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.

Objective:This study aims to construct a machine learning model to predict depression symptoms in the elderly and analyze the key influencing factors of depression in the elderly using the shapley additive interpretation (SHAP) method.Methods:Based on entries from the 2018 China Health and Retirement Longitudinal Study database, a sample of 5 954 elderly individuals was selected. Feature selection using Support Vector Machine Recursive Feature Elimination, Extreme Gradient Boosting (XGBoost) - Recursive Feature Elimination (RFE), and the Lasso algorithm, which was combined with five classifiers-logistic regression, decision trees, random forests, support vector machines, and XGBoost-to explore the classification effectiveness for depressive symptoms in the elderly. Finally, the SHAP method was used to interpret the analysis of the model with the highest receiver operating characteristic curve areas under the curve (AUC).Results:The accuracy of 15 prediction models ranged from 0.702 to 0.743, with AUC between 0.730 and 0.795. Sensitivity was reported at 0.546 to 0.588, while specificity ranges from 0.783 to 0.865. The model XGBoost-RFE-XGBoost presented the highest AUC. Based on SHAP values, the top four factors influencing depressive symptoms in older adults were life satisfaction, duration of nighttime sleep, disability status, and self-rated health.Conclusion:This study developed a highly efficient and interpretable risk prediction model for depressive symptoms in older adults, which could help identify high-risk older adults and give personalized interventions.

Objective:To investigate the drug resistance of newly diagnosed HIV-1 infected patients in Shanghai and to provide reference value for clinical antiretroviral therapy (ART).Methods:The peripheral venous blood plasma of 196 newly diagnosed HIV-1 infected patients screened according to the inclusion and exclusion criteria at the Shanghai Public Health Clinical Center from April to June 2023 was collected, HIV-1 RNA was extracted, the pol region was amplified by reverse transcription-polymerase chain reaction (RT-PCR) for sequencing, the mutation sites and ART drug resistance were analyzed.Results:The plasma of 196 newly diagnosed HIV-1 infected patients was amplified successfully in 162 cases (amplification success rate was 82.65%). The subtypes consisted of CRF07_BC(51.23%), CRF01_AE (27.78%), and others (6.79%), CRF55_01B (5.56%), B (3.70%), CRF01_AE/B (3.70%) and CRF08_BC (1.23%). The overall transmitted drug resistance rate was 7.41%, the protease inhibitors (PIs), non-nucleoside/nucleotide reverse transcriptase inhibitors (NNRTIs), nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs), integrase inhibitors (INSTIs) resistance rates were 3.09%, 3.70%, 0.00% and 0.62%, respectively. The proportion of NNRTIs-related mutation sites in B (66.67%) and CRF55_01B (88.89%) was higher than that in CRF07_BC (13.25%); the proportion of NNRTIs-related mutation sites in CRF55_01B (88.89%) was higher than that in CRF01_AE (22.22%) and other subtypes (18.18%), the difference was statistically significant (all P<0.05). Multivariate logistic regression analysis showed that the probability of PIs-related mutation sites in CRF01_AE/B was 21.71 times that of CRF07_BC[odds ratio ( OR)=21.71, 95% confidence interval ( CI): 3.36-140.27, P=0.001]. Conclusions:The transmitted drug resistance among newly diagnosed HIV-1 infected patients in Shanghai is at the moderate epidemic level, mainly NNRTIs and PIs-related drug resistance, and the INSTIs resistance rate is low, the use of INSTIs in ART regimens should be considered.

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 report the genetic analysis of a family with a fetus suspected of Ellis-van Creveld(EVC)syndrome based on ultrasound findings such as ventricular septal defect(VSD),short long bones in the limbs and polydactyly,and to conduct a literature review to clarify the pathogenic cause.Methods A 27-year-old pregnant woman,who was pregnant for the first time and had no prior deliveries,was admitted to the prenatal diagnosis center of Shijiazhuang Obstetrics and Gynecology Hospital in October 2021.At 17 weeks of gestation,ultrasound detected multiple fetal malformations.The genomic DNA of the fetal proband's amniotic fluid cells and the parents'peripheral blood samples were sequentially subjected to chromosomal karyotype analysis,chromosomal microarray analysis(CMA),and whole exome sequencing(WES).Suspected pathogenic mutations were verified by Sanger sequencing in the proband and its parents.Subsequently,a Minigene in vitro experiment was used to analyze one splicing mutation.Meanwhile,databases such as PubMed were searched,and literature reports were combined for genetic analysis.Results Chromosomal karyotype analysis of the fetus showed no abnormalities,and CMA did not detect any copy number variation(CNV)with clinical significance.WES results revealed two mutations in the EVC gene(NM_153717.2)of the fetus:a nonsense mutation c.1405G>T(p.E469X)in exon 10 and a splicing mutation c.1886+5G>A in intron 13.Family verification using Sanger sequencing showed that the father was a carrier of the c.1405G>T(p.E469X)mutation in exon 10,and the mother was a carrier of the c.1886+5G>A mutation in intron 13.The compound heterozygous mutation of the fetus was inherited from the parents.According to the guidelines of the American College of Medical Genetics and Genomics(ACMG)for classifying genetic variations,c.1405G>T(p.E469X)was classified as likely pathogenic mutation(PVS1+PM2),and c.1886+5G>A was classified as likely pathogenic mutation(PM2+PM3_Strong).The Minigene experiment results showed that the c.1886+5G>A mutation caused a 115-bp segment retention in intron 13,further supporting its pathogenicity.Review of the literature showed that the typical clinical manifestations of EVC syndrome include short limbs,short ribs,postaxial polydactyly,nail and tooth dysplasia,and congenital heart defects.Gene mutations in EVC/EVC2 were found to be the main pathogenic cause through whole exome sequencing,with mutation types including missense mutations,large-scale duplications/deletions,in-frame microdeletions,nonsense mutations,frameshift mutations,and splicing mutations.Conclusions The compound heterozygous mutations in the EVC gene are the pathogenic cause of the fetus.The detection of these mutations expands the genetic variation spectrum of Ellis-van Creveld syndrome.

Objective To explore the relationship between dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and clinical pathological features of invasive breast cancer and lymphovascular invasion(LVI).Methods Imaging and clinical pathological data were retrospectively collected from 508 patients with invasive breast cancer who underwent breast DCE-MRI at the First Medical Center of Chinese PLA General Hospital from January 2019 to August 2021.Patients were divided into the LVI-positive(LVI+)group(n=79)and LVI-negative(LVI-)group(n=429)based on postoperative pathological results.Univariate and multivariate logistic regression analyses were used to identify risk factors for LVI.Results Compared with LVI-group,LVI+group had a higher proportion of patients aged<45 years(44.3%vs.27.0%,P=0.002),non-mass-like enhancement(NME)(31.7%vs.17.7%,P=0.004),Ki-67 expression rate(40.0%vs.30.0%,P<0.001),high Ki-67 expression(94.9%vs.78.1%,P=0.001),Luminal B subtype(76.0%vs.60.1%,P=0.008),and positive axillary lymph nodes rate(72.2%vs.31.5%,P<0.001),while the proportion of Luminal A subtype was lower(2.5%vs.21.5%,P<0.001).Univariate and multivariate logistic regression analyses showed that age≥45 years(OR=0.468,95%CI 0.280-0.783,P=0.004)was an independent protective factor for LVI,while NME(OR=1.987,95%CI 1.126-3.444,P=0.016)was an independent risk factor.Compared with Luminal A subtype,patients with Luminal B subtype(OR=10.482,95%CI 3.164-64.923,P=0.001),HER-2 overexpression subtype(OR=11.571,95%CI 2.755-79.341,P=0.003)and triple-negative subtypes(OR=8.433,95%CI 1.985-57.908,P=0.009)had a higher risk of LVI.Conclusions Age≥45 years is an independent protective factor for LVI,while NME is an independent risk factor.Among molecular subtypes,patients with Luminal B,HER-2 overexpression and triple-negative subtypes have a higher risk of LVI compared with the Luminal A subtype.

Objective To establish a stable,reliable,and clinically relevant porcine model of endotoxin-induced acute respiratory distress syndrome(ARDS).Methods Ten 8-month-old male Bama minipigs were deeply sedated,followed by invasive mechanical ventilation and electrocardiographic monitoring.Lipopolysaccharide(LPS)was intravenously pumped at 600 μg/(kg·h)for 3 hours,then maintained at 15 μg/(kg·h)thereafter.Dynamic monitoring was performed at five time points after LPS injection(LPS 0,1,3,5,and 8 h),including arterial blood gas analysis and chest computed tomography(CT)scans.Pathological examination of lung tissues obtained via bronchoscopic biopsy(HE staining and transmission electron microscopy)was conducted.These indicators were comprehensively used to evaluate the success of the animal model.Results At 5 hours after LPS administration,8 minipigs developed symptoms such as skin cyanosis,elevated body temperature,and respiratory distress.The oxygenation index decreased to<300 mmHg.Chest CT scans showed diffuse pulmonary infiltrates.Histopathology revealed alveolar edema and hyaline membrane formation.Transmission electron microscopy demonstrated disruption of pulmonary blood-air barrier,depletion of lamellar bodies in type Ⅱ pneumocytes,inflammatory cell infiltration,and exudation of plasma proteins and fibrin.Compared with LPS 0 h,at LPS 8 h,the oxygenation index and arterial blood pH were significantly decreased(P<0.001),while blood lactic acid and serum potassium were significantly increased(P<0.05);serum calcium and base excess were significantly decreased(P<0.05),and the lung injury score based on HE-stained lung sections was significantly increased(P<0.01).Conclusion The porcine ARDS model established by continuous LPS injection can dynamically simulate the pathophysiological characteristics and typical pathological manifestations of clinical septic ARDS,making it an effective tool to study the pathogenesis,prevention,and treatment strategies of septic ARDS.

Objective To explore the mediating role of activities of daily living (ADL) in pain and depressive symptoms in the elderly in China. Methods Utilizing the data from 2020 China Health and Retirement Longitudinal Study, 4403 Chinese elderly individuals aged ≥ 60 years old were selected as the research subjects. Depression Scale (CES-D 10) of the Center for Epidemiological Survey and ADL scale were used in the study. The PROCESS4.1 macro was used to test the mediating effect of daily living activities between pain and depressive symptoms, and the Bootstrap method was applied for verification of the mediating variables. Results A total of 2368 cases of depressive symptoms were detected in the elderly in China, with a detection rate of 53.78%. Pain was positively correlated with depressive symptoms (r=0.27, P<0.01), and activities of daily living were negatively correlated with pain and depressive symptoms (r=-0.27, -0.337, P<0.01). The results showed that the total effect value of pain on depressive symptoms was 0.33, the direct effect value was 0.24, and the mediating effect value of daily living activities was 0.09, accounting for 27.27%. Conclusion Pain and activities of daily living are important factors influencing depressive symptoms in the elderly, and activities of daily living play a partial mediating role in the relationship between pain and depressive symptoms in the elderly.

Objective To investigate the key factors influencing the implementation of long-acting injection-promoting policies and propose effective improving paths.Methods Qualitative interviews were carried out for stakeholders involved in the promotion of long-acting injections,based on the consolidated framework for implementation research.Additionally,countermeasures for identified barriers were proposed based on expert recommendations for implementation changes.Results A total of 46 health administrators,healthcare workers,and patients in Beijing were interviewed.The study identified several barriers in the strength and quality of evidence,adaptability,relative advantage,complexity and cost,patient needs and resources,external collaboration,external policies and incentives,organizational structural characteristics,and self-efficacy.Conclusions From the perspectives and experiences of stakeholders,the promotion of long-acting injections has shown initial success but still faces multiple obstacles.It is recommended that efforts should be made to coordinate and adapt policies,improve and incentivize relative organizations,and continuously strengthen the advocacy and education for individuals.
Humans ; Beijing ; Delayed-Action Preparations ; Health Personnel ; Health Policy ; Injections