Aims. This study explored the shared genetic traits and molecular interactions between postmenopausal osteoporosis (POMP) and sarcopenia, both of which substantially degrade elderly health and quality of life. We hypothesized that these motor system diseases overlap in pathophysiology and regulatory mechanisms. Methods. We analyzed microarray data from the Gene Expression Omnibus (GEO) database using weighted gene co-expression network analysis (WGCNA), machine learning, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to identify common genetic factors between POMP and sarcopenia. Further validation was done via differential gene expression in a new cohort. Single-cell analysis identified high expression cell subsets, with mononuclear macrophages in osteoporosis and muscle stem cells in sarcopenia, among others. A competitive endogenous RNA network suggested regulatory elements for these genes. Results. Signal transducer and activator of transcription 3 (STAT3) was notably expressed in both conditions. Single-cell analysis pinpointed specific cells with high STAT3 expression, and microRNA (miRNA)-125a-5p emerged as a potential regulator. Experiments confirmed the crucial role of STAT3 in osteoclast differentiation and muscle proliferation. Conclusion. STAT3 has emerged as a key gene in both POMP and sarcopenia. This insight positions STAT3 as a potential common therapeutic target, possibly improving management strategies for these age-related diseases. Cite this article: Bone Joint Res 2024;13(8):411–426

Despite the vast quantities of published artificial intelligence (AI) algorithms that target trauma and orthopaedic applications, very few progress to inform clinical practice. One key reason for this is the lack of a clear pathway from development to deployment. In order to assist with this process, we have developed the Clinical Practice Integration of Artificial Intelligence (CPI-AI) framework – a five-stage approach to the clinical practice adoption of AI in the setting of trauma and orthopaedics, based on the IDEAL principles (https://www.ideal-collaboration.net/). Adherence to the framework would provide a robust evidence-based mechanism for developing trust in AI applications, where the underlying algorithms are unlikely to be fully understood by clinical teams.

Cite this article: Bone Joint Res 2024;13(9):507–512.

The aim of this study was to review the role of clinical trial networks in orthopaedic surgery. A total of two electronic databases (MEDLINE and EMBASE) were searched from inception to September 2013 with no language restrictions. Articles related to randomised controlled trials (RCTs), research networks and orthopaedic research, were identified and reviewed. The usefulness of trainee-led research collaborations is reported and our knowledge of current clinical trial infrastructure further supplements the review. Searching yielded 818 titles and abstracts, of which 12 were suitable for this review. Results are summarised and presented narratively under the following headings: 1) identifying clinically relevant research questions; 2) education and training; 3) conduct of multicentre RCTs and 4) dissemination and adoption of trial results. This review confirms growing international awareness of the important role research networks play in supporting trials in orthopaedic surgery. Multidisciplinary collaboration and adequate investment in trial infrastructure are crucial for successful delivery of RCTs. Cite this article: Bone Joint Res 2014;3:169–74