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 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.

Objective To observe the value of spectral CT multi-parameter imaging for preoperative predicting lymph node metastasis(LNM)of gastric cancer.Methods Totally 136 patients with gastric adenocarcinoma were retrospectively enrolled.The patients were further divided into LNM group(n=74)and non-LNM group(n=62)according to postoperative pathological findings of lymph nodes status.Clinical data,conventional CT findings and spectral CT parameters were compared between groups.Factors being significant different between groups were included in multivariate logistic regression analysis to screen independent predictors of gastric cancer LNM.Clinical+conventional CT model(model 1),spectrum CT model(model 2)and combined model(model 3)were constructed based on the above independent predictors,respectively.Receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the efficacy of each model for preoperative predicting LNM of gastric cancer.Results CT-N stage,CT-T stage,70,100 and 140 keV CT valuestumor at arterial phase(AP),arterial enhancement fraction(AEF)and normalized iodine concentration at venous phase(NICVP)were all independent predictors of gastric cancer LNM(all P＜0.05).AUC of model 3 was 0.846,higher than that of model 1 and model 2(AUC=0.767,0.774,Z=-0.368,-2.373,both P＜0.05)for preoperative predicting LNM of gastric cancer,while the latter two were not significantly different(Z=-0.152,P=0.879).Conclusion Spectral CT multi-parameter imaging could effectively predict LNM of gastric cancer preoperatively.

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%.

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 invastigate the targets and signaling pathways of Gubiao Fanggan Modified Formula in regulating the defense against influenza A virus in immunosuppressed mice by network pharmacology,and the key targets were verified by immuno-suppressive mice model.Methods:TCMSP database was used to search the active ingredients of Gubiao Fanggan Modified Formula,and GeneCard,OMIM,PharmGkb databases were used to obtain the potential targets of the active ingredients to prevent influenza,and take their intersection targets were taken;STRING11.5 database was used to make protein-protein interaction network analyzed and finded the core targets;Cytoscape3.8.1 was used to build a traditional Chinese medicine-ingredient-disease target network,and GO enrichment analysis and KEGG enrichment analysis were performed.Intraperitoneal injection of cyclophosphamide was used to construct a mouse model of immune function suppression,normal group,model control group,Gubiao Fanggan Modified Formula group and oseltamivir group were set up,followed by prophylactic administration,and influenza virus intervention was performed on the fourth day.After 7 days of intragastric administration,the key targets were verified by mouse spleen index,HE staining,RT-qPCR and immunohistochemistry.Results:There were 82 active ingredients in five traditional Chinese medicines in Gubiao Fanggan Modi-fied Formula,and 72 common targets of drugs and diseases such as IL-6,TNF-α,IL-2,etc,mainly involving IL-17,TNF and AGE-RAGE signaling pathway.Gubiao Fanggan Modified Formula could increase spleen index and significantly reduce mRNA and protein expressions of IL-6 and TNF-α in spleen tissue of mice(P<0.05 or P<0.01).Conclusion:Gubiao Fanggan Modified Formula may regulate body's immune function through targets such as IL-6 and TNF-α,thereby preventing influenza virus infection.

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.

As the main weapon of cellular immunity,CD4+ T cells play a vital role in controlling and eliminating infections,and are an important barrier for the body to resist infections.Respiratory tract infectious diseases caused by influenza virus infection have extremely high infectivity,morbidity and mortality.The infection mechanism is relatively complicated and has not been fully ex-plained.The exuberant immune response induced by the body after influenza virus infection is described as a"cytokine storm"which is related to pro-inflammatory cytokines and tissue damage,which may eventually lead to acute lung injury.Therefore,this article sum-marizes the current research progress,focusing on the mechanism of CD4+T cells in the cytokine storm induced by influenza virus in-fection and the impact of acute lung injury,providing relevant ideas and theoretical guidance for follow-up research,with a view to the disease caused by influenza virus bring new and effective methods of diagnosis and treatment.