Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

Brain’s neural activities encompass both periodic rhythmic oscillations and aperiodic neural fluctuations. Rhythmic oscillations manifest as spectral peaks of neural signals, directly reflecting the synchronized activities of neural populations and closely tied to cognitive and behavioral states. In contrast, aperiodic fluctuations exhibit a power-law decaying spectral trend, revealing the multiscale dynamics of brain neural activity. In recent years, researchers have made notable progress in studying brain aperiodic dynamics. These studies demonstrate that aperiodic activity holds significant physiological relevance, correlating with various physiological states such as external stimuli, drug induction, sleep states, and aging. Aperiodic activity serves as a reflection of the brain’s sensory capacity, consciousness level, and cognitive ability. In clinical research, the aperiodic exponent has emerged as a significant potential biomarker, capable of reflecting the progression and trends of brain diseases while being intricately intertwined with the excitation-inhibition balance of neural system. The physiological mechanisms underlying aperiodic dynamics span multiple neural scales, with activities at the levels of individual neurons, neuronal ensembles, and neural networks collectively influencing the frequency, oscillatory patterns, and spatiotemporal characteristics of aperiodic signals. Aperiodic dynamics currently boasts broad application prospects. It not only provides a novel perspective for investigating brain neural dynamics but also holds immense potential as a neural marker in neuromodulation or brain-computer interface technologies. This paper summarizes methods for extracting characteristic parameters of aperiodic activity, analyzes its physiological relevance and potential as a biomarker in brain diseases, summarizes its physiological mechanisms, and based on these findings, elaborates on the research prospects of aperiodic dynamics.

BACKGROUND:At present,the incidence of scoliosis is increasing year by year,especially in adolescent idiopathic scoliosis.Therefore,it is more and more important to strengthen the research on the treatment of adolescent scoliosis. OBJECTIVE:To summarize the current status,hotspots,emerging trends,and frontiers of global research on the treatment of adolescent idiopathic scoliosis to provide reference and guidance for future related research. METHODS:The literature related to the treatment of adolescent idiopathic scoliosis was retrieved on the Web of Science Core Collection(WOSCC)database from 2013 to 2023.CiteSpace 6.2.R1 software was used for visual analysis of countries,institutions,authors,and keywords. RESULTS AND CONCLUSION:(1)A total of 561 English articles were included in this study.Among countries,institutions,and authors,the United States has contributed the most.Nanjing University and Qiu,Yong(Affiliated Drum Tower Hospital,Nanjing University School of Medicine)are the most published institution and author.The academic journal with the largest number of articles is the European Spine Journal.(2)In the analysis of cited literature,the top 10 most cited articles mainly describe the effects of surgical treatment and conservative treatment on improving adolescent idiopathic scoliosis,especially improving the curvature of patients.(3)Through the summary of highly cited articles and the keyword clustering,keyword prominence in-depth mining,the research hotspots are currently the relationship between Cobb angle and treatment choice,the therapeutic effect of exercise therapy and the therapeutic effect of posterior vertebral fusion.(4)The prognosis of patients with different curvatures has not been studied in depth,and the etiology of adolescent idiopathic scoliosis has not been clarified,so the relationship between curvature and prognosis and the etiology of adolescent idiopathic scoliosis may be a new research trend in the future.

Objective To investigate disparities in health levels among populations across different regions of China and analyze the relationship between these disparities and regional, social, and economic factors, and to provide recommendations to promote health equity. Methods Based on the data from the Seventh National Population Census, this study employed spatial autocorrelation analysis and the spatial Durbin model to conduct spatial and temporal analyses of the health status of the national population from 2012 to 2021, focusing on the regional distribution of health levels and related influencing factors. Results 1. Regional disparities: The mortality rate in Gansu Province rose from 6.05‰ in 2012 to 8.26‰ in 2021, whereas the mortality rate in eastern provinces such as Hainan Province was relatively low in 2021 (5.39‰). 2. Spatial clustering: The spatial correlation of mortality rates was weak (Moran's I: 0.134-0.245), and the high mortality clusters showed a shift from southwest to northeast region. 3. Influencing factors: Economic conditions, education quality, urbanization levels, and healthcare resources significantly impacted population mortality rates. Conclusion The present study identifies pronounced regional disparities in population health, providing a scientific basis for formulating targeted healthcare policies. Additionally, this study highlights the critical importance of spatial analysis in understanding and addressing public health issues to advance health equity.

BACKGROUND: Senescent human skin primary fibroblast (FB) models have been established for studying aging-related, proliferative, and inflammatory skin diseases. The aim of this study was to compare the transcriptome characteristics of human primary dermal FBs from children and the elderly with four senescence models. METHODS: Human skin primary FBs were obtained from healthy children (FB-C) and elderly donors (FB-E). Senescence models were generated by ultraviolet B irradiation (FB-UVB), D-galactose stimulation (FB-D-gal), atazanavir treatment (FB-ATV), and replication exhaustion induction (FB-P30). Flow cytometry, immunofluorescence staining, real-time quantitative polymerase chain reaction, co-culturing with immune cells, and bulk RNA sequencing were used for systematic comparisons of the models. RESULTS: In comparison with FB-C, FB-E showed elevated expression of senescence-related genes related to the skin barrier and extracellular matrix, proinflammatory factors, chemokines, oxidative stress, and complement factors. In comparison with FB-E, FB-UVB and FB-ATV showed higher levels of senescence and expression of the genes related to the senescence-associated secretory phenotype (SASP), and their shaped immune microenvironment highly facilitated the activation of downstream immune cells, including T cells, macrophages, and natural killer cells. FB-P30 was most similar to FB-E in terms of general transcriptome features, such as FB migration and proliferation, and aging-related characteristics. FB-D-gal showed the lowest expression levels of senescence-related genes. In comparisons with the single-cell RNA sequencing results, FB-E showed almost complete simulation of the transcriptional spectrum of FBs in elderly patients with atopic dermatitis, followed by FB-P30 and FB-UVB. FB-E and FB-P30 showed higher similarity with the FBs in keloids. CONCLUSIONS Each senescent FB model exhibited different characteristics. In addition to showing upregulated expression of natural senescence features, FB-UVB and FB-ATV showed high expression levels of senescence-related genes, including those involved in the SASP, and FB-P30 showed the greatest similarity with FB-E. However, D-galactose-stimulated FBs did not clearly present aging characteristics.
Humans ; Fibroblasts/drug effects* ; Cellular Senescence/physiology* ; Skin/metabolism* ; Child ; Transcriptome/genetics* ; Aged ; Ultraviolet Rays ; Cells, Cultured ; Galactose/pharmacology*

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Education is a crucial element in the development of acupuncture as a discipline, providing essential talent support for its future advancement. A structured interview was conducted with renowned acupuncture expert Professor ZHAO Jiping, focusing on key topics such as the core of acupuncture education, the connotation and development of acupuncture textbooks, and acupuncture teaching models. Through in-depth discussion, the current problems in acupuncture education were analyzed, and possible solutions were explored, aiming to offer ideas for the innovative development of acupuncture education.
Acupuncture/trends* ; Humans ; Acupuncture Therapy ; China