AIM: To investigate the correlation of serum leucine-rich α-2 glycoprotein 1(LRG1)and fibroblast growth factor 21(FGF-21)levels with neovascular glaucoma(NVG).METHODS: A total of 110 cases(110 eyes)with NVG admitted to the ophthalmology department from September 2020 to September 2022 were selected as NVG group, with 23 cases of grade II, 44 cases of grade III, and 43 cases of grade IV, while 90 sex and age matched cataract patients(90 eyes)were selected as control group. The levels of LRG1, FGF-21, vascular endothelial growth factor(VEGF), pigment epithelium-derived factor(PEDF), and tumor necrosis factor-α(TNF-α)in serum were detected by ELISA; Pearson correlation analysis was used to analyze the correlation of serum LRG1 and FGF-21 levels with Teich grade, VEGF, PEDF and TNF-α levels.RESULTS: The levels of serum LRG1, FGF-21, VEGF, PEDF and TNF-α in the NVG group were significantly higher than those in the control group(all P<0.01). With the increase of Teich grading, the levels of serum LRG1, FGF-21, VEGF, PEDF and TNF-α in NVG patients significantly increased in turn(all P<0.05). Correlation analysis showed that the levels of LRG1 and FGF-21 in serum of NVG patients were positively correlated with the levels of VEGF, PEDF and TNF-α(all P<0.05).CONCLUSION: The levels of LRG1 and FGF-21 in serum of patients with NVG are obviously increased, which are positively correlated with the levels of VEGF, PEDF and TNF-α, both of which may be related to the development of NVG.

In recent years, the field of cardiovascular surgery has undergone revolutionary changes and made rapid progress in various aspects, bringing more hope and possibilities for the health and well-being of patients. The constant emergence of new technologies brings new opportunities and hope, as well as constant challenges to past concepts. This article aims to provide a comprehensive overview of the latest developments in cardiovascular surgery in recent years, especially since 2023. It introduces cutting-edge knowledge and technologies in the field of cardiovascular surgery, including lifelong management of aortic valve disease, artificial valves, mitral valves, treatment options for hypertrophic obstructive cardiomyopathy, heart transplantation, left ventricular assist, coronary artery surgery, cardiac structural interventions for chronic heart failure, aortic dissection, and comprehensive surgical treatment of atrial fibrillation. It also analyzes and explores future development directions in depth, aiming to provide useful references and inspiration for cardiovascular doctors and jointly promote the continuous progress of cardiovascular surgery in China.

Disorders of consciousness (DOC) are pathological conditions characterized by severely suppressed brain function and the persistent interruption or loss of consciousness. Accurate diagnosis and evaluation of DOC are prerequisites for precise treatment. Traditional assessment methods are primarily based on behavioral scales, which are inherently subjective and rely on observable behaviors. Moreover, traditional methods have a high misdiagnosis rate, particularly in distinguishing minimally conscious state (MCS) from vegetative state/unresponsive wakefulness syndrome (VS/UWS). This diagnostic uncertainty has driven the exploration of objective, reliable, and efficient assessment tools. Among these tools, electroencephalography (EEG) has garnered significant attention for its non-invasive nature, portability, and ability to capture real-time neurodynamics. This paper systematically reviews the application of EEG biomarkers in DOC assessment. These biomarkers are categorized into 3 main types: resting-state EEG features, task-related EEG features, and features derived from transcranial magnetic stimulation-EEG (TMS-EEG). Resting-state EEG biomarkers include features based on spectrum, microstates, nonlinear dynamics, and brain network metrics. These biomarkers provide baseline representations of brain activity in DOC patients. Studies have shown their ability to distinguish different levels of consciousness and predict clinical outcomes. However, because they are not task-specific, they are challenging to directly associate with specific brain functions or cognitive processes. Strengthening the correlation between resting-state EEG features and consciousness-related networks could offer more direct evidence for the pathophysiological mechanisms of DOC. Task-related EEG features include event-related potentials, event-related spectral modulations, and phase-related features. These features reveal the brain’s responses to external stimuli and provide dynamic information about residual cognitive functions, reflecting neurophysiological changes associated with specific cognitive, sensory, or behavioral tasks. Although these biomarkers demonstrate substantial value, their effectiveness rely on patient cooperation and task design. Developing experimental paradigms that are more effective at eliciting specific EEG features or creating composite paradigms capable of simultaneously inducing multiple features may more effectively capture the brain activity characteristics of DOC patients, thereby supporting clinical applications. TMS-EEG is a technique for probing the neurodynamics within thalamocortical networks without involving sensory, motor, or cognitive functions. Parameters such as the perturbational complexity index (PCI) have been proposed as reliable indicators of consciousness, providing objective quantification of cortical dynamics. However, despite its high sensitivity and objectivity compared to traditional EEG methods, TMS-EEG is constrained by physiological artifacts, operational complexity, and variability in stimulation parameters and targets across individuals. Future research should aim to standardize experimental protocols, optimize stimulation parameters, and develop automated analysis techniques to improve the feasibility of TMS-EEG in clinical applications. Our analysis suggests that no single EEG biomarker currently achieves an ideal balance between accuracy, robustness, and generalizability. Progress is constrained by inconsistencies in analysis methods, parameter settings, and experimental conditions. Additionally, the heterogeneity of DOC etiologies and dynamic changes in brain function add to the complexity of assessment. Future research should focus on the standardization of EEG biomarker research, integrating features from resting-state, task-related, and TMS-EEG paradigms to construct multimodal diagnostic models that enhance evaluation efficiency and accuracy. Multimodal data integration (e.g., combining EEG with functional near-infrared spectroscopy) and advancements in source localization algorithms can further improve the spatial precision of biomarkers. Leveraging machine learning and artificial intelligence technologies to develop intelligent diagnostic tools will accelerate the clinical adoption of EEG biomarkers in DOC diagnosis and prognosis, allowing for more precise evaluations of consciousness states and personalized treatment strategies.

This paper systematically summarizes the practical experience of the 2025 Dingri earthquake emergency medical rescue in Tibet. It analyzes the requirements for earthquake medical rescue under conditions of high-altitude hypoxia, low temperature, and low air pressure. The paper provides a detailed discussion on the strategic layout of earthquake medical rescue at the national level, local government level, and through social participation. It covers the construction of rescue organizational systems, technical systems, material support systems, and information systems. The importance of building rescue teams is emphasized. In high-altitude and cold conditions, rapid response, scientific decision-making, and multi-party collaboration are identified as key elements to enhance rescue efficiency. By optimizing rescue organizational structures, strengthening the development of new equipment, and promoting telemedicine technologies, the precision and effectiveness of medical rescue can be significantly improved, providing important references for future similar disaster rescues.

In recent years, with the continuous development and increasing maturity of interventional techniques, interventional treatment for congenital heart disease (CHD) has been progressively disseminated to county- and city-level hospitals in China. Concurrently, the standardized management of adult CHD (particularly patent foramen ovale) and the lifelong management of complex CHD are gaining increasing clinical attention, while the emergence of new techniques and products continuously advances the discipline. This article aims to review the new progress made in the field of interventional treatment for congenital heart disease in China during 2024. It specifically reviews and analyzes the following key aspects: (1) annual statistics on interventional closure procedures for CHD; (2) recent insights into patent foramen ovale closure; (3) advances in transcatheter pulmonary valve replacement; (4) interventional treatment and lifelong management strategies for complex CHD; (5) new interventional techniques for acquired heart disease; and (6) the application of artificial intelligence in CHD management. Through the synthesis and discussion of these topics, this article seeks to provide a detailed analysis of the current landscape of interventional treatment for CHD in China and project its future development trends.

BACKGROUND: The urban-rural disparities in overweight and obesity among children and adolescents are narrowing, and there is a need for long-term and updated data to explain this inequality, understand the underlying mechanisms, and identify priority groups for interventions. METHODS: We analyzed data from seven rounds of the Chinese National Survey on Students Constitution and Health (CNSSCH) conducted from 1985 to 2019, focusing on school-age children and adolescents aged 7-18 years. Joinpoint regression was used to identify inflection points (indicating a change in the trend) in the prevalence of overweight and obesity during the study period, stratified by urban/rural areas and sex. Annual percent change (APC), average annual percent change (AAPC), and 95% confidence interval (CI) were used to describe changes in the prevalence of overweight and obesity. Polynomial regression models were used to predict the prevalence of overweight and obesity among children and adolescents in 2025 and 2030, considering urban/rural areas, sex, and age groups. RESULTS: The prevalence of overweight and obesity in urban boys and girls showed an inflection point of 2000, with AAPC values of 10.09% (95% CI: 7.33-12.92%, t = 7.414, P <0.001) and 8.67% (95% CI: 6.10-11.30%, t = 6.809, P <0.001), respectively. The APC for urban boys decreased from 18.31% (95% CI: 4.72-33.67%, t = 5.926, P = 0.027) to 4.01% (95% CI: 1.33-6.75%, t = 6.486, P = 0.023), while the APC for urban girls decreased from 13.88% (95% CI: 1.82-27.38%, t = 4.994, P = 0.038) to 4.72% (95% CI: 1.43-8.12%, t = 6.215, P = 0.025). However, no inflection points were observed in the best-fit models for rural boys and girls during the period 1985-2019. The prevalence of overweight and obesity for both urban and rural boys is expected to converge at 35.76% by approximately 2027. A similar pattern is observed for urban and rural girls, with a prevalence of overweight and obesity reaching 20.86% in 2025. CONCLUSIONS The prevalence of overweight and obesity among Chinese children and adolescents has been steadily increasing from 1985 to 2019. A complete reversal in urban-rural prevalence is expected by 2027, with a higher prevalence of overweight and obesity in rural areas. Urgent action is needed to address health inequities and increase investments, particularly policies targeting rural children and adolescents.
Humans ; Child ; Adolescent ; Female ; Male ; Rural Population/statistics & numerical data* ; Overweight/epidemiology* ; Prevalence ; China/epidemiology* ; Pediatric Obesity/epidemiology* ; Obesity/epidemiology* ; Urban Population

BACKGROUND: The global rise in diabetes prevalence is a pressing concern. Despite initiatives like "The Healthy Beijing Action 2020-2030" advocating for increased awareness, treatment, and control, the specific situation in Beijing remains unexplored. This study aimed to analyze the trends in diabetes prevalence, awareness, treatment, and control among Beijing adults. METHODS: Through a stratified multistage probability cluster sampling method, a series of representative cross-sectional surveys were conducted in Beijing from 2005 to 2022, targeting adults aged 18-79 years. A face-to-face questionnaire, along with body measurements and laboratory tests, were administered to 111,943 participants. Data from all survey were age- and/or gender-standardized based on the 2020 Beijing census population. Annual percentage rate change (APC) or average annual percentage rate change (AAPC) was calculated to determine prevalence trends over time. Complex sampling logistic regression models were employed to explore the relationship between various characteristics and diabetes. RESULTS: From 2005 to 2022, the total prevalence of diabetes among Beijing adults aged 18-79 years increased from 9.6% (95% CI: 8.8-10.4%) to 13.9% (95% CI: 13.1-14.7%), with an APC/AAPC of 2.1% (95% CI: 1.1-3.2%, P <0.05). Significant increases were observed among adults aged 18-39 years and rural residents. Undiagnosed diabetes rose from 3.5% (95% CI: 3.2-4.0%) to 7.2% (95% CI: 6.6-7.9%) with an APC/AAPC of 4.1% (95% CI: 0.5-7.3%, P <0.05). However, diabetes awareness and treatment rates showed annual declines of 1.4% (95% CI: -3.0% to -0.2%, P <0.05) and 1.3% (95% CI: -2.6% to -0.2%, P <0.05), respectively. The diabetes control rate decreased from 21.5% to 19.1%, although not statistically significant (APC/AAPC = -1.5%, 95% CI: -5.6% to 1.9%). Overweight and obesity were identified as risk factors for diabetes, with ORs of 1.65 (95% CI: 1.38-1.98) and 2.48 (95% CI: 2.07-2.99), respectively. CONCLUSIONS The prevalence of diabetes in Beijing has significantly increased between 2005 and 2022, particularly among young adults and rural residents. Meanwhile, there has been a concerning decrease in diabetes awareness and treatment rates, while control rates have remained stagnant. Regular blood glucose testing, especially among adults aged 18-59 years, should be warranted. Furthermore, being male, elderly, overweight, or obese was associated with higher diabetes risk, suggesting the needs for targeted management strategies.
Humans ; Adult ; Middle Aged ; Male ; Female ; Aged ; Adolescent ; Young Adult ; Cross-Sectional Studies ; Diabetes Mellitus/epidemiology* ; Beijing/epidemiology* ; Prevalence ; China/epidemiology* ; Surveys and Questionnaires

Research on IgG4-related disease (IgG4-RD), an autoimmune condition recognized to be a unique disease entity only two decades ago, has processed from describing patients' symptoms and signs to summarizing its critical pathological features, and further to investigating key pathogenic mechanisms. Challenges in gaining a better understanding of the disease, however, stem from its relative rarity-potentially attributed to underrecognition-and the absence of ideal experimental animal models. Recently, with the development of various high-throughput techniques, "omics" studies at different levels (particularly the single-cell omics) have shown promise in providing detailed molecular features of IgG4-RD. While, the application of omics approaches in IgG4-RD is still at an early stage. In this paper, we review the current progress of omics research in IgG4-RD and discuss the value of machine learning methods in analyzing the data with high dimensionality.
Humans ; Immunoglobulin G4-Related Disease/metabolism* ; Immunoglobulin G/metabolism* ; Machine Learning ; Animals ; Proteomics/methods*