OBJECTIVE To analyze the significance of serum infectious markers in different sources and investigate the prevetion strategy of nosocomial infection in clinical laboratory. METHODS The serum infectious markers(HBsAg,anti-HCV,anti-HIV and syphilis antibody) were detected in outpatient,inpatient and physical check-up people from Jun 2007 to Jun 2008. RESULTS The total percentage of HBsAg,anti-HCV,anti-HIV or syphilis antibody positive in outpatient,inpatient and physical check-up people was 43.30%,13.56% and 7.26%,respectively.The positive rate of HBeAg in outpatient,inpatient and physical check-up people was 12.29%,1.78% and 0.71%,respectively.The average infection rate of syphilis was 1.75% in inpatients,but in inpatients above 60 years old it could be 3.75%.Twenty-one cases were reconfirmed of HIV positive by the reconfirmation laboratory of CDC in Human Province. CONCLUSIONS The positive rates of four serum infectious markers in outpatient or inpatient are markedly higher than that in physical check-up people(? 2=10 117.6,P

Objective To evaluate the prostate imaging reporting and data system(PI-RADS) version 1 and version 2 for detection of prostate cancer (PCa) by multiparametric magnetic resonance imaging (MpMRI) in a consecutive cohort of patients with magnetic resonance imaging/transrectal ultrasonography (MRI-TRUS) fusion-guided biopsy.Methods 30 suspicious lesions including 15 prostate cancer and 15 non cancer at 3.0 T MpMRI were scored according to the PI-RADS V1(≥ 3 scores in at least one MRI sequence)system before MRI-TRUS fusion guided biopsy and correlated to histopathology results.PI-RADS V2 and Likert scores were determined retrospectively,diagnostic accuracy was determined using receiver operating characteristic curve analysis.Results The PI-RADS score of the dominant lesion was significantly higher in patients with PCa compared to patients with negative histopathology (PI-RADS V1:12.10±2.60 vs 7.47±1.98,P<0.05;PI-RADS V2:4.21±1.18 vs 2.79±0.92,P<0.05);Using a Likert score cut-off ≥ 4,a sensitivity of 73.7%,a specificity of 78.9%, positive predictive value of 77.74% and a negative predictive value of 75.00% (AUC=0.778,95%CI:0.63-0.93), a PI-RADS V1 cut-off ≥ 10,a sensitivity of 73.7%,a specificity of 94.7%,positive predictive value of 93.29% and a negative predictive value of 78.26% (AUC=0.911,95%CI:0.82-1.00) and PI-RADS V2 cut-off ≥ 4,a sensitivity of 57.9%, a specificity of 100%, positive predictive value of 100% and a negative predictive value of 73.37% (AUC=0.837,95%CI:0.70-0.97) were achieved.Conclusion The described fusion system is dependable and efficient for targeted MRI-TRUS fusion-guided biopsy.MpMRI PI-RADS scores combined with a novel real-time MRI-TRUS fusion system facilitate sufficient diagnosis of PCa with high sensitivity and specificity,PI-RADS scores appears to be the preferable method for the evaluation of prostate cancer than Likert score, while V2 does not perform better than V1.

Objective To evaluate the diagnostic value of prostate imaging reporting and data system version 1 (PI-RADS V1) and version 2 (PI-RADS V2) for detection of prostate cancer (PCa) in the transition zone (TZ).Methods Seventy-seven patients with suspicious lesions in TZ on mpMRI were scored according to the PI-RADS system (V1 and V2) before MR-TRUS fusion guided biopsy prospectively.In all of the patients with suspicious tumors,respectively at least one lesion with a PI-RADS V1 assessment category of ≥3,was selected for biopsy.Independent sample t test was used to compare scores of PI-RADS V1 and V2 between PCa and benign prostatic hyperplasia (BPH).The diagnostic performance of PI-RADS V 1 and V2 for detection of PCa in the transition zone was compared by analyzing ROC basing on the results of MR-TRUS fusion guided biopsy.Results A cohort of 77 patients was performed including 31 cases of PCa (32 cores) and 46 cases of BPH (51 cores).PCa (V1:1 1.50±2.79;V2:4.28±0.99) had significantly higher scores of both PI-RADS V1 and PI-RADS V2 than BPH(V1:7.51± 1.63;V2∶2.61 ±0.67) (P＜0.05).Using a PI-RADS V1 score cut-off ≥ 11,sensitivity and specificity in group PCa and BPH were calculated,which were 68.8％(22/32) and 96.1％(49/51) with a area under curve of 0.869;using a PI-RADS V2 score cut-off ≥4,which were 75.0％ (24/32) and 90.2％ (46/51) with a area under curve of 0.888,respectively.Conclusions PI-RADS system can indicate the likelihood of PCa of suspicious lesions in TZ on Mp-MRI.PI-RADS V2 perform better than V 1 for the assessment of prostate cancer in TZ.

Objective:To explore the value of spectral CT radiomics quantitative features on differentiating lung cancer nodule from inflammatory nodule.Methods:The spectral CT imaging data of 96 lung cancer nodules and 45 inflammatory nodules from the First Affiliated Hospital of Soochow University were analyzed retrospectively. According to a ratio of two to one, patients were randomly assigned to the training group and validation group, including 64 lung cancer nodules and 30 inflammatory nodules in the training group, 32 lung cancer nodules and 15 inflammatory nodules in the validation group. MaZda software was used for radiomic feature extraction from the 70 keV monochromatic images in arterial phase and venous phase for lung cancer nodules and inflammatory nodules in the training group. Fisher coefficients (Fisher), classification error probability combined average correlation coefficients (POE+ACC) and mutual information (MI) were used to select 10 optimal features for the optimal feature subsets. The optimal feature subsets were analyzed by using linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA) to calculate the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, precise and F1 score in differentiating lung cancer nodule from inflammatory nodule. The prediction model was established using the optimal feature subsets in the training group with artificial neural network (ANN). Then the established prediction model was used to differentiate lung cancer nodule from inflammatory nodule in the validation group. Delong test was used to compare the differences in the AUC of different optimal feature subsets.Results:In arterial phase, the optimal feature subset obtained from MI-NDA had the highest AUC of 0.888 [95% confidence interval (CI) 0.806-0.943], accuracy rate of 88.3%, sensitivity of 87.5% and specificity of 90.0%, on the differential diagnosis of lung cancer nodule and inflammatory nodule in the training group. There was no significant difference in AUC between MI-NDA and Fisher-NDA or (POE+ACC)-NDA method ( Z=1.941, P=0.052; Z=1.683, P=0.092). In venous phase, the optimal feature subset obtained from (POE+ACC)-NDA had the highest AUC of 0.846 (95%CI 0.757-0.912), accuracy rate of 87.2%, sensitivity of 92.2% and specificity of 76.7%, on the differential diagnosis of lung cancer nodule and inflammatory nodule in the training group. There was no significant difference in AUC between(POE+ACC)-NDA and MI-NDA method ( Z=1.354, P=0.18), but significant difference between (POE+ACC)-NDA and Fisher-NDA method ( Z=2.423, P=0.015). In the validation group and training group, the optimal feature subset selected by MI-NDA method had the highest AUC of 0.888(95%CI 0.806-0.943) and 0.871(95%CI 0.741-0.951). Conclusion:Spectral CT radiomics quantitative features have great value on the differential diagnosis of lung cancer nodule and inflammatory nodule.

Objective:To explore the value of different machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features in preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC).Methods:The data of 132 patients with HCC confirmed by pathology in the First Affiliated Hospital of Soochow University from January 2015 to May 2020 were retrospectively analyzed, including 72 cases of positive MVI and 60 cases of negative MVI. According to the proportion of 7∶3, the cases were randomly divided into training set and validation set. The radiomics features of hepatobiliary phase images for HCC were extracted by PyRadiomics software. The clinical and radiomics features of the training set were screened by the least absolute shrinkage and selection operator (LASSO) regression with 5 fold cross-validation, and then the optimal feature subset was obtained. Six machine learning algorithms, including decision tree, extreme gradient boosting, random forest, support vector machine (SVM), generalized linear model (GLM) and neural network, were used to build the prediction models, and the ROC curves were used to evaluate the prediction ability of the models. DeLong test was used to compare the differences of area under the curve (AUC) for 6 machine learning algorithms.Results:Totally 14 features selected by LASSO regression were obtained to form the optimal feature subset, including 2 clinical features (maximum tumor diameter and alpha-fetoprotein) and 12 radiomics features. The AUCs of decision tree, extreme gradient boosting, random forest, SVM, GLM and neural network based on the optimal feature subset were 0.969, 1.000, 1.000, 0.991, 0.966, 1.000 in the training set and 0.781, 0.890, 0.920, 0.806, 0.684, 0.703 in the validation set, respectively. There were significant differences in the AUCs between extreme gradient boosting and GLM or neural network ( Z=2.857, 3.220, P=0.004, 0.001). The differences in AUCs between random forest and SVM, GLM, or neural network were significant ( Z=2.371, 3.190, 3.967, P=0.018, 0.001,<0.001). The difference in AUCs between SVM and GLM was statistically significant ( Z=2.621 , P=0.009). There were no significant differences in the AUCs among the other machine learning models ( P>0.05). Conclusion:Machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features can be used to preoperatively predict MVI of HCC, particularly the extreme gradient boosting and random forest models have high prediction efficiency.

Objective: To evaluate the efficacy and safety of low dose sublingual nifedipine dripping pills (5 mg) in treating moderate and severe hypertension in comparison with normal dose (10 mg) of sublingual nifedipine dripping pills. Methods: This study was designed as a randomized, double-blind, positive drug parallel controlled, multi-center, non-inferiority clinical trial. Patients with moderate and severe hypertension were enrolled by 14 clinical trial centers, randomly divided into the trial group (sublingual 5 mg nifedipine dripping pills) and the control group (sublingual 10 mg nifedipine dripping pills). The changes in blood pressure were monitored continuously within 2 hours after the initial administration, repeated the dose in 20 minutes interval after the initial administration for up to additional 3 doses (maximum 4 doses) if the antihypertensive efficacy was not satisfactory. The efficacy of antihypertensive therapy between the two groups was evaluated by repeated administration rates and blood pressure changes at 60 minutes post the initial administration, and the safety of treatment was evaluated by recording adverse event rate of the two groups. Results: The anti-hypertensive effective rates at 60 minutes after sublingual administration were 83.5% (202/242) and 86.7% (208/240) respectively between the trial group and control group (χ²=1.307, P=0.253) . On the aspect of antihypertensive effectiveness at 60 minutes after single dose of sublingual administration, the anti-hypertension effective rates of the trial group and the control group were 85.6% (154/180) and 87.2% (164/188) respectively (χ²=0.221, P=0.639). Prevalence of the repeated administration was also similar between the two groups (25.6%(62/242) in the trial group and 21.7% (52/240) in the control group, χ²=1.043, P=0.307). On the safety aspect, there was no adverse events/reactions in the trial group, but there were 15 cases of adverse events/reactions occurred in control group (6.25%, χ²=15.611, P<0.001). Conclusions In the treatment of moderate to severe hypertension, the antihypertensive efficacy of low dose nifedipine dripping pills is similar to that of conventional dosage, and the safety profile of low dose nifedipine dripping pills is better than that of the conventional dose.