Brain age prediction, as a significant approach for assessing brain health and early diagnosing neurodegenerative diseases, has garnered widespread attention in recent years. Electroencephalogram (EEG), an non-invasive, convenient, and cost-effective neurophysiological signal, offers unique advantages for brain age prediction due to its high temporal resolution and strong correlation with brain functional states. Despite substantial progress in enhancing prediction accuracy and generalizability, challenges remain in data quality and model interpretability. This review comprehensively examined the advancements in EEG-based brain age prediction, detailing key aspects of data preprocessing, feature extraction, model construction, and result evaluation. It also summarized the current applications of machine learning and deep learning methods in this field, analyzed existing issues, and explored future directions to promote the widespread application of EEG-based brain age prediction in both clinical and research settings.
Humans ; Electroencephalography/methods* ; Brain/physiology* ; Machine Learning ; Aging/physiology* ; Deep Learning ; Signal Processing, Computer-Assisted

The coupling analysis of continuous physiological signals is a crucial tool for revealing the dynamic activities and interactions within physiological systems. To gain a deeper understanding of the coupling relationships between physiological systems, this paper provides a comprehensive review of recent research on coupling analysis methods for continuous physiological signals, both domestically and internationally. It focuses on various coupling analysis techniques, including time-domain, frequency-domain, time-frequency-domain and nonlinear methods, and explores their applications in the coupling assessment of different physiological systems. Finally, the paper discusses the limitations of these methods and their future development trends. By systematically reviewing the current methods of continuous physiological signal coupling analysis, this paper aims to provide valuable references for further research on coupling evaluation methods for physiological systems.
Signal Processing, Computer-Assisted ; Humans

Purpose/Significance To deeply analyze the research progress on the application of medical knowledge graph in the field of stroke,to discuss the problems of the development of stroke knowledge graph in China,and to put forward suggestions for the construc-tion of stroke knowledge graph.Method/Process By reviewing and analyzing the relevant literature,the application of medical knowledge graph in the field of stroke is sorted out and summarized.Result/Conclusion There are still many deficiencies in the development of stroke knowledge graph in China,and in the future,in-depth research can be carried out from four aspects,namely,expanding the ap-plication scope of knowledge graph,promoting the fusion of knowledge graph,developing more efficient algorithms,and upgrading to cog-nitive graph by joint artificial intelligence(AI).

Objective To establish a fast,stable,and sensitive zebrafish model of osteoporosis(OP)using different method.Methods OP models were induced by iron overload or prednisolone(Pred),and bone formation and mortality were observed.The groups were divided into:Control group,model group(include FAC group and Pred group),and positive control group(AC group).Ammonium ferric citrate was used as the model drug in the iron-overload induction method.For the Pred induction models,the modeling time for the Pred-3 days post-fertilization(dpf)method was 3～9 dpf,the modeling time for the Pred-5 dpf method was 5～10 dpf,and Pred was administered from 3 dpf and removed from 7～9 dpf for the Pred withdrawal method.To compare the anti-osteoporosis(OP)effects of commonly used drugs such as Alfacalcidol(AC),Calcitriol(CA),and Alendronate(AL),it's important to select a stable and sensitive positive control drug and to further optimize different staining methods and conditions.Results There was no significant effect of ammonium ferric citrate 500 μg/mL on bone formation.Bone formation and the length of the first vertebra were significantly decreased in the Pred group induced by Pred-3 dpf compared with those in the control group(P＜0.01,P＜0.05),but zebrafish mortality was higher.There was no significant difference between the Pred-5 dpf method,but bone formation was significantly reduced in the Pred withdrawal group(P＜0.01),with no mortality.Alfacalcidol,calcitriol,and alendronate all had anti-OP effects,with CA having the most sensitive and stable anti-OP effect.Alizarin red staining showed that the optimal dye parameters were 0.02%concentration for dyeing 2 h,with washing in 0.5%KOH and glycerol under the conditions of a 3∶1 ratio for 3 h followed by a 1∶1 ratio for 14 h.The result of staining showed that calcein was more sensitive for staining bone nodes and ARS staining was more sensitive for staining the first vertebra.Conclusions The Pred withdrawal method can be used to establish a rapid,stable,and sensitive OP model in zebrafish as a reliable model for studying OP.

Objective:To report the results of national surveillance on the distribution and antimicrobial resistance profile of clinical Gram-positive bacteria isolates from bloodstream infections in China in 2022.Methods:The clinical isolates of Gram-positive bacteria from blood cultures in member hospitals of National Bloodstream Infection Bacterial Resistant Investigation Collaborative System（BRICS）were collected during January 2022 to December 2022. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute（CLSI）. WHONET 5.6 and SPSS 25.0 software were used to analyze the data.Results:A total of 3 163 strains of Gram-positive pathogens were collected from 51 member units，and the top five bacteria were Staphylococcus aureus（ n=1 147，36.3%），coagulase-negative Staphylococci（ n=928，29.3%）， Enterococcus faecalis（ n=369，11.7%）， Enterococcus faecium（ n=296，9.4%）and alpha-hemolyticus Streptococci（ n=192，6.1%）. The detection rates of methicillin-resistant Staphylococcus aureus（MRSA）and methicillin-resistant coagulase-negative Staphylococci（MRCNS）were 26.4%（303/1 147）and 66.7%（619/928），respectively. No glycopeptide and daptomycin-resistant Staphylococci were detected. The sensitivity rates of Staphylococcus aureus to cefpirome，rifampin，compound sulfamethoxazole，linezolid，minocycline and tigecycline were all >95.0%. Enterococcus faecium was more prevalent than Enterococcus faecalis. The resistance rates of Enterococcus faecium to vancomycin and teicoplanin were both 0.5%（2/369），and no vancomycin-resistant Enterococcus faecium was detected. The detection rate of MRSA in southern China was significantly lower than that in other regions（ χ2=14.578， P=0.002），while the detection rate of MRCNS in northern China was significantly higher than that in other regions（ χ2=15.195， P=0.002）. The detection rates of MRSA and MRCNS in provincial hospitals were higher than those in municipal hospitals（ χ2=13.519 and 12.136， P<0.001）. The detection rates of MRSA and MRCNS in economically more advanced regions（per capita GDP≥92 059 Yuan in 2022）were higher than those in economically less advanced regions（per capita GDP<92 059 Yuan）（ χ2=9.969 and 7.606， P=0.002和0.006）. Conclusions:Among the Gram-positive pathogens causing bloodstream infections in China， Staphylococci is the most common while the MRSA incidence decreases continuously with time；the detection rate of Enterococcus faecium exceeds that of Enterococcus faecalis. The overall prevalence of vancomycin-resistant Enterococci is still at a low level. The composition ratio of Gram-positive pathogens and resistant profiles varies slightly across regions of China，with the prevalence of MRSA and MRCNS being more pronounced in provincial hospitals and areas with a per capita GDP≥92 059 yuan.

Objective The aim of this study was to explore the correlation between the methylation status of microRNA let-7a-3 in esophageal squamous cell carcinoma(ESCC)and the expression of insulin-like growth factor 2(IGF-Ⅱ).Methods The methylation specific PCR(qMSP)was used to detect the methylation status of let-7a-3 in 83 cases of esophageal cancer and corre-sponding adjacent normal tissues.The enzyme linked immunosorbent assay(ELISA)was used to detect the expression of IGF-Ⅱ in plasma.Results The degree of let-7a-3 methylation in cancer tissues of 83 patients with ESCC was significantly higher than that in normal tissues adjacent to cancer(P<0.001).The expression of IGF-Ⅱ in the plasma of 83 patients with ESCC was positively corre-lated with the methylation degree of let-7a-3,which was statistically significant(r=0.600,P<0.001).Conclusion microRNAlet-7a-3 may participate in the occurrence and progression of ESCC by regulating the methylation of downstream molecules,which is of great significance for understanding the mechanisms of ESCC development and providing a basis for the diagnosis and prognosis of ESCC.

Electroencephalogram(EEG)is a non-invasive measurement method of brain electrical activity.In recent years,single/few-channel EEG has been used more and more,but various types of physiological artifacts seriously affect the analysis and wide application of single/few-channel EEG.In this paper,the regression and filtering methods,decomposition methods,blind source separation methods and machine learning methods involved in the various physiological artifacts in single/few-channel EEG are reviewed.According to the characteristics of single/few-channel EEG signals,hybrid EEG artifact removal methods for different scenarios are analyzed and summarized,mainly including single-artifact/multi-artifact scenes and online/offline scenes.In addition,the methods and metrics for validating the performance of the algorithm on semi-simulated and real EEG data are also reviewed.Finally,the development trend of single/few-channel EEG application and physiological artifact processing is briefly described.

Objective In this study,we analyzed the transcriptome sequences of Kupffer cells exposed to simulated microgravity for 3 d and conducted biological experiments to determine how microgravity initiates apoptosis in Kupffer cells. Methods Rotary cell culture system was used to construct a simulated microgravity model.GO and KEGG analyses were conducted using the DAVID database.GSEA was performed using the R language.The STRING database was used to conduct PPI analysis.qPCR was used to measure the IL1B,TNFA,CASP3,CASP9,and BCL2L11 mRNA expressions.Western Blotting was performed to detect the level of proteins CASP3 and CASP 9.Flow cytometry was used to detect apoptosis and mitochondrial membrane cells.Transmission electron microscopy was used to detect changes in the ultrastructure of Kupffer cells. Results Transcriptome Sequencing indicated that simulated microgravity affected apoptosis and the inflammatory state of Kupffer cells.Simulated microgravity improved the CASP3,CASP9,and BCL2L11 expressions in Kupffer cells.Annexin-V/PI and JC-1 assays showed that simulated microgravity promoted apoptosis in Kupffer cells.Simulated microgravity causes M1 polarization in Kupffer cells. Conclusion Our study found that simulated microgravity facilitated the apoptosis of Kupffer cells through the mitochondrial pathway and activated Kupffer cells into M1 polarization,which can secrete TNFA to promote apoptosis.

In recent years, the research focus on health-related behavior and chronic non-communicable diseases has shifted from the analysis on independent effects of multiple causes on a single outcome to the evaluation the complex relationships between multiple causes and multiple effects. Complex network theory, an important branch of system science, considers the relationships among factors in a network and can reveal how health-related behaviors interact with chronic diseases through a series of complex network models and indicators. This paper summarizes the definition and development of complex network theory and its commonly used models, indicators, and case studies in the field of health-related behavior and chronic disease to promote the application of complex network theory in the field of health and provide reference and tools for future research of the relationship between health-related behavior and chronic disease.

Objective:To analyze the effect of long-term care insurance on the incidence of falls in old people with disability, and the modification effect of different living arrangements.Methods:Based on the discharge diagnosis records of the hospitalization in urban residents who applied for long-term care insurance in Chengdu from July 2014 to June 2021, those who received long-term care insurance were selected as the intervention group and those who received no long-term care insurance were selected as the control group, the changes in the incidence of falls before and after the long-term care were analyzed using difference-in-differences method, and the modification effects of different living arrangements were explored using stratified analysis.Results:Long-term care insurance significantly reduced the risk for monthly incidence of falls in older adults [average treatment effects on treated (ATT) = -1.21% (95% CI: -1.82%--0.60%)], and the effect increased over time (ATT=-1.26%, 95% CI: -1.93%--0.59%). Long-term care insurance had a significant effect on reducing the incidence of falls in old people living with spouses/family members (ATT=-0.92%, 95% CI: -1.47%--0.37%) and in nursing facilities/hospitals (ATT=-2.10%, 95% CI: -3.81%--0.40%), but it had no significant effect in those living alone. Conclusions:Long-term care insurance can effectively reduce the incidence of falls in old people with disability, showing a positive effect. Living with family members or in nursing facilities/hospitals has a modification effect of the implementation of long-term care insurance policy. It is necessary to pay attention to key populations in the future and expand the coverage of long-term care insurance.