Immune suppression in the tumor microenvironment (TME) is closely related to abnormal glycolysis. Tumor cells gain metabolic advantages and suppress immune responses through the "Warburg effect". Traditional Chinese medicine (TCM) has been shown to regulate key glycolysis enzymes (such as HK2 and PKM2), metabolic signaling pathways (such as PI3K/AKT/mTOR, HIF-1α) and non-coding RNAs at multiple targets, thus synergistically inhibiting lactate accumulation, improving vascular abnormalities, and relieving metabolic inhibition of immune cells. Studies have shown that TCM monomers and formulas can promote immune cell infiltration and functions, improve metabolic microenvironment, and with the assistance by the nano-delivery system, enhance the precision of treatment. However, the dynamic mechanism of the interaction between TCM-regulated glycolysis and TME has not been fully elucidated, for which single-cell sequencing and other technologies provide important technical support to facilitate in-depth analysis and clinical translational research. Future studies should be focused on the synergistic strategy of "metabolic reprogramming-immune activation" to provide new insights into the mechanisms of tumor immunotherapy.
Humans ; Tumor Microenvironment/immunology* ; Glycolysis/drug effects* ; Neoplasms/drug therapy* ; Medicine, Chinese Traditional ; Signal Transduction ; Drugs, Chinese Herbal/pharmacology*

Objective To explore the value of CT-enhanced portal venous phase radiomics combined with machine learning algo-rithms in assessing microvascular invasion(MVI)in hepatocellular carcinoma(HCC).Methods A retrospective analysis was con-ducted on imaging and clinical data of 132 HCC patients.The patients were randomly divided into training set and test set at a 7︰3 ratio.Independent influencing factors for predicting MVI status in HCC patients were identified through univariate and multivariate logistic regression analyses.Radiomics features were selected using the least absolute shrinkage and selection operator(LASSO)algorithm,and a Radiomics score(Radscore)was calculated to construct the final combined model.Four machine learning algorithms including-logistic regression(LR),naive Bayes(NB),support vector machine(SVM),and K-nearest neighbor(KNN)were applied to evaluate the model.The performance of each machine model was assessed using the receiver operating characteristic(ROC)curves and the area under the curve(AUC).Results Univariate and multivariate logistic regression analyses revealed that venous phase CT values were independentinfluencing factors,and six radiomics features were ultimately selected.After the Radscore was calculated,a combined model was constructed using Radscore and venous phase CT values.Machine learning algorithms showed that the combined model achieved the following AUC in the training set:0.895[95%confidence interval(CI)0.821-0.965]for LR,0.892(95%CI 0.831-0.963)for NB,0.644(95%CI 0.532-0.765)for SVM,and 0.855(95%CI 0.783-0.947)for KNN.In the test set,the respective AUC were 0.845(95%CI 0.712-0.961),0.840(95%CI 0.723-0.964),0.492(95%CI 0.311-0.687),and 0.716(95%CI 0.566-0.871).Conclusion Radiomics based on CT-enhanced portal venous phase combined with machine learning algorithms demonstrates high efficiency in preoperative evaluation of MVI in HCC,with the LR model showing the best performance.

Objective To provide a comprehensive review of the modeling method of the empty bottle stimulation(EBS)anxiety model,including commonly used experimental animal strains and genders,animal grouping,modeling procedures,modeling duration,primary behavioral evaluation method,and the underlying pathological mechanisms.This aims to offer a reference for the application of the EBS anxiety model in anxiety disorder research.Methods Searches were conducted in databases such as CNKI and PubMed to collect all literature related to the EBS anxiety model,which were then systematically summarized and organized.Results(1)Male adult SD or Wistar rats are predominantly used as experimental animals;(2)The optimal modeling period is 2 weeks;(3)Behavioral evaluations primarily utilize the open field test,elevated plus maze test,and light-dark box test;(4)Pathological mechanisms involve abnormal neurotransmitter metabolism in brain regions such as the hippocampus,prefrontal cortex,and amygdala.Conclusions The EBS anxiety model exhibits an anxiety-like behavioral phenotype and associated neurobiological mechanisms,validating its utility as an animal model for the study of anxiety disorders.However,further exploration and refinement are required for its standardized construction protocol and the understanding of its mechanistic underpinnings.

Objective To provide a comprehensive review of the modeling method of the empty bottle stimulation(EBS)anxiety model,including commonly used experimental animal strains and genders,animal grouping,modeling procedures,modeling duration,primary behavioral evaluation method,and the underlying pathological mechanisms.This aims to offer a reference for the application of the EBS anxiety model in anxiety disorder research.Methods Searches were conducted in databases such as CNKI and PubMed to collect all literature related to the EBS anxiety model,which were then systematically summarized and organized.Results(1)Male adult SD or Wistar rats are predominantly used as experimental animals;(2)The optimal modeling period is 2 weeks;(3)Behavioral evaluations primarily utilize the open field test,elevated plus maze test,and light-dark box test;(4)Pathological mechanisms involve abnormal neurotransmitter metabolism in brain regions such as the hippocampus,prefrontal cortex,and amygdala.Conclusions The EBS anxiety model exhibits an anxiety-like behavioral phenotype and associated neurobiological mechanisms,validating its utility as an animal model for the study of anxiety disorders.However,further exploration and refinement are required for its standardized construction protocol and the understanding of its mechanistic underpinnings.

Objective To explore the value of CT-enhanced portal venous phase radiomics combined with machine learning algo-rithms in assessing microvascular invasion(MVI)in hepatocellular carcinoma(HCC).Methods A retrospective analysis was con-ducted on imaging and clinical data of 132 HCC patients.The patients were randomly divided into training set and test set at a 7︰3 ratio.Independent influencing factors for predicting MVI status in HCC patients were identified through univariate and multivariate logistic regression analyses.Radiomics features were selected using the least absolute shrinkage and selection operator(LASSO)algorithm,and a Radiomics score(Radscore)was calculated to construct the final combined model.Four machine learning algorithms including-logistic regression(LR),naive Bayes(NB),support vector machine(SVM),and K-nearest neighbor(KNN)were applied to evaluate the model.The performance of each machine model was assessed using the receiver operating characteristic(ROC)curves and the area under the curve(AUC).Results Univariate and multivariate logistic regression analyses revealed that venous phase CT values were independentinfluencing factors,and six radiomics features were ultimately selected.After the Radscore was calculated,a combined model was constructed using Radscore and venous phase CT values.Machine learning algorithms showed that the combined model achieved the following AUC in the training set:0.895[95%confidence interval(CI)0.821-0.965]for LR,0.892(95%CI 0.831-0.963)for NB,0.644(95%CI 0.532-0.765)for SVM,and 0.855(95%CI 0.783-0.947)for KNN.In the test set,the respective AUC were 0.845(95%CI 0.712-0.961),0.840(95%CI 0.723-0.964),0.492(95%CI 0.311-0.687),and 0.716(95%CI 0.566-0.871).Conclusion Radiomics based on CT-enhanced portal venous phase combined with machine learning algorithms demonstrates high efficiency in preoperative evaluation of MVI in HCC,with the LR model showing the best performance.

As a common biliary disease, cholelithiasis has high incidence and recurrence rates, with negative impact on patients′ health and quality of life. The causes of cholelithiasis are complex, and recent studies have shown that microorganisms may be associated with the formation and recurrence of cholelithiasis, but the specific relationship between the two has not been fully elucidated. Bile microbiota may be related to the increase of incidence rate of gallstones. Different types of cholelithiasis have different microbial composition. Microorganisms may participate in the formation and recurrence of gallstones by influencing bile composition, promoting the formation of stone cores and other ways. The authors provide a review on the etiology and pathogenesis of cholelithiasis, classification of cholelithiasis, microenvironment of biliary microbiota, and the relationship between biliary microbiota and stone formation, aiming to provide new ideas and theoretical basis for the prevention and treatment of cholelithiasis.

As a common biliary disease, cholelithiasis has high incidence and recurrence rates, with negative impact on patients′ health and quality of life. The causes of cholelithiasis are complex, and recent studies have shown that microorganisms may be associated with the formation and recurrence of cholelithiasis, but the specific relationship between the two has not been fully elucidated. Bile microbiota may be related to the increase of incidence rate of gallstones. Different types of cholelithiasis have different microbial composition. Microorganisms may participate in the formation and recurrence of gallstones by influencing bile composition, promoting the formation of stone cores and other ways. The authors provide a review on the etiology and pathogenesis of cholelithiasis, classification of cholelithiasis, microenvironment of biliary microbiota, and the relationship between biliary microbiota and stone formation, aiming to provide new ideas and theoretical basis for the prevention and treatment of cholelithiasis.

Urine is produced from the urinary system, and urinary cell-free DNA (cfDNA) carries genomic DNA directly secreted from urinary system.Urine samples are non-invasive, unlimited in quantity and easy to obtain, making urinary cfDNA a promising biomarker for urologic diseases.This article reviews the progress of clinical application of urinary cfDNA in urologic diseases.

AIM:To investigate the effects of Panax notoginseng compound formula(PN)on endometrial fibro-sis by regulating inflammatory reaction and intestinal flora(IF)in a rat model of intrauterine adhesion(IUA).METHODS:The rat IUA model was established by following the mechanical injury method.A total of 50 rats were randomly divided in-to sham group,model group,low-dose(210 mg/kg)PN group,medium-dose(420 mg/kg)PN group and high-dose(840 mg/kg)PN group.After 8 weeks of intragastric administration,the uterus was collected to observe morphological changes with naked eye.The degree of uterine tissue damage and fibrosis was evaluated through hematoxylin-eosin(HE)and Mas-son staining.The collagen type Ⅰ(Col Ⅰ)was detected by immunohistochemistry.The interleukin-6(IL-6)and IL-10 pro-tein expression was detected by Western blot.The levels of IL-6,IL-1β,tumor necrosis factor-α(TNF-α)and vascular endothelial growth factor B(VEGFB)were detected by enzyme-linked immunosorbent assay(ELISA).The IF diversity and population structure were observed by 16S amplicon.RESULTS:Compared with the sham group,the uteruses of rats in the model group showed:reduced elasticity,accompanied by congestion and edema;decreased number of glands and blood vessels,and thinned endometrium(P<0.01);increased collagen fibers and Col Ⅰ protein expression(P<0.01);sig-nificantly increased IL-1β,IL-6,TNF-α and VEGFB levels in the uterine tissue(P<0.01);decreased IL-10 level(P<0.01);and reduced IF diversity(P<0.05).Compared with the model group,the drug intervention groups exhibited:re-covered elasticity of the uterus and relieved congestion and edema;increased number of endometrial glands and blood ves-sels(P<0.05);decreased collagen fibers and Col Ⅰ protein expression(P<0.01);reduced IL-1β,IL-6,and TNF-α lev-els to varying degrees in the uterine tissue(P<0.05);elevated IL-10 level(P<0.01);and improved IF diversity(P<0.05).CONCLUSION:The PN is able to significantly improve the endometrial tissue fibrosis in IUA rats.The under-lying mechanisms may be related to the inhibition of IL-6,IL-1β and TNF-α expression,up-regulation of IL-10,and im-provement of IF diversity.

Objective To observe the value of CT radiomics-based nomogram for predicting difference of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology.Methods Totally 126 patients with gastric cancer diagnosed by surgical pathology were retrospectively analyzed.The patients were divided into concordant group(n=77)and inconsistent group(n=49)according to the concordance between endoscopic biopsy and postoperative pathology results or not,also divided into training set and validation set at the ratio of 2∶1.Clinical predictors were screened,then a clinical prediction model was constructed.Radiomics features were extracted based on venous-phase CT images and screened using L1 regularization.Radiomics models were constructed using 3 machine learning(ML)algorithms,i.e.decision trees,random forests and logistic regression.The nomogram based on clinical and the best ML radiomics model was constructed,and the efficacy and clinical utility of the above models and nomogram for predicting inconsistency of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology were evaluated.Results Patients'age,platelet count,and arterial-phase CT values of tumors were all independent predictors of inconsistency between endoscopic biopsy and postoperative pathology of Lauren types of gastric cancer.CT radiomics model using random forests algorithm showed better predictive efficacy among 3 ML models,with the area under the curve(AUC)of 0.835 in training set and 0.724 in validation set,respectively.The AUC of clinical model,radiomics model and the nomogram in training set was 0.764,0.835 and 0.884,while was 0.760,0.724 and 0.841 in validation set,respectively.In both training set and validation set,the nomogram showed a good fit and considerable clinical utility.Conclusion CT radiomics-based nomogram had potential clinical application value for predicting inconsistency of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology.