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

Small extracellular vesicles (sEVs) have emerged as powerful vectors for liquid biopsy, offering a noninvasive window into the dynamic physiological and pathological states of the body. However, to fully leverage the clinical potential of sEV biomarkers, it is imperative to develop robust and efficient technologies for their isolation and analysis. In this study, we introduce a novel sharp-edge acoustofluidic platform designed for rapid and effective sample preparation, coupled with sensitive detection of specific sEV populations based on their surface markers. Our approach utilizes acoustically activated sharp-edge microstructures to concentrate bead-bound sEVs within the microfluidic device, facilitating immediate visualization by fluorescence microscopy. As a proof of principle, we demonstrate the capability of this portable acoustofluidic chip to selectively isolate and detect epidermal growth factor receptor (EGFR)-expressing vesicles, achieving nearly a 6-fold signal enhancement in EGFR-positive sEVs compared to EGFR-negative populations from sample volumes as small as 50 μl. This advancement not only underscores the potential of our platform for high-sensitivity biomarker detection but also paves the way for its application in isolating organ-specific sEVs. Such capability could be transformative for real-time monitoring of organ function and the simultaneous detection of multiple sEV markers, thereby broadening the scope of diagnostic precision and therapeutic decision-making in clinical practice.

Abnormalities in the ossicular chain, a key middle-ear component that is crucial for sound transmission, can lead to conductive hearing loss; reconstruction offers an effective treatment. Accurate preoperative ossicular-chain measurements are essential for creating prostheses; however, current methods rely on cadaver studies or manual measurements from 2-dimensional images, which are time-intensive and laborious and depend heavily on radiologist expertise. To improve efficiency, we aimed to develop a systematic approach for automated ossicular-chain segmentation and measurement using ultra-high-resolution computed tomography (U-HRCT). One hundred forty patients (226 ears) with normal ear anatomy underwent U-HRCT. Twelve parameters were defined to measure ossicular-chain components. Automated measurements based on automated segmentation of 226 ear images were verified through manual measurements. We analyzed variations by ear side, sex, and age group. Stapes analysis was limited by segmentation accuracy. Complete segmentation of the malleus, incus, and stapes was achieved in 47 ears. Automated measurements of 8 parameters showed no significant differences compared to manual measurements in 47 cases. Significant sex-based differences emerged in all parameters except stapes footplate length, incudostapedial joint angle, and stapes volume (P = 0.205, P = 0.560, and P = 0.170, respectively). Notable side-specific differences were observed in female incus height and male malleus volume (P = 0.017 and P = 0.037, respectively). No statistically significant differences were found in other parameters across different age groups, except for malleus and incus volumes (P = 0.015 and P = 0.031). The proposed algorithm effectively automated ossicular-chain segmentation and measurement, establishing a normative range for ossicular parameters and providing a valuable reference for detecting abnormalities.

Object property perception, as a core component of tactile sensing technology, faces severe challenges due to its inherent complexity and diversity, particularly under the constraints of decoupling difficulty and limited precision. Herein, this paper introduces an innovative approach to object property perception utilizing triboelectric-magnetoelastic sensing. This technology integrates triboelectricity and magnetoelasticity, achieving a self-powered sensing mechanism that requires no external power source for sensing signal generation. Moreover, by deploying a triboelectric array, it comprehensively captures multi-dimensional information of objects. Concurrently, in conjunction with magnetoelastic sensing technology, it provides stable and reliable mechanical information, ensuring that the system can accurately decouple key characteristics of objects, such as material properties, softness, and roughness, even in open environments where temperature, humidity, and mechanical conditions change in real time. Furthermore, by combining deep learning algorithms, it achieves exceptionally high recognition accuracy for object properties (material recognition accuracy: 99%, softness recognition accuracy: 100%, roughness recognition accuracy: 95%). Even in complex scenarios with intertwined multiple properties, the overall recognition accuracy remains consistently above 95%. The multimodal tactile sensing technology proposed in this paper provides robust technical support and theoretical foundation for the intelligent development of robots and the enhancement of real-time tactile perception capabilities.

Soft robots demonstrate remarkable potential in medical applications owing to their minimally invasive nature, exceptional controllability, and shape-adaptive capabilities. However, existing control systems primarily rely on a single permanent magnet or electromagnetic coil for actuation, resulting in limited robotic motion capabilities, weak electromagnetic field gradient forces, and bulky magnetic drive systems. These constraints substantially hinder the robot's flexibility and functional expandability. To address these constraints, this study proposes a highly integrated hybrid electromagnetic coil permanent magnet actuation system. This innovative design enables actuation force amplification and synergistic regulation of locomotion, deformation, and orientation. Experimental validation confirms the broad operational capacity of the miniature ferrofluidic robot (MFR), including controllable motion-deformation coupling within multiscale luminal structures and active directional control in biomimetic gastric models. Leveraging the MFR's robust deformation and locomotion abilities, the empowerment mechanism for passive structures significantly enhanced compatibility with mechanical systems. Based on this mechanism, we achieved the transportation of larger-mass simulated drug particles by empowering passive delivery systems. To further validate the functionality of MFR, we developed an MFR-based capsule that achieves precise temporal and spatial control of drug release through experiments involving magnetothermal effect-accelerated release of simulated drugs and selective occlusion in simulated blood vessels. These advancements markedly enhanced the application potential of microrobots in complex and confined clinical environments.

Optimizing resource allocation for Parkinson's disease (PD) motor rehabilitation necessitates identifying biomarkers of responsiveness and dynamic neuroplasticity signatures underlying efficacy. A cohort study of 52 early-stage PD patients undergoing 2-week multidisciplinary intensive rehabilitation therapy (MIRT) was conducted, which stratified participants into responders and nonresponders. A multimodal analysis of resting-state electroencephalography (EEG) microstates and functional magnetic resonance imaging (fMRI) coactivation patterns was performed to characterize MIRT-induced spatiotemporal network reorganization. Responders demonstrated clinically meaningful improvement in motor symptoms, exceeding the minimal clinically important difference threshold of 3.25 on the Unified PD Rating Scale part III, alongside significant reductions in bradykinesia and a significant enhancement in quality-of-life scores at the 3-month follow-up. Resting-state EEG in responders showed a significant attenuation in microstate C and a significant enhancement in microstate D occurrences, along with significantly increased transitions from microstate A/B to D, which significantly correlated with motor function, especially in bradykinesia gains. Concurrently, fMRI analyses identified a prolonged dwell time of the dorsal attention network coactivation/ventral attention network deactivation pattern, which was significantly inversely associated with microstate C occurrence and significantly linked to motor improvement. The identified brain spatiotemporal neural markers were validated using machine learning models to assess the efficacy of MIRT in motor rehabilitation for PD patients, achieving an average accuracy rate of 86%. These findings suggest that MIRT may facilitate a shift in neural networks from sensory processing to higher-order cognitive control, with the dynamic reallocation of attentional resources. This preliminary study validates the necessity of integrating cognitive-motor strategies for the motor rehabilitation of PD and identifies novel neural markers for assessing treatment efficacy.

The convergence of life sciences and information technology is driving a new wave of scientific and technological innovation, with brain-on-a-chip interfaces (BoCIs) emerging as a prominent area of focus in the brain-computer interface field. BoCIs aim to create an interactive bridge between lab-grown brains and the external environment, utilizing advanced encoding and decoding technologies alongside electrodes. Unlike classical brain-computer interfaces that rely on human or animal brains, BoCIs employ lab-grown brains, offering greater experimental controllability and scalability. Central to this innovation is the advancement of stem cell and microelectrode array technologies, which facilitate the development of neuro-electrode hybrid structures to ensure effective signal transmission in lab-grown brains. Furthermore, the evolution of BoCI systems depends on a range of stimulation strategies and novel decoding algorithms, including artificial-intelligence-driven methods, which has expanded BoCI applications to pattern recognition and robotic control. Biological neural networks inherently grant BoCI systems neuro-inspired computational properties-such as ultralow energy consumption and dynamic plasticity-that surpass those of conventional artificial intelligence. Functionally, BoCIs offer a novel framework for hybrid intelligence, merging the cognitive capabilities of biological systems (e.g., learning and memory) with the computational efficiency of machines. However, critical challenges span 4 domains: optimizing neural maturation and functional regionalization, engineering high-fidelity bioelectronic interfaces for robust signal transduction, enhancing adaptive neuroplasticity mechanisms in lab-grown brains, and achieving biophysically coherent integration with artificial intelligence architectures. Addressing these limitations could offer insights into emergent intelligence while enabling next-generation biocomputing solutions.

Endoscopic submucosal dissection (ESD) has emerged as a critical alternative to laparoscopic excisional surgery for the removal of early gastrointestinal tumors. However, current robotic systems for ESD face challenges with accessibility, dexterity, and precision in confined spaces due to limitations in actuation methods and mechanical design. To overcome these issues, a new motorless, master-slave soft robotic system using hydraulic actuation is introduced for ESD procedures. This system features dual soft robotic arms: one serves as an electrosurgical tool, and the other serves as a 3-jaw soft tubular grasper. Notably, the entire system is powered purely by hydraulic force, eliminating the need for DC motors or complex electronic controllers. Inspired by nature, the grasper ensures even force distribution and removes rotational motion, reducing the risk of iatrogenic injury. Its scalable design and compliant properties allow for effective tissue manipulation in tight spaces, with strong pulling forces generated by the embedded soft actuation network. In vitro and ex vivo experiments on fresh porcine tissues demonstrate the system's ability to grip and perform electrosurgical cutting on simulated lesions. This innovation has the potential to be applied in other areas of endoscopic surgery as well.

Limbless creatures can crawl on flat surfaces by deforming their bodies and interacting with asperities on the ground, offering a biological blueprint for designing efficient limbless robots. Inspired by this natural locomotion, we present a soft robot capable of navigating complex terrains using a combination of rectilinear motion and asymmetric steering gaits. The robot is made of a pair of antagonistic inflatable soft actuators covered with a flexible kirigami skin with asymmetric frictional properties. The robot's rectilinear locomotion is achieved through cyclic inflation of internal chambers with precise phase shifts, enabling forward progression. Steering is accomplished using an asymmetric gait, allowing for both in-place rotation and wide turns. To validate its mobility in obstacle-rich environments, we tested the robot in an arena with coarse substrates and multiple obstacles. Real-time feedback from onboard proximity sensors, integrated with a human-machine interface, allowed adaptive control to avoid collisions. This study highlights the potential of bioinspired soft robots for applications in confined or unstructured environments, such as search-and-rescue operations, environmental monitoring, and industrial inspections.

Soft robots are exceptionally suited to exploring complex environments, including amphibious navigations, due to their flexible and adaptive characteristics. However, achieving efficient actuation and multimodal locomotion or transition in amphibious environments for soft robots is challenging. In this paper, we present a multimodal amphibious robot with radial symmetry configuration and 3 different locomotion modes (crawling on land and underwater, swimming in water). The robot consists of 3 soft electrohydraulic flippers, which can be independently or synergistically actuated to rotate or oscillate in both air and water and generate the propulsion for amphibious locomotion. Theoretical analysis and experimental tests have verified the remarkable amphibious actuation performance of the soft electrohydraulic flippers with effective electrode encapsulation process. Optimal actuation frequencies are also obtained for maximizing the efficiency of the robot's movements in different mediums. Based on the 3 powerful soft electrohydraulic flippers with radial symmetry distribution, the robot can smoothly transition from crawling on land to crawling underwater, and swim up from the bottom to the surface of water, without reconfiguration of the robot. This work demonstrates the first amphibious soft robot based on electrohydraulic actuators with multimodal locomotion transition in an amphibious environment and may open up more possibilities for the development of multimodal soft robots.

Remote ischemic conditioning (RIC) is a novel and promising therapeutic intervention for symptomatic intracranial atherosclerotic stenosis (sICAS). This study aimed to evaluate sex differences in stroke recurrence among patients with sICAS and assess the efficacy of RIC in the RICA (chronic remote ischemic conditioning in patients with symptomatic intracranial atherosclerotic stenosis) trial. The RICA trial was a multicenter, randomized clinical trial conducted across 84 stroke centers in China. Patients with sICAS were randomly assigned on a 1:1 ratio to receive either RIC intervention or sham RIC intervention once daily for 12 months. The primary endpoint was ischemic stroke recurrence. The median follow-up duration was 3.5 years. Of the 3,033 patients enrolled in the RICA trial, 1,079 (35.58%) were women. Female patients were generally older (mean [SD] age 62.9 [8.8] years versus 60 [9.2] years) and had a higher prevalence of hypertension, diabetes, and a higher body mass index than male patients. No significant difference was observed in ischemic stroke recurrence risk between female and male patients during a median follow-up of 3.5 years (20.5% versus 16.6%, adjusted hazard ratio, 1.18; [95% CI, 0.97 to 1.42]). However, RIC significantly reduced the risk of ischemic stroke recurrence in male patients, while no similar effect was observed in female patients (adjusted hazard ratio, 0.88; [95% CI, 0.58 to 1.32]; P for interaction = 0.379). No significant sex-based differences were observed in ischemic stroke recurrence among patients with sICAS over the 3.5-year follow-up period. RIC may have better therapeutic benefits for male patients with good compliance.