expression of circRNA-016901 before treatment as a biomarker. Plasma expression of circRNA-016901 was closely associated with OA patients' disease severity. After treatment, decreased plasma expression levels of circRNA-016901 were only observed in OA patients, while no alteration in plasma circRNA-016901 accumulation was observed in the RA and ON groups. In conclusion, circRNA-016901 is accumulated to high levels in OA and may be applied to improve the diagnostic accuracy of OA. [ABSTRACT FROM AUTHOR]

Osteoarthritis is the most common type of degenerative joint disease and affects millions of people. In this paper, we propose a non-obtrusive and straightforward method to assess the progression of osteoarthritis. In standard medicine praxis, osteoarthritis is observed with X-rays. In this study, we use widely available wearable sensors with gyroscopes to make the observation. Two novel methods are proposed for gyroscope data processing. A small-scale study has shown that these methods can be used to monitor osteoarthritis's progression, and to differentiate between healthy subjects and subjects with femoroacetabular impingement syndrome. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis and condylar resorption of temporomandibular joint (TMJ) has rarely been reported in children as consequence of otologic disease. We describe the management of a case in a 9-year-old female as long-term complication of an otomastoiditis and review the literature currently available on this topic. Case presentation: A nine-years-old female patient referred to Emergency Room of Bambino Gesù Children's Research Hospital, IRCCS (Rome,Italy) for an acute pain in the left preauricular area and reduced mandibular movements. In the medical history an otomastoiditis and periorbital cellulitis was reported at the age of six with complete remission of symptoms after antibiotic treatment. No recent history of facial trauma and no previous orthodontic treatment were reported. She was referred to a pediatric dentist that conducted a clinical examination according to the Diagnostic Criteria of Temporomandibular Disorders (DC/TMD) and was diagnosed with bilateral myalgia of the masticatory muscles and arthralgia at the level of the left TMJ. Then, a complete diagnostic path was performed that included multidisciplinary examinations by a rheumatologist, infectious disease specialist, ear nose and throat (ENT) doctor, a maxillofacial surgeon and a medical imaging specialist. Differential diagnosis included juvenile idiopathic arthritis, idiopathic condylar resorption, trauma, degenerative joint disease, neurological disease. Finally, unilateral post-infective osteoarthritis of the left TMJ with resorption of mandibular condyle was diagnosed. The patient went through a pharmacological therapy with paracetamol associated to counselling, jaw exercises and occlusal bite plate. After 1 month, the patient showed significant reduction of orofacial pain and functional recovery that was confirmed also one-year post-treatment. The novelty of this clinical case lies in the accurate description of the multidisciplinary approach with clinical examination, the differential diagnosis process and the management of TMD with conservative treatment in a growing patient. Conclusions: Septic arthritis of temporomandibular joint and condylar resorption were described as complications of acute otitis media and/or otomastoiditis in children. We evidenced the importance of long-term follow-up in children with acute media otitis or otomastoiditis due to the onset of TMJ diseases. Furthermore, in the multidisciplinary management of orofacial pain the role of pediatric dentist is crucial for the diagnostic and therapeutic pathway to avoid serious impairment of mandibular function. [ABSTRACT FROM AUTHOR]

Background: To perform reliability and validity study of the Functional Index for Hand Osteoarthritis (FIHOA) in the Turkish language.Methods: FIHOA was translated into Turkish following the principles of cross-cultural adaptation. Our translation was firstly tested in 40 patients with hand osteoarthritis. Adapted FIHOA questionnaire was then administrated to 100 hand OA patients successively with Modified Health Assessment Questionnaire (mHAQ), Numerical Rating Scale (NRS), and Short Form-36 (SF-36). Patients filled out the FIHOA questionnaire one more time after five days for test-retest assessment. Patients were divided into two groups as symptomatic or asymptomatic, with a NRS score of 5 or above defining symptomatic OA. Internal consistency was assessed by Cronbach's alpha and intraclass correlation coefficient (ICC) of test-retest reliability. Spearman correlation analysis was used to determine the correlation and validity between data. External construct validity was assessed using the correlation between FIHOA, mHAQ, hand pain NRS, and negative correlation with SF-36 subgroups.Results: According to the total score, Cronbach-alpha was found as 0.90, while ICC was determined as 0.98 for test-retest reliability. When the correlations between the FIHOA questionnaire, mHAQ, and NRS questionnaires were examined, significant correlations were determined, and negative correlations between FIHOA and SF-36 subgroups were observed.Conclusion: Turkish FIHOA is a reliable and valid method for assessing functionality in Turkish patients with hand osteoarthritis. [ABSTRACT FROM AUTHOR]