Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review.

Healthcare-associated infections (HAIs) pose significant challenges in healthcare systems, with preventable surveillance playing a crucial role. Traditional surveillance, although effective, is resource-intensive. The development of new technologies, such as artificial intelligence (AI), can support traditional surveillance in analysing an increasing amount of health data or meeting patient needs.

A Wireless, Mechanically Flexible, 25μm-Thick, 65,536-Channel Subdural Surface Recording and Stimulating Microelectrode Array with Integrated Antennas.

This paper presents a fully wireless microelectrode array (MEA) system-on-chip (SoC) with 65,536 electrodes for non-penetrative cortical recording and stimulation, featuring a total sensing area of 6.8mm×7.4mm with a 26.

Definition of a tool to assess shared decision-making (SDM) on women with breast cancer: A value-based approach.

Background And Aims: In oncology, there is increasing talk of personalized treatment and shared decision-making (SDM), especially when multiple treatment options are available with different outcomes depending on patient preference. The present study aimed to define the set of main dimensions and relative tools to assess the Value brought to patients from a Breast Cancer's Clinical pathway structured according to a dynamic SDM framework.

Methods: Starting from our previous systematic review of the literature, a deep search of the main evidence-based and already validated questionnaires was carried out.

Real-Time Inference With 2D Convolutional Neural Networks on Field Programmable Gate Arrays for High-Rate Particle Imaging Detectors.

We present a custom implementation of a 2D Convolutional Neural Network (CNN) as a viable application for real-time data selection in high-resolution and high-rate particle imaging detectors, making use of hardware acceleration in high-end Field Programmable Gate Arrays (FPGAs). To meet FPGA resource constraints, a two-layer CNN is optimized for accuracy and latency with KerasTuner, and network is further used to minimize the computing resource utilization of the network. We use "High Level Synthesis for Machine Learning" () tools to test CNN deployment on a Xilinx UltraScale+ FPGA, which is an FPGA technology proposed for use in the front-end readout system of the future Deep Underground Neutrino Experiment (DUNE) particle detector.