Objective:To develop and validate a MRI-based radiomics nomogram combining with radiomics signature and clinical factors for the preoperative differentiation of benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT).Methods:From January 2015 to May 2020, 86 patients with parotid tumors confirmed by surgical pathology in the Affiliated Hospital of Qingdao University were enrolled as training sets, and 35 patients in the University of Hong Kong-Shenzhen Hospital from January 2013 to January 2020 were enrolled as independent external validation sets. The logistic regression was used to establish a clinical-factors model based on demographics and MRI findings. Radiomics features were extracted from preoperative T 1WI and fat-saturated T 2WI (fs-T 2WI), a radiomics signature model was constructed, and a radiomics score (Rad-Score) was calculated. A combined diagnostic model and nomogram combining with the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The receiver operating characteristic (ROC) analysis was used to evaluate the performance of each model and DeLong test was used for comparison of area under the ROC curve (AUC). Results:The logistic regression results showed that deep lobe involvement (OR=3.285, P=0.040) and surrounding tissue invasion (OR=15.919, P=0.013) were independent factors for MPGT and constructed the clinical-factors model. A total of 19 features were extracted from the joint T 1WI and fs-T 2WI to build the radiomics signature model. The combined diagnostic model and nomogram incorporating deep lobe involvement, surrounding tissue invasion and Rad-score were established. The AUCs of the clinical-factors model, radiomics signature model and combined diagnostic model for differentiating BPGT from MPGT for the training and validation sets were 0.758, 0.951, 0.953 and 0.752, 0.941 and 0.964 respectively. The AUCs of the radiomics signature model and the combined diagnostic model were significantly higher than those of the clinical-factors model for both training and validation sets (training set: Z=3.95, 4.31, both P<0.001; validation set: Z=2.16, 2.67, P=0.031, 0.008). There was no statistical difference in AUCs between the radiomics signature model and combined diagnostic model (training set: Z=0.39, P=0.697; validation set: Z=1.10, P=0.273). Conclusions:The MRI-based radiomics signature model and radiomics nomogram incorporating deep lobe involvement, surrounding tissue invasion, and Rad-score showed favorable predictive efficacy for differentiating BPGT from MPGT.

Objective To establish a novel real-time quality control method for rapidly identifying the random error of sodium ion con-centration in serum using an artificial intelligence voting algorithm,and evaluate the relevant effectiveness of the model established on this basis.Methods A total of 144 754 test results of serum sodium ion rom the inpatients measured by Beckman AU5400 biochemis-try analyzer from January to May 2021 were obtained retrospectively from laboratory information system of the Department of Clinical La-boratory,Nanjing Drum Tower Hospital,and all the data were used as unbiased data for the current study.The random errors were arti-ficially introduced to generate the corresponding biased data set.Subsequently,the voting algorithm-based internal quality control model(ViQC)was established using the principles of the voting algorithm.The ViQC model and five classical PBRTQC(patient-based real-time quality control)algorithms were performed direct to each biased data.The analytical performance of the ViQC model was evaluated by using classification model criteria.The trimmed average number of patient samples until error detection(tANPed)was used to com-pare the clinical detection efficacy of the ViQC model with those of the five classical algorithms,and the error detection curves were plotted.Results Compare with all the classical algorithms,the ViQC model showed a false positive rate below 0.002 and achieved ac-curacy above 0.951 in detecting all the deviations.When the error factors were 1.5,2.5,and 3.0,the false positive rate of the ViQC model was zero.When the error factor was 2.5,its accuracy reached 0.979.Compared to the five classical PBRTQC algorithms,the ViQC model reduced the overall average tANPed by up to 34％and showed higher sensitivity for error detection.In addition,the ViQC model demonstrated the area under the ROC curve was as high as 0.989 at TEa on the test set,but the value of tANPed wasonly five.Conclusion We successfully established a real-time quality control model for the data of patients based on artificial intelligence algo-rithms,and its efficacy of clinical detection was superior to the traditional PBRTQC algorithms.

Objective:To assess the association between short-term ambient air pollution exposure and arterial stiffness and whether obesity modifies these associations.Methods:A cross-sectional study was conducted based on Fangshan family cohort in Beijing. The 24 hours average air pollutant levels on the day cohort participants took baseline survey were calculated as short-term air pollution. A generalized additive model (GAM) with Gaussian links was used to estimate changes in typical carotid artery intima-media thickness (CIMT), brachial-ankle pulse wave velocity (BAPWV), pulse pressure (PP) and ankle-branchial index (ABI) after short-term exposure to each air pollution (PM 2.5, PM 10, SO 2, NO 2, CO). The cross-product terms of each air pollution, body mass index (BMI), and waist-to-hip ratio were included in the GAM model to test the interaction. Further, they conducted a stratified analysis to test their effects on the relationship between short-term exposure to each air pollution and the arterial stiffness indicators. Results:A total of 4 211 individuals were included in the analysis. Individuals' age was (58.9±8.7) years, of which 2 268 (53.9%) were female. Several covariates, including sociodemographic factors, lifestyle behaviors, and history of drugs, were included in the analysis. The results of the GAM analysis showed that an increase in PM 2.5 ( β=2.912×10 -4, 95% CI: 1.424×10 -4-4.400×10 -4, P<0.001), CO ( β=0.027, 95% CI: 0.011-0.043, P<0.001), SO 2 ( β=2.070×10 -3, 95% CI: 7.060×10 -4-3.430×10 -3, P=0.003), and NO 2 ( β=3.650×10 -4, 95% CI: 2.340×10 -5-7.060×10 -4, P=0.036) were associated with an increase in CIMT, while an increase in PM 10 ( β=0.018, 95% CI: 0.002-0.033, P=0.028) was associated with an increase in PP in the study population. Besides, the waist-to-hip ratio had an effect-modification on the correlation of short-term exposure of PM 2.5 (interaction P=0.015), NO 2 (interaction P=0.008), and CO (interaction P=0.044) with CIMT, and the correlation between short-term exposure of PM 2.5 (interaction P=0.002), NO 2 (interaction P=0.010), CO (interaction P=0.029), PM 10 (interaction P<0.001) with PP. The significant association between CIMT, PP, and air pollution concentrations was more visible in people with lower waist-to-hip ratios. Conclusions:Short-term ambient air pollution exposure was associated with arterial stiffness indicators, and there was an effect modification of waist-to-hip ratio on these associations, and lower waist-to-hip ratios may enhance the association between air pollution exposure and indicators.

Objective:To explore the association between polymorphisms of transforming growth factor-β(TGF-β)signaling pathway and non-syndromic cleft lip with or without cleft palate(NSCL/P)among Asian populations,while considering gene-gene interaction and gene-environment interaction.Methods:A total of 1 038 Asian NSCL/P case-parent trios were ascertained from an international consortium,which conducted a genome-wide association study using a case-parent trio design to investigate the genes affec-ting risk to NSCL/P.After stringent quality control measures,343 single nucleotide polymorphism(SNP)spanning across 10 pivotal genes in the TGF-β signaling pathway were selected from the original genome-wide association study(GWAS)dataset for further analysis.The transmission disequilibrium test(TDT)was used to test for SNP effects.The conditional Logistic regression models were used to test for gene-gene interaction and gene-environment interaction.Environmental factors collected for the study in-cluded smoking during pregnancy,passive smoking during pregnancy,alcohol intake during pregnancy,and vitamin use during pregnancy.Due to the low rates of exposure to smoking during pregnancy and al-cohol consumption during pregnancy(＜3％),only the interaction between maternal smoking during pregnancy and multivitamin supplementation during pregnancy was analyzed.The threshold for statistical significance was rigorously set at P=1.46 × 10-4,applying Bonferroni correction to account for multiple testing.Results:A total of 23 SNPs in 4 genes yielded nominal association with NSCL/P(P＜0.05),but none of these associations was statistically significant after Bonferroni's multiple test correction.How-ever,there were 6 pairs of SNPs rs4939874(SMAD2)and rs1864615(TGFBR2),rs2796813(TGFB2)and rs2132298(TGFBR2),rs4147358(SMAD3)and rs1346907(TGFBR2),rs4939874(SMAD2)and rs1019855(TGFBR2),rs4939874(SMAD2)and rs12490466(TGFBR2),rs2009112(TGFB2)and rs4075748(TGFBR2)showed statistically significant SNP-SNP interaction(P＜1.46 × 10-4).In contrast,the analysis of gene-environment interactions did not yield any significant results after being cor-rected by multiple testing.Conclusion:The comprehensive evaluation of SNP associations and interac-tions within the TGF-β signaling pathway did not yield any direct associations with NSCL/P risk in Asian populations.However,the significant gene-gene interactions identified suggest that the genetic architec-ture influencing NSCL/P risk may involve interactions between genes within the TGF-β signaling path-way.These findings underscore the necessity for further investigations to unravel these results and further explore the underlying biological mechanisms.