Objective To explore and verify the prognostic value of endothelial cells in glioblastoma. Methods Through bioinformatics analysis of the TCGA and CGGA databases, we screened endothelial cell-related markers in GBM single-cell data according to a series of criteria. Moreover, univariate Cox regression analysis was performed to obtain and screen endothelial cell prognosis-related markers and construct endothelial cell-related prognostic risk score. qPCR experiments was used to verify the differences in the expression of prognostic markers in GBM tissues and peritumoral normal brain tissues. Kaplan-Meier method was used to construct the survival curve to identify the prognostic efficacy of the prognostic risk score. Results A total of 2 115 prognostic genes of glioblastoma (GBM) were screened. Among them, 1 494 was upregulated and 621 was downregulated. Seven groups of cells were obtained after GBM single-cell sequencing analysis, including AC-like tumor cells, endothelial cells, monocytes/macrophages, NB-like tumor cells, neurons, OC-like tumor cells, and OPC-like tumor cells. According to the differential genes of endothelial cells and the corresponding screening criteria, four genes (DUSP6, STC1, VWA1, and TM4SF1) were screened for risk-score construction. The expression of the target gene in GBM tissues and normal brain tissues around the tumor was significantly up-regulated detected by qPCR. The risk score=0.171*DUSP6+0.144*STC1+0.041*VWA1−0.004*TM4SF1. Conclusion The glioblastoma endothelial cells’ risk score determined in this study can preferably predict the prognosis of patients.

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.

In recent years,there has been an increase in the prevalence and incidence of acute myocardial infarction.Despite advances in diagnostic and therapeutic technologies related to the disease,challenges remain in the management of the disease after patients are discharged from the hospital.Home-based cardiac rehabilitation has been demonstrated to be an effective means of improving the prognosis of the disease.The utilization of digital health technology has the potential to enhance patient compliance and quality of life in the context of home-based cardiac rehabilitation for those who have experienced an acute myocardial infarction.This article reviews the forms of intervention,specific applications and effects of digital health technologies in home-based cardiac rehabilitation,It analyses the existing problems and puts forward nursing countermeasures.The aim is to further help nursing staff to guide the practice of home cardiac rehabilitation for patients with acute myocardial infarction.

Objective To construct mice hepatocellular carcinoma models with different tumor immune microenvironments(TIME)and explore the differences.Methods H22 and hepa1-6 were used to construct subcutaneous transplantation tumor model of C57 mice as homologous hepatocellular carcinoma cell lines(denoted as H22 group and hepal-6 group,each n=8),and the differences of TIME were evaluated.Immunohistochemistry was used to detect and quantify the infiltration of T cells,CD4+T cells,CD8+T cells,regulatory T cells and B cells in TIME.Flow cytometry was performed to detect the differences of composition of immune cell subpopulations in peripheral blood and tumor parenchyma.Gene expression profile characteristics of tumor tissue were analyzed based on high-throughput transcriptome sequencing technology,and enrichment analyses of immune-related signaling pathways were evaluated combined with gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG).Results H22 group showed cold and hepa1-6 group showed hot TIME characteristics.The number of T cells,CD4+T cells and CD8+T cells in tumor tissue of H22 group were all lower,while the proportion of T cells,CD4+T cells and CD8+T cells in peripheral blood were all higher than those of hepa1-6 group(all P＜0.05).Compared with H22 group,up-regulated genes of tumor tissue in hepa1-6 group expressed significantly enriched in tumor immune activation-related signaling pathways.Conclusion H22 and hepa1-6 hepatocellular carcinoma models showed distinct TIME characteristics of cold and hot tumors,respectively,and the amount of immune cells in tumor tissue of the former were significantly lower than those in the latter.

Objective To construct mice hepatocellular carcinoma models with different tumor immune microenvironments(TIME)and explore the differences.Methods H22 and hepa1-6 were used to construct subcutaneous transplantation tumor model of C57 mice as homologous hepatocellular carcinoma cell lines(denoted as H22 group and hepal-6 group,each n=8),and the differences of TIME were evaluated.Immunohistochemistry was used to detect and quantify the infiltration of T cells,CD4+T cells,CD8+T cells,regulatory T cells and B cells in TIME.Flow cytometry was performed to detect the differences of composition of immune cell subpopulations in peripheral blood and tumor parenchyma.Gene expression profile characteristics of tumor tissue were analyzed based on high-throughput transcriptome sequencing technology,and enrichment analyses of immune-related signaling pathways were evaluated combined with gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG).Results H22 group showed cold and hepa1-6 group showed hot TIME characteristics.The number of T cells,CD4+T cells and CD8+T cells in tumor tissue of H22 group were all lower,while the proportion of T cells,CD4+T cells and CD8+T cells in peripheral blood were all higher than those of hepa1-6 group(all P＜0.05).Compared with H22 group,up-regulated genes of tumor tissue in hepa1-6 group expressed significantly enriched in tumor immune activation-related signaling pathways.Conclusion H22 and hepa1-6 hepatocellular carcinoma models showed distinct TIME characteristics of cold and hot tumors,respectively,and the amount of immune cells in tumor tissue of the former were significantly lower than those in the latter.

In recent years,there has been an increase in the prevalence and incidence of acute myocardial infarction.Despite advances in diagnostic and therapeutic technologies related to the disease,challenges remain in the management of the disease after patients are discharged from the hospital.Home-based cardiac rehabilitation has been demonstrated to be an effective means of improving the prognosis of the disease.The utilization of digital health technology has the potential to enhance patient compliance and quality of life in the context of home-based cardiac rehabilitation for those who have experienced an acute myocardial infarction.This article reviews the forms of intervention,specific applications and effects of digital health technologies in home-based cardiac rehabilitation,It analyses the existing problems and puts forward nursing countermeasures.The aim is to further help nursing staff to guide the practice of home cardiac rehabilitation for patients with acute myocardial infarction.

OBJECTIVE: To accurately measure human energy metabolism with high temporal resolution, a respiratory gas analysis system was designed using a breath-by-breath approach. METHODS: Firstly, indirect calorimetry was employed in respiratory gas analysis to measure the respiratory flow and concentration signals in real-time. Secondly, oxygen consumption QO2 and carbon dioxide production QCO2 were calculated through respiratory characteristic alignment and respiratory signal segmentation. Finally, metabolic indexes were calculated according to the Weir formula. Furthermore, a controlled trial was formulated for validation comparisons with the MGC ULTIMA SYSTEM PFX CARDIO2. RESULTS: The results indicate that the data points of metabolic indexes measured by this system all fall within the confidence interval when compared with those of the MGC. CONCLUSION The system has high consistency with the MGC measurement results. Thus, the system can accurately measure metabolism in real-time and provide data support for clinical nutrition assessment and therapy.
Humans ; Energy Metabolism ; Breath Tests/instrumentation* ; Calorimetry, Indirect/instrumentation* ; Equipment Design

Objective:To investigate the impact of virtual navigation bronchoscopy on perioperative information in patients undergoing thoracoscopic pulmonary resection.Methods:Employed three distinct propensity score matching models to effectively address the baseline data disparities among patients undergoing thoracoscopic pulmonary resection. Categorized the patients into two groups: pulmonary wedge resection and pulmonary segmentectomy. Compared the disparities in clinical characteristics, intraoperative lesion resection, and postoperative recovery between patients who underwent virtual navigation bronchoscopy assisted localization prior to surgery and those who did not employ any specific localization methods.Results:This reserch included a total of 127 patients who underwent localization assisted by virtual navigation bronchoscopy, and 122 patients who did not undergo specialized localization. After propensity score matching, the navigation group demonstrated a statistically significant reduction in intraoperative blood loss[model 3, 40(20, 50) ml vs. 50(20, 100) ml, P=0.027], drainage volume on the first day post-surgery[model 3, 100(50, 175) ml vs. 150(100, 220) ml, P=0.023], and incidence of residual pleural effusion(model 3, 31 cases vs. 38 cases, P=0.046) compared to the non-positioning group among patients undergoing pulmonary wedge resection. In the pulmonary segmentectomy group, we observed a reduction in intraoperative blood loss[model 3, 50(30, 100) ml vs. 100(50, 100) ml, P=0.003] and incidence of residual pneumothorax(model 3, 18 cases vs. 28 cases, P=0.012) in patients who underwent navigation-assisted procedures compared to those without specialized positioning. Conclusion:The utilization of virtual navigation bronchoscopy for preoperative localization assistance in thoracoscopic sublobectomy(including wedge resection and segmental resection) may represent a viable approach to mitigate intraoperative injury and facilitate postoperative recovery.

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.

Ulcerative colitis (UC) is a chronic and non-specific inflammatory bowel disease (IBD). Huanglian Ganjiang decoction (HGD), derived from ancient book Beiji Qianjin Yao Fang, has demonstrated efficacy in treating UC patients traditionally. Previous research established that the compatibility of cold herb Coptidis Rhizoma + Phellodendri Chinensis Cortex (CP) and hot herb Angelicae Sinensis Radix + Zingiberis Rhizoma (AZ) in HGD synergistically improved colitis mice. This study investigated the compatibility mechanisms through which CP and AZ regulated inflammatory balance in colitis mice. The experimental colitis model was established by administering 3% dextran sulphate sodium (DSS) to mice for 7 days, followed by CP, AZ and CPAZ treatment for an additional 7 days. M1/M2 macrophage polarization levels, glucose metabolites levels and pyruvate dehydrogenase kinase 4 (PDK4) expression were analyzed using flow cytometry, Western blot, immunofluorescence and targeted glucose metabolomics. The findings indicated that CP inhibited M1 macrophage polarization, decreased inflammatory metabolites associated with tricarboxylic acid (TCA) cycle, and suppressed PDK4 expression and pyruvate dehydrogenase (PDH) (Ser-293) phosphorylation level. AZ enhanced M2 macrophage polarization, increased lactate axis metabolite lactate levels, and upregulated PDK4 expression and PDH (Ser-293) phosphorylation level. TCA cycle blocker AG-221 and adeno-associated virus (AAV)-PDK4 partially negated CP's inhibition of M1 macrophage polarization. Lactate axis antagonist oxamate and PDK4 inhibitor dichloroacetate (DCA) partially reduced AZ's activation of M2 macrophage polarization. In conclusion, the compatibility of CP and AZ synergistically alleviated colitis in mice through M1/M2 macrophage polarization balance via PDK4-mediated glucose metabolism reprogramming. Specifically, CP reduced M1 macrophage polarization by restoration of TCA cycle via PDK4 inhibition, while AZ increased M2 macrophage polarization through activation of PDK4/lactate axis.
Animals ; Drugs, Chinese Herbal/chemistry* ; Mice ; Macrophages/immunology* ; Glucose/metabolism* ; Pyruvate Dehydrogenase Acetyl-Transferring Kinase/genetics* ; Male ; Mice, Inbred C57BL ; Humans ; Colitis/drug therapy* ; Disease Models, Animal ; Colitis, Ulcerative/drug therapy* ; Metabolic Reprogramming