Objective:To construct a combined prediction model based on dual-layer detector spectral CT radiomics features and quantitative parameters, and to evaluate its value in preoperative prediction of serosal invasion in gastric cancer.Methods:This case-control study retrospectively analyzed data from 253 gastric cancer patients confirmed by postoperative pathology at the First Affiliated Hospital of Soochow University (Center 1) and Changshu No.2 People′s Hospital (Center 2) from January 2022 to December 2023. Patients from Center 1 ( n=157) were randomly divided into training set ( n=110) and test set ( n=47) in a 7∶3 ratio, while patients from Center 2 ( n=96) served as an external validation set. Based on postoperative pathological serosal invasion status, patients were classified into serosal invasion group ( n=164) and non-serosal invasion group ( n=89), with distributions of 70/40, 30/17, and 64/32 in the training, test, and external validation sets, respectively. Spectral CT quantitative parameters, including arterial and venous phase iodine concentration (IC), normalized iodine concentration (NIC), arterial-venous IC differences, arterial-venous NIC differences (NIC pa), arterial enhancement fraction (AEF), and effective atomic number (Z eff), were measured. Radiomics features were extracted from venous-phase 40 keV monochromatic images. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection. The logistic regression classifier (LR-LASSO) was applied to construct the radiomics model. Univariate and multivariate logistic regression analyses identified independent risk factors for serosal invasion, including the radiomics signature (RadScore) and quantitative parameters. A clinical model was built using significant quantitative parameters, and a combined model integrated RadScore. An artificial model was based on cT4 staging assessed by two radiologists using venous-phase CT. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Results:A total of six radiomics features were selected to establish the radiomics model. RadScore ( OR=7.598, 95% CI 2.259-25.562, P=0.001) and NIC pa ( OR=4.598, 95% CI 1.404-15.050, P=0.012) served as independent risk factors. The NIC pa served as the clinical model. The AUCs (95% CI) of the combined model in the training, test, and external validation sets were 0.984 (0.969-1.000), 0.855 (0.728-0.982), and 0.773 (0.665-0.882), respectively. The AUCs of the artificial model were 0.741, 0.670, 0.644; of the clinical model were 0.709, 0.633, 0.626. The AUCs of the radiomics model were 0.963, 0.824, 0.741, respectively. Calibration curves showed good agreement between predicted probability and observed probability. The DCA confirmed higher clinical net benefits for the combined model. Conclusion:The combined model integrating dual-layer detector spectral CT radiomics features and quantitative parameters exhibits high efficacy for preoperative prediction of gastric cancer serosal invasion.

Objective:To construct a combined prediction model based on dual-layer detector spectral CT radiomics features and quantitative parameters, and to evaluate its value in preoperative prediction of serosal invasion in gastric cancer.Methods:This case-control study retrospectively analyzed data from 253 gastric cancer patients confirmed by postoperative pathology at the First Affiliated Hospital of Soochow University (Center 1) and Changshu No.2 People′s Hospital (Center 2) from January 2022 to December 2023. Patients from Center 1 ( n=157) were randomly divided into training set ( n=110) and test set ( n=47) in a 7∶3 ratio, while patients from Center 2 ( n=96) served as an external validation set. Based on postoperative pathological serosal invasion status, patients were classified into serosal invasion group ( n=164) and non-serosal invasion group ( n=89), with distributions of 70/40, 30/17, and 64/32 in the training, test, and external validation sets, respectively. Spectral CT quantitative parameters, including arterial and venous phase iodine concentration (IC), normalized iodine concentration (NIC), arterial-venous IC differences, arterial-venous NIC differences (NIC pa), arterial enhancement fraction (AEF), and effective atomic number (Z eff), were measured. Radiomics features were extracted from venous-phase 40 keV monochromatic images. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection. The logistic regression classifier (LR-LASSO) was applied to construct the radiomics model. Univariate and multivariate logistic regression analyses identified independent risk factors for serosal invasion, including the radiomics signature (RadScore) and quantitative parameters. A clinical model was built using significant quantitative parameters, and a combined model integrated RadScore. An artificial model was based on cT4 staging assessed by two radiologists using venous-phase CT. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Results:A total of six radiomics features were selected to establish the radiomics model. RadScore ( OR=7.598, 95% CI 2.259-25.562, P=0.001) and NIC pa ( OR=4.598, 95% CI 1.404-15.050, P=0.012) served as independent risk factors. The NIC pa served as the clinical model. The AUCs (95% CI) of the combined model in the training, test, and external validation sets were 0.984 (0.969-1.000), 0.855 (0.728-0.982), and 0.773 (0.665-0.882), respectively. The AUCs of the artificial model were 0.741, 0.670, 0.644; of the clinical model were 0.709, 0.633, 0.626. The AUCs of the radiomics model were 0.963, 0.824, 0.741, respectively. Calibration curves showed good agreement between predicted probability and observed probability. The DCA confirmed higher clinical net benefits for the combined model. Conclusion:The combined model integrating dual-layer detector spectral CT radiomics features and quantitative parameters exhibits high efficacy for preoperative prediction of gastric cancer serosal invasion.

Objective To investigate the prevalence of genital Chlamydia trachomatis (GCT) infection and associated risk factors in male clients attending sexually transmitted disease (STD) clinics in Guangdong and provide integrated intervention strategy for this group.Methods Convenient sampling was used to recruit participants from April to June in 2015 in Guangdong province.The information about their socio-demographic characteristics and sexual behaviors were collected by using a questionnaire,and blood samples were taken from them to test the antibodies against HIV,syphilis and HCV.First pass urine was taken to test GCT and gonorrhea.Results A total of 1 749 participants with the average age of 39.53 years were recruited.The majority of them were married (73.87％,1 292/1 749),residents of Guangdong (92.28％,1 614/1 749) and in Han ethnic group (99.49％,1 740/1 749).The positive rates for GCT,HIV,syphilis,HCV,Neisseria gonorrhea,and WBC in urinalysis were 6.06％ (106/1 749),0.46％ (8/1 749),3.43％ (60/1 749),0.45％ (7/1 550),2.74％ (48/1 749),7.89％ (138/1 749) respectively.Multivariate analysis showed that risk factors for GCT infection include IDUs (OR=13.98,95％CI:3.35-58.38),anal sex with men (OR=3.11,95％ CI:1.45-6.71),Neisseria gonorrhea positive (OR =9.64,95％ CI:5.09-18.24),and WBC positive (OR =1.96,95％ CI:1.08-3.55).Conclusions This study demonstrated the high prevalence of GCT infection in male clients attending STD clinics in Guangdong.Therefore precision intervention should target this population at high-risk.

Objective To investigate the effects of cytokines on the expression of angiotensin Ⅱ type 1 receptor (AT1R) in vascular smooth muscle cells (VSMCs) in rats. Methods Primary cultured VSMCs from SD rat thoracic aorta were cultured in serum-free DMEM for 24 h, and then in DMEM supplemented with 10% fetal bovine serum for another 12 h. The cultured VSMCs were randomly divided into 5 groups (n =6 each): control group (group C); 10% cytokine group (group L); 50% cytokine group (group N); 100% cytokine group (group H) and L-arginine methy ester (L-NAME), an inhibitor of nitric oxide synthase) group. In group C, the cellswere cultured continuously for 12 h. In L, N and H groups, 10%, 50% and 100% cytokines (IL-1β 50 ng/ml +TNF-α 100 ng/ml + IFN-γ 500 ng/ml) were added to the culture medium respectively and the cells were then incubated for 12 h. In group L-NAME, 100% cytokines + L-NAME 5 mmol/L were added to the culture medium and the cells were then incubated for 12 h. The expression of AT1R mPNA and protin was determined by RT-PCR and Western blot respectively.Results Cytokines down-regulated AT1R mRNA and protein expression in a concentration-dependent manner (P ＜ 0.05 or 0.01). L-NAME reversed cytokines-induced changes in AT1R mRNA and protein expression ( P ＜ 0.01). Conclusion Cytokines can down-regulate the expression of AT1R in rat VSMCs and the mechanism is related to the NO synthesis.

Objective To study the anti-vertigo function of polysaccharides of Gastrodia elata (GEP) and polysaccharides of Armillaria mellea (AMP). Methods Regard vertigo mice caused by machinery rotation as research object, observe the escaping time of electrical shock in maze experiment and jumping platform test, and observe the food intake. Results GEP and AMP can obviously shorten the escaping time of electrical shock (P