In this paper， by referring to ancient and modern literature， the textual research of Inulae Flos has been conducted to clarify the name， origin， production area， quality evaluation， harvesting， processing and others， so as to provide reference and basis for the development and utilization of famous classical formulas containing this herb. After textual research， it could be verified that the medicinal use of Inulae Flos was first recorded in Shennong Bencaojing of the Han dynasty. In successive dynasties， Xuanfuhua has been taken as the official name， and it also has other alternative names such as Jinfeicao， Daogeng and Jinqianhua. The period before the Song and Yuan dynasties， the main origin of Inulae Flos was the Asteraceae plant Inula japonica， and from the Ming and Qing dynasties to the present， I. japonica and I. britannica are the primary source. In addition to the dominant basal species， there are also regional species such as I. linariifolia， I. helianthus-aquatili， and I. hupehensis. The earliest recorded production areas in ancient times were Henan， Hubei and other places， and the literature records that it has been distributed throughout the country since modern times. The medicinal part is its flower， the harvesting and processing method recorded in the past dynasties is mainly harvested in the fifth and ninth lunar months， and dried in the sun， and the modern harvesting is mostly harvested in summer and autumn when the flowers bloom， in order to remove impurities， dry in the shade or dry in the sun. In addition， the roots， whole herbs and aerial parts are used as medicinal materials. In ancient times， there were no records about the quality of Inulae Flos， and in modern times， it is generally believed that the quality of complete flower structure， small receptacles， large blooms， yellow petals， long filaments， many fluffs， no fragments， and no branches is better. Ancient processing methods primarily involved cleaning， steaming， and sun-drying， supplemented by techniques such as boiling， roasting， burning， simmering， stir-frying， and honey-processing. Modern processing focuses mainly on cleaning the stems and leaves before use. Regarding the medicinal properties， ancient texts describe it as salty and sweet in taste， slightly warm in nature， and mildly toxic. Modern studies characterize it as bitter， pungent， and salty in taste， with a slightly warm nature. Its therapeutic effects remain consistent across eras， including descending Qi， resolving phlegm， promoting diuresis， and stopping vomiting. Based on the research results， it is recommended that when developing famous classical formulas containing Inulae Flos， either I. japonica or I. britannica should be used as the medicinal source. Processing methods should follow formula requirements， where no processing instructions are specified， the raw products may be used after cleaning.

In this paper， by referring to ancient and modern literature， the textual research of Inulae Flos has been conducted to clarify the name， origin， production area， quality evaluation， harvesting， processing and others， so as to provide reference and basis for the development and utilization of famous classical formulas containing this herb. After textual research， it could be verified that the medicinal use of Inulae Flos was first recorded in Shennong Bencaojing of the Han dynasty. In successive dynasties， Xuanfuhua has been taken as the official name， and it also has other alternative names such as Jinfeicao， Daogeng and Jinqianhua. The period before the Song and Yuan dynasties， the main origin of Inulae Flos was the Asteraceae plant Inula japonica， and from the Ming and Qing dynasties to the present， I. japonica and I. britannica are the primary source. In addition to the dominant basal species， there are also regional species such as I. linariifolia， I. helianthus-aquatili， and I. hupehensis. The earliest recorded production areas in ancient times were Henan， Hubei and other places， and the literature records that it has been distributed throughout the country since modern times. The medicinal part is its flower， the harvesting and processing method recorded in the past dynasties is mainly harvested in the fifth and ninth lunar months， and dried in the sun， and the modern harvesting is mostly harvested in summer and autumn when the flowers bloom， in order to remove impurities， dry in the shade or dry in the sun. In addition， the roots， whole herbs and aerial parts are used as medicinal materials. In ancient times， there were no records about the quality of Inulae Flos， and in modern times， it is generally believed that the quality of complete flower structure， small receptacles， large blooms， yellow petals， long filaments， many fluffs， no fragments， and no branches is better. Ancient processing methods primarily involved cleaning， steaming， and sun-drying， supplemented by techniques such as boiling， roasting， burning， simmering， stir-frying， and honey-processing. Modern processing focuses mainly on cleaning the stems and leaves before use. Regarding the medicinal properties， ancient texts describe it as salty and sweet in taste， slightly warm in nature， and mildly toxic. Modern studies characterize it as bitter， pungent， and salty in taste， with a slightly warm nature. Its therapeutic effects remain consistent across eras， including descending Qi， resolving phlegm， promoting diuresis， and stopping vomiting. Based on the research results， it is recommended that when developing famous classical formulas containing Inulae Flos， either I. japonica or I. britannica should be used as the medicinal source. Processing methods should follow formula requirements， where no processing instructions are specified， the raw products may be used after cleaning.

Objective To prepare a multifunctional mesoporous silica-based nanocarrier system(MMSN@Gem)with gemcitabine and investigate its effect on bladder cancer cells BIU-87.Methods The multifunctional meso-porous silica-based nano drug-carrying system was prepared by a modified method.Transmission electron microsco-py,high-performance liquid chromatography,and thermal imaging were used to characterize the morphology,drug-carrying and photothermal properties of MMSN@Gem.The effect of MMSN@Gem on BIU-87 bladder cancer cells was detected by in vitro experiments.Results MMSN@Gem exhibited a well-defined spherical morphology with an average particle size of(102.48±1.03)nm with a drug loading capacity of 25.04％±0.17％,and an average zeta potential of(-24.84±0.07)mV.The photothermal conversion efficiency was 40.7％which significantly enhanced the release of Gem under near-infrared irradiation.In vitro studies showed that MMSN@Gem significantly inhibited BIU-87 cell activity,induced apoptosis of BIU-87 cells,reduced migration and invasion ability,and enhanced its uptake by BIU-87 cells.Conclusions MMSN@Gem exhibits excellent photothermal properties,enhances cellular uptake efficiency,inhibits the proliferation and migration of BIU-87 cells,and promotes apoptosis,providing a promising drug delivery system for the clinical treatment of bladder cancer.

Objective:To construct a prognostic risk model based on lactate metabolism-related genes screened using bioinformatics methods in renal cell carcinoma patients，and investigate the clinical prognosis and immune characteristics of renal cell carcinoma.Methods:Gene expression data and clinical information of patients with renal cell carcinoma were downloaded from the Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma（TCGA-KIRC）dataset. Lactate metabolism-related gene sets were obtained from the Gene Set Enrichment Analysis（GSEA）database. The R package DEseq2 was employed to identify differentially expressed genes associated with lactate metabolism within the TCGA-KIRC dataset. GO and KEGG enrichment analyses were performed using the clusterProfiler package. Prognosis-related genes were screened via univariate Cox regression analysis and the intersection with lactate metabolism-related differentially expressed genes was obtained. A risk model was constructed using LASSO regression followed by multivariate Cox regression analysis to calculate risk scores. This risk model was subsequently validated using the GSE29609 dataset. Patients were stratified into high-risk and low-risk groups based on the median risk score. The expression profiles of key immune molecule genes and immune checkpoint genes were compared between the two groups. Survival analysis curves for immune checkpoint genes were generated using the survival and survminer R packages. Differences in tumor mutation burden（TMB）between the high-risk and low-risk groups were assessed，and corresponding TMB survival analysis curves were plotted. Finally，the tumor immune dysfunction and exclusion（TIDE）algorithm was used to evaluate disparities in immunotherapy response potential between the two risk groups.Results:An optimal prognostic risk model incorporating seven lactate metabolism- and prognosis-related genes（ LDHD，PER2，ACADM，FLI1，LIPA，TCIRG1，SLC25A4）was constructed and successfully validated in the GSE29609 dataset. Univariate Cox regression analysis revealed that a high-risk score was significantly associated with poor prognosis（ HR=2.915，95% CI：2.451-3.470， P<0.001）. Multivariate Cox regression analysis confirmed that this risk model could serve as an independent prognostic factor for patients with renal cell carcinoma（ HR=2.231，95% CI：1.829-2.722， P<0.001）. Patients in the high-risk group exhibited significantly worse outcomes compared to the low-risk group，regardless of whether they had early-stage or advanced-stage renal cell carcinoma（both P<0.001）. Analyses related to the immune microenvironment indicated an upregulated immunosuppressive phenotype in the high-risk group. Furthermore，the TMB was significantly higher in the high-risk group than in the low-risk group（ P=0.032），and patients within the high-risk group exhibiting higher TMB levels demonstrated even poorer survival（ P<0.001）. Finally，the TIDE score was significantly elevated in the high-risk group in comparison to the low-risk group（ P<0.001）. Conclusions:The risk model based on lactate metabolism-related genes constructed in this study can guide the prognosis of renal cell carcinoma. Patients in the high-risk group are more prone to immune escape and formation of an inhibitory immune microenvironment，leading to worse prognoses. This risk model may serve as a biomarker for predicting immunotherapy response.

Objective To investigate the effects of glutathione peroxidase 7(GPX7)on the proliferation and senescence of hepatocellular carcinoma(HCC)cells and its potential value as a therapeutic target.Methods Clinical and transcriptomic data from HCC patients in The Cancer Genome Atlas(TCGA)database were analyzed to evaluate the relationship between GPX7 expression and prognosis.Western blotting,qRT-PCR,CCK-8 assay,EdU staining,and SA-β-Gal staining were employed to compare the expression and function of GPX7 in HCC cell lines and normal liver cells.A stable GPX7-knockdown Hep3B cell line was established,and transcriptomic changes were analyzed using RNA-seq.The PI3K inhibitor LY294002 and its activator 740 Y-P were utilized to determine the role of GPX7 in regulating cell senescence via the PI3K-AKT pathway.An orthotopic xenograft model was constructed using 18 male nude mice(6 weeks old,18～22 g)to observe tumor growth.Tumor and adjacent tissue samples from 56 HCC patients(obtained from the Eighth Medical Center of Chinese PLA General Hospital between January 2012 and December 2022)were analyzed to assess the relationship between GPX7 expression and clinicopathological features.Results TCGA data analysis revealed that GPX7 was significantly highly expressed in HCC tumor tissues(P<0.01),and the patients with high GPX7 expression had obviously shorter overall survival(OS)(P=0.018).Western blotting and qRT-PCR showed that the expression level of GPX7 was notably higher in the HCC cell lines than the normal liver cells(P<0.01).After GPX7 knockdown,the CCK-8 absorbance values of HCC cells were significantly reduced(P<0.01),the proportion of EdU-positive cells was obviously decreased(P<0.01),and the proportion of SA-β-Gal positive cells was notably increased(P<0.01).RNA-seq analysis revealed that differentially expressed genes were significantly enriched in the PI3K-AKT signaling pathway(P<0.05).The PI3K inhibitor LY294002 significantly enhanced the GPX7 knockdown-induced cell senescence(P<0.01),while the activator 740 Y-P partially reversed this effect(P<0.05).In the xenograft model,GPX7 knockdown significantly inhibited tumor growth in nude mice(P<0.01),and the PI3K activator 740 Y-P partially reversed this effect(P<0.01).Clinical sample analysis displayed that high GPX7 expression was negatively correlated with tumor differentiation(P<0.05),and its high expression was associated with shortened OS(HR=2.61,95%CI:1.26～5.39,P=0.005).Conclusion GPX7 regulates cell proliferation and senescence through the PI3K-AKT pathway,and may serve as a potential therapeutic target for HCC treatment.

Objective:To construct a prognostic risk model based on lactate metabolism-related genes screened using bioinformatics methods in renal cell carcinoma patients，and investigate the clinical prognosis and immune characteristics of renal cell carcinoma.Methods:Gene expression data and clinical information of patients with renal cell carcinoma were downloaded from the Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma（TCGA-KIRC）dataset. Lactate metabolism-related gene sets were obtained from the Gene Set Enrichment Analysis（GSEA）database. The R package DEseq2 was employed to identify differentially expressed genes associated with lactate metabolism within the TCGA-KIRC dataset. GO and KEGG enrichment analyses were performed using the clusterProfiler package. Prognosis-related genes were screened via univariate Cox regression analysis and the intersection with lactate metabolism-related differentially expressed genes was obtained. A risk model was constructed using LASSO regression followed by multivariate Cox regression analysis to calculate risk scores. This risk model was subsequently validated using the GSE29609 dataset. Patients were stratified into high-risk and low-risk groups based on the median risk score. The expression profiles of key immune molecule genes and immune checkpoint genes were compared between the two groups. Survival analysis curves for immune checkpoint genes were generated using the survival and survminer R packages. Differences in tumor mutation burden（TMB）between the high-risk and low-risk groups were assessed，and corresponding TMB survival analysis curves were plotted. Finally，the tumor immune dysfunction and exclusion（TIDE）algorithm was used to evaluate disparities in immunotherapy response potential between the two risk groups.Results:An optimal prognostic risk model incorporating seven lactate metabolism- and prognosis-related genes（ LDHD，PER2，ACADM，FLI1，LIPA，TCIRG1，SLC25A4）was constructed and successfully validated in the GSE29609 dataset. Univariate Cox regression analysis revealed that a high-risk score was significantly associated with poor prognosis（ HR=2.915，95% CI：2.451-3.470， P<0.001）. Multivariate Cox regression analysis confirmed that this risk model could serve as an independent prognostic factor for patients with renal cell carcinoma（ HR=2.231，95% CI：1.829-2.722， P<0.001）. Patients in the high-risk group exhibited significantly worse outcomes compared to the low-risk group，regardless of whether they had early-stage or advanced-stage renal cell carcinoma（both P<0.001）. Analyses related to the immune microenvironment indicated an upregulated immunosuppressive phenotype in the high-risk group. Furthermore，the TMB was significantly higher in the high-risk group than in the low-risk group（ P=0.032），and patients within the high-risk group exhibiting higher TMB levels demonstrated even poorer survival（ P<0.001）. Finally，the TIDE score was significantly elevated in the high-risk group in comparison to the low-risk group（ P<0.001）. Conclusions:The risk model based on lactate metabolism-related genes constructed in this study can guide the prognosis of renal cell carcinoma. Patients in the high-risk group are more prone to immune escape and formation of an inhibitory immune microenvironment，leading to worse prognoses. This risk model may serve as a biomarker for predicting immunotherapy response.

Objective To explore the changes of serum angiopoietin-like protein 4(ANGPTL4)and NOD-like receptor protein 3(NLRP3)levels after traumatic brain injury(TBI)and their diagnostic value for sec-ondary massive cerebral infarction.Methods A total of 100 TBI patients admitted to the hospital from Au-gust 2019 to August 2021 were enrolled as the TBI group,meantime,100 healthy people in the hospital were enrolled as the control group.The serum levels of ANGPTL4 and NLRP3 were detected by enzyme-linked im-munosorbent assay(ELISA).The clinical characteristics of TBI patients with and without secondary massive cerebral infarction were compared.Receiver operating characteristic(ROC)curve was applied to analyze the serum levels of ANGPTL4 and NLRP3 on their diagnostic value for TBI patients with secondary massive cere-bral infarction.Multivariate Logistic regression analysis was applied to analyze the factors affecting the occur-rence of secondary massive cerebral infarction in TBI patients.Results The serum ANGPTL4 level in TBI group was lower than that in the control group,and the serum NLRP3 level was higher than that in the con-trol group(P<0.05).There were obvious differences in proportion of brain hernia,proportion of subarach-noid hemorrhage,serum levels of ANGPTL4 and NLRP3 between patients with secondary massive cerebral infarction and patients without secondary massive cerebral infarction(P<0.05).ROC curve analysis showed that the area under the curve(AUC)of serum ANGPTL4 and NLRP3 in diagnosing secondary massive cere-bral infarction in TBI patients was 0.792 and 0.812 respectively,with sensitivity of 77.80%and 83.30%re-spectively,and specificity of 86.60%and 64.60%respectively.The sensitivity,the specificity and AUC of the combined detection were 83.30%,82.90%and 0.867 respectively.Multivariate Logistic regression analysis showed that serum NLRP3 level was a risk factor for TBI patients with secondary massive cerebral infarction(P<0.05).After treatment,it was found that serum ANGPTL4 level increased and NLRP3 level decreased in TBI patients(P<0.05).Conclusion The serum level of ANGPTL4 in TBI patients decreases,while the level of NLRP3 increases,and the level of ANGPTL4 in the serum of patients with secondary massive cerebral in-farction decreases and the level of NLRP3 increases,both of them are of great significance in the diagnosis of secondary massive cerebral infarction in TBI patients.

Objective Given the extensive application of machine learning(ML)in medical models and its remarkable learning and generalization capabilities,this study employed automated ML(AutoML)combined with patient demographics and clinical conditions to early assess the risk of failure in bowel preparation prior to colonoscopy.Methods A retrospective analysis was conducted on patients who underwent colonoscopy examinations in Hospital 1 and Hospital 2 from January 2022 to January 2023,and their general and clinical information was collected.According to the Boston bowel preparation scale(BBPS),a BBPS of≤5 was defined as a failure in bowel preparation,>5 was deemed satisfactory.From the data of the two hospitals,we randomly divided the dataset into a training set(n=303)and a validation set(n=76)at an 8∶2 ratio.Least absolute shrinkage and selection operator(LASSO)logistic regression(LR)model was used for feature selection,a nomogram scoring system was constructed,and models were established using AutoML based on five algorithms.Model performance was evaluated through receiver operator characteristic curve(ROC curve),calibration curves,LR-based decision curve analysis(DCA),SHAP plots,and force plots.Results Among the 379 patients,105 cases(27.7%)experienced bowel preparation failure(BBPS≤5).21 study variables were narrowed down to 10 through LASSO with 5-fold cross-validation,resulting in the development of a Nomogram chart with demonstrated reliability via calibration curves.Using the H2O platform and five algorithms[gradient boosting machine(GBM),deep learning(DL),generalized linear model(GLM),Stacked Ensemble and distributed random forest(DRF)],67 models were developed.Stacked Ensemble outperformed the others with an area under the curve(AUC)of 0.871,LogLoss of 0.403,and RMSE of 0.354,surpassing traditional LR model and other models.Variable importance contribution plots indicated significant predictive influences from factors such as the interval between laxative ingestion and examination,history of constipation,completion of laxative regimen,age,and presence of a companion during the procedure.Finally,SHAP plots and force plots revealed variable distribution patterns in binary classification predictions and the impact of variables on predictive outcomes.Conclusion The AutoML model based on the Stacked Ensemble algorithm exhibits clear clinical utility in early prediction of bowel preparation failure risk.Moreover,a clinically applicable column chart scoring tool is constructed.

Objective To investigate the predictive value of Rotterdam CT score combined with serum soluble cluster of differentiation antigen 40 ligand (sCD40L) and fibulin-5 for prognosis of patients with severe traumatic brain injury (sTBI). Methods A total of 186 sTBI patients were divided into good prognosis group (n=104) and poor prognosis group (n=82). CT images were analyzed and Rotterdam CT scores were obtained; the enzyme-linked immunosorbent assay (ELISA) was used to detect serum sCD40L and fibulin-5 levels on the 1st, 3rd and 7th day of admission; the Spearman analysis was used to assess the correlations of serum sCD40L and fibulin-5 levels on the first day of admission with the Rotterdam CT score and Glasgow Coma Scale (GCS) score; the Multivariate Logistic regression analysis was conducted to explore the risk factors for poor prognosis in sTBI patients; the receiver operating characteristic (ROC) curve was performed to analyze the predictive value of Rotterdam CT score combined with serum sCD40L and fibulin-5 levels on the first day of admission for prognosis of sTBI patients. Results On the 1st, 3rd and 7th day of admission, compared with the good prognosis group, the serum levels of sCD40L and fibulin-5 in the poor prognosis group were significantly elevated (P < 0.05); serum sCD40L and fibulin-5 levels were negatively correlated with GCS scores (r=-0.505, -0.421, P < 0.05) and positively correlated with Rotterdam CT score (r=0.495, 0.397, P < 0.05); sCD40L (OR=2.768, 95%CI, 1.537 to 4.983) and fibulin-5 (OR=2.539, 95%CI, 1.301 to 4.953) were independent risk factors for poor prognosis in sTBI patients (P < 0.001); the area under the curve (AUC) of Rotterdam CT score combined with serum sCD40L and fibulin-5 levels on the first day of admission for predicting poor prognosis in sTBI patients was 0.969 (95%CI, 0.933 to 0.989), with a sensitivity of 86.59% and a specificity of 94.23%; the diagnostic performance of Rotterdam CT score combined with sCD40L and fibulin-5 was superior to that of each individual indicator (Z=4.233, 4.274, 4.433, P < 0.001); the Bootstrap internal validation results showed that the predictive performance curve of the combined prediction model was consistent with the actual clinical occurrence curve. Conclusion Rotterdam CT score combined with serum sCD40L and fibulin-5 has certain predictive value for poor prognosis in sTBI patients.