Thermodynamics-informed modifier selection for molten salt oxidation of simulated cation resin: Synergistic oxygen-vacancy catalysis and enhanced Cs/Sr retention of WO3

Chemical Engineering Journal · 2026-02-18

NEOSTI: A Neuromorphic Electronic-Opto Spatial-Temporal Hybrid Image Sensor

Research Square · 2025-01-20

Millimeter-Sized 0.1pM LoD Wireless 16-Channel Organic-Electrochemical-Transistor-Based Electrochemical Sensing SoC

2025-02-16 · 2 citations

Inflammatory cytokines, such as TNF-alpha and IL-6, play a crucial role as biomarkers in the management of chronic diseases like autoimmune disorders, cardiovascular diseases, and cancer [1]. Since long-term monitoring of these cytokines is essential for an effective disease management, miniaturized electrochemical sensor implant systems offer a less invasive option for monitoring of substance concentrations within the human body [2] [3]. However, it remains a challenge to develop an electrochemical implant system that offers high sensitivity, a wide dynamic range (DR), and a certain level of spatial resolution for detecting inflammatory factors. Existed electrochemical sensing solutions rely on three-electrode (3E) systems or lon-Sensitive Field-Effect Transistors (ISFETs) [4]–[7]. The surface contact between the solution and the electrode limits the current sensitivity. In addition, it also hampers the scalability of the implant system size and the number of channels that can be accommodated [8], [9]. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{In}$</tex> recent years, organic electrochemical transistors (OECTs) have emerged as a promising solution [10]–[12]. However, the high sensitivity of OECTs requires relatively high static current, realizing a much higher power consumption of the sensor interface IC than that of 3E/ISFET.

The role of ROCK1/MLC/NMMHC IIA-actin signaling in ischemic stroke-induced blood-brain barrier disruption: implications for therapeutic intervention

Cellular and Molecular Life Sciences · 2025-10-30 · 4 citations

Abstract Background Disruption of the blood-brain barrier (BBB) is a key event in the onset of ischemic stroke (IS), primarily driven by endothelial cytoskeletal rearrangement. The interaction between non-muscle myosin heavy chain IIA (NMMHC IIA) and actin, along with the ROCK/MLC pathway, is central to this cytoskeletal reorganization. While our previous studies have shown that the Caspase-3/ROCK1/MLC/NMMHC IIA-actin positive feedback loop mediates H 2 O 2 -induced neuronal apoptosis, its role in cerebral ischemia-reperfusion (I/R) injury and BBB disruption remains unclear. Methods In vivo, we used endothelial-specific NMMHC IIA conditional knockdown mice, NMMHC IIA-inducible endothelial conditional knock-in mice and C57BL/6J to establish a middle cerebral artery occlusion/reperfusion model. In vitro, we employed brain microvascular endothelial cells in an oxygen-glucose deprivation/reoxygenation model. The effects of the NMMHC IIA inhibitor blebbistatin, the ROCK1 inhibitor Y-27632, and the actin depolymerizer cytochalasin D were assessed for their impact on I/R-induced activation of the ROCK/MLC/NMMHC IIA-actin pathway, tight junction proteins (TJs) degradation, and brain damage. Results Inhibition of NMMHC IIA expression and stress fiber depolymerization significantly reduced NMMHC IIA-actin interactions, suppressed the ROCK/MLC pathway, decreased TJs degradation, and alleviated cerebral I/R injury. Conversely, overexpression of NMMHC IIA further exacerbated cerebral I/R injury and BBB disruption and amplified activation of the ROCK1/MLC pathway. Y-27632 inhibited the ROCK/MLC/NMMHC IIA-actin pathway, mitigating I/R-induced BBB disruption. Conclusions This study reveals that the ROCK1/MLC/NMMHC IIA-actin pathway is implicated in I/R-induced BBB disruption and operates as a positive feedback loop. These findings offer a promising therapeutic strategy for the treatment of IS and BBB damage.

Single-Channel EEG Algorithm for OSA Screening via Sleep Staging and Respiratory Event Detection

2025-10-16

Obstructive sleep apnea (OSA) is a common sleeping obstacle, affecting approximately 1 billion people worldwide. However, it remains underdiagnosed because of the high cost and operational complexity of gold standard polysomnography (PSG). To address this challenge, this study developed a low-power miniaturized system for simultaneous sleep staging and respiratory event detection using a single-channel prefrontal EEG. The system features wearable hydrogel electrodes for user-friendly signal acquisition. Two dedicated models were proposed: a sleep staging model combining Bi-GRU with attention mechanisms and a respiratory event detector based on SVM. Experimental results in clinical data from 12 OSA patients demonstrated that the sleep staging model achieved an average accuracy of $80.20 \%$ in the classification of five classes under validation of leave-one-subject-out (LOSO), while the respiratory event detector achieved an accuracy of $80.83 \%$. The work offers a cost-effective approach for the screening of sleep health, providing a practical pathway to mitigate the disparities in OSA diagnosis caused by limited healthcare resources.

Controllable preparation of carbon coating Ge nanospheres with a cubic hollow structure for high-performance lithium ion batteries

Journal of Colloid and Interface Science · 2024-08-03 · 16 citations

A Low-Power ABR Characteristic Waveform Automatic Detection Algorithm Design and FPGA Implementation

2024-12-14

Portable, low-power ABR characteristic waveform automatic identification devices have broad application prospects in clinical and scientific research. This paper proposes an FPGA-based ABR characteristic waveform automatic identification device. The device integrates an ABR automatic identification algorithm, which outputs the latencies of waves I, III, and V with the ABR waveform as input into the system. The algorithm consists of two parts: a filtering algorithm and a U-Net neural network algorithm, achieving an average accuracy of 91.96% on a self-built dataset. Compared to using only the U-Net neural network algorithm, this method reduces the energy consumption by 19.64% on the self-built dataset, which is of great practical value in scenarios that require long-term or repeated ABR characteristic waveform automatic detection tasks.

20 Years of Implantable Circuits at BioCAS

2024-10-24 · 1 citations

Over the past two decades, BioCAS has served as a pivotal forum for researchers to share their insights and breakthroughs in the development of implantable microelectronic systems. This review synthesizes the collective knowledge and milestones in implantable circuits as presented at BioCAS, witnessing the transformative impact of these technologies on scientific discovery, healthcare and human life.

A Wireless Anesthesia Depth Monitoring System Based on Features Extracted from Frontal EEG

2024-12-14

This paper proposes a portable anesthesia depth evaluation system, comprising a wireless 5-channel EEG signal acquisition device and a signal processing platform. The system employs a scale of 1 to 5 for anesthesia depth scoring, with score 1 indicating a state of complete anesthesia and score 5 representing a fully awake state. Experimental data were collected from 83 gynecological surgeries at Beijing Tsinghua Changgung Hospital. Compare to the Observer's Assessment of Alertness/Sedation (OAA/S) scale, the proposed design achieved a classification accuracy of 86.84% on the validation set. Notably, the proposed approach enables more responsive and efficient anesthesia monitoring, with a twice per minute update rate, compared to the once per minute update rate of BIS. In general, this approach enables wireless, real-time, and accurate monitoring of anesthesia depth.

Design and FPGA Implementation of a Light-Weight Calibration-Friendly Eye Gaze Tracking Algorithm

2024-05-19 · 1 citations

Eye tracking technique is showing broad application potential in daily life. This work proposes an FPGA-based eye gaze tracking system with a differential convolutional neural network structure, which is naturally compatible with calibration operation. Each newly acquired eye image is fed into the proposed algorithm together with the pre-calibrated eye sample as a reference to obtain the relative gaze direction. The final processing output is generated by combining the results of several reference samples. Our design reports an angular gaze tracking accuracy of 3.28° and 3.05° on MPIIGaze and a self-built dataset, respectively. The entire system is integrated on a Xilinx Ultra 96 single computer board with FPGA for acceleration. The total weight of the board is 123.3 grams. The system achieves a processing speed of 15.2 frames per second.