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

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.

Background: Abnormal alterations in cerebral blood flow (CBF) have been implicated in cognitive decline and neurodegeneration. Maintaining adequate CBF in astronauts during long-duration microgravity is therefore crucial for the success of manned spaceflight. However, the quantitative assessment of CBF during space missions remains challenging. Methods: Thirty-six participants underwent a 90-d -6° head-down tilt bed rest (HDTBR) protocol, a well-established ground-based analog of microgravity. Multimodal imaging data, including internal carotid artery Doppler ultrasound and brain magnetic resonance imaging, were collected during HDTBR. Multiple machine learning (ML) algorithms were developed to investigate carotid-CBF mapping relationship and establish CBF change prediction models. Results: After 90-d HDTBR, significant regional CBF decreases were observed, primarily in the right Heschl's gyrus, right middle cingulate gyrus, and right superior frontal gyrus. The optimal ML model CatBoost showed robust predictive performance for CBF in these regions (right Heschl's gyrus: AUC = 0.88, accuracy = 0.84; right middle cingulate gyrus: AUC = 0.92, accuracy = 0.83; right superior frontal gyrus: AUC = 0.82, accuracy = 0.72). To enhance accessibility and practical utility, the prediction model was implemented as an interactive web application for in-orbit deployment. Conclusion: This study demonstrates the feasibility of constructing ML-driven CBF prediction models under microgravity based on multimodal imaging. The developed prediction models show promise as early warning tools for brain health of astronauts in spaceflight.

Microrobots driven by magnetic and acoustic fields have shown great potential in multiple biomedical applications due to their excellent biocompatibility, wireless actuation, access to confined environments, and tissue penetration. A single physical actuation method often meets inevitable limitations and complications, such as the limited propulsion of the magnetic actuation and difficult direction control of the acoustic actuation. This review summarizes the current progress of hybrid magneto-acoustic actuation to address the limitations of single magnetic or acoustic actuation. First, we review the research on microrobots driven by single magnetic and acoustic fields and clarify the properties of each physical actuation. Then, we summarize 2 forms of hybrid magnetic-acoustic actuation: (a) magnetic steering and acoustic propulsion and (b) magnetic propulsion and acoustic manipulation. The state-of-the-art applications of magneto-acoustic microrobots, including targeted drug delivery, minimally invasive surgery, and medical imaging, are presented to demonstrate their great potential in biology and clinics. This article finally discusses current challenges and potential developments in magneto-acoustic robotics to provide a reliable path for designing and applying hybrid magneto-acoustic actuation methods.

Multi-electrode arrays (MEAs) are a key enabling technology in the development of cybernetic systems, as they provide a means to understand and control the activity of neural populations linking brain microtissue dynamics with electronic systems. MEAs allow high-resolution, noninvasive recordings of neuronal activity, creating a powerful interface for investigating in vitro brain development and dysfunction. In this work, we introduce a novel deep learning framework based on a graph deviation network (GDN) to analyze spiking activity from human forebrain organoids (hFOs) and predict network-level alterations associated with autism spectrum disorder (ASD) risk. Our method extends traditional spike and burst analysis by encoding amplitude-modulated spike trains as dynamic graphs, enabling the extraction of meaningful topological descriptors. These graph-based features are then processed to detect deviations in network organization induced by neurodevelopmental perturbations. As proof of concept, we examine the impact of valproic acid (VPA), a known environmental ASD risk factor. VPA disrupts synaptic signaling in hFOs, reducing efficiency, increasing path length, and decreasing input connectivity. Despite biological variability, the GDN consistently detects early dysfunction within 24 h post-exposure and captures transient millisecond-level events. This supports MEA-coupled hFOs as predictive platforms for ASD risk assessment and real-time neurotoxicity screening.

Acute myeloid leukemia (AML) continues to represent a substantial unmet therapeutic need in clinical practice. In recent years, peptide-drug conjugates and small interfering RNA (siRNA) drugs have gained considerable attention due to their impressive clinical progress in treating various diseases. In this study, we designed a carrier-free "3-in-1" peptide-daunorubicin-siRNA (PDR) nanoassembly, which combines a cell-penetrating and tumor-suppressing peptide, a daunorubicin (DNR) prodrug, and siRNA targeting the LILRB4 gene. After optimizing the molar ratio among peptide, DNR prodrug, and siRNA, we identified the most potent PDR formulation, which exhibited excellent intracellular uptake efficiency, primarily through caveolin-mediated endocytosis, in THP-1 cells. The pH-responsive bond in the DNR prodrug facilitated the endosomal escape of siRNA, leading to significant gene repression of LILRB4. Additionally, the tumor-suppressing peptide p16^MIS effectively inhibited the transition of cells from the S phase to the G2/M phase and induced apoptosis. In a leukemia mouse model, PDR efficiently suppressed leukemia cell invasion, prolonged survival, and reduced leukemia cell infiltration in the bone marrow. Notably, silencing LILRB4 not only promoted T cell maturation in spleen and lymph nodes but also enhanced T cell infiltration in tumor tissues. This study offered a highly promising therapeutic strategy for AML and other diseases.

Multisite electrophysiological monitoring of ex vivo tissues and organ models is essential for basic research and drug toxicity evaluation. However, conventional microelectrode arrays with fixed positions and rigid structures are insufficient for dynamic, curved tissue surfaces. Here, we present a magnetically actuated soft electrode (MSE) with precise navigation, adaptive attachment, and high-fidelity signal acquisition. Operating in a "locate-adhere-record-detach" cycle, the MSE enabled continuous multisite detection on beating ex vivo tissues. In isolated rat heart experiments, the MSE demonstrated millimeter-level navigation accuracy, stable contact, and high signal-to-noise ratio (average 28 dB). By integrating magnetic locomotion with electrophysiological sensing, this work establishes a programmable, actively addressable platform for multisite electrophysiological monitoring of organ models, tissue slices, and engineered constructs, offering broad potential for cardiotoxicity screening and cardiovascular research.

Precision-controlled microscale manipulation tasks-including neural probe implantation, ophthalmic surgery, and cell membrane puncture-often involve minimally invasive membrane penetration techniques with real-time force feedback to minimize tissue trauma. This imposes rigorous design requirements on the corresponding miniaturized instruments with robotic assistance. This paper proposes an integrated piezoelectric module (IPEM) that combines high-frequency vibration-assisted penetration with real-time in situ force sensing. The IPEM features a compact piezoelectric actuator integrated with a central tungsten probe, generating axial micro-vibration (4,652 Hz) to enable smooth tissue penetration while simultaneously measuring contact and penetration forces via the piezoelectric effect. Extensive experiments were conducted to validate the effectiveness and efficacy of the proposed IPEM. Both static and dynamic force-sensing tests demonstrate the linearity, sensitivity (9.3 mV/mN), and accuracy (mean absolute error < 0.3 mN, mean absolute percentage error < 1%) of the embedded sensing unit. In gelatin phantom tests, the module reduced puncture and insertion forces upon activation of vibration. In vivo experiments in mouse brains further confirmed that the system could reduce penetration resistance (from an average of 11.67 mN without vibration to 7.8 mN with vibration, decreased by 33%) through the pia mater and accurately mimic the electrode implantation-detachment sequence, leaving a flexible electrode embedded with minimal trauma. This work establishes a new paradigm for smart surgical instruments by integrating a compact actuator-sensor design with real-time in situ force feedback capabilities, with immediate applications in brain-machine interfaces and microsurgical robotics.

Sound target detection (STD) plays a critical role in modern acoustic sensing systems. However, existing automated STD methods show poor robustness and limited generalization, especially under low signal-to-noise ratio (SNR) conditions or when processing previously unencountered sound categories. To overcome these limitations, we first propose a brain-computer interface (BCI)-based STD method that utilizes neural responses to auditory stimuli. Our approach features the Triple-Region Spatiotemporal Dynamics Attention Network (Tri-SDANet), an electroencephalogram (EEG) decoding model incorporating neuroanatomical priors derived from EEG source analysis to enhance decoding accuracy and provide interpretability in complex auditory scenes. Recognizing the inherent limitations of stand-alone BCI systems (notably their high false alarm rates), we further develop an adaptive confidence-based brain-machine fusion strategy that intelligently combines decisions from both the BCI and conventional acoustic detection models. This hybrid approach effectively merges the complementary strengths of neural perception and acoustic feature learning. We validate the proposed method through experiments with 16 participants. Experimental results demonstrate that the Tri-SDANet achieves state-of-the-art performance in neural decoding under complex acoustic conditions. Moreover, the hybrid system maintains reliable detection performance at low SNR levels while exhibiting remarkable generalization to unseen target classes. In addition, source-level EEG analysis reveals distinct brain activation patterns associated with target perception, offering neuroscientific validation for our model design. This work pioneers a neuro-acoustic fusion paradigm for robust STD, offering a generalizable solution for real-world applications through the integration of noninvasive neural signals with artificial intelligence.

Objective: Advancing high-speed steady-state visually evoked potential (SSVEP)-based brain-computer interface (BCI) systems requires effective electroencephalogram (EEG) decoding through deep learning. However, challenges persist due to data sparsity and the unclear neural basis of most augmentation techniques. Furthermore, effective processing of dynamic EEG signals and accommodating augmented data require a more sophisticated model tailored to the unique characteristics of EEG signals. Approach: This study introduces background EEG mixing (BGMix), a novel data augmentation technique grounded in neural principles that enhances training samples by replacing background noise between different classes. Building on this, we propose the augment EEG Transformer (AETF), a Transformer-based model designed to capture the temporal, spatial, and frequential features of EEG signals, leveraging the advantages of Transformer architectures. Main results: Experimental evaluations of 2 publicly available SSVEP datasets show the efficacy of the BGMix strategy and the AETF model. The BGMix approach notably improved the average classification accuracy of 4 distinct deep learning models, with increases ranging from 11.06% to 21.39% and 4.81% to 25.17% in the respective datasets. Furthermore, the AETF model outperformed state-of-the-art baseline models, excelling with short training data lengths and achieving the highest information transfer rates (ITRs) of 205.82 ± 15.81 bits/min and 240.03 ± 14.91 bits/min on the 2 datasets. Significance: This study introduces a novel EEG augmentation method and a new approach to designing deep learning models informed by the neural processes of EEG. These innovations significantly improve the performance and practicality of high-speed SSVEP-based BCI systems.

Motion recognition, especially the distinction between high-speed and low-speed movements, is a challenging computational task that typically requires substantial resources. The extensive response range required to detect such variations in speed often exceeds the capabilities of traditional CMOS technology. This report introduces a SnS₂-based in-sensor reservoir that offers an effective solution for detecting a variety of motion types at sensory terminals. By leveraging in-sensor reservoir computing, the device excels at classifying different motions across a wide velocity spectrum, providing a novel and promising method for motion recognition. The conductance of SnS₂ channel under light stimulation is governed by the trapping and recombination of photogenerated carriers at the inherent defect states, which contributes to the flexible optically dynamical sensing function of the device to varying illumination times. These attributes make the device versatile for both optical sensing and synaptic emulation. The findings suggest that such a SnS₂-based device could be instrumental in advancing motion recognition capabilities for developing next-generation artificial intelligence systems.