Staphylococcal enterotoxin B exposed to pregnant rats inhibits the hedgehog signaling pathway in thymic T lymphocytes of the offspring.

The Hedgehog (Hh) signaling pathway is involved in T cell differentiation and development and plays a major regulatory part in different stages of T cell development. A previous study by us suggested that prenatal exposure to staphylococcal enterotoxin B (SEB) changed the percentages of T cell subpopulation in the offspring thymus. However, it is unclear whether prenatal SEB exposure impacts the Hh signaling pathway in thymic T cells. In the present study, pregnant rats at gestational day 16 were intravenously injected once with 15 µg SEB, and the thymi of both neonatal and adult offspring rats were aseptically acquired to scrutinize the effects of SEB on the Hh signaling pathway. It firstly found that prenatal SEB exposure clearly caused the increased expression of Shh and Dhh ligands of the Hh signaling pathway in thymus tissue of both neonatal and adult offspring rats, but significantly decreased the expression levels of membrane receptors of Ptch1 and Smo, transcription factor Gli1, as well as target genes of CyclinD1, C-myc, and N-myc in Hh signaling pathway of thymic T cells. These data suggest that prenatal SEB exposure inhibits the Hh signaling pathway in thymic T lymphocytes of the neonatal offspring, and this effect can be maintained in adult offspring via the imprinting effect.

Robust Epileptic Seizure Detection Based on Biomedical Signals Using an Advanced Multi-View Deep Feature Learning Approach.

Epilepsy is a neurological disorder characterized by abnormal neuronal discharges that manifest in life-threatening seizures. These are often monitored via EEG signals, a key aspect of biomedical signal processing (BSP). Accurate epileptic seizure (ES) detection significantly depends on the precise identification of key EEG features, which requires a deep understanding of the data's intrinsic domain. Therefore, this study presents an Advanced Multi-View Deep Feature Learning (AMV-DFL) framework based on machine learning (ML) technology to enhance the detection of relevant EEG signal features for ES. Our method initially applies a fast Fourier transform (FFT) to EEG data for traditional frequency domain feature (TFD-F) extraction and directly incorporates time domain (TD) features from the raw EEG signals, establishing a comprehensive traditional multi-view feature (TMV-F). Deep features are subsequently extracted autonomously from optimal layers of one-dimensional convolutional neural networks (1D CNN), resulting in multi-view deep features (MV-DF) integrating both time and frequency domains. A multi-view forest (MV-F) is an interpretable rule-based advanced ML classifier used to construct a robust, generalized classification. Tree-based SHAP explainable artificial intelligence (T-XAI) is incorporated for interpreting and explaining the underlying rules. Experimental results confirm our method's superiority, surpassing models using TMV-FL and single-view deep features (SV-DF) by 4% and outperforming other state-of-the-art methods by an average of 3% in classification accuracy. The AMV-DFL approach aids clinicians in identifying EEG features indicative of ES, potentially discovering novel biomarkers, and improving diagnostic capabilities in epilepsy management.

Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography.

Deciphering hand motion intention from surface electromyography (sEMG) encounters challenges posed by the requisites of multiple degrees of freedom (DOFs) and adaptability. Unlike discrete action classification grounded in pattern recognition, the pursuit of continuous kinematics estimation is appreciated for its inherent naturalness and intuitiveness. However, prevailing estimation techniques contend with accuracy limitations and substantial computational demands. Kalman estimation technology, celebrated for its ease of implementation and real-time adaptability, finds extensive application across diverse domains. This study introduces a continuous Kalman estimation method, leveraging a system model with sEMG and joint angles as inputs and outputs. Facilitated by model parameter training methods, the approach deduces multiple DOF finger kinematics simultaneously. The method's efficacy is validated using a publicly accessible database, yielding a correlation coefficient (CC) of 0.73. With over 45,000 windows for training Kalman model parameters, the average computation time remains under 0.01 s. This pilot study amplifies its potential for further exploration and application within the realm of continuous finger motion estimation technology.

Histone deacetylase Sir2 promotes the systemic Candida albicans infection by facilitating its immune escape via remodeling the cell wall and maintaining the metabolic activity.

Histone deacetylation affects Candida albicans (C. albicans) pathogenicity by modulating virulence factor expression and DNA damage. The histone deacetylase Sir2 is associated with C. albicans plasticity and maintains genome stability to help C. albicans adapt to various environmental niches. However, whether Sir2-mediated chromatin modification affects C. albicans virulence is unclear. The purpose of our study was to investigate the effect of Sir2 on C. albicans pathogenicity and regulation. Here, we report that Sir2 is required for C. albicans pathogenicity, as its deletion affects the survival rate, fungal burden in different organs and the extent of tissue damage in a mouse model of disseminated candidiasis. We evaluated the impact of Sir2 on C. albicans virulence factors and revealed that the Sir2 null mutant had an impaired ability to adhere to host cells and was more easily recognized by the innate immune system. Comprehensive analysis revealed that the disruption of C. albicans adhesion was due to a decrease in cell surface hydrophobicity rather than the differential expression of adhesion genes on the cell wall. In addition, Sir2 affects the distribution and exposure of mannan and ß-glucan on the cell wall, indicating that Sir2 plays a role in preventing the immune system from recognizing C. albicans. Interestingly, our results also indicated that Sir2 helps C. albicans maintain metabolic activity under hypoxic conditions, suggesting that Sir2 contributes to C. albicans colonization at hypoxic sites. In conclusion, our findings provide detailed insights into antifungal targets and a useful foundation for the development of antifungal drugs. IMPORTANCE: Candida albicans (C. albicans) is the most common opportunistic fungal pathogen and can cause various superficial infections and even life-threatening systemic infections. To successfully propagate infection, this organism relies on the ability to express virulence-associated factors and escape host immunity. In this study, we demonstrated that the histone deacetylase Sir2 helps C. albicans adhere to host cells and escape host immunity by mediating cell wall remodeling; as a result, C. albicans successfully colonized and invaded the host in vivo. In addition, we found that Sir2 contributes to carbon utilization under hypoxic conditions, suggesting that Sir2 is important for C. albicans survival and the establishment of infection in hypoxic environments. In summary, we investigated the role of Sir2 in regulating C. albicans pathogenicity in detail; these findings provide a potential target for the development of antifungal drugs.

Long-range infrared absorption spectroscopy and fast mass spectrometry for rapid online measurements of volatile organic compounds from black tea fermentation.

Fermentation is the key process to determine the quality of black tea. Traditional physical and chemical analyses are time consuming, it cannot meet the needs of online monitoring. The existing rapid testing techniques cannot determine the specific volatile organic compounds (VOCs) produced at different stages of fermentation, resulting in poor model transferability; therefore, the current degree of black tea fermentation mainly relies on the sensory judgment of tea makers. This study used proton transfer reaction mass spectrometry (PTR-MS) and fourier transform infrared spectroscopy (FTIR) combined with different injection methods to collect VOCs of the samples, the rule of change of specific VOCs was clarified, and the extreme learning machine (ELM) model was established after principal component analysis (PCA), the prediction accuracy reached 95% and 100%, respectively. Finally, different application scenarios of the two technologies in the actual production of black tea are discussed based on their respective advantages.

Green Fruit Detection with a Small Dataset under a Similar Color Background Based on the Improved YOLOv5-AT.

Green fruit detection is of great significance for estimating orchard yield and the allocation of water and fertilizer. However, due to the similar colors of green fruit and the background of images, the complexity of backgrounds and the difficulty in collecting green fruit datasets, there is currently no accurate and convenient green fruit detection method available for small datasets. The YOLO object detection model, a representative of the single-stage detection framework, has the advantages of a flexible structure, fast inference speed and excellent versatility. In this study, we proposed a model based on the improved YOLOv5 model that combined data augmentation methods to detect green fruit in a small dataset with a background of similar color. In the improved YOLOv5 model (YOLOv5-AT), a Conv-AT block and SA and CA blocks were designed to construct feature information from different perspectives and improve the accuracy by conveying local key information to the deeper layer. The proposed method was applied to green oranges, green tomatoes and green persimmons, and the mAPs were higher than those of other YOLO object detection models, reaching 84.6%, 98.0% and 85.1%, respectively. Furthermore, taking green oranges as an example, a mAP of 82.2% was obtained on the basis of retaining 50% of the original dataset (163 images), which was only 2.4% lower than that obtained when using 100% of the dataset (326 images) for training. Thus, the YOLOv5-AT model combined with data augmentation methods can effectively achieve accurate detection in small green fruit datasets under a similar color background. These research results could provide supportive data for improving the efficiency of agricultural production.

Living Synthelectronics: A New Era for Bioelectronics Powered by Synthetic Biology.

Bioelectronics, which converges biology and electronics, has attracted great attention due to their vital applications in human-machine interfaces. While traditional bioelectronic devices utilize nonliving organic and/or inorganic materials to achieve flexibility and stretchability, a biological mismatch is often encountered because human tissues are characterized not only by softness and stretchability but also by biodynamic and adaptive properties. Recently, a notable paradigm shift has emerged in bioelectronics, where living cells, and even viruses, modified via gene editing within synthetic biology, are used as core components in a new hybrid electronics paradigm. These devices are defined as "living synthelectronics," and they offer enhanced potential for interfacing with human tissues at informational and substance exchange levels. In this Perspective, the recent advances in living synthelectronics are summarized. First, opportunities brought to electronics by synthetic biology are briefly introduced. Then, strategic approaches to designing and making electronic devices using living cells/viruses as the building blocks, sensing components, or power sources are reviewed. Finally, the challenges faced by living synthelectronics are raised. It is believed that this paradigm shift will significantly contribute to the real integration of bioelectronics with human tissues.

A Secure and Interpretable AI for Smart Healthcare System: A Case Study on Epilepsy Diagnosis Using EEG Signals.

The efficient patient-independent and interpretable framework for electroencephalogram (EEG) epileptic seizure detection (ESD) has informative challenges due to the complex pattern of EEG nature. Automated detection of ES is crucial, while Explainable Artificial Intelligence (XAI) is urgently needed to justify the model detection of epileptic seizures in clinical applications. Therefore, this study implements an XAI-based computer-aided ES detection system (XAI-CAESDs), comprising three major modules, including of feature engineering module, a seizure detection module, and an explainable decision-making process module in a smart healthcare system. To ensure the privacy and security of biomedical EEG data, the blockchain is employed. Initially, the Butterworth filter eliminates various artifacts, and the Dual-Tree Complex Wavelet Transform (DTCWT) decomposes EEG signals, extracting real and imaginary eigenvalue features using frequency domain (FD), time domain (TD) linear feature, and Fractal Dimension (FD) of non-linear features. The best features are selected by using Correlation Coefficients (CC) and Distance Correlation (DC). The selected features are fed into the Stacking Ensemble Classifiers (SEC) for EEG ES detection. Further, the Shapley Additive Explanations (SHAP) method of XAI is implemented to facilitate the interpretation of predictions made by the proposed approach, enabling medical experts to make accurate and understandable decisions. The proposed Stacking Ensemble Classifiers (SEC) in XAI-CAESDs have demonstrated 2% best average accuracy, recall, specificity, and F1-score using the University of California, Irvine, Bonn University, and Boston Children's Hospital-MIT EEG data sets. The proposed framework enhances decision-making and the diagnosis process using biomedical EEG signals and ensures data security in smart healthcare systems.

Custom orthotic design by integrating 3D scanning and subject-specific FE modelling workflow.

The finite element (FE) foot model can help estimate pathomechanics and improve the customized foot orthoses design. However, the procedure of developing FE models can be time-consuming and costly. This study aimed to develop a subject-specific scaled foot modelling workflow for the foot orthoses design based on the scanned foot surface data. Six participants (twelve feet) were collected for the foot finite element modelling. The subject-specific surface-based finite element model (SFEM) was established by incorporating the scanned foot surface and scaled foot bone geometries. The geometric deviations between the scaled and the scanned foot surfaces were calculated. The SFEM model was adopted to predict barefoot and foot-orthosis interface pressures. The averaged distances between the scaled and scanned foot surfaces were 0.23 ± 0.09 mm. There was no significant difference for the hallux, medial forefoot, middle forefoot, midfoot, medial hindfoot, and lateral hindfoot, except for the lateral forefoot region (p = 0.045). The SFEM model evaluated slightly higher foot-orthoses interface pressure values than measured, with a maximum deviation of 7.1%. These results indicated that the SFEM technique could predict the barefoot and foot-orthoses interface pressure, which has the potential to expedite the process of orthotic design and optimization.

Improvement of Cancer Prevention and Control: Reflection on the Role of Emerging Information Technologies.

Cancer has become an important public health problem affecting the health of Chinese residents, as well as residents all over the world. With the improvement of cancer prevention and treatment, the growth of the mortality rate of cancers has slowed down gradually, but the incidence rate is still increasing rapidly, and cancers still impose heavy disease and economic burdens. Cancer screening and early cancer diagnosis and treatment are important ways to reduce the burden of cancer-related diseases. At present, various projects for early cancer diagnosis and treatment have been implemented in China. With the expansion of the coverage of these projects, the problems related to project implementation, operation, and management have emerged gradually. In recent years, emerging information technologies have been applied in the field of health and have facilitated health management and clinical decision-making. Meanwhile, China announced multiple policies to encourage and promote the application of information technologies in the field of health. Therefore, combined with the analysis of major problems in cancer prevention and control projects, this paper probes into how to apply information technologies such as biological information mining, artificial intelligence, and electronic information collection technology to various stages of cancer prevention and control. Information technologies realize the integrated management of prevention and control processes, for example, mobilization and preliminary identification, high-risk assessment, clinical screening, clinical diagnosis and treatment, tracking and follow-up, and biological sample management of high-risk groups, and promote the efficient implementation of cancer prevention and control projects in China.

Loading Recognition for Lumbar Exoskeleton Based on Multi-Channel Surface Electromyography from Low Back Muscles.

Lumbar exoskeleton is an assistive robot, which can reduce the risk of injury and pain in low back muscles when lifting heavy objects. An important challenge it faces involves enhancing assistance with minimal muscle energy consumption. One of the viable solutions is to adjust the force or torque of assistance in response to changes in the load on the low back muscles. It requires accurate loading recognition, which has yet to yield satisfactory outcomes due to the limitations of available measurement tools and load classification methods. This study aimed to precisely identify muscle loading using a multi-channel surface electromyographic (sEMG) electrode array on the low back muscles, combined with a participant-specific load classification method. Ten healthy participants performed a stoop lifting task with objects of varying weights, while sEMG data was collected from the low back muscles using a 3x7 electrode array. Nineteen time segments of the lifting phase were identified, and time-domain sEMG features were extracted from each segment. Participant-specific classifiers were built using four classification algorithms to determine the object weight in each time segment, and the classification performance was evaluated using a 5-fold cross-validation method. The artificial neural network classifier achieved an impressive accuracy of up to 96%, consistently improving as the lifting phase progressed, peaking towards the end of the lifting movement. This study successfully achieves accurate recognition of load on low back muscles during the object lifting task. The obtained results hold significant potential in effectively reducing muscle energy consumption when wearing a lumbar exoskeleton.

A randomized controlled trial investigating the impact of a single flash cupping session on patients with chronic lower back pain using multichannel surface electromyographic assessment.

BACKGROUND: Chronic lower back pain (CLBP) is one of the most common disorders worldwide. Flash cupping has the ability to relieve CLBP; nevertheless, its impact on CLBP and the likely mechanism of action have not been studied. OBJECTIVE: The goal of this study was to assess the impact of a single, brief cupping session on CLBP and low back muscle activity using multichannel surface electromyography (sEMG). METHODS: In this randomized controlled trial, 24 patients with CLBP were enrolled and randomly assigned to the control group (treated by acupuncture) and cupping group (treated by acupuncture and flash cupping). Acupuncture was applied on the shen shu (BL23), dachang shu (BL25), and wei zhong (BL40) acupoints in both the groups. A brief cupping treatment was applied to the shen shu (BL23), qihai shu (BL24), dachang shu (BL25), guanyuan shu (BL26), and xiaochang shu (BL27) acupoints on both sides of the lower back in the cupping group. The numeric rating scale (NRS) was used to assess therapy efficacy for lower back pain (LBP) before and after treatment. Surface EMG data collected during symmetrical trunk flexion-extension movements were utilized to measure lower back muscle activity and the effectiveness of LBP therapy. RESULTS: There was no statistically significant difference (P= 0.63) in pain intensity between the two groups before and after treatment. There was a statistically significant difference (P= 0.04) between the control group and the cupping group in the sEMG topographic map parameter CoGx-To-Midline. CONCLUSION: This study established a connection between the action mechanism of flash cupping and enhanced horizontal synchronization of lower back muscular activity.

Effect Analysis of Wearing an Lumbar Exoskeleton on Coordinated Activities of the Low Back Muscles Using sEMG Topographic Maps.

Lumbar exoskeleton has potential to assist in lumbar movements and thereby prevent impairment of back muscles. However, due to limitations of evaluation tools, the effect of lumbar exoskeletons on coordinated activities of back muscles is seldom investigated. This study used the surface electromyography (sEMG) topographic map based on multi-channel electrodes from low back muscles to analyze the effects. Thirteen subjects conducted two tasks, namely lifting and holding a 20kg-weight box. For each task, three different trials, not wearing exoskeleton (NoExo), wearing exoskeleton but power-off (OffExo), and wearing exoskeleton and power-on (OnExo), were randomly conducted. Root-mean-square (RMS) and median-frequency (MDF) topographic maps of the recorded sEMG were constructed. Three parameters, average pixel values, distribution of center of gravity (CoG), and entropy, were extracted from the maps to assess the muscle coordinated activities. In the lifting task, results showed the average pixel values of RMS maps for the NoExo trial were lower than those for the OffExo trial ( [Formula: see text]) but the same as those for the OnExo trial ( [Formula: see text]0.05). The distribution of CoG showed a significant difference between NoExo and OnExo trials ( [Formula: see text]). In the holding task, RMS and MDF maps' average pixel values showed significant differences between NoExo and OnExo trials ( [Formula: see text]). These findings suggest that active lumbar exoskeletons can reduce the load on low back muscles in the static holding task rather than in the dynamic lifting task. This proves sEMG topographic maps offer a new way to evaluate such effects, thereby helping improve the design of lumbar exoskeleton systems.

A novel unsupervised dynamic feature domain adaptation strategy for cross-individual myoelectric gesture recognition.

Objective.Surface electromyography pattern recognition (sEMG-PR) is considered as a promising control method for human-machine interaction systems. However, the performance of a trained classifier would greatly degrade for novel users since sEMG signals are user-dependent and largely affected by a number of individual factors such as the quantity of subcutaneous fat and the skin impedance.Approach.To solve this issue, we proposed a novel unsupervised cross-individual motion recognition method that aligned sEMG features from different individuals by self-adaptive dimensional dynamic distribution adaptation (SD-DDA) in this study. In the method, both the distances of marginal and conditional distributions between source and target features were minimized through automatically selecting the optimal feature domain dimension by using a small amount of unlabeled target data.Main results.The effectiveness of the proposed method was tested on four different feature sets, and results showed that the average classification accuracy was improved by above 10% on our collected dataset with the best accuracy reached 90.4%. Compared to six kinds of classic transfer learning methods, the proposed method showed an outstanding performance with improvements of 3.2%-13.8%. Additionally, the proposed method achieved an approximate 9% improvement on a publicly available dataset.Significance.These results suggested that the proposed SD-DDA method is feasible for cross-individual motion intention recognition, which would provide help for the application of sEMG-PR based system.

Water-responsive supercontractile polymer films for bioelectronic interfaces.

Connecting different electronic devices is usually straightforward because they have paired, standardized interfaces, in which the shapes and sizes match each other perfectly. Tissue-electronics interfaces, however, cannot be standardized, because tissues are soft1-3 and have arbitrary shapes and sizes4-6. Shape-adaptive wrapping and covering around irregularly sized and shaped objects have been achieved using heat-shrink films because they can contract largely and rapidly when heated7. However, these materials are unsuitable for biological applications because they are usually much harder than tissues and contract at temperatures higher than 90 °C (refs. 8,9). Therefore, it is challenging to prepare stimuli-responsive films with large and rapid contractions for which the stimuli and mechanical properties are compatible with vulnerable tissues and electronic integration processes. Here, inspired by spider silk10-12, we designed water-responsive supercontractile polymer films composed of poly(ethylene oxide) and poly(ethylene glycol)-α-cyclodextrin inclusion complex, which are initially dry, flexible and stable under ambient conditions, contract by more than 50% of their original length within seconds (about 30% per second) after wetting and become soft (about 100 kPa) and stretchable (around 600%) hydrogel thin films thereafter. This supercontraction is attributed to the aligned microporous hierarchical structures of the films, which also facilitate electronic integration. We used this film to fabricate shape-adaptive electrode arrays that simplify the implantation procedure through supercontraction and conformally wrap around nerves, muscles and hearts of different sizes when wetted for in vivo nerve stimulation and electrophysiological signal recording. This study demonstrates that this water-responsive material can play an important part in shaping the next-generation tissue-electronics interfaces as well as broadening the biomedical application of shape-adaptive materials.

A Precise Hip Protection System with Multi-scale Fall Warning Algorithm Based on Offset Displacement.

Falls occur frequently in daily life and the damage to the body is irreversible. Therefore, it is crucial to implement timely and effective warning and protection systems for falls to minimize the damage caused by falls. Currently, the fall warning algorithm has shortcomings such as low recognition rates for falls and fall-risk movements and insufficient lead-time, the time before the subject impacts the floor, making it difficult for falling protection devices to function effectively. In this study, a multi-scale falls warning algorithm based on offset displacement is built, and a hip protection system is designed. The performance of the algorithm and the system is validated using 150 falling and 500 fall-risk actions from 10 volunteers. The results showed that the recognition accuracy for falling actions is 98.7% and the recognition accuracy for fall-risk actions is 99.4%, with an average lead-time of 402ms. The protection rate for falling movements reached 98.7%. This proposed algorithm and hip protection system have the potential to be applied in elderly communities, hospitals, and homes to reduce the damage caused by falls.Clinical Relevance- This study provides important reference for clinicians in analyzing fall behaviors to patients at risk of falls in clinical settings, offering valuable technical support for ensuring the safety of patients in danger of falling. It also contributes to further promoting the development of falling-prevention medical devices.

Adhesive Carbon Paste Electrode for Convenient and Long-term Electrocardiogram Monitoring: A Pilot Study.

Long-term electrocardiogram (ECG) monitoring is an important and widely-used technique in the clinic that helps with the diagnosis of possible diseases that cannot be detected in a short time monitoring. However, the clinically used electrode needs conductive gel to reduce the impedance between the skin and the electrodes, which easily causes the possibility of allergy. Moreover, as the conductive gel becomes dry, the signal's quality will decrease accordingly. In this paper, we proposed a novel adhesive Carbon Paste Electrode (CPE) to achieve convenient and long-term ECG monitoring. By comparing the time-domain waveforms, the R-R peak intervals difference, and the Signal-to-Noise Ratio (SNR) of ECG with the traditional conductive gel-based electrode (Gel) in fixed and unfixed conditions, the performance of the proposed CPE was investigated. The results showed that the CPE could achieve similar ECG monitoring both in fixed and unfixed conditions. When on Day 2, the quality acquired by Gel began to decrease while CPE was still stable, which was obvious especially in unfixed condition. The R-R peak intervals showed that on Day 2, the Gel was unreliable with some abnormal points occurring. Besides, the results of SNR and average heart rate (AHR) also confirmed that the CPE could achieve similar results as Gel on Day 1 and outperformed Gel on Day 2. It is believed that the proposed CPE opens a window of high-quality long-term ECG monitoring with more convenience.

The Changes of Cardiovascular Neurotransmitter Levels under Low-Intensity Focused Ultrasound Stimulation of the Vagus Nerve.

BACKGROUND: Our previous study has shown that stimulation of the vagus nerve with low-intensity focused ultrasound could modulate blood pressure (BP), but the underlying mechanisms remain unclear. This study investigated the changes of cardiovascular neurotransmitter levels to indirectly evaluate the responses of the autonomic nervous system and renin-angiotensin system under low-intensity focused ultrasound stimulation (FUS) of the vagus nerve. METHODS: Cardiovascular neurotransmitter levels of epinephrine (EPI), norepinephrine (NE), and angiotensin II (ANGII) were measured and compared before and after the FUS in seven spontaneously hypertensive rats; and were also measured and compared between a target stimulation group (FUS, n = 6) and non-target stimulation group (Control, n = 5) after stimulation to exclude the influence of potential confounding factors. RESULTS: The t-test results showed that the levels of EPI, NE, and ANGII were significantly decreased (P < 0.05) after stimulation compared to before stimulation. Additionally, the levels of NE and EPI were significantly lower (P < 0.05) in the FUS group than in the Control group after stimulation, indicating that the activities of the sympathetic nervous system and renin-angiotensin system of the vagus nerve might be inhibited by FUS of the vagus nerve. CONCLUSION: These findings reveal the mechanism of BP lowing in response to FUS of the vagus nerve.Clinical Relevance-This study revealed the mechanism of BP lowering in response to focused ultrasound stimulation of the vagus nerve through analyzing the changes of cardiovascular neurotransmitter levels.

Spasticity assessment with muscle coactivation of elbow flexors during passive stretch in Post-stroke Hemiplegia.

the assessment of muscle properties is an essential prerequisite in the treatment of post-stroke muscle spasticity. Previous studies have shown that muscle coactivation, which reflects the simultaneous activation of agonist and antagonist muscle groups, is associated with muscle spasticity during voluntary contraction. However, current spasticity assessment approaches do not often consider muscle coactivation for passive contraction measured with surface electromyography (sEMG). The purpose here is to evaluate the validity and reliability of muscle co-activation based on sEMG for assessing spasticity of post-stroke patients. This study was conducted on 39 chronic hemiplegia post-stroke patients with varying degrees of elbow flexor spasticity. The severity of spasticity was assessed with Modified Ashworth Scale (MAS). The patients produced elbow flexion passively on affected arm. Two-channel surface sEMG recordings were acquired simultaneously for the biceps and triceps muscles. The effectiveness and reliability of the EMG-based spasticity assessment method were evaluated using Spearman's correlation analysis and intra class correlation coefficients (ICCs). The results showed that there was a statistically significant positive relationship between the level of activity and the coactivation index (R=0.710, P=0.003), while the ICCs for intra trial measures ranged between 0.928 and 0.976. Muscle coactivation is a promising tool for continuously quantifying muscle spasticity in post-stroke patients, suggesting that the EMG-based muscle coactivation index could be useful for assessing motor function.

Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors.

Intelligent rehabilitation robotics (RR) have been proposed in recent years to aid post-stroke survivors recover their lost limb functions. However, a large proportion of these robotic systems operate in a passive mode that restricts users to predefined trajectories that rarely align with their intended limb movements, precluding full functional recovery. To address this issue, an efficient Transfer Learning based Convolutional Neural Network (TL-CNN) model is proposed to decode post-stroke patients' motion intentions toward realizing dexterously active robotic training during rehabilitation. For the first time, we use Spatial-Temporal Descriptor based Continuous Wavelet Transform (STD-CWT) as input to TL-CNN to optimally decode limb movement intent patterns. We evaluated the STD-CWT method on three distinct wavelets including the Morse, Amor, and Bump, and compared their decoding outcomes with those of the commonly adopted CWT technique under similar experimental conditions. We then validated the method using electromyogram signals of five stroke survivors who performed twenty-one distinct motor tasks. The results showed that the proposed technique recorded a significantly higher (p<0.05) decoding accuracy and faster convergence compared to the common method. Our method equally recorded obvious class separability for individual motor tasks across subjects. The findings suggest that the STD-CWT Scalograms have the potential for robust decoding of motor intention and could facilitate intuitive and active motor training in stroke RR.Clinical Relevance- The study demonstrated the potential of Spatial Temporal based Scalograms in aiding precise and robust decoding of multi-class motor tasks, upon which dexterously active rehabilitation robotic training for full motor function restoration could be realized.