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. 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.Aims
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The metabolic variations between the cartilage of osteoarthritis (OA) and Kashin-Beck disease (KBD) remain largely unknown. Our study aimed to address this by conducting a comparative analysis of the metabolic profiles present in the cartilage of KBD and OA. Cartilage samples from patients with KBD (n = 10) and patients with OA (n = 10) were collected during total knee arthroplasty surgery. An untargeted metabolomics approach using liquid chromatography coupled with mass spectrometry (LC-MS) was conducted to investigate the metabolomics profiles of KBD and OA. LC-MS raw data files were converted into mzXML format and then processed by the XCMS, CAMERA, and metaX toolbox implemented with R software. The online Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to annotate the metabolites by matching the exact molecular mass data of samples with those from the database.Aims
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Aims. This study aimed to evaluate the BioFire Joint Infection (JI) Panel in cases of hip and knee periprosthetic joint infection (PJI) where conventional microbiology is unclear, and to assess its role as a complementary intraoperative diagnostic tool. Methods. Five groups representing common microbiological scenarios in hip and knee revision arthroplasty were selected from our arthroplasty registry, prospectively maintained PJI databases, and biobank: 1) unexpected-negative cultures (UNCs), 2) unexpected-positive cultures (UPCs), 3) single-positive intraoperative cultures (SPCs), and 4) clearly septic and 5) aseptic cases. In total, 268 archived synovial fluid samples from 195 patients who underwent acute/chronic revision total hip or knee arthroplasty were included. Cases were classified according to the International Consensus Meeting 2018 criteria. JI panel evaluation of synovial fluid was performed, and the results were compared with cultures. Results. The JI panel detected microorganisms in 7/48 (14.5%) and 15/67 (22.4%) cases related to UNCs and SPCs, respectively, but not in cases of UPCs. The correlation between JI panel detection and infection
Achilles tendon re-rupture (ATRR) poses a significant risk of postoperative complication, even after a successful initial surgical repair. This study aimed to identify risk factors associated with Achilles tendon re-rupture following operative fixation. This retrospective cohort study analyzed a total of 43,287 patients from national health claims data spanning 2008 to 2018, focusing on patients who underwent surgical treatment for primary Achilles tendon rupture. Short-term ATRR was defined as cases that required revision surgery occurring between six weeks and one year after the initial surgical repair, while omitting cases with simultaneous infection or skin necrosis. Variables such as age, sex, the presence of Achilles tendinopathy, and comorbidities were systematically collected for the analysis. We employed multivariate stepwise logistic regression to identify potential risk factors associated with short-term ATRR.Aims
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Aims. In this study, we aimed to visualize the spatial distribution characteristics of femoral head necrosis using a novel measurement method. Methods. We retrospectively collected CT imaging data of 108 hips with non-traumatic osteonecrosis of the femoral head from 76 consecutive patients (mean age 34.3 years (SD 8.1), 56.58% male (n = 43)) in two clinical centres. The femoral head was divided into 288 standard units (based on the orientation of units within the femoral head, designated as N[Superior], S[Inferior], E[Anterior], and W[Posterior]) using a new measurement system called the longitude and latitude division system (LLDS). A computer-aided design (CAD) measurement tool was also developed to visualize the measurement of the spatial location of necrotic lesions in CT images. Two orthopaedic surgeons independently performed measurements, and the results were used to draw 2D and 3D heat maps of spatial distribution of necrotic lesions in the femoral head, and for statistical analysis. Results. The results showed that the LLDS has high inter-rater reliability. As illustrated by the heat map, the distribution of Japanese Investigation Committee (JIC)
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). Electronic literature searches were conducted using MEDLINE, Embase, PubMed, and Cochrane. All studies reporting prognostic estimates for factors associated with the revision of a primary TEA were included. The risk of bias was assessed using the Quality In Prognosis Studies (QUIPS) tool, and the quality of evidence was assessed using the modified Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) framework. Due to low quality of the evidence and the heterogeneous nature of the studies, a narrative synthesis was used.Aims
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This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model. The study included 538 joints that underwent primary THA. The patients were divided into groups using unsupervised time series clustering for five-year BMD loss of Gruen zone 7 postoperatively, and a machine-learning model to predict the BMD loss was developed. Additionally, the predictor for BMD loss was extracted using SHapley Additive exPlanations (SHAP). The patient-specific efficacy of bisphosphonate, which is the most important categorical predictor for BMD loss, was examined by calculating the change in predictive probability when hypothetically switching between the inclusion and exclusion of bisphosphonate.Aims
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Fracture-related infection (FRI) is commonly classified based on the time of onset of symptoms. Early infections (< two weeks) are treated with debridement, antibiotics, and implant retention (DAIR). For late infections (> ten weeks), guidelines recommend implant removal due to tolerant biofilms. For delayed infections (two to ten weeks), recommendations are unclear. In this study we compared infection clearance and bone healing in early and delayed FRI treated with DAIR in a rabbit model.
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The present study investigated receptor activator of nuclear factor kappa-Β ligand (RANKL), osteoprotegerin (OPG), and Runt-related transcription factor 2 (RUNX2) gene expressions in giant cell tumour of bone (GCTB) patients in relationship with tumour recurrence. We also aimed to investigate the influence of CpG methylation on the transcriptional levels of RANKL and OPG. A total of 32 GCTB tissue samples were analyzed, and the expression of RANKL, OPG, and RUNX2 was evaluated by quantitative polymerase chain reaction (qPCR). The methylation status of RANKL and OPG was also evaluated by quantitative methylation-specific polymerase chain reaction (qMSP).Aims
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This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization.Aims
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Aims. This study aimed to evaluate the clinical application of the PJI-TNM
Knee osteoarthritis (OA) involves a variety of tissues in the joint. Gene expression profiles in different tissues are of great importance in order to understand OA. First, we obtained gene expression profiles of cartilage, synovium, subchondral bone, and meniscus from the Gene Expression Omnibus (GEO). Several datasets were standardized by merging and removing batch effects. Then, we used unsupervised clustering to divide OA into three subtypes. The gene ontology and pathway enrichment of three subtypes were analyzed. CIBERSORT was used to evaluate the infiltration of immune cells in different subtypes. Finally, OA-related genes were obtained from the Molecular Signatures Database for validation, and diagnostic markers were screened according to clinical characteristics. Quantitative reverse transcription polymerase chain reaction (qRT‐PCR) was used to verify the effectiveness of markers.Aims
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Orthopaedic surgery uses many varied instruments with high-speed, high-impact, thermal energy and sometimes heavy instruments, all of which potentially result in aerosolization of contaminated blood, tissue, and bone, raising concerns for clinicians’ health. This study quantifies the aerosol exposure by measuring the number and size distribution of the particles reaching the lead surgeon during key orthopaedic operations. The aerosol yield from 17 orthopaedic open surgeries (on the knee, hip, and shoulder) was recorded at the position of the lead surgeon using an Aerodynamic Particle Sizer (APS; 0.5 to 20 μm diameter particles) sampling at 1 s time resolution. Through timestamping, detected aerosol was attributed to specific procedures.Aims
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Cartilage injuries rarely heal spontaneously and often require surgical intervention, leading to the formation of biomechanically inferior fibrous tissue. This study aimed to evaluate the possible effect of amelogenin on the healing process of a large osteochondral injury (OCI) in a rat model. A reproducible large OCI was created in the right leg femoral trochlea of 93 rats. The OCIs were treated with 0.1, 0.5, 1.0, 2.5, or 5.0 μg/μl recombinant human amelogenin protein (rHAM+) dissolved in propylene glycol alginate (PGA) carrier, or with PGA carrier alone. The degree of healing was evaluated 12 weeks after treatment by morphometric analysis and histological evaluation. Cell recruitment to the site of injury as well as the origin of the migrating cells were assessed four days after treatment with 0.5 μg/μl rHAM+ using immunohistochemistry and immunofluorescence.Aims
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To investigate the optimal thresholds and diagnostic efficacy of commonly used serological and synovial fluid detection indexes for diagnosing periprosthetic joint infection (PJI) in patients who have rheumatoid arthritis (RA). The data from 348 patients who had RA or osteoarthritis (OA) and had previously undergone a total knee (TKA) and/or a total hip arthroplasty (THA) (including RA-PJI: 60 cases, RA-non-PJI: 80 cases; OA-PJI: 104 cases, OA-non-PJI: 104 cases) were retrospectively analyzed. A receiver operating characteristic curve was used to determine the optimal thresholds of the CRP, ESR, synovial fluid white blood cell count (WBC), and polymorphonuclear neutrophil percentage (PMN%) for diagnosing RA-PJI and OA-PJI. The diagnostic efficacy was evaluated by comparing the area under the curve (AUC) of each index and applying the results of the combined index diagnostic test.Aims
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A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance. MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS).Aims
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This study was designed to characterize the recurrence incidence and risk factors of antibiotic-loaded cement spacer (ALCS) for definitive bone defect treatment in limb osteomyelitis. We included adult patients with limb osteomyelitis who received debridement and ALCS insertion into the bone defect as definitive management between 2013 and 2020 in our clinical centre. The follow-up time was at least two years. Data on patients’ demographics, clinical characteristics, and infection recurrence were retrospectively collected and analyzed.Aims
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The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction. Cite this article:
Dead-space management, following dead bone resection, is an important element of successful chronic osteomyelitis treatment. This study compared two different biodegradable antibiotic carriers used for dead-space management, and reviewed clinical and radiological outcomes. All cases underwent single-stage surgery and had a minimum one-year follow-up. A total of 179 patients received preformed calcium sulphate pellets containing 4% tobramycin (Group OT), and 180 patients had an injectable calcium sulphate/nanocrystalline hydroxyapatite ceramic containing gentamicin (Group CG). Outcome measures were infection recurrence, wound leakage, and subsequent fracture involving the treated segment. Bone-void filling was assessed radiologically at a minimum of six months post-surgery.Aims
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