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

Biological imaging has revolutionized tissue analysis by revealing morphological and physiological dynamics, yet faces inherent limitations in penetration depth and resolution. Micro/nanomotors (MNMs), with autonomous propulsion and spatiotemporal control, offer transformative solutions to traditional static imaging paradigms. These dynamic contrast agents enhance detection sensitivity in ultrasound, fluorescence, photoacoustic, and magnetic resonance imaging via motion-amplified signal modulation, enabling real-time tracking of subcellular events and microenvironmental changes. While MNMs-enhanced bioimaging has advanced rapidly, systematic analysis of their mechanisms and challenges remains limited. Based on our research experience in this field, this paper first summarizes the signal-enhancing mechanisms of MNMs in single-modal imaging. It then explores multimodal applications through MNMs-probe design and discusses artificial intelligence-driven intelligent MNMs for precision imaging. Finally, challenges and outlook are outlined, aiming to provide a theoretical framework and research roadmap for MNMs-mediated bioimaging technologies.

Robotic palpation for in situ tissue biomechanical evaluation is crucial for disease diagnosis, especially in luminal organs. However, acquiring real-time information about the tissue's interaction state and physical characteristics remains a substantial challenge. While commercial surgical robotic systems have integrated tactile feedback, the absence of tactile intelligence and autonomous decision-making limits the surgeon's ability to comprehensively assess tissue mechanics, hindering the efficient detection of abnormalities. Endoscopic optical coherence tomography has emerged as a promising technology for real-time, 3-dimensional visualization of tissue microstructures and subtle lesions in luminal organs. However, it does not address the tactile sensing required for lesion profiling and boundary identification. To bridge this gap, we developed a new robotic bimodal palpation technique that uses a previously proposed optical-coherence-tomography-based tactile sensor, ElastoSight. This technique utilizes circumferential and sliding B-scan modes along with Bayesian optimization for precise lesion center and boundary detection. In tumor phantom models, our technique achieves tumor localization within 30 iterations, with high F₁ scores over 0.976 and a centroid error below 0.032 mm. Using the sliding B-scan mode on the phantom surface, we achieve accurate segmentation of hard tissue inclusions from the surrounding soft tissue, with a precision rate of 0.983 and an area error below 0.25 mm². These results show that the proposed technique effectively tackles real-time lesion localization and segmentation challenges, demonstrating strong performance in simulations and experiments. Our technique can potentially enhance tissue abnormality detection during robot-assisted minimally invasive surgery, improving the precision and efficiency of procedures like tumor removal.

With the increasing use of computed tomography (CT), concerns about radiation dose have grown. Deep-learning-based methods have shown great promise in improving low-dose CT image quality while further reducing patient dose. However, most deep-learning-based methods are trained on vendor-specific CT datasets with varying imaging conditions and dose levels, which results in poor generalizability across vendors due to marked data heterogeneity. Moreover, the centralization of multicenter datasets is restricted by the high costs of data collection and privacy regulations. To overcome these challenges, we propose FedM2CT, a federated metadata-constrained method with mutual learning for all-in-one CT reconstruction. This method enables simultaneous reconstruction of multivendor CT images with different imaging geometries and sampling protocols in one framework. Specifically, FedM2CT consists of 3 modules: task-specific iRadonMAP (TS-iRadonMAP), condition-prompted mutual learning (CPML), and federated metadata learning (FMDL). TS-iRadonMAP performs task-specific low-dose reconstruction, CPML shares condition-prompted knowledge between clients and the server, and FMDL aggregates model parameters with a metamodel to effectively mitigate the effect of data heterogeneity. Extensive experiments under 3 different settings demonstrate that the proposed FedM2CT achieves outstanding results compared to other methods, both qualitatively and quantitatively, showing the potential to achieve the goal of all-in-one CT reconstruction with different low-dose tasks, i.e., low-milliampere-second, sparse-view, and limited-angle.

Growing evidence highlights the importance of body composition (BC), including bone, muscle, and adipose tissue (AT), as a critical biomarker for cardiometabolic risk stratification. However, conventional methods for quantifying BC components using medical images are hindered by labor-intensive workflows and limited anatomical coverage. This study developed BioCompNet-an end-to-end deep learning workflow that integrates dual-parametric magnetic resonance imaging (MRI) sequences (water/fat) with a hierarchical U-Net architecture to enable fully automated quantification of 15 biomechanically critical BC components. BioCompNet targets 10 abdominal compartments (vertebral bone, psoas muscles, core muscles, subcutaneous AT [SAT], superficial SAT, deep SAT, intraperitoneal AT, retroperitoneal AT, visceral AT, and intermuscular AT [IMAT]) and 5 thigh compartments (femur, muscle, SAT, IMAT, and vessels). The workflow was developed on 8,048 MRI slices from a community-based cohort (n = 503) and independently validated on 240 MRI slices from a tertiary hospital (n = 30). The model's performance was benchmarked against expert annotations. On internal and external validation datasets, BioCompNet achieved average Dice similarity coefficients of 0.944 and 0.938 for abdominal compartments and 0.961 and 0.936 for thigh compartments, respectively. Excellent interreader reliability was observed (intraclass correlation coefficient ≥ 0.881) across all quantified features, and IMAT quantification showed a strong linear trend (P _trend < 0.001) compared to physician-rated assessments. The workflow substantially reduced processing time from 128.8 ± 5.6 to 0.12 ± 0.001 min per case. By enabling rapid, accurate, and comprehensive volumetric analysis of BC components, BioCompNet establishes a scalable framework for precision cardiometabolic risk assessment and clinical decision support.

C-tactile afferents are low-threshold mechanoreceptors that innervate the hairy skin of mammals, essential for emotional interactions. Replication of such a mechanism could facilitate emotional interactions between humans and embodied intelligence robotic systems. Herein, we demonstrate a monolithic synaptic device that replicates and integrates tactile sensing and neuromorphic processing functions for in-sensor computing. The device is operable by both mechanical and electrical inputs, with the mechanoelectrical operation mechanism stemming from the synergistic effect of dynamic ionic migration and injection. As a proof of concept, the device effectively converts spatiotemporal tactile stimuli into distinct electrical signals, which are subsequently encoded to enable the microcomputer to classify multiple discrete emotional states, such as happiness, calmness, and excitement. This monolithic integrated device, which converges mild tactile perception with neuromorphic processing, with high tactile sensitivity and low-energy consumption, establishes an approach for emotional interaction between intelligent robots and human beings.

Motivated by the agility of animal and human locomotion, highly dynamic bionic legged robots have been extensively applied across various domains. Legged robotics represents a multidisciplinary field that integrates manufacturing, materials science, electronics, and biology, and other disciplines. Among its core subsystems, the lower limbs are particularly critical, necessitating the integration of structural optimization, advanced modeling techniques, and sophisticated control strategies to fully exploit robots' dynamic performance potential. This paper presents a comprehensive review of recent developments in the structural design of single-legged robots and systematically summarizes prevailing modeling approaches and control strategies. Key challenges and potential future directions are also discussed, serving as a reference for the future application of state-of-the-art manufacturing and control methodologies in legged robotic systems.

Flexible continuum robots exhibit excellent adaptability to a wide range of tasks and environments. However, accurate and efficient modeling and control remain challenging due to their inherent nonlinearities. In this article, a hybrid model-based and online data-driven control method is proposed for a tendon-driven continuum robot, which requires no prior dataset collection or training. The method incorporates the Jacobian derived from the piecewise constant curvature model with online Jacobian error compensation using a Kalman filter. Consecutive Jacobian estimates are constrained to reduce fluctuations and improve stability in real-time estimation. Experimental results validate the effectiveness of the proposed hybrid approach in enhancing tracking accuracy and demonstrate its robustness against external disturbances.

Advancements in health wearable technology hold the potential to prevent critical health issues such as hyponatremia and other hydration-related conditions often triggered by intense physical activities. Approaches to address this issue include the development of thin-film wearable sensors incorporating carbon nanotubes (CNTs), which offer scalability, lightweight design, and exceptional electrical properties. CNT paper serves as an ideal substrate for electrochemical sensors like ion-selective membranes (ISMs), enabling effective on-skin electrolyte monitoring. However, current on-skin devices often face limitations in maintaining performance during human motion. This study introduces a bioinspired surface texturing technique that mimics the microstructures of rose petals to enhance wettability, self-cleaning, and ISM sensitivity. By replicating the mechanical properties of the surface texture found on rose petals, the newly developed ISM achieves accurate measurements across a 2-mm air gap, offering an improved interfacing solution that promotes better sweat recirculation and comfort. This advancement overcomes the constraints of traditional sensors, paving the way for more reliable and effective noninvasive health monitoring in real-world conditions.

In this study, we investigate the complex network dynamics of in vitro neural systems using DishBrain, which integrates live neural cultures with high-density multi-electrode arrays in real-time, closed-loop game environments. By embedding spiking activity into lower-dimensional spaces, we distinguish between spontaneous activity (Rest) and Gameplay conditions, revealing underlying patterns crucial for real-time monitoring and manipulation. Our analysis highlights dynamic changes in connectivity during Gameplay, underscoring the highly sample efficient plasticity of these networks in response to stimuli. To explore whether this was meaningful in a broader context, we compared the learning efficiency of these biological systems with state-of-the-art deep reinforcement learning (RL) algorithms (Deep Q Network, Advantage Actor-Critic, and Proximal Policy Optimization) in a simplified Pong simulation. Through this, we introduce a meaningful comparison between biological neural systems and deep RL. We find that when samples are limited to a real-world time course, even these very simple biological cultures outperformed deep RL algorithms across various game performance characteristics, implying a higher sample efficiency.

Centimeter-scale robots have unique advances such as small size, light weight, and flexible motions, which exhibit great application potential in many fields. Notably, high integration and robustness are 2 key factors determining the locomotion characteristics and practical applications. Here, we propose a novel centimeter-scale quadruped piezo robot. The robot's locomotion is generated by multi-dimensional vibration trajectories at the feet, which are produced through a novel built-in actuation method. The robot achieves high locomotion speed (47.38 body length per second), high carrying capability (28.96 times self-weight), and high-resolution motion (minimum step size of 0.33 μm). Benefiting from the built-in integration method, the robot realizes the built-in integration of actuation, control, communication, and power supply, enabling untethered movement and strong robustness. It has a low startup voltage (10 V _0-p) and an endurance time of 32 min. Furthermore, after enduring 3 consecutive drops, 2 kicks, and being stepped on by an adult (over 3,500 times its own weight), the system remains functional and continues to move afterward. The robot utilizes modular expansion to achieve image sensing applications, including multi-object image capture and object detection. This work provides inspiration for the balance between high-integration design and robustness in centimeter-scale robots.