Xiuyun Liu

Conventional Faraday‐based calculations often fail to accurately predict chitosan hydrogel electrodeposition because the measured deposit is a hydrated polymer gel rather than a dense dry electrochemical product. To address this limitation, an effective deposition coefficient, , was introduced into a corrected Faraday framework to relate electrical input to wet hydrogel volume. A cylindrical growth model was derived for platinum wire cathodes and fitted to thickness data obtained under galvanostatic deposition at 3–25 mA, yielding = 60.4 and R ² = 0.929. The framework was further extended analytically to the planar‐electrode limit, showing that electrode geometry changes the predicted growth law. Characterization of the freeze‐dried deposit showed retained chitosan‐associated infrared features, a diffraction profile distinct from that of the starting powder, and irregular surface openings. Controlled cathode immersion geometry enabled proof‐of‐concept fabrication of stepped chitosan hydrogel structures. This work establishes a calibrated process framework for connecting electrochemical input, electrode geometry, and wet chitosan hydrogel coating formation.

Remote‐controlled genetic circuits use externally applied physical signals to regulate gene expression and cellular functions with spatial and temporal precision. Light, ultrasound, and magnetic fields provide complementary advantages in controllability, tissue accessibility, and biological compatibility, but their performance depends on both physical‐field propagation and efficient conversion of external energy into intracellular signals and predictable gene‐regulatory outputs. This review presents a unified framework of physical transduction, biological sensors, signal processors, and genetic effectors for comparing prokaryotic and eukaryotic systems. Core genetic circuits are distinguished from delivery and auxiliary platforms that support cargo release, cellular uptake, or energy conversion without directly regulating genes. Representative systems are compared by circuit architecture, transduction mechanism, stimulus parameters, dynamic range, basal leakage, response kinetics, reversibility, in vivo actuation, delivery requirements, and safety. The evolution from inducible switches toward multi‐input logic, tunable regulation, genetic memory, and feedback control is discussed. An end‐to‐end codesign workflow and modality‐specific reporting framework are proposed to integrate physical inputs, transducers, genetic circuits, delivery strategies, and stimulation devices. Quantitative benchmarking and standardized safety evaluation are essential for advancing these systems toward predictable and clinically translatable therapeutic platforms.

The blood–brain barrier (BBB), while indispensable for maintaining central nervous system (CNS) homeostasis, constitutes the principal impediment to effective therapeutic delivery for neurodegenerative disorders, particularly hindering spatially resolved modulation of extracellular ions and reactive oxygen species (ROS) within the neural microenvironment. Contemporary electrochemical methodologies have emerged as a paradigm shift for dynamically reconciling these dual parameters, thereby enabling targeted neuroregulation. Critical review of this field reveals a distinct evolution from passive physiological interventions to active electrochemical engineering approaches. Current research, however, encounters persistent translational barriers including insufficient spatiotemporal resolution in neural interfaces, incomplete mechanistic understanding of ROS‐ionic crosstalk, and scalability limitations of nanoscale delivery systems. To transcend these limitations, the synergistic convergence of electrochemical platforms with machine learning (ML)–guided predictive analytics, near‐infrared (NIR) phototherapy, and biocompatible nanocarrier‐mediated delivery systems constitutes a strategic imperative in next‐generation neurotherapeutic development. Such interdisciplinary convergence is not merely incremental but rather a fundamental prerequisite for realizing clinically translatable neural microenvironment modulation.

The intrinsic properties of plants offer numerous opportunities for scientific and technological advancement. Considerable efforts have been directed toward developing plant-on-chip platforms to investigate cellular responses to external stimuli, including chemical, mechanical, and electrical cues. In this study, we present a fluidic platform using polydimethylsiloxane (PDMS) and a printed circuit board (PCB), integrated with electrochemical impedance spectroscopy (EIS) detection. Various experimental conditions were examined, including ionic and pH stimulation, as well as membrane dimensions, with the onion inner membrane treated as a black-box system. The measurement results are presented as Nyquist plots, and a resistance model incorporating multifactorial influences is proposed. Impedance variations in plant cells serve as a basis for electrical modulation. To explore these properties, we converted acoustic signals into electrical inputs and recorded the outputs after being modulated by onion inner epidermal cells. A transfer function analysis was subsequently performed. Our results indicate that the plant cell-on-chip (PCOC) platform holds promise for further investigations into plant cell properties. The impedance results suggest that plant cells can respond to different external stimuli, enabling modulation of the electrical properties. These findings lay the groundwork for future studies on cellular electrical characteristics and the development of preliminary bioelectrical circuits.

The accurate delineation of ischemic stroke lesions in magnetic resonance imaging (MRI) is impeded by heterogeneous lesion morphology and the computational expense of modeling global context in three‑dimensional data. In cerebral infarction assessment, diffusion‑weighted imaging, apparent diffusion coefficient, T2‑weighted imaging and T2star sequences (including susceptibility weighted image processing) each offer complementary information, yet existing fusion strategies often fail to adapt to missing modalities or capture long‑range dependencies efficiently. Here we present GPMNet, a lightweight convolutional framework that integrates an adaptive multimodal feature fusion module—employing dynamic cross‑attention to spatially weight and merge signals from all four MRI sequences—and a gated parallel state‑space module that models global voxel interactions in linear time via dual gated branches. We trained the network end-to-end on the ATLAS R2.0 dataset and our own dataset collected at HuanHu Hospital (Tianjin, China), labeled as HHD. The training used a combined Dice–binary cross-entropy and TOPK10 loss, and the outputs were refined using ensemble inference and connected-domain filtering. GPMNet achieved Dice coefficients of 0.6604 and 0.7171 on the two cohorts respectively, achieving superior results compared to other state-of-the-art algorithms. Moreover, the Grad-CAM–based interpretability analysis confirms that the model’s attention corresponds to true ischemic areas across modalities, offering visual evidence of its diagnostic reliability and enhancing the transparency of the segmentation process. Our approach delivered rapid, high‑precision stroke segmentation and establishes a scalable paradigm for resource‑efficient clinical imaging applications.

Timely identification of harmful brain activities via electroencephalography (EEG) is critical for brain disease diagnosis and treatment, which remains limited in application due to inter-rater variability, resource constraints, and poor generalizability of existing artificial intelligence models. In this study, we describe an automated classifier, VIPEEGNet, which leverages the advantage of transfer learning from ImageNet-pretrained models to distinguish six types of brain activities. For the development cohort, the recall of VIPEEGNet ranges from 36.8% to 88.2%, and the precision ranges from 55.6% to 80.4%, with performance comparable to that of human experts. Notably, the external testing showed Kullback-Leibler divergence (KLD) values of 0.223 (public) and 0.273 (private), ranking second among the existing 2767 competing algorithms, while using only 0.7% of the parameters of the top-ranked algorithm. Its minimal parameter requirements and modular design offer a deployable solution for real-time brain monitoring, potentially expanding access to expert-level EEG interpretation in resource-limited settings.

The quality of the indoor environment is critical for sleep. As smartphone use continues to rise, these devices has become integral to daily life, particularly amongst young adults. Many students engage with short-form video applications pre-bedtime, which has been linked to poorer sleep quality and next-day cognitive performance. To explore aforementioned factors quantitatively, 18 University students were recruited as participants. They were sequentially exposed to three conditions: a 60 min baseline, a 30 min condition and a 0 min condition. Polysomnography technology was used to continuously monitor electroencephalogram, electro-ophthalmogram and other signals during sleep. Subsequently, participants were asked to complete a subjective-response questionnaire and an early-morning cognitive assessment. A comprehensive analysis was then conducted to evaluate sleep quality and quantify its impact. Baseline survey data ( N = 211) revealed that 89.6% of university students used smartphones pre-bedtime and 64.5% watched short-form videos. During the experimental phase, compared to baseline condition, progressively limiting short video exposure to 30 and 0 min increased sleep efficiency by 5.43% and 11.51%, respectively, reduced Wakefulness After Sleep Onset by 35.3 min and 46.4 min, raised positive effect scores by 2.33 and 5.44 points, and improved N-back accuracy by 3.5% and 5.5%.