Objectives. CT-based three-column classification (TCC) has been widely used in the treatment of tibial plateau fractures (TPFs). In its updated version (updated three-column
Aims. Plating displaced proximal humeral fractures is associated with a high rate of screw perforation. Dynamization of the proximal screws might prevent these complications. The aim of this study was to develop and evaluate a new gliding screw
Aims. The incidence of limb fractures in patients living with HIV (PLWH) is increasing. However, due to their immunodeficiency status, the operation and rehabilitation of these patients present unique challenges. Currently, it is urgent to establish a standardized perioperative rehabilitation plan based on the
Aims. 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. Methods. 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. Results. C1 subtype is mainly concentrated in the development of skeletal muscle organs, C2 lies in metabolic process and immune response, and C3 in pyroptosis and cell death process. Therefore, we divided OA into three subtypes: bone remodelling subtype (C1), immune metabolism subtype (C2), and cartilage degradation subtype (C3). The number of macrophage M0 and activated mast cells of C2 subtype was significantly higher than those of the other two subtypes. COL2A1 has significant differences in different subtypes. The expression of COL2A1 is related to age, and trafficking protein particle complex subunit 2 is related to the sex of OA patients. Conclusion. This study linked different tissues with gene expression profiles, revealing different molecular subtypes of patients with knee OA. The relationship between clinical characteristics and OA-related genes was also studied, which provides a new
Aims. The lack of disease-modifying treatments for osteoarthritis (OA) is linked to a shortage of suitable biomarkers. This study combines multi-molecule synovial fluid analysis with machine learning to produce an accurate diagnostic biomarker model for end-stage knee OA (esOA). Methods. Synovial fluid (SF) from patients with esOA, non-OA knee injury, and inflammatory knee arthritis were analyzed for 35 potential markers using immunoassays. Partial least square discriminant analysis (PLS-DA) was used to derive a biomarker model for cohort classification. The ability of the biomarker model to diagnose esOA was validated by identical wide-spectrum SF analysis of a test cohort of ten patients with esOA. Results. PLS-DA produced a streamlined biomarker model with excellent sensitivity (95%), specificity (98.4%), and reliability (97.4%). The eight-biomarker model produced a fingerprint for esOA comprising type IIA procollagen N-terminal propeptide (PIIANP), tissue inhibitor of metalloproteinase (TIMP)-1, a disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS-4), monocyte chemoattractant protein (MCP)-1, interferon-γ-inducible protein-10 (IP-10), and transforming growth factor (TGF)-β3. Receiver operating characteristic (ROC) analysis demonstrated excellent discriminatory accuracy: area under the curve (AUC) being 0.970 for esOA, 0.957 for knee injury, and 1 for inflammatory arthritis. All ten validation test patients were classified correctly as esOA (accuracy 100%; reliability 100%) by the biomarker model. Conclusion. SF analysis coupled with machine learning produced a partially validated biomarker model with cohort-specific fingerprints that accurately and reliably discriminated esOA from knee injury and inflammatory arthritis with almost 100% efficacy. The presented findings and approach represent a new biomarker
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 ( Cite this article:
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Spinopelvic characteristics influence the hip’s biomechanical behaviour. However, to date there is little knowledge defining what ‘normal’ spinopelvic characteristics are. This study aims to determine how static spinopelvic characteristics change with age and ethnicity among asymptomatic, healthy individuals. This systematic review followed the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines to identify English studies, including ≥ 18-year-old participants, without evidence of hip or spine pathology or a history of previous surgery or interventional treatment, documenting lumbar lordosis (LL), sacral slope (SS), pelvic tilt (PT), and pelvic incidence (PI). From a total of 2,543 articles retrieved after the initial database search, 61 articles were eventually selected for data extraction.Aims
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Arthroplasty surgery of the knee and hip is performed in two to three million patients annually. Periprosthetic joint infections occur in 4% of these patients. Debridement, antibiotics, and implant retention (DAIR) surgery aimed at cleaning the infected prosthesis often fails, subsequently requiring invasive revision of the complete prosthetic reconstruction. Infection-specific imaging may help to guide DAIR. In this study, we evaluated a bacteria-specific hybrid tracer (99mTc-UBI29-41-Cy5) and its ability to visualize the bacterial load on femoral implants using clinical-grade image guidance methods.
99mTc-UBI29-41-Cy5 specificity for 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:
The aim of this study was to identify the optimal lip position for total hip arthroplasties (THAs) using a lipped liner. There is a lack of consensus on the optimal position, with substantial variability in surgeon practice. A model of a THA was developed using a 20° lipped liner. Kinematic analyses included a physiological range of motion (ROM) analysis and a provocative dislocation manoeuvre analysis. ROM prior to impingement was calculated and, in impingement scenarios, the travel distance prior to dislocation was assessed. The combinations analyzed included nine cup positions (inclination 30-40-50°, anteversion 5-15-25°), three stem positions (anteversion 0-15-30°), and five lip orientations (right hip 7 to 11 o’clock).Aims
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In this study, we aimed to visualize the spatial distribution characteristics of femoral head necrosis using a novel measurement method. 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.Aims
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The aim of this study was to determine the fracture haematoma (fxH) proteome after multiple trauma using label-free proteomics, comparing two different fracture treatment strategies. A porcine multiple trauma model was used in which two fracture treatment strategies were compared: early total care (ETC) and damage control orthopaedics (DCO). fxH was harvested and analyzed using liquid chromatography-tandem mass spectrometry. Per group, discriminating proteins were identified and protein interaction analyses were performed to further elucidate key biomolecular pathways in the early fracture healing phase.Aims
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This study aimed to demonstrate the promoting effect of elastic fixation on fracture, and further explore its mechanism at the gene and protein expression levels. A closed tibial fracture model was established using 12 male Japanese white rabbits, and divided into elastic and stiff fixation groups based on different fixation methods. Two weeks after the operation, a radiograph and pathological examination of callus tissue were used to evaluate fracture healing. Then, the differentially expressed proteins (DEPs) were examined in the callus using proteomics. Finally, in vitro cell experiments were conducted to investigate hub proteins involved in this process.Aims
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Impaired fracture repair in patients with type 2 diabetes mellitus (T2DM) is not fully understood. In this study, we aimed to characterize the local changes in gene expression (GE) associated with diabetic fracture. We used an unbiased approach to compare GE in the fracture callus of Zucker diabetic fatty (ZDF) rats relative to wild-type (WT) littermates at three weeks following femoral osteotomy. Zucker rats, WT and homozygous for leptin receptor mutation (ZDF), were fed a moderately high-fat diet to induce T2DM only in the ZDF animals. At ten weeks of age, open femoral fractures were simulated using a unilateral osteotomy stabilized with an external fixator. At three weeks post-surgery, the fractured femur from each animal was retrieved for analysis. Callus formation and the extent of healing were assessed by radiograph and histology. Bone tissue was processed for total RNA extraction and messenger RNA (mRNA) sequencing (mRNA-Seq).Aims
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To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory. Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the machine learning algorithm. Risk factors were included in the analysis, along with clinical characteristics and parameters acquired through diagnostic radiology.Aims
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The management of periprosthetic joint infection (PJI) remains a major challenge in orthopaedic surgery. In this study, we aimed to characterize the local bone microstructure and metabolism in a clinical cohort of patients with chronic PJI. Periprosthetic femoral trabecular bone specimens were obtained from patients suffering from chronic PJI of the hip and knee (n = 20). Microbiological analysis was performed on preoperative joint aspirates and tissue specimens obtained during revision surgery. Microstructural and cellular bone parameters were analyzed in bone specimens by histomorphometry on undecalcified sections complemented by tartrate-resistant acid phosphatase immunohistochemistry. Data were compared with control specimens obtained during primary arthroplasty (n = 20) and aseptic revision (n = 20).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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