Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device.

Minimally invasive, high-bandwidth brain-computer-interface (BCI) devices can revolutionize human applications. With orders-of-magnitude improvements in volumetric efficiency over other BCI technologies, we developed a 50-µm-thick, mechanically flexible micro-electrocorticography (µECoG) BCI, integrating 256×256 electrodes, signal processing, data telemetry, and wireless powering on a single complementary metal-oxide-semiconductor (CMOS) substrate containing 65,536 recording and 16,384 stimulation channels, from which we can simultaneously record up to 1024 channels at a given time. Fully implanted below the dura, our chip is wirelessly powered, communicating bi-directionally with an external relay station outside the body. We demonstrated chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from somatosensory, motor, and visual cortices, decoding brain signals at high spatiotemporal resolution.

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. We conducted a scoping review, following the PRISMA-ScR guideline, searching for studies of new digital technologies applied to the surveillance, control, and prevention of HAIs in hospitals and LTCFs published from 2018 to 4 November 2023. The literature search yielded 1292 articles. After title/abstract screening and full-text screening, 43 articles were included. The mean study duration was 43.7 months. Surgical site infections (SSIs) were the most-investigated HAI and machine learning was the most-applied technology. Three main themes emerged from the thematic analysis: patient empowerment, workload reduction and cost reduction, and improved sensitivity and personalization. Comparative analysis between new technologies and traditional methods showed different population types, with machine learning methods examining larger populations for AI algorithm training. While digital tools show promise in HAI surveillance, especially for SSIs, challenges persist in resource distribution and interdisciplinary integration in healthcare settings, highlighting the need for ongoing development and implementation strategies.

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 26.5µm×29µm electrode pitch. Sensing, data telemetry, and powering are monolithically integrated on a single chip, which is made mechanically flexible to conform to the surface of the brain by substrate removal to a total thickness of 25µm allowing it to be contained entirely in the subdural space under the skull.

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. In the second phase, to corroborate this grid, a Delphi survey was conducted to assess each questionnaire identified for each dimension, against the following seven value-based criteria: Clinical Benefit, Safety, Care Team Well Being, Patient Reported Outcomes Measures, Green Oncology, Impact on Health Budget, and Genomic Profile. Results: The resulting 7-dimension questionnaire is composed of 72 questions. Of these, some quantitatively and objectively assess the evolution of the patient's disease state, whereas others aim to ask patients about their active involvement in decisions affecting them and to investigate whether they were free to explore their preferences. Furthermore, to frame the analyzed phenomenon at the right time, for each questionnaire section, the specific, evidence-based timing of administration is indicated. Conclusion: The resulting questionnaire is validated in its entirety and it is composed of a set of questions and relative time point for data collections to assess the Value brought to patients undertaking a Breast Cancer's Clinical pathway, structured according to a dynamic SDM framework. It constitutes a quantitative instrument to integrate patient centeredness with a personalized perspective in the care management of women with breast cancer.

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 quantization is further used to minimize the computing resource utilization of the network. We use "High Level Synthesis for Machine Learning" (hls4ml) 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. We evaluate network accuracy and estimate latency and hardware resource usage, and comment on the feasibility of applying CNNs for real-time data selection within the currently planned DUNE data acquisition system. This represents the first-ever exploration of employing 2D CNNs on FPGAs for DUNE.