In vitro mechanical analysis of X-shaped femoroplasty with polymethyl methacrylate boundary a fall on the greater trochanter.

To evaluate with mechanical testing (MT) using synthetic femurs, an X-shaped femoroplasty technique with polymethyl methacrylate (PMMA), analyzing the results applied to the prophylaxis of proximal femur (PF) fractures caused by low-energy trauma. MT was performed simulating a fall on the greater trochanter, using fifteen Sawbones™ models. They were divided into three experimental groups (n = 5): control (DP) group, drilled without augmentation (DWA) group, and X-shaped augmentation (DX) group. Maximum load, stiffness, absorbed energy and displacement were analyzed primarily in all groups; and secondarily then, morphology and fracture type were verified in all groups while PMMA volume, temperature and time polymerization were analyzed only in the DX group. The MT results obtained for synthetic models respectively in the DP, DWA, and DX groups were: mean maximum load (5562.0 ±â€¯464.8) N, (4798.0 ±â€¯121.2) N, and (7132.0 ±â€¯206.9) N; mean stiffness values (673 ±â€¯64.34) N/mm, (636 ±â€¯8.7) N/mm, and (738 ±â€¯17.13) N/mm, and mean absorbed energy values (36,203 ±â€¯3819) N.mm, (27,617 ±â€¯3011) N.mm, (44,762 ±â€¯3219) N.mm; mean displacement values (13.6 ±â€¯1.45) N, (11.1 ±â€¯0.5) N, and (13.2 ±â€¯0.69) N. The mean volume, temperature reached during filling in the DX group were 9.8 mL, 42.54ºC with 1' 56" of polymerization. The fracture types were similar between the DP and DWA groups, affecting the trochanteric region, as distinctly to those in the DX group, which were restricted to the femoral neck. The values obtained in MT showed statistical significance when analyzed by one-way ANOVA (5%) for maximum load, stiffness, and absorbed energy between groups. In conclusion, X-shaped PMMA augmentation presents a protective biomechanical characteristic against PF fractures generated in synthetic models by boundary a fall on the greater trochanter.

Feature selection for proximity estimation in COVID-19 contact tracing apps based on Bluetooth Low Energy (BLE).

During the COVID-19 pandemic, contact tracing apps based on the Bluetooth Low Energy (BLE) technology found in smartphones have been deployed by multiple countries despite BLE's debatable performance for determining close contacts among users. Current solutions estimate proximity based on a single feature: the mean attenuation of the BLE signal. In this context, a new generation of these apps which better exploits data from the BLE signal and other sensors available on phones can be fostered. Collected data can be used to extract multiple features that feed machine learning models which can potentially improve the accuracy of today's solutions. In this work, we consider the use of machine learning models to evaluate different feature sets that can be extracted from the received BLE signal, and assess the performance gain as more features are introduced in these models. Since indoor conditions have a strong impact in assessing the risk of being exposed to the SARS-CoV-2, we analyze the environment (indoor or outdoor) role in these models, aiming at understanding the need for apps that could increase proximity accuracy if aware of its environment. Results show that a better accuracy can be obtained in outdoor locations with respect to indoor ones, and that indoor proximity estimation can benefit more from the introduction of more features with respect to the outdoor estimation case. Accuracy can be increased about 10% when multiple features are considered if the device is aware of its environment, reaching a performance of up to 83% in indoor spaces and up to 91% in outdoor ones. These results encourage future contact tracing apps to integrate this awareness not only to better assess the associated risk of a given environment but also to improve the proximity accuracy for detecting close contacts.