Cyborg and bionic systems (Washington, D.C.)最新文献

Restoring precise muscular control in the poststroke wrist/hand (W/H) demands sensorimotor integration to correct compensatory neuroplasticity. However, current rehabilitation robots inadequately modulate ascending somatosensory pathways from specific muscles. This study developed an electromyography (EMG)-driven soft robot with electro-vibro-feedback (EVF-robot) for targeted somatosensory priming in W/H muscles. This system integrates (a) focal vibratory stimulation and neuromuscular electrical stimulation for recruiting the somatosensory pathways of the targeted W/H flexors and extensors; (b) an EMG-driven control algorithm for strengthening the voluntary motor control of a driving muscle; and (c) robot assistance to achieve coordinated joint extension and flexion. In a single-arm trial with 20 sessions, 15 chronic stroke participants assisted by the system achieved significant improvements in voluntary W/H behavioral control, somatosensory feedback, and intermuscular coordination in the paretic upper limb (P < 0.05). During their W/H extension, the cortical peaks of corticomuscular coherence shifted contralaterally for W/H extensors, and the ascending corticomuscular coherence from W/H flexors increased (P < 0.05). These improvements persisted at the 3-month follow-up. The findings provide preliminary evidence that sensorimotor integration training with the EMG-driven EVF-robot may modulate compensatory neuroplasticity and facilitate improvements in coordinated motor control of the distal joints in individuals with chronic stroke.

Lower limb musculoskeletal dynamics simulation has been widely used to estimate the lower limb mechanics, but challenges such as heavy reliance on force plates, poor model generalization, and high computational load hindered its application in real-time robot control systems requiring rapid feedback and inference. This study proposed the Marker-GMformer model, a marker trajectories-driven deep learning model designed for efficient and accurate continuous prediction of lower limb kinematics and dynamics. By integrating prior knowledge with global-local and spatial-temporal features from the inputted marker coordinate time series, Marker-GMformer maintained high performance while reducing computational complexity. The model also demonstrated strong generalization, accurately predicting multi-joint kinematics, moments, and ground reaction forces (GRFs) across 13 different motion patterns. The predicted results were compared to those from musculoskeletal multibody dynamics simulations and force plates. Excellent performance was achieved with average Pearson correlation coefficients ( $\rho \ge 0.97$ ) and low root mean square errors (RMSE = 1.95° for angles, RMSE = 0.036 body weight for GRFs, and RMSE = 0.099 N·m/kg for moments) across all patterns. The findings underscored the substantial promise of the proposed method for enabling real-time monitoring of human lower limb mechanics and delivering timely feedback to optimize the control of assistive robots.

Hand movements in task space are typically represented using either Cartesian or polar coordinate systems. While Cartesian coordinates are commonly used in electroencephalography (EEG)-based brain-computer interface (BCI) studies, polar coordinates offer a more natural representation for circular motion by directly encoding angular information. This study investigates the feasibility of continuous decoding of hand motion angles in polar coordinates using EEG signals. In the paradigm, human participants engaged in bimanual circular tracing with a fixed radius while their EEG signals were recorded. To evaluate the feasibility of this approach, 6 deep learning models, including commonly used EEGNet, DeepConvNet, and ShallowConvNet, and their variants incorporating long short-term memory (LSTM) layers, were employed. Performance was assessed using mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (CC) between decoded and actual angles. Across 8 participants, all 6 models significantly outperformed the chance level (P < 0.01), with the best model achieving an MSE of 1.012 rad², an MAE of 0.627 rad, and a CC of 0.895. These results demonstrate the feasibility of continuous angular decoding of circular hand motion in polar coordinates using EEG signals. This approach offers a promising alternative to traditional Cartesian-based decoding methods, particularly for applications involving circular or rotational movements.

White matter microstructure, essential for neural communication, is genetically influenced and often disrupted in schizophrenia. Large-scale genome-wide association studies have identified over 200 genome-wide significant loci for schizophrenia, yet the extent to which schizophrenia shares genetic architecture with white matter microstructure-particularly across multidimensional diffusion tensor imaging (DTI) metrics and hemispheric distinctions-remains incompletely understood. Here, we employed the conditional/conjunctional false discovery rate (cond/conjFDR) approach to investigate the genetic overlap between schizophrenia and white matter microstructure. These analyses utilized large-scale genome-wide association datasets for schizophrenia (N _case = 53,386, N _control = 77,258) and the microstructure of 48 white matter tracts (N = 33,224), derived from individuals of European ancestry. White matter integrity was assessed using fractional anisotropy (FA), mean diffusivity (MD), and 3 eigenvalues. Additionally, we performed comprehensive functional and validation analyses for the shared loci. We identified 435 shared loci, including 154 loci exclusive to 3 eigenvalues. Hemisphere-specific analysis of white matter tracts revealed lateralized patterns, with 25.5% to 34.4% of loci being left-specific and 23.9% to 33.7% right-specific. Enrichment analysis highlighted the shared loci related to nervous system and central nervous system development, supporting their role in neurodevelopmental mechanisms. Validation analyses across diverse methods and datasets further confirmed the reliability of the shared loci. This study demonstrates a complex, shared genetic architecture between schizophrenia and white matter microstructure, highlighting hemispheric genetic asymmetry and the value of multidimensional DTI metrics in uncovering the genetic basis of structural brain abnormalities.

This work presents a novel rolling driving principle (RDP) for stick-slip actuators to achieve high motion consistency, inspired by the rack-and-pinion mechanism. This RDP utilizes a symmetrical driving structure and tangential contact to realize the pure rolling motion between the stator and the slider, requiring just a single lead zirconate titanate (PZT). This configuration ensures a consistent bidirectional driving process with a constant contact force, which improves both motion consistency and linearity. Based on this RDP principle, a linear stick-slip actuator incorporating an isosceles trapezoidal flexible mechanism (ITFM) has been implemented. The corresponding driving principle, operating principle, and the RDP's advantages have been analyzed and revealed. Design optimization was performed to investigate the optimal structural parameters of the ITFM. The superior performance of the proposed RDP-type actuator was experimentally verified across both high- and low-frequency ranges. The results indicate that the presented design exhibits forward and reverse output speed values of 0.410 and 0.417 mm/s at 10 Hz with linear correlation coefficients of 0.99969 and 0.99962, indicating an excellent motion consistency with a velocity difference ratio of 1.96%. When working at 560 Hz, the presented actuator reaches 37.73 and 34.99 mm/s for the forward and reverse output speed, yielding high linearity values of 0.99999 and 0.99999 due to the tiny speed fluctuation, and maintains a reasonable motion consistency with a velocity difference ratio of 7.54%. Finally, an RDP-type actuator-based magnetic resonance imaging (MRI)-compatible microsurgical instrument was proposed and prototyped, which enables opening-closing motions and cutting motions for intraoperative MRI surgical applications.

Background: Bladder cancer is associated with poor clinical prognosis due to their immunosuppressive microenvironment and therapeutic resistance. Methods: To address the low response rate of immune checkpoint inhibitors (ICIs) and the lack of effective drug delivery strategies, this study developed a mannose-modified pH/glutathione (GSH) dual-responsive nano-delivery system (MPP@IKE-aPD-1/diABZI) that synergistically activates ferroptosis and immune responses to achieve efficient antitumor therapy. This nanosystem uses Mannose-PEG-s-s-PCL/CDM-PEG-PCL as carriers to co-load the ferroptosis inducer IKE, STING agonist diABZI, and anti-PD-1 antibody (aPD-1), enabling tumor microenvironment-specific drug release and lymph node-targeted delivery. Results: In vitro experiments demonstrated rapid drug release under acidic/high GSH conditions, inducing ferroptosis in bladder cancer cells and activating dendritic cells through the release of danger signals such as HMGB1. It showed marked enrichment of the nanosystem in tumors and draining lymph nodes, suppressing orthotopic bladder tumor growth (94.5% inhibition rate) and lung metastasis (92% reduction in metastatic foci) while extending median survival in mice to 35 d. Mechanistic studies revealed that ferroptosis-induced immunogenic cell death synergized with STING pathway activation to enhance CD8⁺ T cell infiltration and granzyme B expression, while blocking the PD-1/PD-L1 axis alleviated immunosuppression. Furthermore, the treatment group exhibited long-term immune memory, effectively preventing tumor recurrence. Conclusion: This study provides an innovative multi-mechanism synergistic strategy to overcome immunotherapy resistance in bladder cancer, demonstrating significant clinical translation potential.

Cable-driven robotic systems are widely adopted for transport tasks due to their high load-bearing efficiency. However, their deployment in unstructured or unknown environments is hindered by the challenge of rapidly and reliably anchoring the cable endpoint. This work introduces a deployable cable-driven transport system that combines a tethered unmanned aerial vehicle (UAV) with a winch mechanism to autonomously form a topologically stable entanglement for cable anchoring. At the core of the system is a modular knot planner that integrates human-in-the-loop enclosing plane extraction, frontier-based enclosing path search, and knotting trajectory generation, incorporating metrics such as enclosing planarity, tether visibility, and tether clearance. In real-world experiments conducted in an urbanized outdoor environment, the system autonomously interpreted high-level user commands, executed a full knotting operation around a target structure, and successfully lifted a 15.3-kg payload to a height of 3.5 m. Beyond real-world trials, simulation studies confirmed the system's shape-agnostic knotting capability. A set of ablation experiments further demonstrated the necessity and effectiveness of these joint optimization metrics. Together, these results highlight the practicality and robustness of the proposed system for autonomous heavy-load transport in complex and previously unprepared environments, offering new capabilities for rapidly deployable robotic logistics.

The healthcare sector is challenged by critical workforce shortages, and this is causing an urgent need for innovative technologies to support or augment human roles. Although much of the research effort has focused on support and training of functional tasks, the emotional impacts that humans bring to the loop have often been overlooked. This gap is particularly pressing in healthcare and therapy, where empathy and emotional support are central to patient well-being. Unlike machines, humans possess a unique capacity for empathy, connecting emotionally with others and providing the essential support that fosters healing. Bridging this gap requires integrating affective elements, such as empathy, into therapeutic systems, which is the key to improving their effectiveness. This review explores groundbreaking techniques that integrate interpersonal interactions within therapy and healthcare, focusing on multiplayer games that strengthen real-time social connections, alongside social robots and virtual agents designed to simulate human-like affective interactions. Using artificial intelligence, these technologies aim to replicate complex human dynamics and foster artificial empathy, thus revolutionizing how we deliver care and support.

As a mechano-biological interface, the meninges dissipate external forces, maintain neuroimmune homeostasis, and dynamically regulate the brain's microenvironment. A comprehensive study of the regional heterogeneities in meninges can improve predictions of extra-axial hemorrhage and enhance bio-fidelity of finite element (FE) modeling of head trauma under multiple injury scenarios and pathological conditions. Here, we characterized the mechanical properties of porcine leptomeninges by performing rheological shear modeling and atomic force microscopy indentation experiments. Anatomical areas encompassed the piriform, occipital, frontal, parietal, and temporal lobes, along with the cerebellum lobe. Both macromechanical and micromechanical properties indicate that the modulus of the cerebellar lobe region is much higher than that of other lobes of the pia mater. Meanwhile, the regions of the leptomeninges also displayed local mechanical anisotropy. Regional variations in the mechanical properties were further characterized by analyzing the spatial distribution in protein compositions (collagen and elastin) through 2-photon microscopy and RNA sequencing. The cerebellum lobe was found to exhibit markedly elevated levels of collagen, elastin, and cell junction proteins. Additionally, the cerebellum lobe was also identified to have markedly greater thickness compared to other lobes. Taken together, the results revealed the intricate biomechanical architecture of the leptomeninges and underscore the need to analyze its heterogeneities when modeling FE models or other computational models during traumatic brain injury.

Addressing the variability in cancer immunotherapeutic outcomes among patients and the challenge of devising safe strategies to overcome immune evasion in solid tumors are crucial in advancing cancer therapy. This study investigated the anti-tumor effect of millimeter waves (MMWs) alone and in combination with the anti-programmed cell death-ligand 1 (α-PD-L1) antibody in a 4T1 "cold tumor" model. The results show that MMWs not only inhibit tumor growth but also improve tumor metabolism and the immune microenvironment and enhance anti-tumor immune responses by inducing conformational changes of key immune proteins. Further experiments conducted on cellular and animal models demonstrated that the anti-tumor efficacy of MMWs, which plays a pivotal role, was substantially enhanced with the aid of α-PD-L1. This collaboration resulted in a synergistic effect that not only inhibited tumor progression but also promoted a sustained immune response and prevented recurrence. The additional CT26 "cold tumor" model validates the applicability of this strategy across other "cold tumor" types, particularly in reprogramming the immunosuppressed state of "cold tumor". These findings underscore the unique potential of MMWs as a nonionizing, nonthermal therapeutic tool that complements cancer immunotherapy, offering a novel approach for the precision treatment of solid tumors.