Inflammatory response toward a Mg-based metallic biomaterial implanted in a rat femur fracture model.

The immune system plays an important role in fracture healing, by modulating the pro-inflammatory and anti-inflammatory responses occurring instantly upon injury. An imbalance in these responses can lead to adverse outcomes, such as non-union of fractures. Implants are used to support and stabilize complex fractures. Biodegradable metallic implants offer the potential to avoid a second surgery for implant removal, unlike non-degradable implants. However, considering our dynamic immune system it is important to conduct in-depth studies on the immune response to these implants in living systems. In this study, we investigated the immune response to Mg and Mg-10Gd in vivo in a rat femur fracture model with external fixation. In vivo imaging using liposomal formulations was used to monitor the fluorescence-related inflammation over time. We combine ex vivo methods with our in vivo study to evaluate and understand the systemic and local effects of the implants on the immune response. We observed no significant local or systemic effects in the Mg-10Gd implanted group compared to the SHAM and Mg implanted groups over time. Our findings suggest that Mg-10Gd is a more compatible implant material than Mg, with no adverse effects observed in the early phase of fracture healing during our 4-week study. STATEMENT OF SIGNIFICANCE: Degradable metallic implants in form of Mg and Mg-10Gd intramedullary pins were assessed in a rat femur fracture model, alongside a non-implanted SHAM group with special respect to the potential to induce an inflammatory response. This pre-clinical study combines innovative non-invasive in vivo imaging techniques associated with multimodal, ex vivo cellular and molecular analytics. The study contributes to the development and evaluation of degradable biometals and their clinical application potential. The study results indicate that Mg-10Gd did not exhibit any significant harmful effects compared to the SHAM and Mg groups.

AI-based automated detection and stability analysis of traumatic vertebral body fractures on computed tomography.

PURPOSE: We developed and tested a neural network for automated detection and stability analysis of vertebral body fractures on computed tomography (CT). MATERIALS AND METHODS: 257 patients who underwent CT were included in this Institutional Review Board (IRB) approved study. 463 fractured and 1883 non-fractured vertebral bodies were included, with 190 fractures unstable. Two readers identified vertebral body fractures and assessed their stability. A combination of a Hierarchical Convolutional Neural Network (hNet) and a fracture Classification Network (fNet) was used to build a neural network for the automated detection and stability analysis of vertebral body fractures on CT. Two final test settings were chosen: one with vertebral body levels C1/2 included and one where they were excluded. RESULTS: The mean age of the patients was 68 ± 14 years. 140 patients were female. The network showed a slightly higher diagnostic performance when excluding C1/2. Accordingly, the network was able to distinguish fractured and non-fractured vertebral bodies with a sensitivity of 75.8 % and a specificity of 80.3 %. Additionally, the network determined the stability of the vertebral bodies with a sensitivity of 88.4 % and a specificity of 80.3 %. The AUC was 87 % and 91 % for fracture detection and stability analysis, respectively. The sensitivity of our network in indicating the presence of at least one fracture / one unstable fracture within the whole spine achieved values of 78.7 % and 97.2 %, respectively, when excluding C1/2. CONCLUSION: The developed neural network can automatically detect vertebral body fractures and evaluate their stability concurrently with a high diagnostic performance.

A meta-analysis of previous falls and subsequent fracture risk in cohort studies.

The relationship between self-reported falls and fracture risk was estimated in an international meta-analysis of individual-level data from 46 prospective cohorts. Previous falls were associated with an increased fracture risk in women and men and should be considered as an additional risk factor in the FRAX® algorithm. INTRODUCTION: Previous falls are a well-documented risk factor for subsequent fracture but have not yet been incorporated into the FRAX algorithm. The aim of this study was to evaluate, in an international meta-analysis, the association between previous falls and subsequent fracture risk and its relation to sex, age, duration of follow-up, and bone mineral density (BMD). METHODS: The resource comprised 906,359 women and men (66.9% female) from 46 prospective cohorts. Previous falls were uniformly defined as any fall occurring during the previous year in 43 cohorts; the remaining three cohorts had a different question construct. The association between previous falls and fracture risk (any clinical fracture, osteoporotic fracture, major osteoporotic fracture, and hip fracture) was examined using an extension of the Poisson regression model in each cohort and each sex, followed by random-effects meta-analyses of the weighted beta coefficients. RESULTS: Falls in the past year were reported in 21.4% of individuals. During a follow-up of 9,102,207 person-years, 87,352 fractures occurred of which 19,509 were hip fractures. A previous fall was associated with a significantly increased risk of any clinical fracture both in women (hazard ratio (HR) 1.42, 95% confidence interval (CI) 1.33-1.51) and men (HR 1.53, 95% CI 1.41-1.67). The HRs were of similar magnitude for osteoporotic, major osteoporotic fracture, and hip fracture. Sex significantly modified the association between previous fall and fracture risk, with predictive values being higher in men than in women (e.g., for major osteoporotic fracture, HR 1.53 (95% CI 1.27-1.84) in men vs. HR 1.32 (95% CI 1.20-1.45) in women, P for interaction = 0.013). The HRs associated with previous falls decreased with age in women and with duration of follow-up in men and women for most fracture outcomes. There was no evidence of an interaction between falls and BMD for fracture risk. Subsequent risk for a major osteoporotic fracture increased with each additional previous fall in women and men. CONCLUSIONS: A previous self-reported fall confers an increased risk of fracture that is largely independent of BMD. Previous falls should be considered as an additional risk factor in future iterations of FRAX to improve fracture risk prediction.