Transforming growth factor-beta2 (TGF-β2) is recognized as a versatile cytokine that plays a vital role in regulation of joint development, homeostasis, and diseases, but its role as a biological mechanism is understood far less than that of its counterpart, TGF-β1. Cartilage as a load-resisting structure in vertebrates however displays a fragile performance when any tissue disturbance occurs, due to its lack of blood vessels, nerves, and lymphatics. Recent reports have indicated that TGF-β2 is involved in the physiological processes of chondrocytes such as proliferation, differentiation, migration, and apoptosis, and the pathological progress of cartilage such as osteoarthritis (OA) and rheumatoid arthritis (RA). TGF-β2 also shows its potent capacity in the repair of cartilage defects by recruiting autologous mesenchymal stem cells and promoting secretion of other growth factor clusters. In addition, some pioneering studies have already considered it as a potential target in the treatment of OA and RA. This article aims to summarize the current progress of TGF-β2 in cartilage development and diseases, which might provide new cues for remodelling of cartilage defect and intervention of cartilage diseases

Osteoarthritis (OA) is mainly caused by ageing, strain, trauma, and congenital joint abnormalities, resulting in articular cartilage degeneration. During the pathogenesis of OA, the changes in subchondral bone (SB) are not only secondary manifestations of OA, but also an active part of the disease, and are closely associated with the severity of OA. In different stages of OA, there were microstructural changes in SB. Osteocytes, osteoblasts, and osteoclasts in SB are important in the pathogenesis of OA. The signal transduction mechanism in SB is necessary to maintain the balance of a stable phenotype, extracellular matrix (ECM) synthesis, and bone remodelling between articular cartilage and SB. An imbalance in signal transduction can lead to reduced cartilage quality and SB thickening, which leads to the progression of OA. By understanding changes in SB in OA, researchers are exploring drugs that can regulate these changes, which will help to provide new ideas for the treatment of OA. Cite this article: Bone Joint Res 2023;12(9):536–545

Aims. Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. Methods. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Results. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Conclusion. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice. Cite this article: Bone Joint J 2024;106-B(11):1216–1222

Osteoarthritis (OA), one of the most common motor system disorders, is a degenerative disease involving progressive joint destruction caused by a variety of factors. At present, OA has become the fourth most common cause of disability in the world. However, the pathogenesis of OA is complex and has not yet been clarified. Long non-coding RNA (lncRNA) refers to a group of RNAs more than 200 nucleotides in length with limited protein-coding potential, which have a wide range of biological functions including regulating transcriptional patterns and protein activity, as well as binding to form endogenous small interference RNAs (siRNAs) and natural microRNA (miRNA) molecular sponges. In recent years, a large number of lncRNAs have been found to be differentially expressed in a variety of pathological processes of OA, including extracellular matrix (ECM) degradation, synovial inflammation, chondrocyte apoptosis, and angiogenesis. Obviously, lncRNAs play important roles in regulating gene expression, maintaining the phenotype of cartilage and synovial cells, and the stability of the intra-articular environment. This article reviews the results of the latest research into the role of lncRNAs in a variety of pathological processes of OA, in order to provide a new direction for the study of OA pathogenesis and a new target for prevention and treatment. Cite this article: Bone Joint Res 2021;10(2):122–133

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To systematically review the predominant complication rates and changes to patient-reported outcome measures (PROMs) following osteochondral allograft (OCA) transplantation for shoulder instability.

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The aims of this study were to identify and evaluate the current literature examining the prognostic factors which are associated with failure of total elbow arthroplasty (TEA).

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The aims of this study were to validate the outcome of total elbow arthroplasty (TEA) in patients with rheumatoid arthritis (RA), and to identify factors that affect the outcome.

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Metabolic profiling is a top-down method of analysis looking at metabolites, which are the intermediate or end products of various cellular pathways. Our primary objective was to perform a systematic review of the published literature to identify metabolites in human synovial fluid (HSF), which have been categorized by metabolic profiling techniques. A secondary objective was to identify any metabolites that may represent potential biomarkers of orthopaedic disease processes.

This systematic review examines the current literature regarding surgical techniques for restoring articular cartilage in the hip, from the older microfracture techniques involving perforation to the subchondral bone, to adaptations of this technique using nanofractures and scaffolds. This review discusses the autologous and allograft transfer systems and the autologous matrix-induced chondrogenesis (AMIC) technique, as well as a summary of the previously discussed techniques, which could become common practice for restoring articular cartilage, thus reducing the need for total hip arthroplasty. Using the British Medical Journal Grading of Recommendations, Assessment, Development and Evaluation (BMJ GRADE) system and Grade system. Comparison of the studies discussed shows that microfracture has the greatest quantity and quality of research, whereas the newer AMIC technique requires more research, but shows promise.

Cite this article: W. E. Hotham, A. Malviya. A systematic review of surgical methods to restore articular cartilage in the hip. Bone Joint Res 2018;7:336–342. DOI: 10.1302/2046-3758.75.BJR-2017-0331.