Search

In order to solve the problems of methods that use a single form of sensing, the ease of causing deformation damage to the targets with a low hardness during grasping, and the slow sliding inhibition of a prosthetic hand when the grasping target slides, which are problems that exist in most current intelligent prosthetic hands, this study introduces an adaptive control strategy for prosthetic hands based on multi-sensor sensing. Using a force-sensing resistor (FSR) to collect changes in signals generated after contact with a target, a prosthetic hand can classify the target’s hardness level and adaptively provide the desired grasping force so as to reduce the deformation of and damage to the target in the process of grasping. A fiber-optic sensor collects the light reflected by the object to identify its surface roughness, so that the prosthetic hand adaptively adjusts the sliding inhibition method according to the surface roughness information to improve the grasping efficiency. By integrating information on the hardness and surface roughness of the target, an adaptive control strategy for a prosthetic hand is proposed. The experimental results showed that the adaptive control strategy was able to reduce the damage to the target by enabling the prosthetic hand to achieve stable grasping; after grasping the target with an initial force and generating sliding, the efficiency of slippage inhibition was improved, the target could be stably grasped in a shorter time, and the hardness, roughness and weight ranges of targets that could be grasped by the prosthetic hand were enlarged, thus improving the success rate of stable grasping under extreme conditions.

The electromyography(EMG) signal is the biocurrent associated with muscle contraction and can be used as the input signal to a myoelectric intelligent bionic hand to control different gestures of the hand. Increasing the number of myoelectric-signal channels can yield richer information of motion intention and improve the accuracy of gesture recognition. However, as the number of acquisition channels increases, its effect on the improvement of the accuracy of gesture recognition gradually diminishes, resulting in the improvement of the control effect reaching a plateau. To address these problems, this paper presents a proposed method to improve gesture recognition accuracy by virtually increasing the number of EMG signal channels. This method is able to improve the recognition accuracy of various gestures by virtually increasing the number of EMG signal channels and enriching the motion intention information extracted from data collected from a certain number of physical channels, ultimately providing a solution to the issue of the recognition accuracy plateau caused by saturation of information from physical recordings. Meanwhile, based on the idea of the filtered feature selection method, a quantitative measure of sample sets (separability of feature vectors [SFV]) derived from the divergence and correlation of the extracted features is introduced. The SFV value can predict the classification effect before performing the classification, and the effectiveness of the virtual-dimension increase strategy is verified from the perspective of feature set differentiability change. Compared to the statistical motion intention recognition success rate, SFV is a more representative and faster measure of classification effectiveness and is also suitable for small sample sets.

In order to compare the effects of visual and auditory instructions on the crew when guiding astronauts to complete the procedural tasks in the space station, in this study, subjects were recruited to complete the programmed task of starting from the node module, locating the scientific cabinet and spectrometer, and finally operating the orbital replaceable unit on the spectrometer. Meanwhile, the task performance, eye movement parameters, and cognitive load induced by 2 kinds of instructions in the task were statistically analyzed. The results showed that there were highly significant differences in terms of task completion time, the NASA-TLX (Task Load Index) total score, and eye movement index between the 2 instructions (P < 0.01). There were also significant differences in error rate and effort (P < 0.05). This study proves that visual instruction interaction is better than auditory instruction. Our work provides important reference for the selection of human–computer interaction mode for procedural tasks on space stations. It also provides the experience and theoretical evidence missing so far and proves the benefits of augmented reality assistance in terms of task performance and human factors.

In this paper, we develop a prosthetic bionic hand system to realize adaptive gripping with two closed-loop control loops by using a linear discriminant analysis algorithm (LDA). The prosthetic hand contains five fingers and each finger is driven by a linear servo motor. When grasping objects, four fingers except the thumb would adjust automatically and bend with an appropriate gesture, while the thumb is stretched and bent by the linear servo motor. Since the change of the surface electromechanical signal (sEMG) occurs before human movement, the recognition of sEMG signal with LDA algorithm can help to obtain people’s action intention in advance, and then timely send control instructions to assist people to grasp. For activity intention recognition, we extract three features, Variance (VAR), Root Mean Square (RMS) and Minimum (MIN) for recognition. As the results show, it can achieve an average accuracy of 96.59%. This helps our system perform well for disabilities to grasp objects of different sizes and shapes adaptively. Finally, a test of the people with disabilities grasping 15 objects of different sizes and shapes was carried out and achieved good experimental results.

Myoelectric prosthesis has become an important aid to disabled people. Although it can help people to recover to a nearly normal life, whether they can adapt to severe working conditions is a subject that is yet to be studied. Generally speaking, the working environment is dominated by vibration. This paper takes the gripping action as its research object, and focuses on the identification of grasping intentions under different vibration frequencies in different working conditions. In this way, the possibility of the disabled people who wear myoelectric prosthesis to work in various vibration environment is studied. In this paper, an experimental test platform capable of simulating 0–50 Hz vibration was established, and the Surface Electromyography (sEMG) signals of the human arm in the open and grasping states were obtained through the MP160 physiological record analysis system. Considering the reliability of human intention recognition and the rapidity of algorithm processing, six different time-domain features and the Linear Discriminant Analysis (LDA) classifier were selected as the sEMG signal feature extraction and recognition algorithms in this paper. When two kinds of features, Zero Crossing (ZC) and Root Mean Square (RMS), were used as input, the accuracy of LDA algorithm can reach 96.9%. When three features, RMS, Minimum Value (MIN), and Variance (VAR), were used as inputs, the accuracy of the LDA algorithm can reach 98.0%. When the six features were used as inputs, the accuracy of the LDA algorithm reached 98.4%. In the analysis of different vibration frequencies, it was found that when the vibration frequency reached 20 Hz, the average accuracy of the LDA algorithm in recognizing actions was low, while at 0 Hz, 40 Hz and 50 Hz, the average accuracy was relatively high. This is of great significance in guiding disabled people to work in a vibration environment in the future.

Feature extraction, as an important method for extracting useful information from surface electromyography (SEMG), can significantly improve pattern recognition accuracy. Time and frequency analysis methods have been widely used for feature extraction, but these methods analyze SEMG signals only from the time or frequency domain. Recent studies have shown that feature extraction based on time-frequency analysis methods can extract more useful information from SEMG signals. This paper proposes a novel time-frequency analysis method based on the Stockwell transform (S-transform) to improve hand movement recognition accuracy from forearm SEMG signals. First, the time-frequency analysis method, S-transform, is used for extracting a feature vector from forearm SEMG signals. Second, to reduce the amount of calculations and improve the running speed of the classifier, principal component analysis (PCA) is used for dimensionality reduction of the feature vector. Finally, an artificial neural network (ANN)-based multilayer perceptron (MLP) is used for recognizing hand movements. Experimental results show that the proposed feature extraction based on the S-transform analysis method can improve the class separability and hand movement recognition accuracy compared with wavelet transform and power spectral density methods.

The bipedal robot is one of the most attractive robots types given its similarity to the locomotion of human beings and its ability to assist people to walk during rehabilitation. This review summarizes the chronological historical development of bipedal robots and introduces some current popular bipedal robots age. Then, the basic theory-stability control and key technology-motion planning of bipedal robots are introduced and analyzed. Bipedal robots have a wide range of applications in the service, education, entertainment, and other industries. After that, we specifically discuss the applications of bipedal robots in lower limb rehabilitation, including wearable exoskeleton robots, rehabilitation equipment, soft exoskeleton robots, and unpowered exoskeleton robots, and their control methods. Lastly, the future development and the challenges in this field are discussed.

Nanoliposomes have a broad range of applications in the treatment of autoimmune inflammatory diseases because of their ability to considerably enhance drug transport. For their clinical application, nanoliposomes must be able to realize on-demand release of drugs at disease sites to maximize drug-delivery efficacy and minimize side effects. Therefore, responsive drug-release strategies for inflammation treatment have been explored; however, no specific design has been realized for a responsive drug-delivery system based on pyroptosis-related inflammation. Herein, we report a pioneering strategy for self-adaptive pyroptosis-responsive liposomes (R8-cardiolipin-containing nanoliposomes encapsulating dimethyl fumarate, RC-NL@DMF) that precisely release encapsulated anti-pyroptotic drugs into pyroptotic cells. The activated key pyroptotic protein, the N-terminal domain of gasdermin E, selectively integrates with the cardiolipin of liposomes, thus forming pores for controlled drug release, pyroptosis, and inflammation inhibition. Therefore, RC-NL@DMF exhibited effective therapeutic efficacies to alleviate autoimmune inflammatory damages in zymosan-induced arthritis mice and dextran sulfate sodium-induced inflammatory bowel disease mice. Our novel approach holds great promise for self-adaptive pyroptosis-responsive on-demand drug delivery, suppressing pyroptosis and treating autoimmune inflammatory diseases.

An enclosed microgroove is a vital surface microstructure that is difficult to prepare on the surfaces of difficult-to-machine electrically conductive materials using conventional methods. Electrical discharge machining (EDM) is suitable for this task. It is obtained primarily through layer-by-layer scanning EDM with micro-column electrodes or reciprocating EDM with foil electrodes. However, owing to the small cross-sectional size of the electrodes, a stable machining process is challenging to achieve, which seriously reduces the machining efficiency. To solve these problems, this study proposed ultrasonic vibration-assisted EDM using a laminated electrode possessed strong anti-interference properties for enclosed microgrooves. The effects of ultrasonic vibration on the EDM of microgrooves with single foil and laminated electrodes were investigated. The results show that ultrasonic vibration could improve the machining efficiency of the two types of electrodes and significantly improve the profile quality of the microgrooves prepared using laminated electrodes. When the amplitude was 3 μm and the frequency was 30 kHz, the bottom sidewall corner radius of microgrooves machined by brass foils in laminated electrodes was 28 μm, the width, depth, and roughness of microgrooves machined by copper foils in laminated electrodes were approximately 78 μm, 229 μm and 0.7 μm, the material removal rate was 0.0072 mm3/min. Based on the optimized parameters, inverted “convex” microgrooves with fine consistency were successfully prepared using laminated electrodes on Ti–6Al–4V alloy surfaces. The machining time was reduced by 40% for each microgroove compared with using the single foil electrode. This study provides a new method for preparing complex microgrooves.

BackgroundDespite advances in neuro-oncology, treatments of glioma and tools for predicting the outcome of patients remain limited. The objective of this research is to construct a prognostic model for glioma using the Homologous Recombination Deficiency (HRD) score and validate its predictive capability for glioma.MethodsWe consolidated glioma datasets from TCGA, various cancer types for pan-cancer HRD analysis, and two additional glioma RNAseq datasets from GEO and CGGA databases. HRD scores, mutation data, and other genomic indices were calculated. Using machine learning algorithms, we identified signature genes and constructed an HRD-related prognostic risk model. The model’s performance was validated across multiple cohorts. We also assessed immune infiltration and conducted molecular docking to identify potential therapeutic agents.ResultsOur analysis established a correlation between higher HRD scores and genomic instability in gliomas. The model, based on machine learning algorithms, identified seven key genes, significantly predicting patient prognosis. Moreover, the HRD score prognostic model surpassed other models in terms of prediction efficacy across different cancers. Differential immune cell infiltration patterns were observed between HRD risk groups, with potential implications for immunotherapy. Molecular docking highlighted several compounds, notably Panobinostat, as promising for high-risk patients.ConclusionsThe prognostic model based on the HRD score threshold and associated genes in glioma offers new insights into the genomic and immunological landscapes, potentially guiding therapeutic strategies. The differential immune profiles associated with HRD-risk groups could inform immunotherapeutic interventions, with our findings paving the way for personalized medicine in glioma treatment.

Electrode wear during electrical discharge machining (EDM) is inevitable, and tool electrodes of different materials exhibit different wear rates. Unlike a single-material tool electrode, for a laminated electrode consisting of various material foils (LE-VMF), the components suffer from significantly different amounts of wear and also influence each other in EDM, however, the form and cause of the wear are not clear. Thus, the LE-VMFs including symmetric and asymmetric LE-VMFs were prepared and used in EDM in this paper. The wear of foils in LE-VMFs and their mutual influence during the EDM process were investigated through experiments. The wear forms of a single foil electrode and a foil of the same material in the LE-VMF were also compared. The experimental results show that copper foil with a lower wear rate had a protective effect on an adjacent brass foil with a larger wear rate in the LE-VMF, while the protective effect of the brass foil in the symmetric LE-VMF was larger than that in the asymmetric LE-VMF. The closer the distance from the copper foil, the stronger was the protection effect and the smaller was the wear. In addition, the working surface of the brass foil in the LE-VMF did not show any concavity-like wear in the middle region of its thickness after EDM, as was the case for the single brass foil electrode, even if the thickness of the brass foil was greater than 150 μm.

Background: Hepatic fibrosis is caused by various liver diseases and eventually develops into liver cancer. There is no specific drug approved for the treatment of hepatic fibrosis in the world. Acacetin (AC), a natural flavonoid, is widely present in nature in various plants, such as black locust, Damiana, Silver birch. It has been reported that acacetin can inhibit the proliferation of cancer cells and induce apoptosis. Purpose: In this study, we investigated the effect of acacetin on hepatic stellate cell apoptosis, thereby improving hepatic fibrosis, and combined experimental validation and molecular docking to reveal the underlying mechanism. Result: First, we discovered that acacetin inhibited hepatic stellate cell proliferation as well as the expression of fibrosis-related proteins α-smooth muscle actin (α-SMA) and collagen type I 1 gene (COL1A1) in LX2 cells. Acacetin was then found to promote apoptosis of hepatic stellate cells through the caspase cascade pathway. Network pharmacology screening showed that TP53, CASP3, CASP8, BCL2, PARP1, and BAX were the most important targets related to apoptosis in the PPI network. GO and KEGG analyses of these six important targets were performed, and the top 10 enriched biological processes and related signaling pathways were revealed. Further network pharmacology analysis proved that apoptosis was involved in the biological process of acacetin's action against hepatic stellate cells. Finally, molecular docking revealed that acacetin binds to the active sites of six apoptotic targets. In vitro experiments further confirmed that acacetin could promote the apoptosis of LX2 cells by inducing the activation of P53, thereby improving hepatic fibrosis. Conclusion: acacetin induces P53 activation and promotes apoptosis of hepatic stellate cells thereby ameliorating hepatic fibrosis.

Rheumatoid arthritis (RA) is a common autoimmune disease leading to pain, disability, and even death. Although studies have revealed that aberrant activation of STING was implicated in various autoimmune diseases, the role of STING in RA remains unclear. In the current study, we demonstrated that STING activation was pivotal in RA pathogenesis. As the accumulation of dsDNA, a specific stimulus for STING, is a feature of RA, we developed a spherical polyethyleneimine-coated mesoporous polydopamine nanoparticles loaded with STING antagonist C-176 (PEI-PDA@C-176 NPs) for treating RA. The fabricated NPs with biocompatibility had high DNA adsorption ability and could effectively inhibit the STING pathway and inflammation in macrophages. Intra-articular administration of PEI-PDA@C-176 NPs could effectively reduce joint damage in mice models of dsDNA-induced arthritis and collagen-induced arthritis by inhibiting STING pathway. We concluded that materials with synergistic effects of STING inhibition might be an efficacious strategy to treat RA.

While mesenchymal stem cell (MSC) shows great potentials in treating intervertebral disc degeneration, most MSC die soon after intradiscal transplantation, resulting in inferior therapeutic efficacy. Currently, bulk hydrogels are the common solution to improve MSC survival in tissues, although hydrogel encapsulation impairs MSC migration and disrupts extracellular microenvironment. Cell hydrogel encapsulation has been proposed to overcome the limitation of traditional bulk hydrogels, yet this technique has not been used in treating disc degeneration. Using a layer-by-layer self-assembly technique, we fabricated alginate and gelatin microgel to encapsulate individual MSC for treating disc degeneration. The small size of microgel allowed intradiscal injection of coated MSC. We demonstrated that pyroptosis was involved in MSC death under oxidative stress stimulation, and microgel coating suppressed pyroptosis activation by maintaining mitochondria homeostasis. Microgel coating protected MSC in the harsh disc microenvironment, while retaining vital cellular functions such as migration, proliferation, and differentiation. In a rat model of disc degeneration, coated MSC exhibits prolonged retention in the disc and better efficacy of attenuating disc degeneration, as compared with bare MSC treatment alone. Further, microgel-coated MSC exhibited improved therapeutic effects in treating disc degeneration via suppressing the activation of pyroptosis in the disc. For the first time, microgel-encapsulated MSC was used to treat disc degeneration and obtain encouraging outcomes. The developed biocompatible single-cell hydrogel is an effective strategy to protect MSC and maintain cellular functions and may be an efficacious approach to improving the efficacy of MSC therapy in treating disc degeneration. The objective of this study is to improve the efficacy of cell therapy for treating disc degeneration using single-cell hydrogel encapsulation and further to understand related cytoprotective mechanisms.