Aim

In trauma surgery, the development of biomaterial-associated infections (BAI) is one of the most common complications affecting trauma patients, requiring prolonged hospitalization and the intensive use of antibiotics. Following the attachment of bacteria on the surface of the biomaterial, the biofilm-forming bacteria could initiate a chronic implant-related infection. Despite the use of conventional local and systemic antibiotic therapies, persistent biofilms involve various resistance mechanisms that contribute to therapeutic failures. The development of in vivo chronic BAI models to optimize antibiofilm treatments is a major challenge. Indeed, the biofilm pathogenicity and the host response need to be finely regulated, and compatible with the animal lifestyle. Previously, a Galleria mellonella larvae model for the formation of an early-stage biofilm on the surface of a Kirschner (K)-wire was established. In the present study, two models of mature biofilm using clinical Staphylococcus aureus strains were assessed: one related to contaminated K-wires (in vitro biofilm maturation) and the second to hematogenous infections (in vivo biofilm maturation). Rifampicin was used as a standard drug for antibiofilm treatment.

Aims

The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular fracture involvement, on plain radiographs.

Aims

The optimal procedure for the treatment of ossification of the posterior longitudinal ligament (OPLL) remains controversial. The aim of this study was to compare the outcome of anterior cervical ossified posterior longitudinal ligament en bloc resection (ACOE) with posterior laminectomy and fusion with bone graft and internal fixation (PTLF) for the surgical management of patients with this condition.

Introduction

The functional ante-inclination (AI) of the cup after total hip arthroplasty (THA) is a key component in the combined sagittal index (CSI) to predict joint stability after THA. To accurately predict AI, we deducted a mathematic algorithm between the radiographic anteversion (RA), radiographic inclincation (RI), pelvic tilting (PT), and AI. The current study aims (1) to validate the mathematic algorithm; (2) to convert the AI limits in the CSI index (standing AI ≤ 45°, sitting AI ≥ 41°) into coronal functional safe zone (CFSZ) and explore the influences of the stand-to-sit pelvic motion (PM) and pelvic incidence (PI) on CFSZ; (3) to locate a universal cup orientation that always fulfill the AI criteria of CSI safe zone for all patients or subgroups of PM(PM ≤ 10°, 10° < PM ≤ 30°, and PM > 30°) and PI (PI≤ 41°, 41°< PI ≤ 62°, and PI >62°), respectively.

Aims

There is a lack of evidence about the risk factors for local recurrence of a giant cell tumour (GCT) of the sacrum treated with nerve-sparing surgery, probably because of the rarity of the disease. This study aimed to answer two questions: first, what is the rate of local recurrence of sacral GCT treated with nerve-sparing surgery and second, what are the risk factors for its local recurrence?

Objectives

Previous genome-wide association studies (GWAS) have reported significant association of the single nucleotide polymorphism (SNP) rs8044769 in the fat mass and obesity-associated gene (FTO) with osteoarthritis (OA) risk in European populations. However, these findings have not been confirmed in Chinese populations.

Summary Statement

3D porous and nano-structured polyetheretherketone (PEEK) surface embedded with biofunctional groups can not only induce the up-regulation of osteogenic genes and proteins in-vitro, but also help promote new bone formation in-vivo.

Purpose: Subject-specific computational models of anatomical components can now be generated from image data and used in the assessment of orthopaedic interventions. Whilst such models are becoming more commonplace, there are few studies that have investigated the effects of element size or the level of morphological detail that is required to model complex hierarchical structures such as trabecular bone [1,2]. The purpose of this study was to investigate the modelling of the mechanical properties of a trabecular-like material under compression, and in particular to evaluate the relationship between the grey-scale dependent density, determined from microCT, and the elastic modulus at different levels of mesh density.

Materials and Methods: A specific open cell rigid foam (pcf 7.5, Sawbone, Sweden), with a porosity over 95% and cell size between 1.5 and 2.5 mm, was chosen to represent human cancellous bone. Five cylindrical specimens (24 mm diameter, 19 mm height) were compressed under a maximum displacement of 0.35 mm in a materials testing machine. All the specimens were imaged by microCT (ƒÝCT80, Scanco Medical, Switzerland) before the test to provide the geometry and greyscale distribution. Corresponding finite element models of each specimen were generated using proprietary software (ScanFE, Simpleware, UK) with an element size of 1.5 mm. The elastic modulus of each element was based on the image greyscale using a conversion factor that was determined, through trial-and-error, by matching the predicted displacement with the experimental measurement in the linear region (> 0.2 mm). For each specimen, models of higher and lower mesh densities were constructed by down-sampling the microCT output to different levels. The smallest element size was limited to 0.5 mm due to computational restrictions, however a smaller 8 mm cube of the material was also analysed, with element sizes down to 0.25 mm.

Results: From the experimental tests, the mean apparent elastic modulus of the specimens was found to be 15 MPa, as compared with 18 MPa specified by the manufacturer. For the computational models, the predicted elastic modulus of the whole specimen models was found to decrease with decreasing element size. In particular, there was a large drop in the predicted values when the element size was of the same order as the cell size. With larger elements, the results indicated some convergence and there was reasonable agreement with the experimental results. For the cube model with smaller element sizes, the predicted moduli values again appeared to converge as the element size was reduced to 0.25 mm.

Conclusion: The results of this study show that the optimum conversion factor from an image greyscale value to an elastic modulus varies with element size. If the finite element method is to be used effectively to model bone, then the mesh size must either be sufficiently large to use a continuum approach or sufficiently small to capture the behaviour of the individual trabeculae. Different conversion factors will be required to determine the material properties from the image greyscale in each case.