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.

OBJECTIVES: To investigate the genomic characteristics and prognostic factors of juvenile myelomonocytic leukemia (JMML) with RAS mutations. METHODS: A retrospective analysis was conducted on the clinical data of JMML children with RAS mutations treated at the Hematology Hospital of Chinese Academy of Medical Sciences, from January 2008 to November 2022. RESULTS: A total of 34 children were included, with 17 cases (50%) having isolated NRAS mutations, 9 cases (27%) having isolated KRAS mutations, and 8 cases (24%) having compound mutations. Compared to children with isolated NRAS mutations, those with NRAS compound mutations showed statistically significant differences in age at onset, platelet count, and fetal hemoglobin proportion (P<0.05). Cox proportional hazards regression model analysis revealed that hematopoietic stem cell transplantation (HSCT) and hepatomegaly (≥2 cm below the costal margin) were factors affecting the survival rate of JMML children with RAS mutations (P<0.05); hepatomegaly was a factor affecting survival in the non-HSCT group (P<0.05). CONCLUSIONS Children with NRAS compound mutations have a later onset age compared to those with isolated NRAS mutations. At initial diagnosis, children with NRAS compound mutations have poorer peripheral platelet and fetal hemoglobin levels than those with isolated NRAS mutations. Liver size at initial diagnosis is related to the prognosis of JMML children with RAS mutations. HSCT can improve the prognosis of JMML children with RAS mutations.
Humans ; Leukemia, Myelomonocytic, Juvenile/therapy* ; Mutation ; Male ; Female ; Child, Preschool ; Retrospective Studies ; Child ; Infant ; GTP Phosphohydrolases/genetics* ; Membrane Proteins/genetics* ; Adolescent ; Hematopoietic Stem Cell Transplantation ; Proportional Hazards Models ; Proto-Oncogene Proteins p21(ras)/genetics* ; Prognosis

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 explore the feasibility and corresponding implementation methods of constructing a sample resource bank based on individual-level data of completed clinical trials and using it to construct external controls for drug/device clinical trials.Methods Taking the pre-marketing clinical trial of transcatheter active valve replacement(TAVR)for the treatment of aortic valve stenosis as an example,the individual-level databases of multiple trials were standardized to form a sample bank.The original data of any trial in the sample bank were selected as the experimental group,and the remaining samples were selected as the control group.The potential confounding was handled by using the propensity score matching and stratification methods to clarify the process of constructing external controls based on the sample bank of individual-level data of clinical trials.Results This study included individual-level data of single-group trials of 4 TAVR devices,with a total of 569 subjects(59.2%male).The number of subjects in Trials 1 to 4 was 120,120,163,and 166,respectively.Propensity score matching enabled the matching of 113,117,125,and 147 subjects with comparable or similar characteristics from individual-level data from other trials,respectively,demonstrating a high matching success rate.The PS score distribution plot after stratification showed that the proportions of subjects in the experimental and control groups in strata 1 to 5 in scheme 1 were 4/103,11/103,22/92,32/87,and 51/64,respectively.For all constructed external controlled trials,a certain number of control samples with similar baseline characteristics to the experimental groups were distributed within each propensity score stratum.The results of the simulation test also reflected the potential differences between different devices in the 12-month all-cause mortality rate.Conclusions The sample bank constructed with individual-level data from clinical trials,as a high-quality data source,can serve as a source of external control for single-arm trials in the same field,and as a useful supplement to the external control scenario of real-world evidence to support drug and device development.At the same time,targeted research on research methods and bias control measures in related fields is also needed.

Objective To explore the feasibility and corresponding implementation methods of constructing a sample resource bank based on individual-level data of completed clinical trials and using it to construct external controls for drug/device clinical trials.Methods Taking the pre-marketing clinical trial of transcatheter active valve replacement(TAVR)for the treatment of aortic valve stenosis as an example,the individual-level databases of multiple trials were standardized to form a sample bank.The original data of any trial in the sample bank were selected as the experimental group,and the remaining samples were selected as the control group.The potential confounding was handled by using the propensity score matching and stratification methods to clarify the process of constructing external controls based on the sample bank of individual-level data of clinical trials.Results This study included individual-level data of single-group trials of 4 TAVR devices,with a total of 569 subjects(59.2%male).The number of subjects in Trials 1 to 4 was 120,120,163,and 166,respectively.Propensity score matching enabled the matching of 113,117,125,and 147 subjects with comparable or similar characteristics from individual-level data from other trials,respectively,demonstrating a high matching success rate.The PS score distribution plot after stratification showed that the proportions of subjects in the experimental and control groups in strata 1 to 5 in scheme 1 were 4/103,11/103,22/92,32/87,and 51/64,respectively.For all constructed external controlled trials,a certain number of control samples with similar baseline characteristics to the experimental groups were distributed within each propensity score stratum.The results of the simulation test also reflected the potential differences between different devices in the 12-month all-cause mortality rate.Conclusions The sample bank constructed with individual-level data from clinical trials,as a high-quality data source,can serve as a source of external control for single-arm trials in the same field,and as a useful supplement to the external control scenario of real-world evidence to support drug and device development.At the same time,targeted research on research methods and bias control measures in related fields is also needed.

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.

Objective:To evaluate the role of minimal residual disease (MRD) monitoring during early induction therapy for the treatment of childhood acute lymphoblastic leukemia (ALL).Methods:This was a multicenter retrospective cohort study. Clinical data of 1 164 ALL patients first diagnosed between October 2016 and June 2019 was collected from 16 hospitals in South China Children′s Leukemia Group. According to MRD assay on day 15 of early induction therapy, they were divided into MRD<0.10% group, MRD 0.10%-<10.00% group and MRD≥10.00% group. According to MRD assay on day 33, they were divided into MRD<0.01% group, MRD 0.01%-<1.00% group and MRD≥1.00% group. Age, onset white blood cell count, central nervous system leukemia (CNSL), molecular genetic characteristics and other data were compared between groups. Kaplan-Meier method was used for survival analysis. Cox regression model was used to analyze prognostic factors.Results:Of the 1 164 enrolled patients, there were 692 males and 472 females. The age of diagnosis was 4.7 (0.5, 17.4) years. The white blood cell count at initial diagnosis was 10.7 (0.4, 1 409.0) ×10 9/L. Among all patients, 53 cases (4.6%) had CNSL. The follow-up time was 47.6 (0.5, 68.8) months. The 5-year overall survival (OS) and 5-year relapse-free survival (RFS) rates were (93.1±0.8) % and (90.3±1.1) %. On day 15 of early induction therapy, there were 466 cases in the MRD<0.10% group, 523 cases in the MRD 0.10%-<10.00% group and 175 cases in the MRD≥10.00% group. The 5-year OS rates of the MRD<0.10% group, MRD 0.10%-<10.00% group and MRD≥10.00% group were (95.4±1.0) %, (93.3±1.1) %, (85.4±2.9) %, respectively, while the RFS rates were (93.2±1.6) %, (90.8±1.4) %, (78.9±4.3) %, respectively ( &chi;2=16.47, 21.06, both P<0.05). On day 33 of early induction therapy, there were 925 cases in the MRD <0.01% group, 164 cases in the MRD 0.01%-<1.00% group and 59 cases in the MRD≥1.00% group. The 5-year RFS rates in the MRD 0.01%-<1.00% group was lowest among three groups ((91.4±1.2) % vs. (84.5±3.2) % vs. (87.9±5.1) %). The difference between three groups is statistically significant ( &chi;2=9.11, P=0.010). Among ALL patients with MRD≥10.00% on day 15 of induction therapy, there were 80 cases in the MRD <0.01% group on day 33, 45 cases in the MRD 0.01%-<1.00% group on day 33 and 45 cases in the MRD≥1.00% group on day 33. The 5-year RFS rates of three groups were (83.9±6.0)%, (67.1±8.2)%, (83.3±6.9)% respectively ( &chi;2=6.90, P=0.032). Univariate analysis was performed in the MRD≥10.00% group on day 15 and the MRD 0.01%-<1.00% group on day 33.The 5-year RFS rate of children with CNSL was significantly lower than that without CNSL in the MRD≥10.00% group on day 15 ((50.0±20.4)% vs. (80.3±4.4)%, &chi;2=4.13, P=0.042). Patients with CNSL or MLL gene rearrangement in the MRD 0.01%-<1.00% group on day 33 had significant lower 5-year RFS rate compared to those without CNSL or MLL gene rearrangement ((50.0±25.0)% vs. (85.5±3.1)%, &chi;2=4.06, P=0.044;(58.3±18.6)% vs. (85.7±3.2)%, &chi;2=9.44, P=0.002). Multivariate analysis showed that age ( OR=0.58, 95% CI 0.35-0.97) and white blood cell count at first diagnosis ( OR=0.43, 95% CI 0.27-0.70) were independent risk factors for OS. The MRD level on day 15 ( OR=0.55,95% CI 0.31-0.97), ETV6-RUNX1 fusion gene ( OR=0.13,95% CI 0.03-0.54), MLL gene rearrangement ( OR=2.55,95% CI 1.18-5.53) and white blood cell count at initial diagnosis ( OR=0.52,95% CI 0.33-0.81) were independent prognostic factors for RFS. Conclusions:The higher the level of MRD in early induction therapy, the worse the OS. The MRD levels on day 15 is an independent prognostic factor for RFS.The MRD in early induction therapy guided accurate risk stratification and individualized treatment can improve the survival rate of pediatric ALL.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.