Objective To prepare a thermosensitive hydrogel scaffold loaded with bone marrow mesenchymal stem cells（BMSCs） and resveratrol liposomes （RSV-LIP） to form a therapeutic unit and evaluate its treatment efficacy for traumatic brain injury （TBI）. Methods BMSCs were extracted from rats, and RSV-LIP was prepared and characterized. Cell models were constructed to investigate the pharmacological effects of BMSCs combined with RSV-LIP. BMSCs and RSV-LIP were then loaded into the hydrogel, and a TBI mouse model was established to evaluate the therapeutic effects of the hydrogel. Results The RSV-LIP had a particle size of 127.8 nm, a Zeta potential of −4.9 mV, an encapsulation efficiency of 78.50%, and a drug loading content of 2.37%. Live-dead staining indicated good biocompatibility of the hydrogel. The combination of BMSCs and RSV-LIP significantly inhibited TNF-α and reduced ROS levels, promoting cell migration in scratch assays. Compared to the control group, the hydrogel group showed significantly lower mNSS scores （P<0.01）, higher hanging scores （P<0.001）, and reduced stepping errors （P<0.001）. Conclusion The combination of BMSCs and RSV-LIP exhibited antioxidative stress, anti-inflammatory, and neurogenic cell migration-promoting effects. When loaded into a hydrogel scaffold and locally implanted, it could improve the motor and sensory functions in TBI mice.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

6-gingerol, a bioactive compound from ginger, has demonstrated promising anticancer properties across various cancer models by inducing apoptosis and inhibiting cell proliferation and invasion. In this study, we explore its mechanisms against glioblastoma multiforme (GBM), a notably aggressive and treatment-resistant brain tumor. We found that 6-gingerol crosses the blood-brain barrier more effectively than curcumin, enhancing its potential as a therapeutic agent for brain tumors. Our experiments show that 6-gingerol reduces cell proliferation and triggers apoptosis in GBM cell lines by disrupting cellular energy homeostasis. This process involves an increase in mitochondrial reactive oxygen species (mtROS) and a decrease in mitochondrial membrane potential, primarily due to the downregulation of manganese superoxide dismutase (MnSOD). Additionally, 6-gingerol reduces ERK phosphorylation by inhibiting EGFR and RAF, leading to G1 phase cell cycle arrest. These findings indicate that 6-gingerol promotes cell death in GBM cells by modulating MnSOD and ROS levels and arresting the cell cycle through the ERFR-RAF-1/MEK/ ERK signaling pathway, highlighting its potential as a therapeutic agent for GBM and setting the stage for future clinical research.

6-gingerol, a bioactive compound from ginger, has demonstrated promising anticancer properties across various cancer models by inducing apoptosis and inhibiting cell proliferation and invasion. In this study, we explore its mechanisms against glioblastoma multiforme (GBM), a notably aggressive and treatment-resistant brain tumor. We found that 6-gingerol crosses the blood-brain barrier more effectively than curcumin, enhancing its potential as a therapeutic agent for brain tumors. Our experiments show that 6-gingerol reduces cell proliferation and triggers apoptosis in GBM cell lines by disrupting cellular energy homeostasis. This process involves an increase in mitochondrial reactive oxygen species (mtROS) and a decrease in mitochondrial membrane potential, primarily due to the downregulation of manganese superoxide dismutase (MnSOD). Additionally, 6-gingerol reduces ERK phosphorylation by inhibiting EGFR and RAF, leading to G1 phase cell cycle arrest. These findings indicate that 6-gingerol promotes cell death in GBM cells by modulating MnSOD and ROS levels and arresting the cell cycle through the ERFR-RAF-1/MEK/ ERK signaling pathway, highlighting its potential as a therapeutic agent for GBM and setting the stage for future clinical research.

6-gingerol, a bioactive compound from ginger, has demonstrated promising anticancer properties across various cancer models by inducing apoptosis and inhibiting cell proliferation and invasion. In this study, we explore its mechanisms against glioblastoma multiforme (GBM), a notably aggressive and treatment-resistant brain tumor. We found that 6-gingerol crosses the blood-brain barrier more effectively than curcumin, enhancing its potential as a therapeutic agent for brain tumors. Our experiments show that 6-gingerol reduces cell proliferation and triggers apoptosis in GBM cell lines by disrupting cellular energy homeostasis. This process involves an increase in mitochondrial reactive oxygen species (mtROS) and a decrease in mitochondrial membrane potential, primarily due to the downregulation of manganese superoxide dismutase (MnSOD). Additionally, 6-gingerol reduces ERK phosphorylation by inhibiting EGFR and RAF, leading to G1 phase cell cycle arrest. These findings indicate that 6-gingerol promotes cell death in GBM cells by modulating MnSOD and ROS levels and arresting the cell cycle through the ERFR-RAF-1/MEK/ ERK signaling pathway, highlighting its potential as a therapeutic agent for GBM and setting the stage for future clinical research.

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.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

To explore the ideal pretarsal shows and morphological characteristics of double eyelids in Chinese populations through a morphometric analysis.

Objective To investigate the characteristics of TCM constitution distribution in hepatitis B cirrhosis patients with dysplastic nodules(DN),and to provide a basis for the prevention and treatment of precancerous lesions of liver cancer.Methods This study was conducted among 113 hepatitis B cirrhosis patients with DN,105 hepatitis B cirrhosis patients with regenerative nodules(RN),and 70 hepatitis B cirrhosis patients with small hepatocellular carcinoma(sHCC)who were hospitalized in Guangdong Provincial Hospital of Traditional Chinese Medicine from May 2015 to March 2023.Related data were collected,including age,sex,liver function Child-Pugh class,TCM constitution type,and laboratory markers.A one-way analysis of variance was used for comparison of normally distributed continuous data between multiple groups,and the least significant difference t-test was used for further comparison between two groups;the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between multiple groups;the chi-square test was used for comparison of categorical data between groups,and the Bonferroni correction method was used for further comparison between two groups.Results The main TCM constitution types of hepatitis B cirrhosis patients with DN were Qi-deficiency constitution(27 patients,23.89%),blood-stasis constitution(26 patients,23.01%),and phlegm-dampness constitution(23 patients,20.35%).There were significant differences between the three groups in the proportion of patients with phlegm-dampness constitution or damp-heat constitution(&chi;2=6.822 and 6.383,both P<0.05);the hepatitis B cirrhosis patients with RN had the highest proportion of patients with phlegm-dampness constitution(30.48%),followed by those with DN(20.35%)and those with sHCC(14.29%),while the hepatitis B cirrhosis patients with sHCC had the highest proportion of patients with damp-heat constitution(27.14%),followed by those with DN(16.81%)and those with RN(12.38%).There were significant differences between the hepatitis B cirrhosis DN patients with different TCM constitution types in sex,age,Child-Pugh class,prealbumin,albumin(Alb),aspartate aminotransferase,total bilirubin(TBil),total bile acid,and alpha-fetoprotein(all P<0.05).Compared with the male hepatitis B cirrhosis DN patients,female patients showed a significantly higher proportion of patients with Qi-deficiency constitution(&chi;2=4.895,P=0.027).Among the patients with Qi-deficiency constitution,the patients with Child-Pugh class A liver function accounted for a significantly lower proportion than those with Child-Pugh class B liver function(&chi;2=6.380,P=0.012),while among the patients with phlegm-dampness constitution,the patients with Child-Pugh class A liver function accounted for a significantly higher proportion than those with Child-Pugh class B liver function(&chi;2=8.515,P=0.004).The patients with phlegm-dampness constitution had significantly higher levels of prealbumin and Alb than those with the other four constitutions(all P<0.05),as well as significantly lower levels of TBil and total bile acid than those with damp-heat constitution(P<0.05);the patients with Yin-deficiency constitution had a significantly lower level of Alb than those with qi-deficiency constitution,blood-stasis constitution,or phlegm-dampness constitution(all P<0.05);the patients with Yin-deficiency constitution had a significantly lower proportion of patients with abnormal alpha-fetoprotein than those with non-Yin-deficiency constitutions(&chi;2=4.448,P=0.035).Conclusion Hepatitis B cirrhosis patients with DN mainly have the TCM constitution types of Qi deficiency,blood stasis,and phlegm dampness.The patients with phlegm-dampness constitution seem to have a low probability of carcinogenesis,while those with damp-heat constitution and Yin-deficiency constitution have a relatively high risk of carcinogenesis.

Objective This article aims to investigate the association between hypertension and the risk of GSD by conducting a national multicenter study,a systematic review,and a meta-analysis.Methods The study was conducted in three stages.In the first stage,subjects were recruited for health examination in four hospitals in Chengdu,Tianjin,Beijing,and Chongqing,China,from 2015 to 2020,and the multivariate logistic regression analysis was used to investigate the association between hypertension and the risk of GSD in each center.In the second stage,Embase,PubMed,Wanfang Data,VIP,and CNKI databases were searched for related studies published up to May 2021,and a meta-analysis was conducted to further verify such association.In the third stage,the random effects model was used for pooled analysis of the results of the multicenter cross-sectional study and the findings of previous literature.Results A total of 633 948 participants were enrolled in the cross-sectional study,and the prevalence rate of GSD was 7.844%.The multivariate logistic regression analysis showed that hypertension was positively associated with the risk of GSD(P＜0.05).Subgroup analysis showed that there was no significant difference in the association between hypertension and GSD between individuals with different sexes,ages,and subtypes of GSD.A total of 80 articles were included in the systematic review and the meta-analysis,and the results showed that the risk of GSD was increased by 1.022 times for every 10 mmHg increase in diastolic pressure and 1.014 times for every 10 mmHg increase in systolic pressure.Conclusion Hypertension significantly increases the risk of GSD,and the findings of this study will provide a basis for the etiology of GSD and the identification of high-risk groups.