3D Bioprinted Spheroidal Droplets for Engineering the Heterocellular Coupling between Cardiomyocytes and Cardiac Fibroblasts

Since conventional human cardiac two-dimensional (2D) cell culture and multilayered three-dimensional (3D) models fail in recapitulating cellular complexity and possess inferior translational capacity, we designed and developed a high-throughput scalable 3D bioprinted cardiac spheroidal droplet-organoid model with cardiomyocytes and cardiac fibroblasts that can be used for drug screening or regenerative engineering applications. This study helped establish the parameters for bioprinting and cross-linking a gelatin-alginate-based bioink into 3D spheroidal droplets. A flattened disk-like structure developed in prior studies from our laboratory was used as a control. The microstructural and mechanical stability of the 3D spheroidal droplets was assessed and was found to be ideal for a cardiac scaffold. Adult human cardiac fibroblasts and AC16 cardiomyocytes were mixed in the bioink and bioprinted. Live-dead assay and flow cytometry analysis revealed robust biocompatibility of the 3D spheroidal droplets that supported the growth and proliferation of the cardiac cells in the long-term cultures. Moreover, the heterocellular gap junctional coupling between the cardiomyocytes and cardiac fibroblasts further validated the 3D cardiac spheroidal droplet model.

Learning Data Correction for Myoelectric Hand Based on “Survival of the Fittest”

In recent years, myoelectric hands have become multi-degree-of-freedom (DOF) devices, which are controlled via machine learning methods. However, currently, learning data for myoelectric hands are gathered manually and thus tend to be of low quality. Moreover, in the case of infants, gathering accurate learning data is nearly impossible because of the difficulty of communicating with them. Therefore, a method that automatically corrects errors in the learning data is necessary. Myoelectric hands are wearable robots and thus have volumetric and weight constraints that make it infeasible to store large amounts of data or apply complex processing methods. Compared with general machine learning methods such as image processing, those for myoelectric hands have limitations on the data storage, although the amount of data to be processed is quite large. If we can use this huge amount of processing data to correct the learning data without storing the processing data, the machine learning performance is expected to improve. We then propose a method for correcting the learning data through utilisation of the signals acquired during the use of the myoelectric hand. The proposed method is inspired by “survival of the fittest.” The effectiveness of the method was verified through offline analysis. The method reduced the amount of learning data and learning time by approximately a factor of 10 while maintaining classification rates. The classification rates improved for one participant but slightly deteriorated on average among all participants. To solve this problem, verifying the method via interactive learning will be necessary in the future.

Classification of Soft Tissue Sarcoma Specimens with Raman Spectroscopy as Smart Sensing Technology

Intraoperative confirmation of negative resection margins is an essential component of soft tissue sarcoma surgery. Frozen section examination of samples from the resection bed after excision of sarcomas is the gold standard for intraoperative assessment of margin status. However, it takes time to complete histologic examination of these samples, and the technique does not provide real-time diagnosis in the operating room (OR), which delays completion of the operation. This paper presents a study and development of sensing technology using Raman spectroscopy that could be used for detection and classification of the tumor after resection with negative sarcoma margins in real time. We acquired Raman spectra from samples of sarcoma and surrounding benign muscle, fat, and dermis during surgery and developed (i) a quantitative method (QM) and (ii) a machine learning method (MLM) to assess the spectral patterns and determine if they could accurately identify these tissue types when compared to findings in adjacent H&E-stained frozen sections. High classification accuracy (>85%) was achieved with both methods, indicating that these four types of tissue can be identified using the analytical methodology. A hand-held Raman probe could be employed to further develop the methodology to obtain spectra in the OR to provide real-time in vivo capability for the assessment of sarcoma resection margin status.

Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model

The deep learning gesture recognition based on surface electromyography plays an increasingly important role in human-computer interaction. In order to ensure the high accuracy of deep learning in multistate muscle action recognition and ensure that the training model can be applied in the embedded chip with small storage space, this paper presents a feature model construction and optimization method based on multichannel sEMG amplification unit. The feature model is established by using multidimensional sequential sEMG images by combining convolutional neural network and long-term memory network to solve the problem of multistate sEMG signal recognition. The experimental results show that under the same network structure, the sEMG signal with fast Fourier transform and root mean square as feature data processing has a good recognition rate, and the recognition accuracy of complex gestures is 91.40%, with the size of 1 MB. The model can still control the artificial hand accurately when the model is small and the precision is high.

Self-Assembly of DNA Molecules: Towards DNA Nanorobots for Biomedical Applications

DNA nanotechnology takes DNA molecule out of its biological context to build nanostructures that have entered the realm of robots and thus added a dimension to cyborg and bionic systems. Spurred by spring-like properties of DNA molecule, the assembled nanorobots can be tuned to enable restricted, mechanical motion by deliberate design. DNA nanorobots can be programmed with a combination of several unique features, such as tissue penetration, site-targeting, stimuli responsiveness, and cargo-loading, which makes them ideal candidates as biomedical robots for precision medicine. Even though DNA nanorobots are capable of detecting target molecule and determining cell fate via a variety of DNA-based interactions both in vitro and in vivo, major obstacles remain on the path to real-world applications of DNA nanorobots. Control over nanorobot’s stability, cargo loading and release, analyte binding, and dynamic switching both independently and simultaneously represents the most eminent challenge that biomedical DNA nanorobots currently face. Meanwhile, scaling up DNA nanorobots with low-cost under CMC and GMP standards represents other pertinent challenges regarding the clinical translation. Nevertheless, DNA nanorobots will undoubtedly be a powerful toolbox to improve human health once those remained challenges are addressed by using a scalable and cost-efficient method.

Self-Cognizant Bionic Liquid Sensor for Pathogen Diagnosis

As observed in the outbreaks of SARS and swine flu, as well as many other infectious diseases, the huge volume of human traffic across numerous enclosed public venues has posed immense challenges to preventing the spread of communicable diseases. There is an urgent need for effective disease surveillance management in public areas under pandemic outbreaks. The physicochemical properties associated with ionic liquids make them particularly suited for molecular communications in sensing networks where low throughput is quite adequate for pathogen detection. This paper presents a self-cognizant system for rapid diagnosis of infectious disease using a bionic sensor such that testing can be supported without collecting a fluid sample from a subject through any invasive methods. The system is implemented for testing the performance of the proposed bionic liquid sensing network.

Pt/CNT Micro-Nanorobots Driven by Glucose Catalytic Decomposition

Swimming micro-nanorobots have attracted researchers’ interest in potential medical applications on target therapy, biosensor, drug carrier, and others. At present, the experimental setting of the swimming micro-nanorobots was mainly studied in pure water or H2O2 solution. This paper presents a micro-nanorobot that applied glucose in human body fluid as driving fuel. Based on the catalytic properties of the anode and cathode materials of the glucose fuel cell, platinum (Pt) and carbon nanotube (CNT) were selected as the anode and cathode materials, respectively, for the micro-nanorobot. The innovative design adopted the method of template electrochemical and chemical vapor deposition to manufacture the Pt/CNT micro-nanorobot structure. Both the scanning electron microscope (SEM) and transmission electron microscope (TEM) were employed to observe the morphology of the sample, and its elements were analyzed by energy-dispersive X-ray spectroscopy (EDX). Through a large number of experiments in a glucose solution and according to Stoker’s law of viscous force and Newton’s second law, we calculated the driving force of the fabricated micro-nanorobot. It was concluded that the structure of the Pt/CNT micro-nanorobot satisfied the required characteristics of both biocompatibility and motion.

Human Somatosensory Processing and Artificial Somatosensation

In the past few years, we have gained a better understanding of the information processing mechanism in the human brain, which has led to advances in artificial intelligence and humanoid robots. However, among the various sensory systems, studying the somatosensory system presents the greatest challenge. Here, we provide a comprehensive review of the human somatosensory system and its corresponding applications in artificial systems. Due to the uniqueness of the human hand in integrating receptor and actuator functions, we focused on the role of the somatosensory system in object recognition and action guidance. First, the low-threshold mechanoreceptors in the human skin and somatotopic organization principles along the ascending pathway, which are fundamental to artificial skin, were summarized. Second, we discuss high-level brain areas, which interacted with each other in the haptic object recognition. Based on this close-loop route, we used prosthetic upper limbs as an example to highlight the importance of somatosensory information. Finally, we present prospective research directions for human haptic perception, which could guide the development of artificial somatosensory systems.

Application of Interactive Video Games as Rehabilitation Tools to Improve Postural Control and Risk of Falls in Prefrail Older Adults

The purpose of this study was to examine whether interactive video game (IVG) training is an effective way to improve postural control outcomes and decrease the risk of falls. A convenience sample of 12 prefrail older adults were recruited and divided into two groups: intervention group performed IVG training for 40 minutes, twice per week, for a total of 16 sessions. The control group received no intervention and continued their usual activity. Outcome measures were centre of pressure (COP), mean velocity, sway area, and sway path. Secondary outcomes were Berg Balance Scale, Timed Up and Go (TUG), Falls Efficacy Scale International (FES-I), and Activities-Specific Balance Confidence (ABC). Assessment was conducted with preintervention (week zero) and postintervention (week eight). The intervention group showed significant improvement in mean velocity, sway area, Berg Balance Scale, and TUG (p<0.01) compared to the control group. However, no significant improvement was observed for sway path (p=0.35), FES-I (p=0.383), and ABC (p=0.283). This study showed that IVG training led to significant improvements in postural control but not for risk of falls.

Survey on Main Drive Methods Used in Humanoid Robotic Upper Limbs

Humanoid robotic upper limbs including the robotic hand and robotic arm are widely studied as the important parts of a humanoid robot. A robotic upper limb with light weight and high output can perform more tasks. The drive system is one of the main factors affecting the weight and output of the robotic upper limb, and therefore, the main purpose of this study is to compare and analyze the effects of the different drive methods on the overall structure. In this paper, we first introduce the advantages and disadvantages of the main drive methods such as tendon, gear, link, fluid (hydraulic and pneumatic), belt, chain, and screw drives. The design of the drive system is an essential factor to allow the humanoid robotic upper limb to exhibit the structural features and functions of the human upper limb. Therefore, the specific applications of each drive method on the humanoid robotic limbs are illustrated and briefly analyzed. Meanwhile, we compared the differences in the weight and payload (or grasping force) of the robotic hands and robotic arms with different drive methods. The results showed that the tendon drive system is easier to achieve light weight due to its simple structure, while the gear drive system can achieve a larger torque ratio, which results in a larger output torque. Further, the weight of the actuator accounts for a larger proportion of the total weight, and a reasonable external placement of the actuator is also beneficial to achieve light weight.