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Objective:To explore the value of multiphasic CT-based radiomics signature in predicting the invasive behavior of pancreatic solid pseudopapillary neoplasm (pSPN).Methods:The multiphasic CT images of patients with pSPN confirmed by postoperative pathology in the First Affiliated Hospital of Zhengzhou University from January 2012 to January 2021 were analyzed retrospectively. There were 23 cases of invasiveness and 59 cases of non-invasiveness. The region of interest(ROI) was artificially delineated layer by layer in the plain scan, arterial-phase and venous-phase images, respectively. The 1 316 image features were extracted from each ROI. The data set was divided into training and validation sets with a ratio of 7∶3 by stratified random sampling, and synthetic minority oversampling technique (SMOTE) algorithm was used for oversampling in the training set to generate invasive and non-invasive balanced data for building the training model. The constructed model was validated in the validation set. The receiver operating characteristic(ROC) analysis was used to evaluate model performance and the Delong′s test was applied to compare the area under the ROC curve (AUC) of different predict models. The improvement for classification efficiency of each independent model or their combinations were also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices.Results:After feature extraction, 2, 6 and 3 features were retained to construct plain-scanned model, arterial-phase and venous-phase models, respectively. Seven independent-phase and combined-phase models were established. Except the plain-scanned model, the AUC values of other models were greater than 0.800. The arterial-phase model had the best efficiency for classification among all independent-phase models. The AUC values of arterial-phase model in the SMOTE training and validation sets were 0.913 and 0.873, respectively. By combining the radiomics signature of the arterial-phase and venous-phase models, the AUC values of training and validation sets increased to 0.934 and 0.913 respectively. There were no significant differences of the AUC values between the scan-arterial venous-phase model and arterial venous-phase model in both training and validation sets (both P>0.05). The NRI and IDI indexes showed that the combined form of plain-scan model and arterial-venous-phase model could not significantly improve the classification efficiency in the validation set (both NRI and IDI<0). Conclusions:The arterial-phase CT-based radiomics model has a good predictive performance in the invasive behavior of pSPN, and the combination with a venous-phase radiomics model can further improve the model performance.
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Objective:To explore the classification performance of combined model constructed from CT signs combined with radiomics for discriminating COVID-19 pneumonia and other viral pneumonia.Methods:The clinical and CT imaging data of 181 patients with viral pneumonia confirmed by reverse transcription-polymerase chain reaction in 15 hospitals of Yunnan Province from March 2015 to March 2020 were analyzed retrospectively. The 181 patients were divided into COVID-19 group (89 cases) and non-COVID-19 group (92 cases), which were further divided into training cohort (126 cases) and test cohort (55 cases) at a ratio of 7∶3 using random stratified sampling. The CT signs of pneumonia were determined and the radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models for predicting COVID-19 pneumonia. The diagnostic performance of the models were evaluated using receiver operating characteristic (ROC) analysis, continuous net reclassification index (NRI) calibration curve and decision curve analysis.Results:The combined models consisted of 3 significant CT signs and 14 selected radiomics features. For the radiomics model alone, the area under the ROC curve (AUC) were 0.904 (sensitivity was 85.5%, specificity was 84.4%, accuracy was 84.9%) in the training cohort and 0.866 (sensitivity was 77.8%, specificity was 78.6%, accuracy 78.2%) in the test cohort. After combining CT signs and radiomics features, AUC of the combined model for the training cohort was 0.956 (sensitivity was 91.9%, specificity was 85.9%, accuracy was 88.9%), while that for the test cohort was 0.943 (sensitivity was 88.9%, specificity was 85.7%, accuracy was 87.3%). The AUC values of the combined model and the radiomics model in the differentiation of COVID-19 group and the non-COVID-19 group were significantly different in the training cohort ( Z=-2.43, P=0.015), but difference had no statistical significance in the test cohort ( Z=-1.73, P=0.083), and further analysis using the NRI showed that the combined model in both the training cohort and the test cohort had a positive improvement ability compared with radiomics model alone (training cohort: continuous NRI 1.077, 95 %CI 0.783-1.370; test cohort: continuous NRI 1.421, 95 %CI 1.051-1.790). The calibration curve showed that the prediction probability of COVID-19 predicted by the combined model was in good agreement with the observed value in the training and test cohorts; the decision curve showed that a net benefit greater than 0.6 could be obtained when the threshold probability of the combined model was 0-0.75. Conclusion:The combination of CT signs and radiomics might be a potential method for distinguishing COVID-19 and other viral pneumonia with good performance.
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Objective:To explore the relationship between air pollution and the number of pneumonia hospitalization in a children's hospital in Changsha.Methods:Children who have been in this hospital for the treatment of pneumonia between December 2013 and December 2015 were enrolled in this study.Based on daily meteorological data and air pollution data from December 2013 to December 2015 in Changsha,we constructed a generalized additive model to analyze the relationship between air pollution and the number of pneumonia hospitalization.Results:During the research,the average concentration of PM2.5 and PM10 exceeded the Grade Ⅱ national standards for air quality.The average concentration of SO2 exceeded the Grade Ⅰ national standards.The change of all the 3 main air pollution indexes showed strong statistical relationship with the change of the number of pneumonia hospitalization (P＜0.05),among which,the impact of SO2 ranked number 1,followed by PM2.5 and PM10.Effect of atmospheric pollution on the number of pneumonia boys was basically same as that in the total pneumonia children (P＜0.05).The effect on girls showed no statistical relationship in both models (P＞0.05).Conclusion:The concentrations of SO2'PM2.5 and PM10 are positively correlated with pneumonia hospitalization number of children,and their effect on boys is more obvious than that in the girls.
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The violence behavior is a kind of aggressive behaviors or attempt to hurt another person psychologically,physically or in other forms.Recently,violent incidents occur more and more frequently,and especially among teenagers.A number of concerns on violence continue to rise,and interpersonal violence is the most concerned type.Perpetrators are violence implementers who determine the occurrence and outcome of violence.Many studies provided the risk factors of interpersonal violence,that expounds the influence of personal level,interpersonal relationship,community background and social factors of interpersonal violence.The establishment of the socioecological risk-factor structural model which focuses on the perpetrators' indi vidual,is of great significance for the effective intervention for interpersonal violence.
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OBJECTIVE@#To comprehensively evaluate the interventional effect on unexpected injury among children and adolescents in China, and to provide scientific basis for the injury control strategy. @*METHODS@#Meta analysis was utilized to analyze the selected literatures. After heterogeneity test of the data, a relevant model was chosen to estimate the combined effect values relative risk (RR) and the corresponding 95% confidence interval (95% CI). Subgroup analysis were performed based on the intervention measures, objects and places. Sensitivity and publication bias were analyzed. @*RESULTS@#A total of 18 papers were included in the Meta analysis with a sample size of 32 599. The combined RR value was 0.54 (95% CI 0.44 to 0.68). Subgroup analysis showed that the RR value of health education and comprehensive intervention were 0.59 and 0.50, respectively, with no significant difference between them (P>0.05). The RR values of the interventions in school alone, in school and community or in community alone were 0.51, 0.78 or 0.63. The RR values on children alone, children and parents or parents alone were 0.53, 0.65 or 0.35. The differences were significant when the interventions were performed among different places or objects (P<0.05). Sensitivity analysis revealed that meta-analysis results were relatively stable. @*CONCLUSION@#The targeted interventions were significant in the prevention of unexpected injuries among children and adolescents. It is worth further promoting and spreading.
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Adolescent , Child , Humans , China , Schools , Wounds and Injuries , EpidemiologyABSTRACT
Objective To understand health literacy levels in elderly people aged 60 years and above in Hunan, and explore factors related to health literacy.Methods Hunan residents aged 60 years and above were randomly recruited by the multistage stratified cluster sampling method.A total of 611 elderly people from 13 counties of Hunan were included in the study, which was conducted through questionnaires.Results The median health literacy score for elderly people aged 60 years and above was 51.00 (4-90), and it was lower than that for people under 60 years old, which was 56.00.Single factor analysis found that education level, occupation, number of family members, and household income each had an influence on health literacy scores (H=59.526, 20.609, 17.214, and 50.749, respectively;P=0.000, 0.002, 0.001 and 0.000, respectively).The number of people with basic health literacy accounted for 10.6％ (65/611) of the total.Multiple logistic regression analysis found that chronic disease was a factor affecting basic health literacy.Compared with elderly people without chronic diseases, a higher percentage of people with basic health literacy was among elderly people with chronic diseases (OR =1.870, 95％ CI: 1.037-3.373).Conclusions The health literacy level is lower in elderly people aged 60 years and above than those under 60 years old in Hunan.Only 10.6％ (65/611) of them show basic health literacy.Education level, occupation, number of family members, and household income are the factors related to health literacy.Health education about healthy lifestyle and behavior as well as chronic disease prevention and control should be increased in order to improve the health literacy level in elderly people.