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.

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 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.

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.

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.

Objective To observe the value of dual-energy CT(DECT)radiomics combined with clinical and CT features for predicting differentiation degree of gastric adenocarcinoma(GAC).Methods Totally 254 patients with GAC were prospectively analyzed and divided into high-grade group(low differentiation GAC,n=88)and low-grade group(middle-high differentiation GAC,n=166)according to pathological results.The patients were also divided into training set(n=203,including 70 high-grade and 133 low-grade GAC)and verification set(n=51,including 18 high-grade and 33 low-grade GAC)at the ratio of 8∶2.Radiomics features were extracted and screened based on venous phase single-level(40,70,100 and 140 keV)DECT,and a multi-energy radiomics model was constructed to predict GAC classification.Univariate analysis and multivariate logistic regression were used to analyze clinical and CT features as well as DECT parameters in training set to construct a clinic-CT model.Then a combined model was constructed through combining clinic-CT model with radiomics model.The predictive efficacy of the models were evaluated,and the calibration degree of the combined model was assessed.Results The area under the curve(AUC)of clinic-CT model,multi-energy radiomics model and combined model was 0.74,0.75 and 0.78 in training set,and 0.73,0.77 and 0.78 in verification set,respectively.Except for AUC of combined model was higher than that of clinic-CT model in training set(P＜0.05),no significant difference of AUC was found among models in training set nor verification set(all P＞0.05).The calibration degree of combined model was good in both training set and verification set(both P＞0.05).Conclusion DECT radiomics combined with clinical and CT features could effectively predict differentiation degree of GAC.

Objective To observe the value of spectral CT parameters for evaluating T staging of advanced gastric cancer(AGC).Methods Totally 155 AGC patients were collected and divided into T2 stage(n=40)and T3/4a stage(n=115)according to postoperative pathology.CT values,water concentration(WC)and iodine concentration(IC)of AGC lesions on 40-140 keV arteriovenous phase single energy level images were measured,and the standardized IC(nIC)and spectral curve slopes k1 and k2 were calculated.Clinical variables and spectral quantitative parameters were compared between groups,and receiver operating characteristic curve was plotted,the area under the curve(AUC)was calculated to evaluate the value of each parameter and model for identifying T2 and T3/4a stage AGC.Results Tumor thickness,proportion of low differentiation degree,CT100kev,CT140kev,and WC values in T3/4a group were all significantly higher than those in T2 group(all P＜0.05).CT140keV of AGC lesions on venous phase images presented the highest discrimination efficacy among single parameters,with AUC of 0.782.AUC of clinical-arterial phase-venous phase model was 0.848,higher than that of clinical model and arterial phase model alone(both P＜0.05)but not significantly different compared with AUC of venous phase model(P＞0.05).Conclusion Spectral CT quantitative parameters,especially venous phase parameters could be used to effectively identify T stage of AGC.Multi-parameter combined models had higher diagnostic value.