Journal of Orthopaedic Research®最新文献

Having osteoarthritis in one knee is reported as an independent risk factor for developing contralateral knee osteoarthritis (KOA). However, no study has been designed to predict the development of contralateral KOA (cKOA). The authors hypothesized that specific risk factors for cKOA development exist and that it could be accurately predicted with the assistance of machine learning. KOA was defined using the Kellgren-Lawrence grade (KLG) of 2 or higher. Data from 1353 unilateral KOA patients (900 from the Osteoarthritis Initiative [OAI] and 453 from the Multicenter Osteoarthritis Study [MOST]) over 4–5 years of follow-up were examined. The risk factors for cKOA development were analyzed, and a machine learning model was developed to predict cKOA using OAI as the development data set and MOST as the test data set. cKOA developed in 172 (19.1%) and 178 (39.3%) of the patients (OAI and MOST, respectively) over a period of 4–5 years. A machine learning model was developed using the Tree-based Pipeline Optimization Tool algorithm. This model utilized nine variables, including baseline lateral joint space narrowing grade of the contralateral knee (odds ratio 4.475). The area under the curve of the receiver operating characteristics curve, along with accuracy, precision, and F1-score, were recorded as 0.69, 0.60, 0.50, and 0.58, respectively, in the test data set. The development of cKOA could be effectively predicted using a limited number of variables through machine learning. Surgeons should consider the development of cKOA in patients with identified risk factors when managing KOA patients.

Individuals who undergo anterior cruciate ligament reconstruction are at elevated risk for developing early-onset patellofemoral joint osteoarthritis. Our objective was to use K-means clustering to ascertain whether individuals at risk for patellofemoral joint osteoarthritis could be identified as determined by the presence of multiple co-existing anatomical and patella alignment risk factors. Forty participants (20 after anterior cruciate ligament reconstruction, 20 healthy controls) underwent magnetic resonance imaging assessment of the patellofemoral joint. Measures of hypothesized risk factors for patellofemoral joint osteoarthritis were obtained including patella alignment (lateral patella displacement and tilt), trochlear morphology (sulcus angle, lateral inclination angle), patella height (Insall-Salvati ratio and patella articulating overlap), and patellofemoral joint contact area. K-means clustering (k = 2) was used to ascertain whether a high-risk group could be identified. Following clustering, two distinct groups were detected. Participants assigned to cluster 1 exhibited features consistent with patellofemoral joint osteoarthritis including greater lateral patellar displacement and tilt, flatter trochlear grooves and lower lateral trochlear inclination, less patella articulating overlap, and reduced contact area. The proportion of females after anterior cruciate ligament reconstruction assigned to cluster 1 was 75% (N = 15) compared to 25% of healthy females (N = 5). K-means clustering was capable of characterizing individuals at elevated risk for patellofemoral joint osteoarthritis based on the presence of multiple co-existing anatomical and patella alignment risk factors. The fact that a significant percentage of females were assigned to the high-risk cluster supports the clinical observation that these individuals may be at higher risk of early-onset patellofemoral joint osteoarthritis.

The field of bone regeneration has primarily focused on investigating fracture healing and nonunion in isolated musculoskeletal injuries. Compared to isolated fractures, which frequently heal well, fractures in patients with multiple bodily injuries (polytrauma) may exhibit impaired healing. While some papers have reported the overall cytokine response to polytrauma conditions, significant gaps in our understanding remain in how fractures heal differently in polytrauma patients. We aimed to characterize fracture healing and the temporal local and systemic immune responses to polytrauma in a murine model of polytrauma composed of a femur fracture combined with isolated chest trauma. We collected serum, bone marrow from the uninjured limb, femur fracture tissue, and lung tissue over 3 weeks to study the local and systemic immune responses and cytokine expression after injury. Immune cell distribution was assessed by flow cytometry. Fracture healing was characterized using microcomputed tomography (microCT), histological staining, immunohistochemistry, mechanical testing, and small angle X-ray scattering. We detected more innate immune cells in the polytrauma group, both locally at the fracture site and systemically, compared to other groups. The percentage of B and T cells was dramatically reduced in the polytrauma group 6 h after injury and remained low throughout the study duration. Fracture healing in the polytrauma group was impaired, evidenced by the formation of a poorly mineralized and dysregulated fracture callus. Our data confirm the early, dysregulated inflammatory state in polytrauma that correlates with disorganized and impaired fracture healing.

A major barrier that hampers our understanding of the precise anatomic distribution of pain sensing nerves in and around the joint is the limited view obtained from traditional two dimensional (D) histological approaches. Therefore, our objective was to develop a workflow that allows examination of the innervation of the intact mouse knee joint in 3D by employing clearing-enabled light sheet microscopy. We first surveyed existing clearing protocols (SUMIC, PEGASOS, and DISCO) to determine their ability to clear the whole mouse knee joint, and discovered that a DISCO protocol provided the optimal transparency for light sheet microscopy imaging. We then modified the DISCO protocol to enhance binding and penetration of antibodies used for labeling nerves. Using the pan-neuronal PGP9.5 antibody, our protocol allowed 3D visualization of innervation in and around the mouse knee joint. We then implemented the workflow in mice intra-articularly injected with nerve growth factor (NGF) to determine whether changes in the nerve density can be observed. Both 3D and 2D analytical approaches of the light sheet microscopy images demonstrated quantifiable changes in midjoint nerve density following 4 weeks of NGF injection in the medial but not in the lateral joint compartment. We provide, for the first time, a comprehensive workflow that allows detailed and quantifiable examination of mouse knee joint innervation in 3D.

Aseptic loosening remains one of the top causes of revision surgery of total knee arthroplasty (TKA). Radiostereometric analysis (RSA) is used in research to measure implant migration, however limitations prevent its clinical use. New methods have allowed the same measurements as RSA to be performed with computed tomography (CT) scanners (CT-RSA). The objective of this study is to determine inducible displacement measurements from weight-bearing computed tomography (WBCT) and conventional RSA to assess implant stability. Participants (n = 17) completed RSA exams in the supine and standing position, and WBCT exams in the seated (leg extended) and standing position. Double examinations were performed in the seated (WBCT) or supine (RSA) positions. Inducible displacements were measured with model-based RSA (MBRSA) for RSA exams, and a novel CT-RSA software, V3MA, for WBCT exams. Precision of each technique was calculated between double examinations. Precision data for tibial component total translations and rotations were 0.05 mm and 0.118°, respectively with WBCT-RSA, and were 0.108 mm and 0.269°, respectively with MBRSA. MTPM precision was 0.141 mm with WBCT-RSA and was 0.168 mm with MBRSA. Inducible displacement MTPM of the tibial component was 0.244 ± 0.220 mm with WBCT-RSA and 0.662 ± 0.257 mm with MBRSA. Inducible displacement measurements with MBRSA were significantly different from WBCT-RSA for tibial component anterior tilt (p = 0.0002). WBCT-RSA demonstrated comparable precision to MBRSA, and both techniques measured inducible displacements consistent with stable components. Clinical Significance: As the availability of WBCT increases, its use as an alternative to MBRSA is supported to measure the instantaneous fixation of implant components.

Diclofenac etalhyaluronate (DF-HA) sustained diclofenac release with the effects of hyaluronic acid (HA), offering long-term analgesia in osteoarthritis. In this study, the effects of DF-HA on pain improvement and osteoarthritis were evaluated in a rat knee monoiodoacetate-induced osteoarthritis model compared to HA. Eight rats per group had been injected with monoiodoacetate (2.0 mg) or saline in the right knee for 4 weeks and were injected with either DF-HA (1.25 mg/kg; 0.5 mg), HA (0.5 mg), vehicle which was a substrate without DF-HA (50 μL), or saline and followed for 4 weeks. Mechanical plantar skin sensitivity was assessed weekly using the von Frey assay. Osteoarthritis changes were monitored with Larsen scores via CT imaging at every 2 weeks. The articular cartilage was analyzed using OARSI scores through H&E, Safranin-O staining at 8 weeks. The percentage of Iba-1 positive microglia in the spinal dorsal horn and of FG + CGRP-labeled cells among FG-positive cells in the dorsal root ganglion were evaluated by immunohistochemical staining. TNF-α and IL-6 mRNA expression levels in the knee synovium were evaluated by PCR. The DF-HA showed significantly improved pain hypersensitivity compared with the HA at 6–8 weeks. The percentage of Iba-1-positive microglia was significantly lower than that in the vehicle and the percentage of FG + CGRP/FG was significantly lower than that in the HA. OARSI scores did not differ among treatment groups, Larsen scores indicated lower in the DF-HA than in the vehicle. DF-HA was as effective as HA in joint protection and significantly improved inflammatory pain compared to HA.

Bone fracture healing is a complex physiological process influenced by biomechanical and biomolecular factors. Mechanical stability is crucial for successful healing, and disruptions can lead to delayed healing or nonunion. Bone commonly heals itself through secondary fracture healing, which is governed by the mechanical strain at the fracture site. To investigate these phenomena, a validated methodology for capturing the mechanoregulatory process in specimen-specific models of fracture healing could provide insight into the healing process. This study implemented a prognostic healing simulation framework to predict healing trajectories based on mechanical stimuli. Sixteen sheep were subjected to a 3 mm transverse tibial mid-shaft osteotomy, stabilized with a custom plate, and equipped with displacement transducer sensors to measure interfragmentary motion over 8 weeks. Computed tomography scans were used to create specimen-specific bone geometries for finite element analysis. Virtual mechanical testing was performed iteratively to calculate strains in the callus region, which guided tissue differentiation and consequently, healing. The predicted healing outcomes were compared to continuous in vivo sensor data, providing a unique validation data set. Healing times derived from the in vivo sensor and in silico sensor showed no significant differences, suggesting the potential for these predictive models to inform clinical assessments and improve nonunion risk evaluations. This study represents a crucial step towards establishing trustworthy computational models of bone healing and translating these to the preclinical and clinical setting, enhancing our understanding of fracture healing mechanisms.

Clinical significance: Prognostic bone fracture healing simulation could assist in non-union diagnosis and prediction.