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.

The Expert Consensus on Clinical Application of Qinbaohong Zhike Oral Liquid in Treatment of Acute Bronchitis and Acute Attack of Chronic Bronchitis （GS/CACM 337-2023） was released by the China Association of Chinese Medicine on December 13^th， 2023. This expert consensus was developed by experts in methodology， pharmacy， and Chinese medicine in strict accordance with the development requirements of the China Association of Chinese Medicine （CACM） and based on the latest medical evidence and the clinical medication experience of well-known experts in the fields of respiratory medicine （pulmonary diseases） and pediatrics. This expert consensus defines the application of Qinbaohong Zhike oral liquid in the treatment of cough and excessive sputum caused by phlegm-heat obstructing lung， acute bronchitis， and acute attack of chronic bronchitis from the aspects of applicable populations， efficacy evaluation， usage， dosage， drug combination， and safety. It is expected to guide the rational drug use in medical and health institutions， give full play to the unique value of Qinbaohong Zhike oral liquid， and vigorously promote the inheritance and innovation of Chinese patent medicines.

Objective To analyze the trends in incidence and mortality of breast cancer among Chinese women from 1992 to 2021,assess the impact of age,period,and cohort on its incidence and mortality rates,and predict future trends to provide a basis for developing effective intervention strategies.Methods Utilizing the 2021 Global Burden of Disease(GBD2021)database,the Joinpoint regression model was employed to analyze the trends in age-standardized incidence and mortality rates of breast cancer among Chinese women from 1992 to 2021.The age-period-cohort model was applied to estimate the age,period,and cohort effects on the incidence and mortality of breast cancer among Chinese women during the same period.The autoregressive integrated moving average(ARIMA)model was used to predict the age-standardized incidence and mortality rates of breast cancer among Chinese women from 2022 to 2026.A stratified analysis was conducted to explore the impact of different risk factors[including smoking,alcohol consumption,high body mass index(BMI),hyperglycemia,physical inactivity,and diet]on breast cancer mortality.Results From 1992 to 2021,the incidence and mortality rates of breast cancer among Chinese women showed an overall upward trend,with incidence rates rising from 15.95/100,000 in 1992 to 55.54/100,000 in 2021,and mortality rates increasing from 7.35/100,000 to 12.41/100,000.The age-standardized incidence rate also exhibited an upward trend,rising from 18.51/100,000 to 37.00/100,000,with an average annual percentage change(AAPC)of 2.43%.However,the age-standardized mortality rate showed an overall downward trend,decreasing from 9.05/100,000 to 8.24/100,000,with an AAPC of-0.35%.The APC model analysis revealed that the age,period,and cohort effects on incidence and mortality were statistically significant(P<0.001).Within the same birth cohort,breast cancer incidence increased in women aged 15-89 years but decreased in those≥90 years.Breast cancer mortality showed a steady increase with age.With the increase in years,the risk of breast cancer incidence gradually increased,reaching the highest between 2017 and 2021,with a relative risk(RR)value of 1.37.Conversely,the risk of breast cancer mortality decreased with the increase in years,with the lowest mortality between 2012 and 2016,and an RR value of 0.86.With the increase in the birth cohort year,the risk of breast cancer incidence gradually increased,while the risk of mortality gradually decreased.The ARIMA model prediction results showed that the age-standardized incidence rate of breast cancer among women would continue to rise from 2022 to 2026,reaching 40.25/100,000 by 2026,while the age-standardized mortality rate would tend to stabilize at 8.28/100,000 by 2026.Among the risk factors for breast cancer,diet was found to have the highest impact on breast cancer mortality.Conclusions The incidence rate of breast cancer among Chinese women continues to rise,indicating that the prevention and control situation remains severe.Future efforts should focus on developing precise screening programs for high-risk populations and optimizing early screening strategies and treatment resource allocation based on predicted trend.

Objective To investigate the potential association between methylation levels of CpG locus of brain-derived neurotrophic factor(BDNF)gene and risk of acute stress disorder(ASD)[post-traumatic stress disorder(PTSD)or post-traumatic depression].Methods(1)30 specific pathogen-free(SPF)-grade male ICR mice were randomly divided into control group,24 h restraint stress(Res)group,and Res+5-Aza-2'deoxycytidine(Res+5-Aza-cdR)group,with 10 mice in each group.An ASD mouse model was established using 24h restraint stress.The body weight of the mice was recorded daily.At 24 h and 48 h after the successful establishment of the model,open field tests were conducted to evaluate locomotor activity level and anxiety-like behavior of the mice.Reverse transcription-polymerase chain reaction(RT-PCR)and Western blotting were used to detect BDNF mRNA and protein expression levels in the hippocampus of mice in each group.(2)Relevant genome-wide association study(GWAS)summary data were utilized for instrumental variable(IV)selection.A two-sample Mendelian randomization(TSMR)approach was applied to analyze the causal relationship between CpG site methylation of BDNF gene and ASD risk.The leave-one-out method was used to verify the results.Results(1)The results of body weight monitoring and the open field test showed that compared with control group,the body weight,the residence time in the central area,the total movement distance,and the activity times of mice in Res group were significantly decreased(P＜0.001).Compared with Res group,the body weight,the residence time in the central area,the total movement distance and activity times of mice in Res+5-Aza group were significantly increased(P＜0.00l).The results of RT-qPCR and Western blotting showed that compared with control group,the expression levels of BDNF mRNA and protein in the hippocampus of the mice in Res group were significantly decreased(P＜0.05).Compared with Res group,the expression level of BDNF mRNA and protein in Res+5-Aza group was significantly higher(P＜0.001).(2)TSMR analysis results showed that the cg10635145 site in BDNF gene(OR=0.862,95％CI 0.793-0.938,P=0.043)was negatively correlated with the risk of PTSD.The cg08362738 site(OR=0.975,95％CI 0.960-0.992,P=0.003)was negatively correlated with the risk of trauma-related depression,whereas the cg18117895 site(OR=1.008,95％CI 1.000-1.016,P=0.047)was positively correlated with the risk of trauma-related depression.The results of Leave-one-out test showed that the causal relationship between the single nucleotide polymorphism(SNP)of CpG site in BDNF gene and ASD was not affected by a strong Ⅳ.Conclusion The CpG loci cg1063145 and cg08362738 of BDNF gene maybe protective factors for ASD-related diseases,while cg18117895 maybe a riskfactor.

Objective To establish and evaluate a nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer.Methods A retrospective study was conducted on 1298 patients with HBV-related primary liver cancer hospitalized in the Kunming Third People's Hospital from January 2012 to December 2022.General data and serological indicators were collected,and patients were divided into infection group(n=262)and control group(n=1036)based on the occurrence of spontaneous peritonitis.Univariate and LASSO regression analyses were used to screen variables,followed by binary logistic regression to analyze the influencing factors of spontaneous peritonitis in HBV-related primary liver cancer patients,leading to the establishment of a nomogram prediction model.Finally,the Hosmer-lemeshow(H-L)goodness of fit test,receiver operating characteristic(ROC)curve,calibration curve,decision curve analysis(DCA)and clinical impact curve(CIC)were utilized to evaluate the fit degree,accuracy,calibration,and clinical practicability of the nomogram prediction model.Results Single factor analysis revealed significant differences between infection group and control group in portal vein cancer thrombus(PVTT),Child-Pugh grade,China Liver Cancer Staging(CNLC)stage,alcohol consumption history,smoking history,white blood cell count(WBC),neutrophil count(NE),hemoglobin(Hb),fibrinogen(FIB),abnormal prothrombin(PIVKA-Ⅱ),aspartate aminotransferase(AST),alanine aminotransferase(ALT),total protein(TP),prealbumin(PA),γ-glutamyltransferase(GGT),alkaline phosphatase(ALP),cholinesterase(CHE),total bile acid(TBA),total cholesterol(TC),low density lipoprotein(LDL),creatinine(Cr),HBV DNA,CD3+T cells count,CD4+T cells count,CD8+T cells count,CD4+T cells/CD8+T cells ratio,procalcitonin(PCT),serum amyloid A(SAA),interleukin-6(IL-6),high-sensitivity C-reactive protein(hs-CRP),alpha-fetoprotein(AFP),and IL-4(P＜0.05).LASSO regression analysis identified 5 variables:Child-Pugh grade,PVTT,WBC,CHE and hs-CRP.Binary logistic regression analysis indicated that Child-Pugh grade(Grade B:OR=5.780,95％CI 3.271-10.213,P＜0.001;Grade C:OR=14.818,95％CI 7.697-28.526,P＜0.001),PVTT(OR=2.893,95％CI 2.037-4.108,P＜0.001),WBC(OR=1.088,95％CI 1.031-1.148,P=0.002),and hs-CRP(OR=1.005,95％CI 1.001-1.010,P=0.026)were the independent risk factors of spontaneous peritonitis in HBV-related primary liver cancer patients.Using these 4 variables,a nomogram prediction model was constructed and evaluated.The P-value of the H-L goodness of fit test was 0.760.Moreover,the area under ROC curve(AUC)was 0.866,with a sensitivity of 0.870 and a specificity of 0.716.The average absolute error of the calibration curve is 0.022.DCA and CIC analyses demonstrated that the nomogram prediction model possessed some clinical utility.Conclusion The nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer patients,constructed using Child-Pugh grade,PVTT,WBC and hs-CRP,exhibits a high fitting degree and accuracy,with the prediction probability highly consistent with the actual occurrence probability,and possesses certain clinical practicability.

Glioblastoma multiforme(GBM)is the most common primary malignant brain tumor of the central nervous system in adults,with a median survival of less than 15 months.The tumor microenvironment(TME)of GBM includes extracellular matrix and a variety of immune cells,including tumor-associated macrophages,microglia and myeloid-derived suppressor cells.The interaction between these cells and tumor cells plays a key role in the occurrence and development of GBM.The heterogeneity of GBM microenvironment is one of the main reasons for the poor efficacy of many therapies.Therefore,understanding the interaction between GBM and its tumor microenvironment is helpful to explore new targeted therapeutic strategies,which is expected to provide better treatment options for patients,thereby improving patient prognosis.

Objective To investigate the types and mechanisms of microglial cell death induced by interaction between palmitic acid(PA)and lipopolysaccharide(LPS).Methods BV-2 microglial cells were divided into three groups for apoptosis research,BSA group,PA treatment group,and staurosporine(STA)group.They were further divided into four groups for necrosis research,BSA group,BSA+inhibitor group,PA group,and PA+inhibitor group.Western blotting was conducted to assess the expression levels of key proteins involved in apoptosis and necrosis pathways.The effect of PA on microglial cells was validated through feeding a high-fat diet to Institute of Cancer Research(ICR)mice.Results Apoptotic microglia were observed in both BSA group and PA group,PA significantly induced the activation of caspase-3,caspase-7,and poly ADP-ribose polymerase(PARP).However,compared to the BSA group,the level of activated Caspase-7 in the STA group did not change significantly.Inhibition of ferroptosis,necroptosis,or autophagy did not protect against PA-induced cell damage,while the Caspase-11 inhibitor,wedelolactone(WE),significantly improved PA induced cell damage.This study also found that PA could promote LPS entry into microglial cells and induce pyroptosis.This phenomenon and the protective effect of WE were further confirmed in a high-fat diet mouse model through immunofluorescent staining and Western blotting.Conclusion PA induces apoptosis and pyroptosis in microglial cells,while simultaneously promoting LPS entry into microglial cells and inducing pyroptosis.

Objective To explore the application value of fractional anisotropy(FA)analysis of RGB component transformation in different directions of fibers in substantia nigra in Parkinson's disease(PD).Methods There were 35 cases of PD and 37 cases of normal control group.After being performed by brain diffusion tensor imaging(DTI)scanning,the sequence was imported into 3DSlicense 5.6.0,and the diffusion module was used to implement pseudo color mapping based on FA,locate and segment substantia nigra,and use the substantia nigra mask as the tracking starting point.After forming tracing,fibers were imported into DTIANALYSIS 1.51,converting the RGB components into FA values for analysis,and visualized the analysis result.At the same time,fiber length,fiber density,and segmented FA point cloud percentage were compared.Results Compared with the normal group,the length of substantia nigra fibers in the PD group was shorter[(95.14±19.85)mm vs(115.99±21.39)mm,P＜0.01],and there was a statistical difference between the two groups.There was no statistical difference in fiber density[(0.07±0.05)/mm3 vs(0.10±0.12)/mm3,P＞0.05]between control group and PD group.The percentage of FA segment point clouds in the PD group was lower than that in the normal group at 0.9-1,but the principal component characteristics of the point cloud ratios in each FA segment were not significant.Conclusion Based on the transformation of RGB components into FA analysis,the length,density,and FA values of substantia nigra nerve fibers in PD patients can be quantified and visualized,providing a basis for the study of PD neural pathways.

In recent years,the incidence and mortality of heatstroke have been increasing annually alongside global warming,with a marked rise in cases exhibiting atypical symptoms.To address the increasingly complex challenges in heatstroke prevention and treatment,Heatstroke Prevention and Treatment Research Center of Chinese PLA,Expert Group of Heatstroke Prevention and Treatment of Chinese PLA,and Chinese PLA Professional Committee of Critical Care Medicine have jointly developed this guideline(2025 edition).Utilizing the Grading of Recommendations Assessment,Development and Evaluation(GRADE)system,Appraisal of Guidelines for Research and Evaluation(AGREE)criteria,and Reporting Items for Practice Guidelines in Healthcare(RIGHT)standards,and based on the 2015 draft"Expert Consensus on the Standardized Diagnosis and Treatment of Heatstroke"and the 2019"Chinese Expert Consensus on the Diagnosis and Treatment of Heatstroke",this guideline has been crafted.This guideline provides 25 evidence-based recommendations to guide the prevention,treatment and research of heatstroke,which thoroughly covers 8 critical domains:clinical classification,pathophysiological mechanisms,clinical manifestations,diagnostic criteria,differential diagnosis,treatment protocols,rehabilitation and return to work,and prevention.