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.

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.

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.

Objective To develop and evaluate a diagnostic model for autoimmune liver disease(AILD)using clinical indicators.Methods A total of 181 AILD patients(AILD group)diagnosed in the Department of Gastroenterology or Rheumatology and Immunology,the First Medical Center of Chinese PLA General Hospital,from June 2019 to February 2024,133 patients with hepatitis B virus cirrhosis(HBVC)(HBVC group),and 100 healthy individuals(HC group)were included in the study cohort for retrospective analysis.Clinical data,blood routine tests,and biochemical indicators were compared among the groups.Propensity score matching was used to balance the effects of factors such as gender and age.Multivariate logistic regression analysis was applied to identify characteristic indicators for AILD.Receiver operating characteristic(ROC)curves were used to evaluate the diagnostic efficacy of single and combined indicators,and the optimal diagnostic model for AILD was constructed.The Hosmer-Lemeshow test was performed to assess the model's goodness-of-fit,while ROC analysis was employed to evaluate the predictive validity.Additionally,an independent validation cohort,including 91 AILD patients(AILD validation group),82 HBVC patients(HBVC validation group),and 40 healthy individuals(HC validation group)who visited during the same period,was used to validate the diagnostic model.False-positive and false-negative rates were calculated to assess the diagnostic efficacy and clinical utility of the model.Results The age and the proportion of male patients in AILD and HBVC groups were higher than those in HC group,and the proportion of male patients in AILD group was higher than in HBVC group,the differences were statistically significant(P＜0.05).Prior to matching,compared with HC group,HBVC group and AILD group had significantly lower levels of hemoglobin(HB),red blood cell count(RBC),white blood cell count(WBC),platelet count(PLT),and albumin(ALB)(P＜0.05),but significantly higher levels of alanine aminotransferase(ALT),aspartate aminotransferase(AST),alkaline phosphatase(ALP),gamma-glutamyl transferase(GGT),total bilirubin(TBIL),and direct bilirubin(DBIL)(P＜0.05).Compared with HBVC group,AILD group had significantly higher levels of WBC,PLT,ALB,ALP,and GGT(P＜0.05 or P＜0.001)but significantly lower levels of TBIL and DBIL(P＜0.001).After maching,compared with HC group,AILD group still had significantly lower levels of HB,RBC,WBC,PLT,and ALB,but significantly higher levels of ALT,AST,ALP,GGT,TBIL,and DBIL(P＜0.05).Compared with HBVC group,AILD group had significantly higher levels of HB,RBC,WBC,PLT,ALB,ALT,ALP,and GGT,but significantly lower levels of DBIL(P＜0.05).ROC analysis results showed that AST,GGT,ALP,and DBIL had high predictive values for differentiating AILD from HC(AUC=0.843,0.804,0.802,0.773),while PLT,WBC,GGT,and ALP had high predictive values for differentiating AILD from HBVC(AUC=0.780,0.739,0.729,0.702).For distinguishing AILD from HC,the combined model of AST,ALP,and DBIL(Model e)had an AUC of 0.887,with a sensitivity of 82.2％,and a specificity of 94.7％.For distinguishing AILD from HBVC,the combined model of ALP,GGT,and PLT(Model D)had an AUC of 0.829,with a sensitivity of 78.5％and a specificity of 80.6％.Conclusions The combined diagnostic model based on AST,ALP,and DBIL can effectively differentiate healthy individuals without liver diseases from AILD patients,while the combined diagnostic model based on PLT,ALP,and GGT can effectively distinguish HBVC from AILD patients.

Traumatic brain injury (TBI) is intricately linked to the most severe clinical manifestations of brain damage. It encompasses dynamic pathological mechanisms, including hemodynamic disorders, excitotoxic injury, oxidative stress, mitochondrial dysfunction, inflammation, and neuronal death. This review provides a comprehensive analysis and summary of biomaterial-based tissue engineering scaffolds and nano-drug delivery systems. As an example of functionalized biomaterials, nano-drug delivery systems alter the pharmacokinetic properties of drugs. They provide multiple targeting strategies relying on factors such as morphology and scale, magnetic fields, pH, photosensitivity, and enzymes to facilitate the transport of therapeutics across the blood-brain barrier and to promote selective accumulation at the injury site. Furthermore, therapeutic agents can be incorporated into bioscaffolds to interact with the biochemical and biophysical environment of the brain. Bioscaffolds can mimic the extracellular matrix environment, regulate cellular interactions, and increase the effectiveness of local treatments following surgical interventions. Additionally, stem cell-based and exosome-dominated extracellular vesicle carriers exhibit high bioreactivity and low immunogenicity and can be used to design therapeutic agents with high bioactivity. This review also examines the utilization of endogenous bioactive materials in the treatment of TBI.

Adenosine-to-inosine (A-to-I), one of the most prevalent RNA modifications, has recently garnered significant attention. The A-to-I modification actively contributes to biological and pathological processes by affecting the structure and function of various RNA molecules, including double-stranded RNA, transfer RNA, microRNA, and viral RNA. Increasing evidence suggests that A-to-I plays a crucial role in the development of human disease, particularly in cancer, and aberrant A-to-I levels are closely associated with tumorigenesis and progression through regulation of the expression of multiple oncogenes and tumor suppressor genes. Currently, the underlying molecular mechanisms of A-to-I modification in cancer are not comprehensively understood. Here, we review the latest advances regarding the A-to-I editing pathways implicated in cancer, describing their biological functions and their connections to the disease.
Humans ; Adenosine/genetics* ; Inosine/genetics* ; RNA Editing ; Neoplasms/pathology* ; Animals ; MicroRNAs/metabolism*

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.

BACKGROUND: The available evidence regarding the benefits of percutaneous coronary intervention (PCI) on patients receiving dialysis with coronary artery disease (CAD) is limited and inconsistent. This study aimed to evaluate the association between PCI and clinical outcomes as compared with medical therapy alone in patients undergoing dialysis with CAD in China. METHODS: This multicenter, retrospective study was conducted in 30 tertiary medical centers across 12 provinces in China from January 2015 to June 2021 to include patients on dialysis with CAD. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke. Secondary outcomes included all-cause death, the individual components of MACE, and Bleeding Academic Research Consortium criteria types 2, 3, or 5 bleeding. Multivariable Cox proportional hazard models were used to assess the association between PCI and outcomes. Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) were performed to account for potential between-group differences. RESULTS: Of the 1146 patients on dialysis with significant CAD, 821 (71.6%) underwent PCI. After a median follow-up of 23.0 months, PCI was associated with a 43.0% significantly lower risk for MACE (33.9% [ n  = 278] vs . 43.7% [ n  = 142]; adjusted hazards ratio 0.57, 95% confidence interval 0.45-0.71), along with a slightly increased risk for bleeding outcomes that did not reach statistical significance (11.1% vs . 8.3%; adjusted hazards ratio 1.31, 95% confidence interval, 0.82-2.11). Furthermore, PCI was associated with a significant reduction in all-cause and cardiovascular mortalities. Subgroup analysis did not modify the association of PCI with patient outcomes. These primary findings were consistent across IPTW, PSM, and competing risk analyses. CONCLUSION This study indicated that PCI in patients on dialysis with CAD was significantly associated with lower MACE and mortality when comparing with those with medical therapy alone, albeit with a slightly increased risk for bleeding events that did not reach statistical significance.
Humans ; Percutaneous Coronary Intervention/methods* ; Male ; Female ; Coronary Artery Disease/drug therapy* ; Retrospective Studies ; Renal Dialysis/methods* ; Middle Aged ; Aged ; China ; Proportional Hazards Models ; Treatment Outcome

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.