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

To explore the status and characteristics of clinical trials of therapeutic cancer vaccines, and provide the overall trend of clinical translational research of therapeutic cancer vaccines.

To explore the status and characteristics of clinical trials of therapeutic cancer vaccines, and provide the overall trend of clinical translational research of therapeutic cancer vaccines.

[Objective]To evaluate the efficacy and safety of total oral regimens containing pomalidomide as a second-line treatment strategy in multiple myeloma.[Methods]A total of 22 patients with multiple myeloma placed on total oral regimens containing pomalidomide as a second-line therapy from March 2020 to December 2023 were retrospectively analyzed to evaluate the treatment response,survival and safety.[Results]The median age of the 22 patients was 71.5 years old. The total oral treatment regimens containing pomalidomide included IPD (7 cases),PCD (11 cases),XPD (2 cases),and PD (2 cases). The median number of treatment cycles was 14. Among the 13 patients with prior lenalidomide exposure,ORR was 53.85％,of which 23.08％ was ≥VGPR. In 9 patients without prior lenalidomide exposure,the ORR was 77.78％,and of which 55.56％ was ≥VGPR. There was no significant difference in ORR between these two groups (P=0.38). In 12 patients with high genetic risk,the ORR was 50％,and ≥VGPR was 16.67％. The median follow-up time was 10.6 months. Disease progressed in 10 patients and death occurred in 6 patients of them. The median progression free survival (PFS) was not reached (not reached and 10.6 months in non-lenalidomide-exposure patients or lenalidomide-exposure patients,respectively).The high grade treatment-related adverse events (AEs)(≥3 ) were reported in 18.18％ patients,including granulocytopenia,thrombocytopenia,and pulmonary infection. There was no treatment-related death.[Conclusion]Total oral regimens containing pomalidomide as a second-line therapy is generally effective and safe for multiple myeloma patients.

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.

With the changing disease spectrum, the incidence of tumors is increasing and tends to occur among the youth. The long-term survival rate of patients with cancer has increased significantly, and attention to their reproductive rights is growing. Surgery, chemotherapy, radiotherapy, immunotherapy, and molecular targeted therapy are the conventional treatment methods for cancer, with each exerting different effects on the fertility of patients. Common fertility preservation techniques currently include sperm cryopreservation, embryo and oocyte cryopreservation, ovarian tissue cryopreservation, uterine transplantation, and assisted reproductive technology. This article systematically summarizes the influence of different antitumor treatments on fertility, as well as the current status and prospects of fertility preservation in patients with cancer. This study aims to improve cooperation between clinical oncologists and reproductive medicine doctors to enhance fertility preservation for patients with cancer.

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.