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*

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.

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.

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.

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.

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.

This paper summarized professor YOU Zhaoling's experience in treating intrauterine adhesions （IUA） with medicinal flowers. According to the collateral disease theory， deficiency and stasis of uterus vessels and collaterals is the key pathogenesis of IUA. Medicinal flowers are mostly fragrant， light and soft， with the function of diffusing， unblocking， penetrating and dissipating， which can be used to diffuse and dissipate stasis and stagnation in the blood， and finally unblock the collaterals. In clinical practice， Renshenhua （Flos Ginseng）， Sanqihua （Flos Notoginseng）， Jinyinhua （Flos Lonicerae Japonicae）， Daidaihua （Flos Citri Aurantii Amarae）， Lyu'emei （Flos Mume）， Baihehua （Flos Bulbus Lilii）， Xuelianhua （Herba Saussureae Lanicepsis）， Yuejihua （Flos Rosae Chinensis）， Juhua （Flos Chrysanthemi） and other medicinal flowers are commonly used for IUA， as well as self-made compound formulas contained of these herbs such as No.1 IUA Formula which is used in the perioperative period of transcervical resection of adhesion， No.2 IUA Formula which is used to treat postoperative thin endometrium， and Zhuluan Formula （助卵方） for IUA patients who want to have children.

Objective:To assess the quality of low-keV monoenergetic images using deep learning image reconstruction (DLIR) algorithm combined with dual energy CT (DECT) in gastric cancer and to compare them with images from the conventional adaptive statistical iterative reconstruction (ASiR-V) algorithm.Methods:In this cross-sectional study, DECT images of 31 gastric cancer patients in the First Affiliated Hospital of Zhengzhou University were prospectively collected from September 2022 to March 2023. The 55 keV monoenergy images were reconstructed using the DLIR algorithm at low-, medium-, and high-intensity levels (DLIR-L, DLIR-M, and DLIR-H) based on arterial phase and venous phase images, respectively. The 70 keV 40% mixing coefficient (ASiR-V40%) images were reconstructed using the ASiR-V algorithm. In the objective evaluation of images, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for both lesions and muscle were calculated across four sets of reconstructed images. In the subjective evaluation of images, scores were assigned to the overall image quality, lesion visibility, and diagnostic confidence for each set of reconstructed images. Comparisons of SNR and CNR between the 4 groups were made by One-way repeated-measures ANOVA or Friedman′s test. Comparisons of scores were made by Friedman′s test. The P value of pairwise comparison was adjusted using Bonferroni correction methods. Results:In the objective evaluations, CNR lesion, SNR lesion and SNR muscle were highest on the 55 keV DLIR-H images in the arterial and venous phases, and showed a gradually increasing trend on the 70 keV ASiR-V40%, 55 keV DLIR-L, DLIR-M, DLIR-H images ( P<0.05). In subjective evaluations, compared to the 70 keV ASiR-V40% images, overall image quality scores were numerically higher for the 55 keV DLIR-H ( P>0.05), similar or slightly worse for the 55 keV DLIR-M, and significantly lower for the 55 keV DLIR-L ( P<0.05). The lesion visibility and diagnostic confidence on the 55 keV DLIR reconstruction images were higher in both arterial and venous phases than 70 keV ASiR-V40% images ( P<0.05). Conclusions:Compared to the conventional 70 keV ASiR-V40% images, the 55 keV DLIR-H images had higher lesion contrast and diagnostic confidence with lower image noise. The 55 keV DLIR-M images had comparable overall image quality to 70 keV ASiR-V40% images, but the former had higher lesion contrast and diagnostic confidence. The 55 keV DLIR-L was unable to improve image quality to the level of 70 keV ASiR-V40%.