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

In recent years, several serious traffic accidents have exposed the severity of safety issues in autonomous driving technology. Traditional decision-making methods are unable to address potential risky behaviors caused by the functional insufficiencies or machine performance limitations, and human intervention is still needed. This study proposes an intelligent safety decision-making algorithm with passengers' risk assessment by analyzing passenger physiological states online using functional near-infrared spectroscopy (fNIRS). This algorithm is developed based on twin-delayed deep deterministic policy gradient (TD3), and it can overcome the functional insufficiencies of traditional TD3 and guide TD3 using passengers' risk assessment by analyzing passenger physiological states online while confronting risky scenarios. Three experiments have been conducted in autonomous emergency braking, front vehicle cutting-in, and pedestrian crossing scenarios. The results show that the proposed algorithm demonstrates faster convergence and superior safety and comfort performance compared with traditional TD3. This study highlights the applicability of fNIRS technology in enhancing the safety and comfort of autonomous vehicles in the future.

Sono-immunotherapy is expected to effectively enhance treatment efficacy and reduce mortality in patients with pancreatic cancer. Hence, efficient applicable sono-immunotherapy systems are urgently needed for the treatment of this condition. In this study, hollow mesoporous carbon (HMC) nanoparticles were prepared using the sacrificial template method. These nanoparticles had a porphyrin-like structure and could generate singlet oxygen more efficiently than commercial TiO₂. Cellular assays showed that HMC killed tumor cells in the presence of ultrasonication, primarily by inducing apoptosis. HMC could also accelerate the release of immune factors by tumor cells, thereby activating dendritic cells and enhancing the efficacy of immunotherapy. Experiments in tumor-bearing mice and in situ pancreatic cancer tests showed that HMC, in combination with the small-molecule inhibitors of programmed cell death ligand 1, could reduce tumor growth via the generation of reactive oxygen species following ultrasonication. HMC could enhance the efficacy of immunotherapy by disrupting the immunosuppressive tumor microenvironment and promoting the accumulation of immune cells. Accordingly, in vivo sono-immunotherapy was achieved, and the growth of transplanted tumors and in situ tumors could be reduced. In conclusion, this study proposes a novel method for the preparation of HMC nanoparticles and demonstrates their potential in tumor treatment. Additionally, owing to their unique structure, these HMC nanoparticles could be used for different combination therapies tailored based on specific clinical requirements.

Ischemic stroke exacts a heavy toll in death and disability worldwide. After ischemic stroke, the accumulation of pathobiomolecules in the brain extracellular space (ECS) will exacerbate neurological damage and cognitive impairment. Photobiomodulation (PBM) has been demonstrated to improve cognitive function in Alzheimer's disease mouse models by accelerating molecular transportation in the brain ECS. This suggests that PBM may have a potential role in the accumulation of pathobiomolecules in the brain ECS following ischemic stroke. In this study, we developed a PBM therapy apparatus with custom parameters. By evaluating the treatment effect, we identified that 755 nm was the optimal light wavelength for ischemic stroke in rats with transient middle cerebral artery occlusion/reperfusion. Extracellular diffusion and interstitial fluid (ISF) drainage were measured using a tracer-based magnetic resonance imaging method. Our results showed that PBM accelerated molecular transportation in the brain ECS and ISF drainage, promoting the clearance of pro-inflammatory cytokines and reducing the deposition of pathological proteins. Consequently, the infarct volume decreased and neurological cognitive function was improved. Besides, the acceleration of ISF drainage was achieved by reducing expression and restoring polarization of aquaporin 4 (AQP4) in the peri-infarct area. In summary, we demonstrated that PBM could alleviate ischemia-reperfusion injury and prevent post-stroke cognitive impairment by accelerating molecular transportation in the brain ECS, paving a pathway for ischemic stroke treatment via the light-ECS interaction.

Primates possess a more developed central nervous system and a higher level of intelligence than rodents. Detecting and modulating deep brain activity in primates enhances our understanding of neural mechanisms, facilitates the study of major brain diseases, enables brain-computer interactions, and supports advancements in artificial intelligence. Traditional imaging methods such as magnetic resonance imaging, positron emission computed tomography, and scalp electroencephalogram are limited in spatial resolution. They cannot accurately capture deep brain signals from individual neurons. With the progress of microelectromechanical systems and other micromachining technologies, single-neuron level detection and stimulation technology in rodents based on microelectrodes has made important progress. However, compared with rodents, human and nonhuman primates have larger brain volume that needs deeper implantation depth, and the test object has higher safety and device preparation requirements. Therefore, high-resolution devices suitable for long-term detection in the brains of primates are urgently needed. This paper reviewed electrode array devices used for electrophysiological and electrochemical detections in primates' deep brains. The research progress of neural recording and stimulation technologies was introduced from the perspective of electrode type and device structures, and their potential value in neuroscience research and clinical disease treatments was discussed. Finally, it is speculated that future electrodes will have a lot of room for development in terms of flexibility, high resolution, deep brain, and high throughput. The improvements in electrode forms and preparation process will expand our understanding of deep brain neural activities, and bring new opportunities and challenges for the further development of neuroscience.

In modern medical treatment, ultrasound scanning provides a radiation-free medical imaging method for the diagnosis of soft tissues via skin contact. However, the exerted contact force heavily relies on the skill and experience of the operator, which poses great inspection instability. This article reports on a robotic ultrasound scanning system with a constant-force end-effector. Its uniqueness is the introduction of a hybrid active-passive force control approach to maintaining a constant contact force between the ultrasound probe and the continually changing surface. In particular, the passive constant-force mechanism provides strong buffering to the force variation. The active force control system improves flexibility and provides long-stroke positioning. Experimental tests on both silicone models and human volunteers demonstrate the capability of the proposed robotic ultrasound scanning system for obtaining qualified ultrasound images with high repeatability. Moreover, the ease of operation of the robotic US scanning system is verified. This work provides a promising method to assist doctors in conducting better and cushier ultrasound scanning imaging.

Lateral walking exercise is beneficial for the hip abductor enhancement. Accurate gait recognition and continuous hip joint angle prediction are essential for the control of exoskeletons. We propose a dual-task learning framework, the "Twin Brother" model, which fuses convolutional neural network (CNN), long short-term memory (LSTM), neural networks (NNs), and the squeezing-elicited attention mechanism to classify the lateral gait stage and estimate the hip angle from electromyography (EMG) signals. The EMG signals of 6 muscles from 10 subjects during lateral walking were collected. Four gait phases were recognized, and the hip angles of both legs were continuously estimated. The sliding window length of 250 ms and the sliding increment of 3 ms were determined by the requirements of response time and recognition accuracy of the real-time system. We compared the performance of CNN-LSTM, CNN, LSTM, support vector machine, NN, K-nearest neighbor, and the "Twin Brother" models. The "Twin Brother" model achieved a recognition accuracy (mean ± SD) of 98.81% ± 0.14%. The model's predicted root mean square error (RMSE) for the left and right hip angles are 0.9183° ± 0.024° and 1.0511° ± 0.027°, respectively, where the R ² are 0.9853 ± 0.006 and 0.9808 ± 0.008. The accuracy of recognition and estimation are both better than comparative models. For gait phase percentage prediction, RMSE and R ² predicted by the model can reach 0.152° ± 0.014° and 0.986 ± 0.011, respectively. These results demonstrate that the method can be applied to lateral walking gait recognition and hip joint angle prediction.

Fusobacterium nucleatum, a bacterium linked to colorectal cancer, possesses a specific gene called fadA that serves as an early diagnostic biomarker. The CRISPR/Cas12a system has demonstrated marked potential for nucleic acid detection due to its satisfactory selectivity and trans-cleavage ability. However, most CRISPR/Cas-based sensors suffer from problems such as probe entanglement or local aggregation, reducing the Cas enzyme efficiency. In this study, an amplification-free biosensing platform for ultrasensitive detection of F. nucleatum was developed by integrating the highly specific CRISPR/AsCas12a with an improved electrochemiluminescence (ECL) biosensor. Different from the conventional 1- or 2-dimensional probes, the platform was constructed by tetrahedral DNA nanostructure (TDN) probes conjugated with quenchers and coralliform gold (CFAu) functionalized with luminescent agents. The TDN serves as an exceptional scaffold to modulate the recognition unit, substantially enhancing the recognition and cleavage efficiency of AsCas12a toward the probes. Furthermore, the high surface area of CFAu provides extensive landing sites for the luminescent agents, thereby improving the detection sensitivity. The prepared ECL biosensor exhibited a wider linear range (10 fM to 100 nM) and was capable of detecting F. nucleatum down to 1 colony-forming unit/ml. Additionally, the high mismatch sensitivity of AsCas12a to protospacer adjacent motifs and nearby areas provides a strategy for distinguishing mutant from wild-type sequences. Finally, by designing CRISPR RNA (crRNA), this diagnostic method can also be easily modified to detect other bacteria or biomarkers for the early diagnosis of various diseases.

Materials that establish functional, stable interfaces to targeted tissues for long-term monitoring/stimulation equipped with diagnostic/therapeutic capabilities represent breakthroughs in biomedical research and clinical medicine. A fundamental challenge is the mechanical and chemical mismatch between tissues and implants that ultimately results in device failure for corrosion by biofluids and associated foreign body response. Of particular interest is in the development of bioactive materials at the level of chemistry and mechanics for high-performance, minimally invasive function, simultaneously with tissue-like compliance and in vivo biocompatibility. This review summarizes the most recent progress for these purposes, with an emphasis on material properties such as foreign body response, on integration schemes with biological tissues, and on their use as bioelectronic platforms. The article begins with an overview of emerging classes of material platforms for bio-integration with proven utility in live animal models, as high performance and stable interfaces with different form factors. Subsequent sections review various classes of flexible, soft tissue-like materials, ranging from self-healing hydrogel/elastomer to bio-adhesive composites and to bioactive materials. Additional discussions highlight examples of active bioelectronic systems that support electrophysiological mapping, stimulation, and drug delivery as treatments of related diseases, at spatiotemporal resolutions that span from the cellular level to organ-scale dimension. Envisioned applications involve advanced implants for brain, cardiac, and other organ systems, with capabilities of bioactive materials that offer stability for human subjects and live animal models. Results will inspire continuing advancements in functions and benign interfaces to biological systems, thus yielding therapy and diagnostics for human healthcare.

Recent advances in brain-computer interfaces (BCIs) have demonstrated the potential to decode language from brain activity into sound or text, which has predominantly focused on alphabetic languages, such as English. However, logosyllabic languages, such as Mandarin Chinese, present marked challenges for establishing decoders that cover all characters, due to its unique syllable structures, extended character sets (e.g., over 50,000 characters for Mandarin Chinese), and complex mappings between characters and syllables, thus hindering practical applications. Here, we leverage the acoustic features of Mandarin Chinese syllables, constructing prediction models for syllable components (initials, tones, and finals), and decode speech-related stereoelectroencephalography (sEEG) signals into coherent Chinese sentences. The results demonstrate a high sentence-level offline decoding performance with a median character accuracy of 71.00% over the full spectrum of characters in the best participant. We also verified that incorporating acoustic-related features into the design of prediction models substantially enhances the accuracy of initials, tones, and finals. Moreover, our findings revealed that effective speech decoding also involves subcortical structures like the thalamus in addition to traditional language-related brain regions. Overall, we established a brain-to-sentence decoder for logosyllabic languages over full character set with a large intracranial electroencephalography dataset.

Patients with lower limb amputation experience ambulation disorders since they rely exclusively on visual information in addition to the tactile information they receive from stump-socket interface. The lack of sensory feedback in commercial lower limb prostheses is essential in their abandonment by patients with transtibial amputation (TTA) or transfemoral amputation (TFA). Recent studies have obtained promising results using invasive interfaces with peripheral nervous system presenting drawbacks related to surgery. This paper aims to (a) investigate the potential of transcutaneous electrical nerve stimulation (TENS) as noninvasive means for restoring somatotopic sensory feedback in lower limb amputees and (b) evaluate the effect of the system over a 4-week experimental protocol. The first phase of the study involved 13 participants (6 with TTA and 7 with TFA), and the second one evaluated the long-term effect of TENS on ambulation performance of 2 participants (S1 with TTA and S7 with TFA). The proposed system enhanced participant's ambulation significantly increasing the body weight distribution between legs (S1: from 134% to 143%, P < 0.0055; S7: from 66% to 72%, P < 0.0055) and gait symmetry (S1: step length symmetry index from 11% to 5%, P < 0.0055; S7: stance phase symmetry index from -4% to -2%, P < 0.0055). It led to a postamputation neuropathic pain reduction in S1 (neuropathic pain symptom inventory score diminished from 6 to 0). This demonstrates how TENS enhanced prosthesis embodiment, enabling greater load bearing and more physiological gait patterns. This study highlights TENS as noninvasive solution for restoring somatotopic sensory feedback, addressing the current limitations and paving the way for further research.