With the development of information technology, the massive growth of pharmaceutical electronic data and the significant increase in the reports of drug adverse event reports have brought great challenges to pharmacovigilance research. Data mining techniques can automatically extract the risk signals of adverse drug reaction from real-world data. Therefore, efficient data mining of massive adverse event reporting is a necessary measure to realize the automatic detection of adverse drug reactions. By introducing the current major large-scale adverse drug event reporting databases and related data mining methods, this study reviews the application and limitations of adverse drug reaction data mining technology in pharmacovigilance, which provides reference for pharmacovigilance-related institutions and researchers.

Objective Evaluation of Chinese-thyroid imaging reporting and data system(C-TIRADS)combined with contrast-enhanced ultrasound(CEUS)for the assessment of category 4 nodules in the setting of Hashimoto's thyroiditis.Methods Retrospective analysis of 120 C-TIRADS category 4 thyroid nodules from 79 patients with confirmed Hashimoto's thyroiditis who attended the Yiyang Central Hospital from June to December 2022.Thyroid nodules exhibiting one or more benign or malignant features that were suspicious on CEUS were treated as downgraded or upgraded one level.Using the final surgical pathology results as the gold standard,working characteristic(ROC)curves of subjects based on C-TIRADS grading before and after CEUS adjustment were plotted to compare diagnostic efficacy.Results The sensitivity,specificity,and accuracy of the CEUS-adjusted C-TIRADS were 93.0％,87.8％and 90.8％,respectively(P<0.05).The area under the ROC curve was 0.811 and 0.904,respectively(P<0.05).Conclusion C-TIRADS combined with CEUS has better diagnostic efficacy in evaluating category 4 nodules in Hashimoto's thyroiditis.

Objective: To develop an assessment tool for risky road behavior tendencies among middle school students in western China, as well as to determine the relevant indices and their weights, so as to provide the reference for road safety prevention and control for middle school students in western China. Methods: A Delphi study was employed to construct the assessment tool for risky road behavior tendencies among middle school students in western China. In August 2023, eighteen experts in related fields such as traffic safety, education, and healthcare were invited to achieve Delphi consensus. The final indices were initially selected based on the consulting results,followed by the determination of their individual and combined weights using the analytic hierarchy process. Results: The finalized assessment tool comprised 3 primary indicators, 13 secondary indicators, and 100 tertiary indicators. The positivity coefficient of experts was 100%, accompanied by the authority coefficient 0.90. The mean importance scores for the three primary indicators varied from 4.67 to 4.78, while those for the 13 secondary indicators ranged from 4.22 to 4.89. The Kendall coefficient W was statistically significant at 0.32 ( χ 2=96.83, P <0.05). The weights assigned to the three primary indicators were:ability (0.329 4), opportunity (0.337 3), and motivation (0.333 3). The secondary indicators with the top three highest combined weights were social influence (0.027 4), knowledge (0.027 3), and skills (0.026 7). Conclusions The assessment tool for risky road behavior tendencies among middle school students in western China demonstrates high expert consensus, with balanced weighting of primary and secondary indicators. Expanded use of the assessment tool would provide the data support for intervention work.

Objective To understand the serotype, drug resistance and pulsed field gel electrophoresis (PFGE) typing of Salmonella isolated from meat products in Hengyang from 2020 to 2022, so as to provide scientific data for the prevention and control of food-borne Salmonella infection in our city. Methods All 101 Salmonella isolated from meat products were serotyped, drug sensitivity tests were performed with micro broth dilution method, molecular typing was performed using PFGE, clustering analysis was performed with BioNumerics software, and statistical analysis was performed using SPSS 18.0 software. Results The total detection rate of Salmonella from meat sources in Hengyang City from 2020 to 2022 was 38.55% (101/262). Totally 23 different serotypes were detected in the 101 strains of Salmonella among which S. London (21.78%, 22/101),was the dominant serotypes. Seventy nine Salmonella strains showed different levels of drug resistance, with a multi drug resistance rate of 42.57% (43/101). Eighty nine different PFGE bands were found in the 101 strains of Salmonella, with a similarity of approximately 55% to 100%. Conclusion Different Salmonella Serotype are widely distributed, and the antibiotic resistance rate is very high. The PFGE map are polymorphic, and the homology of PFGE bands in Salmonella from different sources is relatively low.

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.

Objective To establish the rapid identification method of Hedysari Radix wild and cultivated products by integrating the identification characteristics of TCM traits obtained by intelligent senses such as electronic nose and colorimeter based on the multivariate statistical analysis method;To provide new ideas and methods for the formulation of commodity specification standards and the application research of market quality control for Hedysari Radix.Methods Totally 29 batches of samples of Hedysari Radix were detected based on colorimeter and electronic nose technology to obtain their sensory information,and the effective components of Hedysari Radix were determined by high performance liquid chromatography(HPLC)and other methods for joint analysis.After establishing the optimal experimental conditions of Hedysari Radix electronic nose,multivariate statistical analysis methods,such as principal component analysis(PCA),orthogonal partial least squares-discriminant analysis(OPLS-DA)and clustering analysis,were used to establish the identification model of Hedysari Radix wild and cultivated commodities.Results The optimum test conditions of Hedysari Radix electronic nose(particle size of 65 mesh):the sample weight was 2.0 g,the optimum temperature of the sample was 50℃,and the time was 25 min.A single intelligent sensory result could not quickly and accurately identify the two,but the fusion information could quickly identify the wild and cultivated commodities of Hedysari Radix,and the chemical composition had a certain correlation with the color and taste.Conclusion Electronic nose and colorimeter can quickly and accurately distinguish wild and cultivated Hedysari Radix after multivariate statistical analysis,which is simple and feasible.The combined analysis of its related properties and active components can be used for the quality evaluation of Hedysari Radix.

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%.

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.