Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Stem cells play a crucial role in maintaining tissue regenerative capacity and homeostasis. However, mechanisms associated with stem cell senescence require further investigation. In this study, we conducted a proteomic analysis of human dental pulp stem cells (HDPSCs) obtained from individuals of various ages. Our findings showed that the expression of NUP62 was decreased in aged HDPSCs. We discovered that NUP62 alleviated senescence-associated phenotypes and enhanced differentiation potential both in vitro and in vivo. Conversely, the knocking down of NUP62 expression aggravated the senescence-associated phenotypes and impaired the proliferation and migration capacity of HDPSCs. Through RNA-sequence and decoding the epigenomic landscapes remodeled induced by NUP62 overexpression, we found that NUP62 helps alleviate senescence in HDPSCs by enhancing the nuclear transport of the transcription factor E2F1. This, in turn, stimulates the transcription of the epigenetic enzyme NSD2. Finally, the overexpression of NUP62 influences the H3K36me2 and H3K36me3 modifications of anti-aging genes (HMGA1, HMGA2, and SIRT6). Our results demonstrated that NUP62 regulates the fate of HDPSCs via NSD2-dependent epigenetic reprogramming.
Humans ; Dental Pulp/cytology* ; Nuclear Pore Complex Proteins/genetics* ; Cellular Senescence/genetics* ; Stem Cells/metabolism* ; Epigenesis, Genetic ; Cell Proliferation ; Cell Differentiation ; Histone-Lysine N-Methyltransferase/metabolism* ; Cells, Cultured ; Cellular Reprogramming ; Cell Movement ; Proteomics

Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

OBJECTIVES: To investigate the role and mechanism of Brahma-related gene 1 (Brg1) in regulating the Wnt/β-catenin signaling pathway in a bronchopulmonary dysplasia (BPD) model. METHODS: Wild-type C57BL/6 and Brg1^f1/f1 mice were randomly divided into four groups: wild-type control, wild-type BPD, Brg1^f1/f1 control, and Brg1^f1/f1 BPD (n=5 each). Immortalized mouse pulmonary alveolar type 2 cells (imPAC2) were cultured, and Brg1 gene was knocked down using lentivirus transfection technology. Cells were divided into three groups: control, empty vector, and Brg1 knockdown. Hematoxylin and eosin staining and immunofluorescence were used to detect pathological changes in mouse lung tissue. Western blot and real-time fluorescent quantitative PCR were used to measure Brg1 protein and mRNA expression levels in mouse lung tissue. Western blot and immunofluorescence were used to detect the expression of homeodomain-containing protein homeobox (HOPX), surfactant protein C (SPC), and Wnt/β-catenin signaling pathway proteins in mouse lung tissue and imPAC2 cells. The CCK8 assay was used to assess the proliferation of imPAC2 cells, and co-immunoprecipitation was performed to verify the interaction between Brg1 and β-catenin proteins in imPAC2 cells. RESULTS: Compared to the Brg1^f1/f1 control group and wild-type BPD group, the Brg1^f1/f1 BPD group showed increased alveolar diameter and SPC protein expression, and decreased relative density of pulmonary vasculature and HOPX protein expression (P<0.05). Compared to the control group, the Brg1 knockdown group showed increased cell proliferation ability, protein expression levels of SPC, Wnt5a and β-catenin, and β-catenin protein fluorescence intensity, along with decreased HOPX protein expression (P<0.05). An interaction between Brg1 and β-catenin proteins was confirmed. CONCLUSIONS The Brg1 gene may promote the proliferation of alveolar type 2 epithelial cells by regulating the Wnt/β-catenin signaling pathway, thus influencing the occurrence and development of BPD.
Animals ; DNA Helicases/genetics* ; Transcription Factors/genetics* ; Wnt Signaling Pathway/physiology* ; Nuclear Proteins/genetics* ; Mice ; Bronchopulmonary Dysplasia/etiology* ; Mice, Inbred C57BL ; beta Catenin/physiology* ; Disease Models, Animal ; Cell Proliferation ; Lung/pathology* ; Male

Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector ma-chine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.

Objective:To explore the clinical value of plasma von Willebrand factor antigen(VWF:Ag)and VWF activity(VWF:GPIbM)based on ABO blood group in the risk assessment of deep vein thrombosis(DVT).Methods:A total of 163 patients with DVT who sought medical treatment from March 2021 to December 2022 were selected as the case group,and 135 healthy volunteers during the same period were selected as the control group.The differences of ABO blood groups,plasma VWF:Ag and VWF:GPIbM levels between the two groups were compared.Receiver operating characteristic(ROC)curves were used to evaluate the clinical value of VWF testing in predicting DVT events.Logistic regression analysis was applied to identify risk factors for DVT.Results:The levels of plasma VWF:Ag and VWF:GPIbM in the DVT group were significantly higher than those in the control group both overall and across ABO blood type subgroups(P＜0.01).Within the DVT group,the levels of plasma VWF:Ag and VWF:GPIbM in patients with non-O blood type were significantly higher than those with blood type O[VWF:Ag:219.74％±63.64％vs 162.21％±56.03％,P＜0.01;VWF:GPIbM:228.10％(185.15％,249.10％)vs 148.25％(116.48％,225.48％),P＜0.01].The area under the ROC curve(AUC)of VWF:Ag for predicting DVT events was 0.855,with a cut-off value of 142.4％,sensitivity of 82.2％and specificity of 72.6％;the AUC of VWF:GPIbM was 0.861,with a cut-off value of 141.2％,sensitivity of 84.7％,and specificity of 71.1％.Univariate analysis showed that both VWF:Ag and VWF:GPIbM were influencing factors for DVT events(P＜0.05).Multivariate logistic regression analysis indicated that VWF:Ag＞142.4％(OR=13.961,95％CI:7.654-25.464,P＜0.01)and VWF:GPIbM＞141.2％(OR=17.615,95％CI:9.155-33.892,P＜0.01)were independent risk factors for DVT events.Conclusion:Levels of VWF:Ag and VWF:GPIbM are significantly elevated in non-O blood type DVT patients.VWF:Ag＞142.4％and VWF:GPIbM＞141.2％are independent risk factors for DVT events.VWF testing based on ABO blood group aids in the precision prevention and control of DVT.

Objective:To explore the clinical value of plasma von Willebrand factor antigen(VWF:Ag)and VWF activity(VWF:GPIbM)based on ABO blood group in the risk assessment of deep vein thrombosis(DVT).Methods:A total of 163 patients with DVT who sought medical treatment from March 2021 to December 2022 were selected as the case group,and 135 healthy volunteers during the same period were selected as the control group.The differences of ABO blood groups,plasma VWF:Ag and VWF:GPIbM levels between the two groups were compared.Receiver operating characteristic(ROC)curves were used to evaluate the clinical value of VWF testing in predicting DVT events.Logistic regression analysis was applied to identify risk factors for DVT.Results:The levels of plasma VWF:Ag and VWF:GPIbM in the DVT group were significantly higher than those in the control group both overall and across ABO blood type subgroups(P＜0.01).Within the DVT group,the levels of plasma VWF:Ag and VWF:GPIbM in patients with non-O blood type were significantly higher than those with blood type O[VWF:Ag:219.74％±63.64％vs 162.21％±56.03％,P＜0.01;VWF:GPIbM:228.10％(185.15％,249.10％)vs 148.25％(116.48％,225.48％),P＜0.01].The area under the ROC curve(AUC)of VWF:Ag for predicting DVT events was 0.855,with a cut-off value of 142.4％,sensitivity of 82.2％and specificity of 72.6％;the AUC of VWF:GPIbM was 0.861,with a cut-off value of 141.2％,sensitivity of 84.7％,and specificity of 71.1％.Univariate analysis showed that both VWF:Ag and VWF:GPIbM were influencing factors for DVT events(P＜0.05).Multivariate logistic regression analysis indicated that VWF:Ag＞142.4％(OR=13.961,95％CI:7.654-25.464,P＜0.01)and VWF:GPIbM＞141.2％(OR=17.615,95％CI:9.155-33.892,P＜0.01)were independent risk factors for DVT events.Conclusion:Levels of VWF:Ag and VWF:GPIbM are significantly elevated in non-O blood type DVT patients.VWF:Ag＞142.4％and VWF:GPIbM＞141.2％are independent risk factors for DVT events.VWF testing based on ABO blood group aids in the precision prevention and control of DVT.

As a physical treatment technique, modified electro-convulsive therapy (MECT) is used to treat mental and certain neurological disorders by causing seizures with short, suitable electrical currents applied to the brain while the patient is under general anesthesia and muscle relaxants. MECT is recognized for its therapeutic efficacy and clinical safety, rendering it one of the most prevalent interventions in psychiatric care. To enhance clinical outcomes and minimize adverse effects, this consensus document delineates the indications, therapeutic parameters, therapeutic procedures, potential adverse effects, and associated management strategies for MECT. These guidelines are informed by the latest clinical research and expert consensus, integrating evidence-based medicine methodologies. The objective is to furnish clinicians with precise operational guidelines and to advance the standardization of MECT practices in clinical settings.