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.

AIM To investigate the effects of Yunpi Tongchang Formula on intestinal mucosal barrier damage in rats with opioid-induced constipation(OIC)of Spleen-Kidney Yang Deficiency Syndrome.METHODS In contrast to the 10 rats of the blank group,the 50 rats of the modeling group were induced into models of OIC of Spleen-Kidney Yang Deficiency Pattern by 7 days consecutive administration of both subcutaneous loperamide injection and alternating gavage of activated carbon ice water and vinegar.Following successful modeling,rats were randomly allocated into the model group,the mosapride citrate tablet group(1.35 mg/kg),and the high-dose,medium-dose,and low-dose Yunpi Tongchang Formula groups(15.12,7.56,3.78 g/kg),with 8 mice in each group.Upon the completion of the 14 days treatment,the rats had their TCM Syndrome scores assessed;their fecal water content,initial black stool excretion time,and small intestine propulsion rate measured;their colon tissue morphology observed by HE staining;their serum levels of IL-6,TNF-α,and IL-1β detected by ELISA;their expressions of occludin and zonula occludens-1(ZO-1)in colon tissues detected by immunohistochemistry;their mRNA expressions of MyD88,TLR4 and NF-κB p65 in the colon tissues detected by RT-qPCR;and their protein expressions of MyD88,TLR4 and NF-κB p65 in the colon tissues detected by Western blot.RESULTS Compared to the blank group,the model group had higher TCM Syndrome scores(P＜0.01);lower fecal water content and small intestine propulsion rate(P＜0.05,P＜0.01);longer initial black stool excretion time(P＜0.01);more mucosal edema in colon tissue,obvious inflammatory infiltration,and glandular disorder;increased serum levels of IL-6,TNF-α and IL-1 β(P＜0.05);decreased colon expressions of ZO-1 and occludin(P＜0.01);and increased mRNA and protein expressions of TLR4,MyD88 and NF-κB p65(P＜0.01).Compared to the model group,both the medium-dose Yunpi Tongchang Formula group and the mosapride citrate tablet group demonstrated effectively reduced TCM syndrome scores(P＜0.01);increased fecal water content and small intestine propulsion rate(P＜0.05,P＜0.01);and shorter initial black stool excretion time(P＜0.01);improved colon mucosal edema and inflammatory infiltration;decreased serum levels of IL-6,TNF-α and IL-1β(P＜0.01);upregulated protein expressions of ZO-1 and occludin(P＜0.01);and downregulated mRNA and protein expressions of TLR4,MyD88 and NF-κB p65(P＜0.05,P＜0.01).CONCLUSION Yunpi Tongchang Formula significantly ameliorates constipation symptoms in OIC rat models of Spleen-Kidney Yang Deficiency Syndrome because of its efficacy in attenuating intestinal inflammation and preserving the integrity of intestinal epithelial barrier structure,with its mechanistic action in downregulating TLR4/MyD88/NF-κB signaling pathway activation.

Objective:By analyzing the differential miRNA in seminal plasma between individuals with normal and abnormal sperm DNA fragmentation index(DFI),we aim to identify miRNA that may impact sperm DNA integrity and target genes,and attempt to analyze their potential mechanisms of action.Methods:A total of 161 study subjects were collected and divided into normal con-trol group,DFI-medium group and DFI-abnormal group based on the DFI detection values.Differential miRNA were identified through miRNA chip analysis.Through bioinformatics analysis and target gene prediction,miRNA related to DFI and specific target genes were identified.The relative expression levels of differential miRNA and target genes in each group were compared to explore the impact of their differential expression on DFI.Results:Through miRNA chip analysis,a total of 11 differential miRNA were detected.Bioin-formatics analysis suggested that miR-26a-5p may be associated with reduced sperm DNA integrity.And gene prediction indicated that PTEN was a specific target gene of miR-26a-5p.Compared to the normal control group,the relative expression levels of miR-26a-5p in both the DFI-medium group and the DFI-abnormal group showed a decrease,while the relative expression levels of PTEN showed an in-crease.The relative expression levels of miR-26a-5p in all groups were negatively correlated with DFI values,while the relative expres-sion levels of PTEN showed a positive correlation with DFI values in the DFI-medium group and the DFI-abnormal group.The AUC of miR-26a-5p in the DFI-medium group was 0.740(P＜0.05),with a sensitivity of 73.6％and a specificity of 71.5％;the AUC of PTEN was 0.797(P＜0.05),with a sensitivity of 76.5％and a specificity of 78.4％.In the DFI-abnormal group,the AUC of miR-26a-5p was 0.848(P＜0.05),with a sensitivity of 81.3％and a specificity of 78.1％.While the AUC of PTEN was 0.763(P＜0.05),with a sensitivity of 77.2％and a specificity of 80.2％.Conclusion:miR-26a-5p affects the integrity of sperm DNA by regulating the expression of PTEN negatively.The relative expression levels of seminal plasma miR-26a-5p and PTEN have good diag-nostic value for sperm DNA integrity damage,which can help in the etiological diagnosis and prognosis analysis of abnormal DFI.This provides a diagnostic and treatment approach for the study and diagnosis of DFI abnormalities without clear etiology.

Objective To explore the risk factors for noncontiguous spinal fractures(NSFs)in the elderly.Methods The clinical data of 614 elderly patients with spinal fracture from January 2013 to December 2019 were analyzed retrospectively.Patients were divided into the NSFs group and the Non-NSFs group according to whether NSFs occurred or not.Univariate analysis and multivariate Logistic regression analysis were used to screen the risk factors of NSFs.Results Univariate analysis showed that female(P=0.003),high-energy violent injury(P=0.032),osteoporosis(P=0.004),fracture in spring(P=0.020),and previous spinal fracture history(P<0.001)were associated with the occurrence of NSFs.Multivariate Logistic regression analysis showed that fracture in spring(P=0.024),previous spinal fracture history(P<0.001)and high-energy violent injury(P=0.038)were the independent risk factors for the occurrence of NSFs in the elderly.Conclusion High-energy violent injury,fracture in spring and previous spinal fracture history are the independent risk factors for the occurrence of NSFs in the elderly.Therefore,elderly patients with the above risk factors should be examined more carefully and comprehensively to avoid missed diagnosis and delayed diagnosis.In order to reduce the incidence of this disease,corresponding measures should be taken according to the preventable risk factors.

AIM To investigate the effects of Yunpi Tongchang Formula on intestinal mucosal barrier damage in rats with opioid-induced constipation(OIC)of Spleen-Kidney Yang Deficiency Syndrome.METHODS In contrast to the 10 rats of the blank group,the 50 rats of the modeling group were induced into models of OIC of Spleen-Kidney Yang Deficiency Pattern by 7 days consecutive administration of both subcutaneous loperamide injection and alternating gavage of activated carbon ice water and vinegar.Following successful modeling,rats were randomly allocated into the model group,the mosapride citrate tablet group(1.35 mg/kg),and the high-dose,medium-dose,and low-dose Yunpi Tongchang Formula groups(15.12,7.56,3.78 g/kg),with 8 mice in each group.Upon the completion of the 14 days treatment,the rats had their TCM Syndrome scores assessed;their fecal water content,initial black stool excretion time,and small intestine propulsion rate measured;their colon tissue morphology observed by HE staining;their serum levels of IL-6,TNF-α,and IL-1β detected by ELISA;their expressions of occludin and zonula occludens-1(ZO-1)in colon tissues detected by immunohistochemistry;their mRNA expressions of MyD88,TLR4 and NF-κB p65 in the colon tissues detected by RT-qPCR;and their protein expressions of MyD88,TLR4 and NF-κB p65 in the colon tissues detected by Western blot.RESULTS Compared to the blank group,the model group had higher TCM Syndrome scores(P＜0.01);lower fecal water content and small intestine propulsion rate(P＜0.05,P＜0.01);longer initial black stool excretion time(P＜0.01);more mucosal edema in colon tissue,obvious inflammatory infiltration,and glandular disorder;increased serum levels of IL-6,TNF-α and IL-1 β(P＜0.05);decreased colon expressions of ZO-1 and occludin(P＜0.01);and increased mRNA and protein expressions of TLR4,MyD88 and NF-κB p65(P＜0.01).Compared to the model group,both the medium-dose Yunpi Tongchang Formula group and the mosapride citrate tablet group demonstrated effectively reduced TCM syndrome scores(P＜0.01);increased fecal water content and small intestine propulsion rate(P＜0.05,P＜0.01);and shorter initial black stool excretion time(P＜0.01);improved colon mucosal edema and inflammatory infiltration;decreased serum levels of IL-6,TNF-α and IL-1β(P＜0.01);upregulated protein expressions of ZO-1 and occludin(P＜0.01);and downregulated mRNA and protein expressions of TLR4,MyD88 and NF-κB p65(P＜0.05,P＜0.01).CONCLUSION Yunpi Tongchang Formula significantly ameliorates constipation symptoms in OIC rat models of Spleen-Kidney Yang Deficiency Syndrome because of its efficacy in attenuating intestinal inflammation and preserving the integrity of intestinal epithelial barrier structure,with its mechanistic action in downregulating TLR4/MyD88/NF-κB signaling pathway activation.

Objective:To explore the independent risk factors for uremic encephalopathy(UE)in maintenance hemodialysis(MHD)patients and provide evidence for early clinical warning and intervention.Methods:A case-control study was conducted,enrolling 67 MHD patients diagnosed with UE(UE group)at Laibin People's Hospital from January 2010 to December 2024,and 67 non-UE patients during the same period(control group).Demographic characteristics,dialysis parameters,laboratory indicators,and infection events were collected.Univariate and multivariate logistic regression analyses were used to identify independent risk factors for UE.Results:The UE group had significantly higher rates of infection(58.2％vs.29.9％),serum creatinine(789 vs.702 μmol/L),and iPTH levels(568 vs.385 pg/mL)compared to the control group(P＜0.05).Multivariate analysis revealed that concurrent infection(OR=3.022,95％CI:1.312-6.958),elevated serum creatinine(OR=1.004,95％CI:1.000-1.008),and elevated iPTH(OR=1.002,95％CI:1.001-1.003)were independent risk factors for UE(P＜0.05).The combined prediction model achieved an AUC of 0.878(95％CI:0.822-0.934),with 82.1％sensitivity and 80.6％specificity.Conclusion:Infection,elevated serum creatinine,and elevated iPTH significantly increase the risk of UE in MHD patients.Clinical management should emphasize infection prevention,toxin clearance optimization,and parathyroid function regulation to reduce UE incidence.

This review analyzes the application status and pros-pect of big data and artificial intelligence technology in the field of pharmacology in recent years.Big data and artificial intelli-gence technology is the inevitable result of the information age,which not only promotes the development of biomedicine,but al-so opens up new ways for the development of pharmacology.Mo-reover,artificial intelligence(AI)is a multifaceted and evolving field applied to pharmaceutical R&D,health management,and new drug R&D.Consequently,this review discusses the applica-tion of artificial intelligence in pharmacology at different stages,discusses the existing shortcomings,and finally makes an out-look.

Objective:This study employs a combination of single-cell sequencing and Mendelian randomization to explore the genetic associations and molecular mechanisms of Eomes in RCC.Methods:In this study,single-cell transcriptomic data from RCC tissues and adjacent normal tissues were extracted from the GEO database.The data were analyzed using R language and various packages such as Seurat,limma,and CellChat for cell cluster annotation,intercellular communication analysis,and differential expression analysis.Additionally,eQTL data related to differentially expressed genes were retrieved from the GWAS database as exposure variables,with RCC used as the outcome variable in Mendelian randomization analysis to identify the role of Eomes in RCC.Finally,GO functional enrichment and KEGG pathway analyses were conducted to explore the potential molecular mechanisms of Eomes.Results:Single-cell RNA sequencing revealed that B cells play a significant role in the heterogeneity of RCC.Mendelian randomization analysis indicated that Eomes is an important risk factor for RCC(P＜0.05).Furthermore,seven highly correlated specific SNPs were identified,including rs 17021298,rs2247056,rs2617170,rs3806624,rs55908509,rs6590334,and rs9420589.GO and KEGG enrichment analyses suggest that Eomes may be involved in early cell fate determination in renal cell carcinoma and participate in the regulation of Th1 and Th2 cell differentiation,HPV infection,and the Notch signaling pathway.Conclusions:This study is the first to combine single-cell sequencing and Mendelian randomization analysis in RCC,confirming a strong positive causal relationship between Eomes and RCC(OR＞1).Our findings offer new insights into the pathogenesis of RCC,suggesting that Eomes could serve as a novel target for early diagnosis and personalized treatment of RCC.

Objective To explore the effect and predictive value of ultrasound-guided needling assisted motor evoked potentials(MEP)and electromyography(EMG)monitoring on neurological recovery in spinal surgery.Methods A retrospective analysis was conducted on the clinical data of 80 patients who underwent spinal surgery at Jiangsu Province Hospital of Chinese Medicine from January 2020 to December 2024.A total of 41 patients in the observation group received ultrasound-guided needling assisted MEP and EMG monitoring,and 39 patients in the control group received conventional method for MEP and EMG monitoring.The operative time,intraoperative blood loss,and the proportions of intraoperative MEP and EMG warnings were compared between the two groups,and the sensitivity and specificity of intraoperative MEP monitoring were compared between the two groups.The receiver operating characteristic(ROC)curve was plotted,and the area under the curve(AUC)was calculated to analyze the efficiency of MEP warning in predicting the dysfunction of postoperative spinal cord.Results There were no significant differences in the operative time,intraoperative blood loss,or the proportions of intraoperative MEP and EMG warnings(P>0.05).The sensitivity,specificity and AUC of intraoperative MEP monitoring in the observation group were significantly higher than those in the control group,with statistically significant differences(P<0.05).The sensitivity,specificity,and AUC of postoperative MEP warning in predicting the dysfunction of spinal cord in the observation group were higher than those in the control group,with statistically significant differences(P<0.05).Conclusion Ultrasound-guided needling assisted MEP and EMG monitoring can effectively enhance the intraoperative neural monitoring accuracy,and postoperative MEP warning demonstrates superior predictive value for postoperative neurological dysfunction.