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.

ObjectiveTo explore the association between the concentration of maternal serum biomarkers， including free beta-human chorionic gonadotropin （fβ-hCG）， alpha fetoprotein （AFP）， unconjugated estriol （uE3）， and inhibin A （INH A） during the second trimester pregnancy and the risk of fetal presenting with copy number variation （CNV）. MethodsA retrospective analysis was conducted on the diagnostic results of pregnant women who were identified as high risk in the second trimester quadruple serological screening. These pregnant women underwent the interventional prenatal diagnosis and performed amniotic fluid karyotype analysis and comparative genomic hybridization array （aCGH） or CNV sequencing （CNV-seq） simultaneously.The detection rates of different diagnostic methods were calculated. The concentration of serum biomarkers， fβ-hCG， AFP， uE3， and INH A were divided into three levels： abnormally low， normal， and abnormally high. The prevalence of these serum biomarkers concentration in pregnant women with aberrant aCGH/CNV-seq results compared to normal controls was statistically calculated. ResultsAmong the 1 516 cases with high-risk quadruple serological screening in the second trimester， 59 chromosomal abnormalities were revealed by karyotype analysis， while 87 abnormalities were detected by aCGH/CNV-seq， indicating a significant statistical difference in detection rate. In comparison to the result of karyotype analysis， aCGH/CNV-seq identified an additional 46 cases of fetal CNV abnormalities， whereas it failed to detect 18 cases of chromosomal structural abnormalities. Among the 1 457 cases with normal karyotype results， the most frequent abnormality was observed in the concentration of fβ-hCG， followed by abnormalities in INH A and uE3 concentrations， yet the least common abnormality was noted in AFP concentration. When the maternal serum uE3 concentration was less than 0.5 multiples of median （MoM）， there was a significant difference in the risk of fetal CNV compared to the CNV results corresponding to normal concentration of uE3. No significant differences were found in the levels of fβ-hCG， AFP， and INH A between the two groups. ConclusionLow serum uE3 concentration （<0.5 MoM） is associated with an increased risk of fetal presenting with aberrant CNV.

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.

Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

Intelligence encompasses various abilities, including logical reasoning, comprehension, self-awareness, learning, planning, creativity, and problem-solving. Extensive research and practical experience suggest that there are sex differences in intellectual development, with females typically maturing earlier than males. However, the key genes and molecular network mechanisms underlying these sex differences in intellectual development remain unclear. To date, Genome-Wide Association Studies (GWAS) have identified 507 genes that are significantly associated with intelligence. This study first analyzed RNA sequencing data from different stages of brain development (from BrainSpan), revealing that during the late embryonic stage, the average expression levels of intelligence-related genes are higher in males than in females, while the opposite is observed during puberty. This study further constructed interaction networks of intelligence-related genes with sex-differential expression in the brain, including the prenatal male network (HELP-M: intelligence genes with higher expression levels in prenatal males) and the pubertal female network (HELP-F: intelligence genes with higher expression levels in pubertal females). The findings indicate that the key genes in both networks are Ep300 and Ctnnb1. Specifically, Ep300 regulates the transcription of 53 genes in both HELP-M and HELP-F, while Ctnnb1 regulates the transcription of 45 genes. Ctnnb1 plays a more prominent role in HELP-M, while Ep300 is more crucial in HELP-F. Finally, this study conducted sequencing validation on rats at different developmental stages, and the results indicated that in the prefrontal cortex of female rats during adolescence, the expression levels of the intelligence genes in HELP-F, as well as key genes Ep300 and Ctnnb1, were higher than those in male rats. These genes were also involved in neurodevelopment-related biological processes. The findings reveal a sex-differentiated intelligence gene network and its key genes, which exhibit varying expression levels during the neurodevelopmental process.
Female ; Intelligence/physiology* ; Male ; Sex Characteristics ; Animals ; Brain/growth & development* ; E1A-Associated p300 Protein/physiology* ; beta Catenin/physiology* ; Transcriptome ; Rats ; Gene Expression Profiling ; Genome-Wide Association Study

Following the release of the Technical Requirements on Quality Control and Standard Establishment of Traditional Chinese Medicine Formula Granules by the National Medical Products Administration in 2021, Chinese Pharmacopoeia Commission has promulgated 296 national drug standards so far, and most provinces have started the work of establishing provincial standards as supplements. The promulgation of standards fostered high-quality development of the industry. Since the implementation of national and provincial standards for more than three years, enterprises have gained deep understanding and hands-on experiences on the characteristics, technical requirements, production process, and quality control of traditional Chinese medicine(TCM) formula granules. Meanwhile, challenges have emerged restricting the high-quality development of this industry, including how to formulate quality control strategies for medicinal materials and decoction pieces, how to reduce manufacturing costs, and how to improve the pass rate and product stability under high standards. Based on the work experiences from standard management and process research, this article analyzed the distribution of sources, processing methods, dry extract rate ranges, process requirements for volatile oil-containing decoction pieces, control measures of safety indices, characteristics and trends of setting characteristic chromatograms or fingerprints, characteristics and trends of setting content ranges, and main differences between national standards and provincial standards. On the one hand, this article aims to present main characteristics for deeply understanding different indicators in standards and provide basic ideas for establishing quality and process control systems. On the other hand, from the perspective of industrial practice, suggestions are put forward on the important aspects that need to be focused on in the quality and process control of TCM formula granules.
Drugs, Chinese Herbal/chemistry* ; Quality Control ; Medicine, Chinese Traditional/standards* ; China ; Drug Industry/standards*

The chemical constituents were systematically separated from the roots of Berberis polyantha by various chromatographic methods, including silica gel column chromatography, HP20 column chromatography, polyamide column chromatography, reversed-phase C_(18) column chromatography, and preparative high-performance liquid chromatography. The structures of the compounds were identified by physicochemical properties and spectroscopic techniques(1D NMR, 2D NMR, UV, MS, and CD). Four phenylpropanoids were isolated from the methanol extract of the roots of B. polyantha, and they were identified as(2R)-1-(4-hydroxy-3,5-dimethoxyphenyl)-1-propanone-O-β-D-glucopyranoside(1), methyl 4-hydroxy-3,5-dimethoxybenzoate(2),(+)-syringaresinol(3), and syringaresinol-4-O-β-D-glucopyranoside(4). Compound 1 was a new compound, and other compounds were isolated from this plant for the first time. The anti-inflammatory activity of these compounds was evaluated based on the release of nitric oxide(NO) in the culture of lipopolysaccharide(LPS)-induced RAW264.7 macrophages. At a concentration of 10 μmol·L~(-1), all the four compounds inhibited the LPS-induced release of NO in RAW264.7 cells, demonstrating potential anti-inflammatory properties.
Plant Roots/chemistry* ; Animals ; Mice ; Berberis/chemistry* ; RAW 264.7 Cells ; Macrophages/immunology* ; Drugs, Chinese Herbal/isolation & purification* ; Nitric Oxide/metabolism* ; Molecular Structure ; Anti-Inflammatory Agents/isolation & purification*

High-performance liquid chromatography(HPLC) was used to determine the content of three major components in Citri Reticulatae Semen(CRS), including limonin, nomilin, and obacunone. The chromaticity of the CRS sample during salt processing and stir-frying was measured using a color difference meter. Next, the relationship between the color and content of the salt-processed CRS sample was investigated through correlation analysis. By integrating the oil bath technique for processing simulation with HPLC, the changes in the relative content of nomilin and its transformation products were analyzed, with its structural transformation pattern during processing identified. Additionally, RAW264.7 cells were induced with lipopolysaccharides(LPSs) to establish an inflammatory model, and the anti-inflammatory activity of nomilin and its transformation product, namely obacunone was evaluated. The results indicated that as processing progressed, E~*ab and L~* values showed a downward trend; a~* values exhibited a slow increase over a certain period, followed by no significant changes, and b~* values remained stable with no significant changes over a certain period and then started to decrease. The limonin content remained barely unchanged; the nomilin content decreased, and the obacunone increased significantly. The changing trends in content and color parameters during salt-processing and stir-frying were basically consistent. The content of nomilin and obacunone was significantly correlated with the colorimetric values(L~*, a~*, b~*, and E~*ab), while limonin content showed no significant correlation with these values. By analyzing HPLC patterns of nomylin at different heating temperatures and time, it was found that under conditions of 200-250 ℃ for heating of 5-60 min, the content of nomilin significantly decreased, while the obacunone content increased pronouncedly. The in vitro anti-inflammatory activity results indicated that compared to the model group, the group with a high concentration of nomilin and the groups with varying concentrations of obacunone showed significantly reduced release of nitric oxide(NO)(P<0.01). When both were at the same concentration, obacunone showed better performance in inhibiting NO release. In this study, the obvious correlation between the color and content of major components during the processing of CRS samples was identified, and the dynamic patterns of quality change in CRS samples during processing were revealed. Additionally, the study revealed and confirmed the transformation of nomilin into obacunone during processing, with the in vitro anti-inflammatory activity of obacunone significantly greater than that of nomilin. These findings provided a scientific basis for CRS processing optimization, tablet quality control, and its clinical application.
Mice ; Animals ; Drugs, Chinese Herbal/pharmacology* ; RAW 264.7 Cells ; Limonins/chemistry* ; Chromatography, High Pressure Liquid ; Citrus/chemistry* ; Color ; Benzoxepins/chemistry* ; Anti-Inflammatory Agents/chemistry*

The quality of traditional Chinese medicine(TCM) is a critical foundation for ensuring the stability of its efficacy, as well as the safety and effectiveness of its clinical use. The identification of critical quality attributes(CQAs) is one of the core components of TCM preparation quality control. This study focuses on Jianwei Xiaoshi Tablets and explores their CQAs related to property and flavor from the perspective of taste receptor proteins. Three taste receptor proteins, T1R2, T1R3, and TRPV1, were selected, and a biosensor based on high-electron-mobility transistor(HEMT) was constructed to detect the interactions between Jianwei Xiaoshi Tablets and taste receptor proteins. Simultaneously, liquid chromatography-mass spectrometry(LC-MS) technology was used to analyze the chemical composition of Jianwei Xiaoshi Tablets. In examining the interaction strength, the results indicated that the interaction between Jianwei Xiaoshi Tablets and TRPV1 protein was the strongest, followed by T1R3, with the interaction with T1R2 being relatively weaker. By combining biosensing technology with LC-MS, 16 chemical components were identified from Jianwei Xiaoshi Tablets, among which six were selected as CQAs for sweetness and seven for pungency. Further validation experiments demonstrated that CQAs such as hesperidin and hesperetin had strong interactions with their corresponding taste receptor proteins. Through the combined use of multiple technological approaches, this study successfully determined the property and flavor-related CQAs of Jianwei Xiaoshi Tablets. It provides novel ideas and approach for the identification of CQAs in TCM preparations and offers comprehensive theoretical support for TCM quality control, contributing to the improvement and development of TCM preparation quality control systems.
Drugs, Chinese Herbal/chemistry* ; Biosensing Techniques/methods* ; TRPV Cation Channels/chemistry* ; Tablets/chemistry* ; Receptors, G-Protein-Coupled/genetics* ; Quality Control ; Taste ; Humans ; Mass Spectrometry

Functional dyspepsia (FD), characterized by persistent or recurrent dyspeptic symptoms without identifiable organic, systemic or metabolic causes, is an increasingly recognized global health issue. The objective of this guideline is to equip clinicians and nursing professionals with evidence-based strategies for the management and treatment of adult patients with FD using traditional Chinese medicine (TCM). The Guideline Development Group consulted existing TCM consensus documents on FD and convened a panel of 35 clinicians to generate initial clinical queries. To address these queries, a systematic literature search was conducted across PubMed, EMBASE, the Cochrane Library, China National Knowledge Infrastructure (CNKI), VIP Database, China Biology Medicine (SinoMed) Database, Wanfang Database, Traditional Medicine Research Data Expanded (TMRDE), and the Traditional Chinese Medical Literature Analysis and Retrieval System (TCMLARS). The evidence from the literature was critically appraised using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. The strength of the recommendations was ascertained through a consensus-building process involving TCM and allopathic medicine experts, methodologists, pharmacologists, nursing specialists, and health economists, leveraging their collective expertise and empirical knowledge. The guideline comprises a total of 43 evidence-informed recommendations that span a range of clinical aspects, including the pathogenesis according to TCM, diagnostic approaches, therapeutic interventions, efficacy assessments, and prognostic considerations. Please cite this article as: Zhang SS, Zhao LQ, Hou XH, Bian ZX, Zheng JH, Tian HH, Yang GH, Hong WS, He YY, Liu L, Shen H, Li YP, Xie S, Shu J, Zeng BF, Li JX, Liu Z, Xiao ZH, Xiao JD, Zheng PY, Huang SG, Chen SL, Fei GJ. International clinical practice guideline on the use of traditional Chinese medicine for functional dyspepsia (2025). J Integr Med. 2025; 23(5):502-518.
Dyspepsia/drug therapy* ; Humans ; Medicine, Chinese Traditional/methods* ; Practice Guidelines as Topic ; Drugs, Chinese Herbal/therapeutic use*