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

Three-dimensional (3D) neural cultures are increasingly recognized for their complexity and resemblance to in vivo neural microenvironments. In this paper, we present a novel 3D cell culturing and noninvasive characterization technique of neural spheroids. Based on embedded platinum wires, the cultured cells are lollipop-shaped spheroids where axons are extended and integrated around the embedded wires. Electrical microstimulation enhanced the connectivity between spheroids and demonstrated signal propagation among them. The resultant axonal elongation facilitated the formation of robust neural tracts interconnecting the neural spheroids. Variation of cells' density allows to adjust the spheroid's diameter, identifying 1 million cells as good number of cells for robust spheroid formation. Recordings of spheroid activities reveal higher-quality neural signal measurement from interior cells compared to those obtained from exterior cells. Viability assays confirmed the efficacy of the proposed culturing technique for sustained growth of neural spheroids over a 1-month period. The proposed spheroid culturing technique holds potential applications in various fields, such as development of brain organoids, which enables real-time interconnection characterization and sensing of environment conditions.

Tissues larger than 400 μm in size lacking microvascular networks cannot survive for long periods of time in vitro. The development of microfluidic technology provides an efficient research tool for constructing microvascular models in vitro. However, traditional single-layer microfluidic chips faced the limitation of spatial layout and could not provide diverse fluidic environments within a single chip. In this paper, we present a novel microfluidic chip design with a 3-layer configuration that utilizes a polycarbonate (PC) porous membrane to separate the culture fluid channels from the tissue chambers, featuring flexibly designable multitissue chambers. PC porous membranes act as the capillary in the vertical direction, enabling precise hydrogel patterning and successfully constructing a microfluidic environment suitable for microvascular tissue growth. The chip demonstrates the ability to build microvascular networks of different shapes such as triangle, rectangle, and inverted triangle on a single chip for more than 10 days. The microvascular networks cultured for 12 days were successfully perfused with 70-kDa fluorescein isothiocyanate, which indicated that the generated networks had good barrier properties. A correlation between tissue chamber shape and shear stress was demonstrated using COMSOL, and a preliminary validation of the flow direction of interstitial flow and the important effect of shear stress on microvascular growth was demonstrated by vascularization experiments. This flexible and scalable design is ideal for culturing multiple vascularized organ tissues on a single microfluidic chip, as well as for studying the effects of different fluidic factors on microvascular growth.

Soft structures driven by magnetic fields exhibit the characteristics of being unencumbered and rapidly responsive, enabling the fabrication of various soft robots according to specific requirements. However, soft structures made from a single magnetic material cannot meet the multifunctional demands of practical scenarios, necessitating the development of soft robot fabrication technologies with composite structures of diverse materials. A novel enhanced digital light processing (DLP) 3-dimensional (3D) printing technology has been developed, capable of printing composite magnetic structures with different materials in a single step. Furthermore, a soft robot with a hard magnetic material-superparamagnetic material composite was designed and printed, demonstrating its thermal effect under high-frequency magnetic fields and the editability of the magnetic domains of the hard magnetic material. The robot exhibits a range of locomotive behaviors, including crawling, rolling, and swimming. Under the influence of a 1-Hz actuation magnetic field, the normalized velocities for these modes of motion are recorded as 0.31 body length per second for crawling, 1.88 body length per second for rolling, and 0.14 body length per second for swimming. The robot has demonstrated its capacity to navigate uneven terrain, surmount barriers, and engage in directed locomotion, along with the ability to capture and transport objects. Additionally, it has showcased swimming capabilities within environments characterized by low Reynolds numbers and high fluid viscosities, findings that corroborate simulation analyses. The multimaterial 3D printing technology introduced in this research presents extensive potential for the design and manufacturing of multifunctional soft robots.

Insect cyborgs have been proposed for application in future rescue operations, environmental monitoring, and hazardous area surveys. An energy harvester for insect carrying is critical to the long-lasting life of insect cyborgs, and designing an energy harvester with superior energy output within the load capacity of tiny flying insects is very important. In this study, we measured the thorax vibration frequency of bees during loaded flight conditions. We propose a piezoelectric vibration energy harvester for bees that has a mass of only 46 mg and can achieve maximum effective output voltage and energy density of 5.66 V and 1.27 mW/cm³, respectively. The harvester has no marked effect on the bees' normal movement, which is verified by experiments of mounting the harvester on bees. These results indicate that the proposed harvester is expected to realize a self-power supply of tiny insect cyborgs.

A quadruped robot with a manipulator that combines dynamic motion and manipulation capabilities will greatly expand its application scenarios. However, the addition of the manipulator raises the center of mass of the quadruped robot, increasing complexity in motion control and posing new challenges for maintaining balance on sloped terrains. To address this, a balance control method based on whole-body synergy is proposed in this study, emphasizing adaptive adjustment of the robot system's overall balance through effective utilization of the manipulator's active motion. By establishing a mapping relationship between the manipulator and the robot's attitude angle under system equilibrium, the desired manipulator motion is guided by real-time estimates of terrain angles during motion, enhancing motion efficiency while ensuring robot balance. Furthermore, to enhance motion tracking accuracy, the optimization of system angular momentum and manipulator manipulability is incorporated into hierarchical optimization tasks, improving manipulator controllability and overall system performance. Simulation and experimental results demonstrate that the quadruped robot with a manipulator exhibits reduced velocity and attitude angle fluctuations, as well as smoother foot-end force dynamics during climbing motions with the addition of manipulator adaptive adjustment. These results validate the effectiveness and superiority of the manipulator-based adaptive adjustment strategy proposed in this paper.

Magnetic soft robots have recently become a promising technology that has been applied to minimally invasive cardiovascular surgery. This paper presents the analytical modeling of a novel multi-section magnetic soft robot (MS-MSR) with multi-curvature bending, which is maneuvered by an associated collaborative multirobot navigation system (CMNS) with magnetic actuation and ultrasound guidance targeted for intravascular intervention. The kinematic and dynamic analysis of the MS-MSR's telescopic motion is performed using the optimized Cosserat rod model by considering the effect of an external heterogeneous magnetic field, which is generated by a mobile magnetic actuation manipulator to adapt to complex steering scenarios. Meanwhile, an extracorporeal mobile ultrasound navigation manipulator is exploited to track the magnetic soft robot's distal tip motion to realize a closed-loop control. We also conduct a quadratic programming-based optimization scheme to synchronize the multi-objective task-space motion of CMNS with null-space projection. It allows the formulation of a comprehensive controller with motion priority for multirobot collaboration. Experimental results demonstrate that the proposed magnetic soft robot can be successfully navigated within the multi-bifurcation intravascular environment with a shape modeling error $3.62\pm {1.28}^{\circ }$ and a tip error of $1.08\pm 0.45\mathrm{mm}$ under the actuation of a CMNS through in vitro ultrasound-guided vasculature interventional tests.

With the prevalence of cardiovascular disease, it is imperative that medical monitoring and treatment become more instantaneous and comfortable for patients. Recently, wearable and implantable optoelectronic devices can be seamlessly integrated into human body to enable physiological monitoring and treatment in an imperceptible and spatiotemporally unconstrained manner, opening countless possibilities for the intelligent healthcare paradigm. To achieve biointegrated cardiac healthcare, researchers have focused on novel strategies for the construction of flexible/stretchable optoelectronic devices and systems. Here, we overview the progress of biointegrated flexible and stretchable optoelectronics for wearable and implantable cardiac healthcare devices. Firstly, the device design is addressed, including the mechanical design, interface adhesion, and encapsulation strategies. Next, the practical applications of optoelectronic devices for cardiac physiological monitoring, cardiac optogenetics, and nongenetic stimulation are presented. Finally, an outlook on biointegrated flexible and stretchable optoelectronic devices and systems for intelligent cardiac healthcare is discussed.

Personalized pain medicine aims to tailor pain treatment strategies for the specific needs and characteristics of an individual patient, holding the potential for improving treatment outcomes, reducing side effects, and enhancing patient satisfaction. Despite existing pain markers and treatments, challenges remain in understanding, detecting, and treating complex pain conditions. Here, we review recent engineering efforts in developing various sensors and devices for addressing challenges in the personalized treatment of pain. We summarize the basics of pain pathology and introduce various sensors and devices for pain monitoring, assessment, and relief. We also discuss advancements taking advantage of rapidly developing medical artificial intelligence (AI), such as AI-based analgesia devices, wearable sensors, and healthcare systems. We believe that these innovative technologies may lead to more precise and responsive personalized medicine, greatly improved patient quality of life, increased efficiency of medical systems, and reducing the incidence of addiction and substance use disorders.

The medial entorhinal cortex of rodents is known to contain grid cells that exhibit precise periodic firing patterns based on the animal's position, resulting in a distinct hexagonal pattern in space. These cells have been extensively studied due to their potential to unveil the navigational computations that occur within the mammalian brain and interesting phenomena such as so-called grid cell distortions have been observed. Previous neuronal models of grid cells assumed their firing fields were independent of environmental boundaries. However, more recent research has revealed that the grid pattern is, in fact, dependent on the environment's boundaries. When rodents are placed in nonsquare cages, the hexagonal pattern tends to become disrupted and adopts different shapes. We believe that these grid cell distortions can provide insights into the underlying neural circuitry involved in grid cell firing. To this end, a calibration circuit for grid cells is proposed. Our simulations demonstrate that this circuit is capable of reproducing grid distortions observed in several previous studies. Our model also reproduces distortions in place cells and incorporates experimentally observed distortions of speed cells, which present further opportunities for exploration. It generates several experimentally testable predictions, including an alternative behavioral description of boundary vector cells that predicts behaviors in nonsquare environments different from the current model of boundary vector cells. In summary, our study proposes a calibration circuit that reproduces observed grid distortions and generates experimentally testable predictions, aiming to provide insights into the neural mechanisms governing spatial computations in mammals.

Cardiovascular diseases are a prominent cause of mortality, emphasizing the need for early prevention and diagnosis. Utilizing artificial intelligence (AI) models, heart sound analysis emerges as a noninvasive and universally applicable approach for assessing cardiovascular health conditions. However, real-world medical data are dispersed across medical institutions, forming "data islands" due to data sharing limitations for security reasons. To this end, federated learning (FL) has been extensively employed in the medical field, which can effectively model across multiple institutions. Additionally, conventional supervised classification methods require fully labeled data classes, e.g., binary classification requires labeling of positive and negative samples. Nevertheless, the process of labeling healthcare data is time-consuming and labor-intensive, leading to the possibility of mislabeling negative samples. In this study, we validate an FL framework with a naive positive-unlabeled (PU) learning strategy. Semisupervised FL model can directly learn from a limited set of positive samples and an extensive pool of unlabeled samples. Our emphasis is on vertical-FL to enhance collaboration across institutions with different medical record feature spaces. Additionally, our contribution extends to feature importance analysis, where we explore 6 methods and provide practical recommendations for detecting abnormal heart sounds. The study demonstrated an impressive accuracy of 84%, comparable to outcomes in supervised learning, thereby advancing the application of FL in abnormal heart sound detection.