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.

ObjectiveUlcerative colitis is a prevalent immunoinflammatory disease. Th17/Treg cell imbalance and gut microbiota dysregulation are key factors in ulcerative colitis pathogenesis. The actin cytoskeleton contributes to regulating the proliferation, differentiation, and migration of Th17 and Treg cells. Wdr63, a gene containing the WD repeat domain, participates in the structure and functional modulation of actin cytoskeleton. Recent research indicates that WDR63 may serve as a regulator of cell migration and metastasis via actin polymerization inhibition. This article aims to explore the effect of Wdr63 deletion on Th17/Treg cells and ulcerative colitis. MethodsWe constructed Wdr63^-/- mice, induced colitis in mice using dextran sulfate sodium salt, collected colon tissue for histopathological staining, collected mesenteric lymph nodes for flow cytometry analysis, and collected healthy mouse feces for microbial diversity detection. ResultsCompared with wild-type colitis mice, Wdr63^-/- colitis mice had a more pronounced shortening of colonic tissue, higher scores on disease activity index and histological damage index, Treg cells decreased and Th17 cells increased in colonic tissue and mesenteric lymph nodes, a lower level of anti-inflammatory cytokine IL-10, and a higher level of pro-inflammatory cytokine IL-17A. In addition, WDR63 has shown positive effects on maintaining intestinal microbiota homeostasis. It maintains the balance of Bacteroidota and Firmicutes, promoting the formation of beneficial intestinal bacteria linked to immune inflammation. ConclusionWdr63 deletion aggravates ulcerative colitis in mice, WDR63 inhibits colonic inflammation likely by regulating Th17/Treg balance and maintains intestinal microbiota homeostasis.

AIM To investigate the effects of Heixiaoyao Powder on neuroinflammation in APP/PS1 transgenic mice.METHODS The 16-week-old male APP/PS1 transgenic mice were randomly divided into the model group,the BBG group(P2X7R specific antagonist,30 mg/kg)and high,medium and low dose Heixiaoyao Powder groups of(22.10,11.05,5.53 g/kg),in contrast to the male C57BL/6J mice of the same age and the same strain of the blank groups,with 12 mice in each group.When normal saline by gavage was dosed upon the blank group and the model group,and the other groups were treated with corresponding drug by gavage.After 90 days of administration,the mice had their learning and memory ability detected by Morris water maze test;their hippocampal pathological changes observed with HE staining;their MyD88 expression detected by immunofluorescence staining;their hippocampal levels of pro-inflammatory factors(TNF-α,IL-6),anti-inflammatory factors(IL-10),ATP and amyloid protein β(Aβ)detected by ELISA;their hippocampal mRNA expressions of P2X7R,TLR4,MyD88 and NF-κB-P65 detected by RT-qPCR method;and their hippocampal protein expressions of P2X7R,TLR4,MyD88,NF-κB-P65 and p-NF-κB-P65 detected by Western blot.RESULTS Compared with the blank group,the model group demonstrated prolonged escape latency and reduced frequency in crossing platform(P＜0.01);decreased number of hippocampal neurons,deranged neurons,and darker cytoplasm staining;increased immunofluorescence expression of hippocampal MyD88(P＜0.01);decreased IL-10 level(P＜0.01);increased levels of TNF-α,IL-6,ATP and Aβ(P＜0.01);increased mRNA and protein expressions ofP2X7R,TLR4 and MyD88(P＜0.01),and increased protein expression of p-NF-κB-P65(P＜0.01).Compared with the model group,the groups intervened with Heixiaoyao Powder or BBG demonstrated shortened escape latency and increased frequency in crossing platform(P＜0.01);more number of neatly arranged hippocampal neurons;increased hippocampal IL-10 level(P＜0.01),decreased levels of TNF-α,IL-6,ATP and Aβ(P＜0.05,P＜0.01);decreased mRNA and protein expressions of P2X7R,TLR4 and MyD88(P＜0.05,P＜0.01);and decreased protein expression of p-NF-κB-P65(P＜0.05,P＜0.01).Except the low dose Heixiaoyao Powder group,the other treatment groups shared decreased immunofluorescence expression of MyD88(P＜0.05,P＜0.01).CONCLUSION Heixiaoyao Powder can significantly improve the learning and memory ability of APP/PS1 model mice,and its mechanism may lie in its function in alleviating cerebral neuroinflammation by reducing the abnormal Aβ aggregation via inhibiting the activation of ATP/P2X7R/NF-κB signaling pathway.

A mounting body of research suggests that circRNAs significantly contribute to the develop-ment of myocardial fibrosis.The microarray results of human circular RNA expression profile indicated that circHERC4_041 expression increased in the myocardium of patients with heart failure,RT-qPCR a-nalysis confirmed that the myocardial expression level of circHERC4_041 in individuals with heart failure were considerably elevated compared to that in healthy organ donors.Fluorescence in situ hybridization(FISH)confirmed that circHERC4_041 was abundant in the cytoplasm of human cardiomyocyte AC16.Overexpression of circHERC4_041 in mouse myocardial fibroblasts(mCFs)mediated by adenovirus in-hibited the expression of fibrosis-related proteins in mCFs.Experiments involving cell proliferation,wound healing,and Transwell assays demonstrated that overexpression of circHERC4_041 suppressed the growth and mobility of mCFs(P＜0.001).Sequence analysis results suggested that circHERC4_041 con-tains potential ribosome entry sequence(IRES)and open reading frame(ORF).Western blot confirmed that circHERC4_041 could translate the 516 amino acid HERC4-516aa protein,which was mainly located in the cytoplasm of the cell.Cell functional experiments confirmed that circHERC4_041 inhibited the fi-brotic phenotype of mCFs by specifically translating HERC4-516aa(P＜0.05).The specific interaction between HERC4-516aa and transglutaminase 2(TGM2)was confirmed by IP-MS screening and Co-IP i-dentification.Further results found that the degradation of TGM2 was promoted through proteasome path-way.The overexpression of TGM2 in mCFs facilitated by adenoviral vectors could counteract the suppres-sive effects of HERC4-516aa on the fibrotic phenotype of mCFs.Therefore,this study confirmed that the HERC4-516aa protein translated by circHERC4_041 can specifically bind to TGM2 to inhibit the fibrotic phenotype of myocardial fibroblasts.

Objective:To evaluate the impact of Diagnosis-Intervention Packet(DIP)payment reform on medical service costs and efficiency for inpatients in public hospitals,and to compare differences between surgical and medical groups.Methods:A quasi-experimental design was employed,using 605 636 discharged patients from a tertiary hospital in Hebei Province between January 2020 and March 2025 as the sample.The difference-in-differences(DID)model was used to analyze the changes in key indicators between the DIP settlement group(intervention group)and the non-DIP settlement group(control group).Results:Total hospitalization costs,out-of-pocket expenses,and medication costs were significantly reduced in the DIP settlement group(P<0.05),while costs for examinations,nursing,laboratory tests,and treatments increased significantly(P<0.05).Material costs increased by 30.7%in the surgical group(P<0.1)and decreased by 19.8%in the medical group(P<0.01).In terms of efficiency,the average length of stay,time,and cost consumption index all decreased(P<0.01),while the proportion of medical services increased(P<0.01).The case mix index(CMI)showed no significant changes.Conclusion:The DIP reform effectively controlled costs and improved efficiency,but it also resulted in cost shifting and departmental disparities.Therefore,it is necessary to optimize cost control and departmental management policies.

PURPOSE: To investigate the protective effect of sub-hypothermic mechanical perfusion combined with membrane lung oxygenation on ischemic hypoxic injury of yorkshire brain tissue caused by traumatic blood loss. METHODS: This article performed a random controlled trial. Brain tissue of 7 yorkshire was selected and divided into the sub-low temperature anterograde machine perfusion group (n = 4) and the blank control group (n = 3) using the random number table method. A yorkshire model of brain tissue injury induced by traumatic blood loss was established. Firstly, the perfusion temperature and blood oxygen saturation were monitored in real-time during the perfusion process. The number of red blood cells, hemoglobin content, NA⁺, K⁺, and Ca²⁺ ions concentrations and pH of the perfusate were detected. Following perfusion, we specifically examined the parietal lobe to assess its water content. The prefrontal cortex and hippocampus were then dissected for histological evaluation, allowing us to investigate potential regional differences in tissue injury. The blank control group was sampled directly before perfusion. All statistical analyses and graphs were performed using GraphPad Prism 8.0 Student t-test. All tests were two-sided, and p value of less than 0.05 was considered to indicate statistical significance. RESULTS: The contents of red blood cells and hemoglobin during perfusion were maintained at normal levels but more red blood cells were destroyed 3 h after the perfusion. The blood oxygen saturation of the perfusion group was maintained at 95% - 98%. NA⁺ and K⁺ concentrations were normal most of the time during perfusion but increased significantly at about 4 h. The Ca²⁺ concentration remained within the normal range at each period. Glucose levels were slightly higher than the baseline level. The pH of the perfusion solution was slightly lower at the beginning of perfusion, and then gradually increased to the normal level. The water content of brain tissue in the sub-low and docile perfusion group was 78.95% ± 0.39%, which was significantly higher than that in the control group (75.27% ± 0.55%, t = 10.49, p < 0.001), and the difference was statistically significant. Compared with the blank control group, the structure and morphology of pyramidal neurons in the prefrontal cortex and CA1 region of the hippocampal gyrus were similar, and their integrity was better. The structural integrity of granulosa neurons was destroyed and cell edema increased in the perfusion group compared with the blank control group. Immunofluorescence staining for glail fibrillary acidic protein and Iba1, markers of glial cells, revealed well-preserved cell structures in the perfusion group. While there were indications of abnormal cellular activity, the analysis showed no significant difference in axon thickness or integrity compared to the 1-h blank control group. CONCLUSIONS Mild hypothermic machine perfusion can improve ischemia and hypoxia injury of yorkshire brain tissue caused by traumatic blood loss and delay the necrosis and apoptosis of yorkshire brain tissue by continuous oxygen supply, maintaining ion homeostasis and reducing tissue metabolism level.
Animals ; Perfusion/methods* ; Disease Models, Animal ; Brain Injuries/etiology* ; Swine ; Male ; Hypothermia, Induced/methods*

OBJECTIVE: To investigate the effect of daratumumab, lenalidomide and dexamethasone on quality of life in transplant-ineligible (TIE) patients with newly diagnosed multiple myeloma (NDMM). METHODS: The clinical data of 93 TIE NDMM patients in our hospital from January 2020 to December 2022 were retrospectively analyzed. The patients were divided into D-Rd group (48 cases) and Rd group (45 cases) according to treatment regimen. The patients in Rd group were treated with lenalidomide and dexamethasone, while those in D-Rd group were treated with daratumumab on the basis of Rd group. The QLQ-C30 and EQ-5D VAS scores of the two groups were compared at baseline and after 3, 6 and 12 treatment cycles. The last follow-up date was June 30, 2023, and overall survival (OS) was compared between the two groups. RESULTS: The median follow-up period in the D-Rd group was 21 (7-38) months, and the median OS was 34 months, while that in the Rd group was 16 (5-35) months, and the median OS was 28 months. There was significant difference in OS between the two groups ( P <0.05). After 3, 6 and 12 treatment cycles, the QLQ-C30 score and EQ-5D VAS score of the two groups were significantly improved (all P <0.05). After 3 and 12 treatment cycles, the QLQ-C30 score and EQ-5D VAS score of D-Rd group were significantly higher than those of Rd group (all P <0.05). There were no significant differences in the improvement of QLQ-C30 GHS and pain scores between the two groups of patients with age <75 years and ECOG 0-1 score after 3, 6 and 12 treatment cycles (P >0.05). In D-Rd group of patients with age≥75 years, the improvement of QLQ-C30 GHS scores after 3 and 12 treatment cycles and QLQ-C30 pain scores after 3, 6 and 12 treatment cycles was significantly superior to that in Rd group (all P <0.05). In D-Rd group of patients with ECOG 2 scores, the improvement of QLQ-C30 GHS and pain scores after 3, 6 and 12 treatment cycles was significantly superior to that in Rd group (all P <0.05). CONCLUSION Daratumumab, lenalidomide, and dexamethasone can significantly improve OS in TIE NDMM patients without decrease of quality of life, especially in those with age≥75 years or ECOG 2 scores.
Humans ; Multiple Myeloma/drug therapy* ; Lenalidomide/therapeutic use* ; Quality of Life ; Dexamethasone/therapeutic use* ; Retrospective Studies ; Antibodies, Monoclonal/therapeutic use* ; Female ; Male ; Middle Aged ; Aged ; Stem Cell Transplantation

OBJECTIVE: To investigate the regulatory effects of two traditional mineral medicines (TMMs), Gypsum Fibrosum (Shigao, GF) and Terra Flava Usta (Zaoxintu, TFU), on gut-beneficial bacteria in mice, and preliminarily explore their mechanisms of action. METHODS: Mice were randomly divided into 3 groups (n=10 per group): the control group (standard diet), the GF group (diet supplemented with 2% GF), and the TFU group (diet supplemented with 2% TFU). After 4-week intervention, 16S rRNA gene sequencing was used to analyze the changes in the gut microbiota (GM). Scanning electron microscopy, in combination with coumarin A tetramethyl rhodamine conjugate and Hoechst stainings, was used to observe the bacteria and biofilm formation. RESULTS: Principal coordinate analysis revealed that GF and TFU significantly altered the GM composition in mice. Further analysis revealed that GF and TFU affected different types of gut bacteria, suggesting that different TMMs may selectively modulate specific bacterial populations. For certain bacteria, such as Faecalibaculum and Ileibacterium, both GF and TFU exhibited growth-promoting effects, implying that they may be sensitive to TMMs and that different TMMs can increase their abundance through their respective mechanisms. Notably, Lactobacillus reuteri, a widely recognized and used probiotic, was significantly enriched in the GF group. Random forest analysis identified Ileibacterium valens as a potential indicator bacterium for TMMs' impact on GM. Further mechanistic studies showed that gut bacteria formed biofilm structures on the TFU surface. CONCLUSIONS This study provides new insights into the interaction between TMMs and GM. As safe and effective natural clays, GF and TFU hold promise as potential candidates for prebiotic development.
Animals ; Gastrointestinal Microbiome/drug effects* ; Bacteria/growth & development* ; Mice ; Biofilms/drug effects* ; Male ; RNA, Ribosomal, 16S/genetics*

Glutamine provides carbon and nitrogen to support the proliferation of cancer cells. However, the precise reason why cancer cells are particularly dependent on glutamine remains unclear. In this study, we report that glutamine modulates the tumor suppressor F-box and WD repeat domain-containing 7 (FBW7) to promote cancer cell proliferation and survival. Specifically, lysine 604 (K604) in the sixth of the 7 substrate-recruiting WD repeats of FBW7 undergoes glutaminylation (Gln-K604) by glutaminyl tRNA synthetase. Gln-K604 inhibits SCFFBW7-mediated degradation of c-Myc and Mcl-1, enhances glutamine utilization, and stimulates nucleotide and DNA biosynthesis through the activation of c-Myc. Additionally, Gln-K604 promotes resistance to apoptosis by activating Mcl-1. In contrast, SIRT1 deglutaminylates Gln-K604, thereby reversing its effects. Cancer cells lacking Gln-K604 exhibit overexpression of c-Myc and Mcl-1 and display resistance to chemotherapy-induced apoptosis. Silencing both c-MYC and MCL-1 in these cells sensitizes them to chemotherapy. These findings indicate that the glutamine-mediated signal via Gln-K604 is a key driver of cancer progression and suggest potential strategies for targeted cancer therapies based on varying Gln-K604 status.
Glutamine/metabolism* ; Myeloid Cell Leukemia Sequence 1 Protein/genetics* ; Humans ; Proto-Oncogene Proteins c-myc/genetics* ; Cell Proliferation ; Signal Transduction ; Neoplasms/pathology* ; F-Box-WD Repeat-Containing Protein 7/genetics* ; Cell Survival ; Cell Line, Tumor ; Apoptosis