Mismatch Between Preoperative MRI Findings and Postoperative Histological Results in the Treatment of Tenosynovial Giant Cell Tumor.

Background/aim: Tenosynovial giant cell tumor (TGCT) is a rare disease of young adults with a high number of cases going unreported. Despite an international consensus meeting of experts in June 2022, the majority of such patients repeatedly pose problems, even for experienced clinicians. This study deals with the question of how often postoperative histopathological findings are consistent with preoperative magnetic resonance imaging (MRI) findings.

Real-Time sEMG Processing With Spiking Neural Networks on a Low-Power 5K-LUT FPGA.

The accurate modeling of hand movement based on the analysis of surface electromyographic (sEMG) signals offers exciting opportunities for the development of complex prosthetic devices and human-machine interfaces, moving from discrete gesture recognition, towards continuous movement tracking. In this study, we present two solutions for real-time sEMG processing, based on lightweight Spiking Neural Networks (SNNs) and efficiently implemented on a Lattice iCE40-UltraPlus FPGA, especially suitable for low-power applications. We first assess the performance in the discrete finger gesture recognition task, considering as a reference the NinaPro DB5 dataset, and demonstrating an accuracy of 83.

Maternal Phenylketonuria and Offspring Outcome: A Retrospective Study with a Systematic Review of the Literature.

Phenylketonuria (PKU) poses significant challenges for maternal and neonatal outcomes, requiring strict adherence to dietary protocols to maintain optimal maternal phenylalanine (Phe) levels during pregnancy. This study retrospectively analyzed outcomes of pregnancies in PKU-affected women and conducted a systematic review on the timing of dietary management and its impact on outcomes. This retrospective study included data from nine PKU-affected women and 14 pregnancies followed at the Regional Reference Center for Metabolic Diseases in Catania.