Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Consciousness, which underpins human perception, cognition, and behavioural responses, has long been a major focus of neuroscience and medicine. Alterations in consciousness occur not only during reversible processes such as sleep and anaesthesia, but also in pathological conditions, including disorders of consciousness (DOC) and brain injury. With recent advances in the synchronized acquisition and analysis of multimodal physiological signals, such as electroencephalography (EEG) and electrocardiography (ECG), the dynamic interactions between the heart and brain have become increasingly apparent. A growing body of evidence indicates that heart-brain interactions are closely associated with the maintenance and transition of consciousness states, offering a novel perspective for elucidating their underlying mechanisms and supporting relevant clinical assessments. Current approaches to heart-brain interaction analysis for consciousness-state assessment can be broadly grouped into three categories: ECG-based methods, EEG-based methods, and heart-brain coupling analysis. These approaches characterize heart-brain interactions during changes in consciousness from the complementary perspectives of autonomic regulation, cortical interoceptive processing, and cross-system information transfer, respectively. Nevertheless, this field remains at an early stage. Most studies have focused on a single experimental context or a fixed analytical method, with insufficient systematic consideration of the applicability and limitations of different approaches. Moreover, the mechanisms underlying the emergence and alteration of consciousness remain incompletely understood. Accordingly, this review examines heart-brain interaction methods employed in recent consciousness-state assessment studies in accordance with progressively deeper analytical perspectives, and systematically evaluates the advantages and limitations of these methods across distinct consciousness-related conditions. Heart rate variability (HRV) is one of the classical ECG-based indices used to investigate heart-brain interactions. It refers to fluctuations in consecutive interbeat intervals over a specified time period and is regarded as a physiological measure of autonomic nervous system regulation that may indirectly reflect the strength of heart-brain interactions. Depending on the analytical dimension, HRV analysis is commonly divided into time-domain, frequency-domain, and complexity-index analyses. These approaches characterize autonomic activity in terms of temporal fluctuations, the distribution of frequency components, and nonlinear dynamical properties, respectively. Although extensive research supports their physiological interpretation, they cannot characterize directional interactions from the heart to the brain. Among EEG-based methods for heart-brain interaction analysis, EEG activity directly related to cardiac fluctuations has received particular attention. One of the most widely used measures is the heartbeat-evoked potential (HEP). HEP-based approaches are considered to directly reflect the capacity of the cerebral cortex to process cardiac afferent signals and may be especially advantageous for characterizing rapidly changing consciousness levels, such as those observed during sleep. However, they do not comprehensively capture dynamic bidirectional information exchange between the brain and heart and are susceptible to cardiac artefacts, respiration, and variation in preprocessing procedures. Heart-brain coupling analysis quantifies functional associations between ECG and EEG through cross-system interaction measures, thereby assessing dynamic interactions between the heart and brain. It emphasizes information transfer, feedback regulation, and cross-timescale coordination between the two systems, offering a more comprehensive perspective for investigating changes in consciousness during sleep, anaesthesia, and DOC. However, the neurophysiological mechanisms and physiological interpretation of individual computational indices have not yet been fully established, and the robustness of their results and their potential for clinical generalization require further validation. Finally, this review summarizes the commonalities and differences among the three classes of methods across research domains involving different consciousness states. It further argues that heart-brain interaction research requires deeper investigation at both the mechanistic level of consciousness and the level of clinical application. Current challenges and key issues are identified, and future directions and feasible pathways for heart-brain interaction research are discussed, with the aim of providing new perspectives for researchers in related fields.

Traditional mass measurement methods are not applicable in microgravity environments,and the main challenge for in-orbit body mass measurement technology based on inertial principles is to address the random errors brought about by the weightless environment.These include additional torques due to shifts in the center of mass,nonlinear accelerations due to non-rigid human bodies,mechanical energy consumption due to organ vibrations,and random vibrations of the measurement device itself.To address the above difficulties,the project proposes a technical scheme based on the principle of linear acceleration,designs and constructs a ground-specific air-floating experimental and simulation platform,studies key data such as motion trajectory,acceleration change,and vibration frequency amplitude during the mass measurement process,and simulates the changes in the center of mass and random vibrations of the human body in a weightless environment.The project has designed an adjustable posture bracket to adapt to changes in the center of mass,enhance body restraint,and greatly reduce shaking;it has also developed an integrated four-bar linkage motion guidance mechanism,high-precision integrated photoelectric distance measurement,and modular motion constant force measurement device to ensure the accurate measurement of acceleration and constant force data.The product has undergone simulation calculations,ground human applicability tests,and in-orbit applicability verification in the space station.Ground test results show that the device achieves a body mass measurement accuracy better than 0.5%,and the dispersion is better than 0.38%;after flight mission verification and evaluation,the in-orbit body mass measurement dispersion is less than 0.4%,which is superior to the SLAMMD,a mass measurement device of the same principle on the International Space Station,and is at the forefront internationally,achieving accurate body mass measurement.

Objective Electrocardiogram(ECG)signal quality degrades when the level of activity is high and motion artifacts are severe.Poor quality signals may result in false alarms,poor patient monitoring,imprecise measurement,and misleading diagnosis.The quantitative assessment of ECG signal quality forms the basis of automatic ECG noise reduction and heart disease diagnosis.Methods The ECG signal quality index(SQI)was obtained by statistically analyzing the heartbeat similarity and the slope character,respectively,namely rSQI and kSQI.Results Using MIT-BIH Noise Stress Test Database to test,both rSQI and kSQI decreased when the Signal Noise Ratio(SNR)decreased,which revealed the ECG signal quality.Based on the quasiperiodic property,the waveform similarity,as a beat-to-beat index,is obtained by cross correlation between two ECG cycles with high precision but heavy computation.Slope-based method dispenses with QRS detection and is very simple and real-time,but its sensitivity is lower than similarity-based method and it only get statistical data.Conclusion Both morphology similarity and slope character algorithms could provide objective estimation of ECG quality.Slope-based method is an attractive measure due to its simplicity and mathematical convenience,while similarity-based method is more accurate and robust for ECG quality assessment.

As a comprehensive high technology,robotics has gained extensive attention from numerous researchers.Intelligent soft robots,representing a new direction in robotics research,are shining brightly in fields such as fragile objects manipulating,medical assistance,and environment exploration due to their unique advantages.In the field of specialized medicine,intelligent soft robots can adapt to the complex and extreme space working environment due to their small stiffness,strong variability and high flexibility,and have broad application prospects in specialized medicine like aerospace,navigation and diving.In this paper,the research status of intelligent soft robots is reviewed from four aspects:materials,structure,actuation modalities,modeling and simulation.Furthermore,the application of intelligent soft robots in the field of specialized medicine is summarized and prospected in this paper.

Anxiety disorder is a highly prevalent psychological illness, and research has shown that obesity is a significant risk factor for its development. This study explored the ameliorative effects and mechanisms of saponins from Panax japonicus(SPJ) on anxiety disorder in mice fed a high-fat diet(HFD). Fifty C57BL/6J mice were randomly divided into normal control diet(NCD) group, HFD group, and low-and high-dose SPJ groups. At week 12, six mice from the HFD group were further divided into a control group(treated with DMSO) and an exogenous fibroblast growth factor 21(FGF21) group(administered rFGF21). The anxiety-like behavior of the mice was assessed using the open field test and elevated plus maze test. Hematoxylin-eosin(HE) staining and oil red O staining were performed to observe pathological changes in the liver and adipose tissue. Glucose metabolism was evaluated through the glucose tolerance test(GTT) and insulin tolerance test(ITT). Western blot analysis was performed to detect the expression of FGF21 and its downstream-related proteins in the liver and cortex, along with the expression of brain-derived neurotrophic factor(BDNF), disks large homolog 4(DLG4), and synaptophysin(SYP) in the cortex. Real-time quantitative fluorescent PCR(qPCR) was used to detect the expression of FGF21 and its receptor genes in the liver and cortex. Immunofluorescence staining was employed to examine the expression of neuronal activator c-Fos, FGF21, and the FGF21 co-receptor β-klotho in the cerebral cortex. The results showed that SPJ significantly improved the frequency of activity in the open arms of the elevated plus maze and the central area of the open field in HFD mice, up-regulated the expression of BDNF, DLG4, and SYP, and effectively alleviated anxiety-like behaviors in HFD mice. Compared with the NCD group, HFD mice exhibited up-regulated expression of FGF21 in the liver and cerebral cortex, while the expression of fibroblast growth factor receptor 1(FGFR1) and β-klotho was significantly down-regulated, suggesting that HFD mice exhibited FGF21 resistance. SPJ markedly up-regulated the β-klotho levels in HFD mice, reversing FGF21 resistance. Further comparison with exogenously administered FGF21 revealed that SPJ activates brain cortical regions in a consistent manner, and additionally, SPJ promotes the number and colocalization of c-Fos and β-klotho positive cells in the brain cortex. In summary, SPJ effectively alleviates anxiety-like behaviors in HFD mice. Its mechanism is associated with up-regulation of β-klotho expression in the brain, reversal of FGF21 resistance, and subsequent activation of neurons in the cerebral cortex and amygdala.
Animals ; Diet, High-Fat/adverse effects* ; Fibroblast Growth Factors/genetics* ; Mice ; Male ; Panax/chemistry* ; Mice, Inbred C57BL ; Anxiety/etiology* ; Saponins/administration & dosage* ; Brain-Derived Neurotrophic Factor/genetics* ; Humans ; Liver/metabolism* ; Drugs, Chinese Herbal/administration & dosage*

Since the promulgation of the first Drug Administration Law of the People's Republic of China 40 years ago in 1984, China has undergone four main stages in the traditional Chinese medicine(TCM) regulation: the initial establishment of TCM regulation rules(1984-1997), the formation of a modern TCM regulatory system(1998-2014), the reform of the review and approval system for new TCM drugs(2015-2018), and the construction of a scientific regulation system for TCM(2019-2024). Over the past five years, a series of milestone achievements of TCM regulation in China have been achieved in the six aspects, including its strategic objectives and the establishment of a science-based regulatory system, the reform of the review and approval system for new TCM drugs, the optimization and improvement of the TCM standard system and its formation mechanism, comprehensive enhancement of regulatory capabilities for TCM safety, international harmonization of TCM regulation and its role in promoting innovation. Looking ahead, centered on advancing TCMRS to establish a sound regulatory framework tailored to the unique characteristics of TCM, TCM regulation will evolve into new reform patterns, advancing and extending across eight critical fronts, including the legal framework and policy architecture, the review and approval system for new TCM drugs, the quality standard and management system of TCM, the comprehensive quality & safety regulation and traceability system, the research and transformation system for TCMRS, AI-driven innovations in TCM regulation, the coordination between high-quality industrial development and high-level regulation, and the leadership in international cooperation and regulatory harmonization. In this way, a unique path for the development of modern TCM regulation with Chinese characteristics will be pioneered.
Humans ; China ; Drugs, Chinese Herbal/standards* ; History, 20th Century ; History, 21st Century ; Medicine, Chinese Traditional/trends*

Moringa oleifera, widely utilized in Ayurvedic medicine, is recognized for its leaves, seeds, and velamen possessing traditional effects such as vātahara(wind alleviation), sirovirecaka(brain clearing), and hridya(mental nourishment). This study aims to identify the medicinal part of ■ in the Sārasvata ghee formulation as described in the Bower Manuscript, while investigating the ameliorative effects of different medicinal parts of M. oleifera on learning and memory deficits in mice and elucidating the underlying molecular mechanisms. A total of 144 male ICR mice were randomly assigned to the following groups: control, model(scopolamine hydrobromide, Sco, 2 mg·kg~(-1)), donepezil(donepezil hydrochloride, Don, 3 mg·kg~(-1)), M. oleifera leaf low-, medium-, and high-dose groups(0.5, 1, 2 g·kg~(-1)), M. oleifera seeds low-, medium-, and high-dose groups(0.25, 0.5, 1 g·kg~(-1)), and M. oleifera velamen low-, medium-, and high-dose groups(0.31, 0.62, 1.24 g·kg~(-1)). Learning and memory abilities were assessed using the passive avoidance test and Morris water maze. Nissl and HE staining were employed to examine histopathological changes in the hippocampus. Transcriptomics and targeted metabolomics were used to screen differential genes and metabolites, with MetaboAnalyst 6.0 and O2PLS methods applied to identify key disease-related targets and pathways. RESULTS:: demonstrated that M. oleifera leaf(1 g·kg~(-1)) significantly ameliorated Sco-induced learning and memory deficits, outperforming M. oleifera seeds(0.25 g·kg~(-1)) and M. oleifera velamen(1.24 g·kg~(-1)). This was evidenced by improved behavioral performance, reversal of neuronal damage, and reduced acetylcholinesterase(AChE) activity. Multi-omics analysis revealed that M. oleifera leaf upregulated Tuba1c gene expression through the synaptic vesicle cycle, enhancing glutamate(Glu), dopamine(DA), and acetylcholine(ACh) release via Tuba1c-Glu associations for neuroprotection. M. oleifera seeds targeted the dopaminergic synapse pathway, promoting memory consolidation through Drd2-ACh associations. M. oleifera velamen was associated with the cocaine addiction pathway, modulating dopamine metabolism via Adora2a-DOPAC, with limited relevance to learning and memory. In conclusion, M. oleifera leaf exhibits superior efficacy and mechanistic advantages over M. oleifera seeds and velamen, suggesting that the ■ in the Sārasvata ghee formulation is likely M. oleifera leaf, providing scientific evidence for its identification in ancient texts.
Animals ; Moringa oleifera/chemistry* ; Male ; Mice ; Seeds/chemistry* ; Plant Leaves/chemistry* ; Mice, Inbred ICR ; Memory Disorders/psychology* ; Transcriptome/drug effects* ; Memory/drug effects* ; Learning/drug effects* ; Metabolomics ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Maze Learning/drug effects*

OBJECTIVE: To compare the effects of different concentrations of ropivacaine femoral nerve block on postoperative pain and early exercise fllowing total knee arthroplasty(TKA). METHODS: A total of 90 patients who underwent primary TKA between September 2022 and February 2023 were consecutively enrolled in this study. The cohort consisted of 34 males and 56 females, with a mean age of (66.66±7.03) years old. According to different concentrations of ropivacaine, patients were divided into 0.1% group, 0.2% group and 0.4% group, with 30 patients in each group. The age, gender, body mass index(BMI), American Society of Aneshesiologists(ASA) grade, operation time, anesthesia time, tourniquet using time, Post Anesthesia care unit(PACU) stay duration, ambulation time, first reaching to Bromage 0 grade time, visual analogue scale(VAS), hospitalization period and postoperative adverse reactions were compared among the three groups. RESULTS: All 90 patients were followed up for an average of (31.56±5.62) days, and no postoperative adverse reactions occurred. There were no significant differences among the three groups in terms of age, gender, BMI, ASA classification, operation time, anesthesia time, tourniquet application time, PACU stay duration, and hospitalization period (P>0.05). Significant differences were observed in VAS scores at 1, 2, 4, 6, and 12 hours post-operation among the three groups (P<0.05). Additionally, significant variations were noted in ambulation time and the first reaching to Bromage level 0 time among the three groups (P<0.05). In terms of postoperative pain, the VAS of the 0.1% group at 1, 2, 4, 6, and 12 hours after surgery(1.93±0.52), (2.57±0.77), (3.10±0.71), (3.10±0.71), (3.07±0.45) points were higher than those of the 0.4% group (1.57±0.50), (2.10±0.55), (2.23±0.57), (2.47±0.73), (2.50±0.57) points, and the differences were statistically significant (P<0.05);the VAS of the 0.1% group at 4, 6, and 12 hours after surgery were higher than those of the 0.2% group (2.43±0.57), (2.53±0.57), (2.63±0.56) points, and the differences were statistically significant (P<0.05);there was no statistically significant difference in VAS between the 0.2% group and the 0.4% group (P>0.05). In terms of early postoperative mobility, the time to ambulation time (8.30±2.76) h and the time to achieve the first Bromage grade 0 (6.13±2.18) h were significantly prolonged in the 0.4% group compared to both the 0.1% group (6.93±1.76) h, (4.17±1.18) h and the 0.2% group (6.53±1.59) h, (4.87±1.53) h. No statistically significant differences were observed between the 0.1% and 0.2% groups (P>0.05). CONCLUSION 0.2% ropivacaine femoral nerve block can effectively reduce postoperative pain after TKA and can perform early exercise earlier.
Humans ; Male ; Female ; Ropivacaine/administration & dosage* ; Arthroplasty, Replacement, Knee/adverse effects* ; Aged ; Nerve Block/methods* ; Femoral Nerve/drug effects* ; Middle Aged ; Pain, Postoperative/drug therapy* ; Anesthetics, Local/administration & dosage* ; Amides