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.

Objective To observe the clinical efficacy and safety of vericiguat tablets combined with sacubitril valsartan sodium(Sac/Val)tablets in the treatment of patients with heart failure with reduced ejection fraction(HFrEF).Methods The HFrEF patients were divided into control group and treatment group according to the cohort method.The control group was treated with Sac/Val tablets 200 mg per time,bid,orally.On the basis of control group,the treatment group was treated with vericiguat tablets 2.5 mg per time,qd,taken with meal.Two groups were treated for 3 months.The clinical efficacy,left ventricular ejection fraction(LVEF),left ventricular end-diastolic dimension(LVEDD)and end-systolic diameter(LVESD),levels of high sensitivity C-reactive protein(hs-CRP),interleukin-6(IL-6),nitric oxide(NO),N-terminal pro-brain natriuretic peptide(NT-proBNP),blood urea nitrogen(BUN)and serum creatinine(SCr),and safety were compared between the two groups.During follow-up,the heart failure rehospitalization rates and major adverse cardiovascular events were compared between the two groups.Results Treatment group was enrolled 53 patients,control group was enrolled 53 patients.After treatment,the total effective rates of treatment and control groups were 94.34％(50 cases/53 cases)and 81.13％(43 cases/53 cases)with statistical significant difference(P＜0.05).After treatment,the LVEF of treatment and control groups were(48.02±5.20)％and(43.02±4.33)％,the LVEDDs were(52.85±6.30)and(55.63±6.88)mm,the LVESDs were(41.64±6.40)and(44.22±5.85)mm,the levels of hs-CRP were(10.22±2.63)and(14.60±2.98)mg L-1,the levels of IL-6 were(14.48±2.40)and(17.36±2.52)pg·mL-1,the levels of NO were(102.60±20.16)and(92.16±16.33)μmol·L-1,the levels of NT-proBNP were(898.74±102.20)and(1315.60±182.64)ng·L-1,the levels of BUN were(12.02±2.28)and(13.45±2.33)mmol·L-1,the levels of SCr were(82.22±5.89)and(85.64±6.03)μmol·L-1,the heart failure rehospitalization rates were 5.66％and 13.21％,respectively;the differences were statistical significant between two groups(all P＜0.05).The adverse drug reactions of treatment group were hyperkalemia,hypotension,renal dysfunction,dizziness and headache,while those in control group were renal dysfunction,hyperkalemia,and hypotension.The major adverse cardiovascular events of treatment group were angina pectoris and acute myocardial infarction,while those in control group were angina pectoris,acute myocardial infarction and atrial fibrillation.The incidences of total adverse drug reactions in treatment and control groups were 13.21％and 7.55％,the incidences of major adverse cardiovascular events were 5.66％and 13.21％,respectively,without statistically significant differences(all P＞0.05).Conclusion Vericiguat tablets combined with Sac/Val tablets have a definitive clinical efficacy in the treatment of HFrEF patients,which can improve cardiac and endothelial function,reduce inflammatory response and readmission times,without increasing the incidences of adverse drug reactions.

Objective:To evaluate the diagnostic performance of artificial intelligence-assisted diagnostic systems in cervical cytopathological examination.Methods:Cervical cytology slide data were retrospectively collected from four hospitals for the external validation of the developed artificial intelligence-assisted diagnostic system. Subsequently, prospective data collection was conducted for human-machine assisted studies.Results:In the retrospective study, a total of 3 162 valid samples were collected as external validation data. The system showed an area under the curve (AUC) of 0.890 (95% CI: 0.878-0.902), accuracy of 0.885 (95% CI: 0.873-0.896), sensitivity of 0.928 (95% CI: 0.914-0.941), and specificity of 0.852 (95% CI: 0.834-0.867). In the prospective study, 212 valid samples were collected, and five junior cytologists participated in the human-machine assisted study. Without artificial intelligence assistance, the average AUC for the five cytologists was 0.686 (95% CI: 0.650-0.722), the accuracy was 0.699 (95% CI: 0.671-0.727), the sensitivity was 0.653 (95% CI: 0.599-0.703), the specificity was 0.719 (95% CI: 0.685-0.750), the Fleiss κ value was 0.510, and the reading time was 223 seconds. With artificial intelligence assistance, the AUC, accuracy, sensitivity, and specificity increased by 0.166, 0.143, 0.225, and 0.107, respectively. Additionally, Fleiss κ was 0.730 and the reading time decreased by 188 seconds. All differences were statistically significant (all P<0.001). Conclusions:Artificial intelligence-assisted diagnosis system shows excellent performance and good generalizability, significantly improving the diagnostic accuracy, consistency, and efficiency of junior cytologists. It can be an effective auxiliary tool for junior cytologists in clinical practice.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective:To evaluate the diagnostic performance of artificial intelligence-assisted diagnostic systems in cervical cytopathological examination.Methods:Cervical cytology slide data were retrospectively collected from four hospitals for the external validation of the developed artificial intelligence-assisted diagnostic system. Subsequently, prospective data collection was conducted for human-machine assisted studies.Results:In the retrospective study, a total of 3 162 valid samples were collected as external validation data. The system showed an area under the curve (AUC) of 0.890 (95% CI: 0.878-0.902), accuracy of 0.885 (95% CI: 0.873-0.896), sensitivity of 0.928 (95% CI: 0.914-0.941), and specificity of 0.852 (95% CI: 0.834-0.867). In the prospective study, 212 valid samples were collected, and five junior cytologists participated in the human-machine assisted study. Without artificial intelligence assistance, the average AUC for the five cytologists was 0.686 (95% CI: 0.650-0.722), the accuracy was 0.699 (95% CI: 0.671-0.727), the sensitivity was 0.653 (95% CI: 0.599-0.703), the specificity was 0.719 (95% CI: 0.685-0.750), the Fleiss κ value was 0.510, and the reading time was 223 seconds. With artificial intelligence assistance, the AUC, accuracy, sensitivity, and specificity increased by 0.166, 0.143, 0.225, and 0.107, respectively. Additionally, Fleiss κ was 0.730 and the reading time decreased by 188 seconds. All differences were statistically significant (all P<0.001). Conclusions:Artificial intelligence-assisted diagnosis system shows excellent performance and good generalizability, significantly improving the diagnostic accuracy, consistency, and efficiency of junior cytologists. It can be an effective auxiliary tool for junior cytologists in clinical practice.

Objective To observe the clinical efficacy and safety of vericiguat tablets combined with sacubitril valsartan sodium(Sac/Val)tablets in the treatment of patients with heart failure with reduced ejection fraction(HFrEF).Methods The HFrEF patients were divided into control group and treatment group according to the cohort method.The control group was treated with Sac/Val tablets 200 mg per time,bid,orally.On the basis of control group,the treatment group was treated with vericiguat tablets 2.5 mg per time,qd,taken with meal.Two groups were treated for 3 months.The clinical efficacy,left ventricular ejection fraction(LVEF),left ventricular end-diastolic dimension(LVEDD)and end-systolic diameter(LVESD),levels of high sensitivity C-reactive protein(hs-CRP),interleukin-6(IL-6),nitric oxide(NO),N-terminal pro-brain natriuretic peptide(NT-proBNP),blood urea nitrogen(BUN)and serum creatinine(SCr),and safety were compared between the two groups.During follow-up,the heart failure rehospitalization rates and major adverse cardiovascular events were compared between the two groups.Results Treatment group was enrolled 53 patients,control group was enrolled 53 patients.After treatment,the total effective rates of treatment and control groups were 94.34％(50 cases/53 cases)and 81.13％(43 cases/53 cases)with statistical significant difference(P＜0.05).After treatment,the LVEF of treatment and control groups were(48.02±5.20)％and(43.02±4.33)％,the LVEDDs were(52.85±6.30)and(55.63±6.88)mm,the LVESDs were(41.64±6.40)and(44.22±5.85)mm,the levels of hs-CRP were(10.22±2.63)and(14.60±2.98)mg L-1,the levels of IL-6 were(14.48±2.40)and(17.36±2.52)pg·mL-1,the levels of NO were(102.60±20.16)and(92.16±16.33)μmol·L-1,the levels of NT-proBNP were(898.74±102.20)and(1315.60±182.64)ng·L-1,the levels of BUN were(12.02±2.28)and(13.45±2.33)mmol·L-1,the levels of SCr were(82.22±5.89)and(85.64±6.03)μmol·L-1,the heart failure rehospitalization rates were 5.66％and 13.21％,respectively;the differences were statistical significant between two groups(all P＜0.05).The adverse drug reactions of treatment group were hyperkalemia,hypotension,renal dysfunction,dizziness and headache,while those in control group were renal dysfunction,hyperkalemia,and hypotension.The major adverse cardiovascular events of treatment group were angina pectoris and acute myocardial infarction,while those in control group were angina pectoris,acute myocardial infarction and atrial fibrillation.The incidences of total adverse drug reactions in treatment and control groups were 13.21％and 7.55％,the incidences of major adverse cardiovascular events were 5.66％and 13.21％,respectively,without statistically significant differences(all P＞0.05).Conclusion Vericiguat tablets combined with Sac/Val tablets have a definitive clinical efficacy in the treatment of HFrEF patients,which can improve cardiac and endothelial function,reduce inflammatory response and readmission times,without increasing the incidences of adverse drug reactions.

Objective To investigate the clinical features of chronic hepatitis C(CHC)patients with hepatitis C virus genotype 3(HCV GT3)infection and the risk factors for disease progression.Methods A multicenter retrospective cohort study was conducted among 1 002 CHC patients from 11 clinical centers in Northwest China from December 2017 to November 2023,and according to their genotype,they were divided into GT1,GT2,GT3,and GT6 groups.Clinical features were compared between the patients with different genotypes.The one-way analysis of variance was used for comparison of normally distributed continuous data between groups,and the Scheffe test was used for further comparison between two groups.The Kruskal-Wallis H test was used for comparison of data with skewed distribution between groups;the chi-square test or Fisher test was used for comparison of categorical data between groups.The multivariate logistic regression analysis was used to explore the influencing factors for the progression of CHC to liver cirrhosis.Results In terms of the genotype,there were 427 patients with GT1 infection,242 with GT2 infection,299 with GT3 infection(210 patients with GT3a infection,87 with GT3b infection,and 2 with unclassified genotype),and 34 with GT6 infection.The patients with GT3 infection had a significantly younger age than those with GT1 infection(51.3±0.5 years vs 53.2±0.6 years,P<0.05)or GT2 infection(51.3±0.5 years vs 53.7±0.8 years,P<0.05),and for the patients with liver cirrhosis,the patients with GT3 infection had a significantly younger age than those with GT1 infection(52.1±0.5 years vs 59.4±0.9 years,P<0.001)or GT2 infection(52.1±0.5 years vs 58.1±1.1 years,P<0.001).Among the patients with GT3 infection,male patients accounted for 77.9%and the patients with liver cirrhosis accounted for 46.2%,which were significantly higher than those among the patients with GT1,GT2 or GT6 infection(all P<0.001).At baseline,the patients with GT3 infection had significantly higher levels of alanine aminotransferase(ALT)and aspartate aminotransferase(AST)than those with GT1 or GT2 infection,significantly higher aspartate aminotransferase-to-platelet ratio index(APRI)and fibrosis-4(FIB4)than those with GT1,GT2 or GT6 infection,a significantly lower platelet count(PLT)than those with GT2 or GT6 infection,a significantly higher level of alpha-fetoprotein than those with GT2 or GT6 infection,and a significantly lower level of albumin(Alb)than those with GT6 infection(all P<0.05).There were no significant differences between the patients with GT3a infection and those with GT3b infection in age,sex,the proportion of patients with liver cirrhosis,comorbidities,HCV RNA quantification,PLT,ALT,AST,alkaline phosphatase,Alb,APRI,and FIB-4(all P>0.05).The multivariate logistic regression analysis showed that PLT≤150×109/L(odds ratio[OR]=10.72,95%confidence interval[CI]:5.76-35.86,P<0.001)and Alb≤35 g/L(OR=3.74,95%CI:1.22-11.45,P=0.021)were risk factors for liver cirrhosis.Conclusion Most CHC patients with GT3 infection are male in Northwest China,and compared with the patients with other genotypes,such patients tend to have a younger age of onset and higher degrees of liver inflammation activity and fibrosis.Low PLT and a low level of Alb are risk factors for progression to liver cirrhosis in CHC patients with GT3 infection.

To address the challenges of extracting nonlinear features from motor imagery electroencephalogram(EEG)signals and effectively capturing functional connectivity between EEG channels,a classification and recognition method for motor imagery EEG signals is proposed based on mutual information and adaptive graph convolutional network.The proposed method extracts frequency domain information by sub-frequency banding on the original motor imagery EEG signals,uncovers the nonlinear relationships within EEG signals by an adjacency matrix constructed with mutual information neural estimation method,and finally achieve null-frequency feature extraction by capturing the dynamic correlation strength between channels with an adaptive graph convolutional network incorporating convolutional block attention module.On the BCI Competition Ⅳ 2a and BCI Competition Ⅲ 3a datasets,the proposed method has average accuracies of 83.14％and 88.19％,respectively,demonstrating that it can effectively reveal functional connectivity between EEG channels,providing a new approach for decoding motor imagery EEG signals.

Objective: To explore the association between parent-child connectedness and romantic relationships of secondary vocational school students and the moderating effect of peers romantic behavior, providing scientific basis for family and school health education. Methods: From March to April 2021，2 426 students from six secondary vocational and technical schools in Shanghai and Shaanxi Province were selected to conduct the survey by combining convenience sampling and cluster sampling.Electronic questionnaires were used to collect data on students family characteristics,oneself and peer romantic behaviors, and parent-child bonding. The t-test was employed for inter group comparisons, and binary Logistic regression analysis was conducted to examine the relationship between parent-child bonding levels, peer romantic behavior, and the romantic behavior of secondary vocational students. Results: The mother-child connection (2.63±0.77) was higher than that of father-child connection (2.48±0.78), with statistically significant difference ( t =6.83, P <0.01). Multivariable Logistic regression showed that overall father-child connectedness was negatively associated with students romantic relationships( OR =0.86，95% CI =0.76-0.97， P =0.02)and was only associated to girls romantic relationships when stratified by gender( OR =0.79，95% CI =0.66-0.93， P =0.01). Peers romantic relationships were positively associated with students romantic relationships ( OR =3.19-5.12, all P <0.01), and there was a moderating effect of the association between maternal connectedness and boys romantic relationships ( OR =1.67, 95% CI =1.05-2.66, P =0.03). Among boys without romantic peers, mother-child connectedness was negatively associated with their romantic relationships ( OR = 0.60 , 95% CI =0.36-0.99, P <0.05). In the total sample of Shanghai and girls of Shaanxi, father-child connectedness was negatively correlated with the romantic relationships of secondary vocational school students ( OR =0.84,0.65,95% CI =0.71-1.00,0.50-0.85,both P <0.05). Peer romantic relationships exhibited a negative moderating effect on the influence of mother-child connectedness on the romantic relationships of males in Shanghai ( OR =1.91, 95% CI =1.03-3.57, P <0.05). Conclusions The father-daughter connectedness is negatively correlated with girls romantic behavior, and peer romantic behavior weakens the correlation between mother-child connectedness and boys romantic behavior. Efforts should be made to enhance the parent-child connectedness of secondary vocational students and their ability to cope with peer influence, providing proper guidance for adolescents heterosexual interactions.

Objective To investigate the risk factors for pneumonia complicating infectious mononucleosis(IM)in adults and construct a nomogram prediction model.Methods A retrospective analysis was conducted on 198 IM patients admitted to the Second Affiliated Hospital of Air Force Medical University from January 2015 to December 2021.Patients were divided into pneumonia group(n=52)and non-pneumonia group(n=146)based on whether pulmonary infection occurred during hospitalization.The baseline data(age,gender,place of onset,etc.),clinical manifestations(maximum body temperature,lymph node enlargement,splenomegaly,etc.),and inflammatory indicators[white blood cell count(WBC),C-reactive protein(CRP),etc.]were compared between the two groups.Kaplan-Meier curves were plotted to analyze the key indicators affecting the hospital stay of IM patients.Multivariate logistic regression was used to analyze the independent risk factors for pneumonia complicating IM in adults and construct a nomogram prediction model based on the identified risk factors.The predictive efficacy of the model was evaluated using the receiver operating characteristic(ROC)curve and the consistency of the model was assessed using the calibration curve.The fit of the model was evaluated using the Hosmer-Lemeshow test.Additionally,the sensitivity,specificity,and accuracy of the model were assessed using confusion matrix.Results Compared with non-pneumonia group,the pneumonia group had a significantly higher proportion of patients from rural areas,with body mass index(BMI)≥24 kg/m2,smoking history,hepatomegaly,fever duration of≥7 d,as well as increased total hospitalization costs and average daily hospitalization costs,and prolonged hospital stay(P<0.05).The proportion of patients with a history of antibiotic use was lower in the pneumonia group(P<0.05).Kaplan-Meier survival analysis showed that patients from rural areas,with BMI≥24 kg/m2,smoking history,no prophylactic use of antibiotics,fever duration≥7 d,and hepatomegaly had significantly prolonged hospital stays(P<0.05).Multivariate logistic regression analysis revealed that living in a rural area(OR=4.089,P<0.05),hepatomegaly(OR=4.082,P<0.05),and elevated WBC(OR=1.205,P<0.05)were independent risk factors for pneumonia complicating IM in adults,while the prophylactic use of antibiotics(OR=0.142,P<0.05)was an independent protective factor.The area under the ROC curve of the constructed nomogram prediction model was 0.827(95%CI 0.762-0.892),and the slope of the calibration curve was close to 1,and the Hosmer-Lemeshow test showed χ2=5.299,P=0.725,indicating good consistency and fit of the prediction model.The results of the confusion matrix assessment showed that the sensitivity of the model was 0.669(0.624-0.773),the specificity was 0.827(0.724-0.930),and the accuracy was 0.732(0.665-0.793).Conclusion The nomogram prediction model based on place of onset,hepatomegaly,the prophylactic use of antibiotics and WBC has excellent fit and discrimination,providing an effective quantitative tool for prognosis assessment of IM.