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Objective To explore the application of seasonal autoregressive integrated moving average (ARIMA) model in the prediction of brucellosis in Urumqi, and to use this model to predict the incidence trend of brucellosis in Urumqi. Methods The monthly incidence data of brucellosis in Urumqi from January 2010 to December 2021 were selected to construct the ARIMA prediction model. The prediction effect of the model was evaluated by mean standard deviation (RMSE) and mean absolute error (MAE). The monthly incidence of brucellosis in Urumqi in 2022 was predicted by the constructed model. Results The incidence of brucellosis in Urumqi had obvious seasonal distribution, and the cases were concentrated from May to July. ARIMA(1,1,1)(1,0,1)12 was the optimal prediction model, with RMSE=0.883 and MAE=5.24. The monthly incidence of brucellosis in Urumqi in 2022 was predicted to be 7, 4, 4, 6, 9, 9, 10, 7, 7, 5, 5, and 5 cases, respectively. Conclusion ARIMA model can well fit and predict the monthly incidence of brucellosis in Urumqi and provide a basis for the monitoring and prevention of brucellosis.
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Objective:To investigate the value of radiomics based on three-dimensional high resolution MR vessel wall imaging (3D HRMR-VWI) for identifying culprit plaques in symptomatic patients with middle cerebral atherosclerosis.Methods:The clinical and imaging features of 117 patients (139 middle cerebral artery plaques) with cerebrovascular diseases in Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from October 2018 to October 2020 were respectively reviewed. Stratified random sampling was used to divide 139 plaques into training set (97 plaques) and validation set (42 plaque) at the ratio of 7∶3. The plaques were divided into 69 culprit plaques and 70 non-culprit plaques based on plaque MR features and clinical symptoms. The clinical and imaging characteristics of culprit plaques and non-culprit plaques were compared by independent sample t-test, Mann-Whitney U test and χ 2 test, and factors with significant difference between two groups in univariate analysis were further analyzed by multivariate logistic regression to find out the independent predictors of culprit plaques. Radiomics features were extracted, screened and radiomics model was constructed using pre-and post-contrast 3D HRMR-VWI based on the training set. The combined model was constructed by combining the independent predictors and radiomics model. Receiver operating characteristic curve and area under curve (AUC) were used to evaluate the efficacy of each model, and DeLong test was used to compare the efficacy of different models. Results:Significant difference was found in intraplaque hemorrhage, lumen area of stenosis, stenosis diameter, stenosis rate, plaque burden and enhancement rate between culprit and non-culprit plaques (all P<0.05). Multivariate logistic regression analysis confirmed that only intraplaque hemorrhage was the independent predictor for culprit plaques (OR=7.045,95%CI 1.402-35.397, P=0.018). In the validation set, the AUC of the pre-contrast 3D HRMR-VWI model was lower than that of the post-contrast 3D HRMR-VWI model ( Z=-2.01, P=0.044). The AUC of pre+post-contrast 3D HRMR-VWI model was not significantly different from that of post-contrast 3D HRMR-VWI model ( Z=0.79, P=0.427). The AUC showed no significant difference between combined model and pre+post-contrast 3D HRMR-VWI model ( Z=-0.59, P>0.05). The combined model showed the best performance in predicting culprit plaques of middle cerebral artery (AUC=0.939), with the sensitivity, specificity and accuracy of 95.24%, 76.19% and 85.71%. Conclusion:Radiomics based on 3D HRMR-VWI has potential values in identifying culprit plaques in symptomatic patients with middle cerebral atherosclerosis.
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Objective To sort out the scientific research achievements in the direction of early warning of infectious diseases in China from 2001 to 2022, and analyze the research hotspots and trends in the direction of early warning of infectious diseases in China in recent 20 years, so as to provide reference for relevant policies and exploration directions. Methods Relevant literature retrieved from CNKI Chinese database was used as the data source, and Excel 2019 and Citespace 6.1.R2 software were used for visual analysis of research hotspots and frontier literature. Results A total of 1276 papers meeting requirements were obtained, and most of the research groups were relatively small and had little cooperation with others. The types of research institutions were relatively single, and most of them were domestic universities. “Infectious diseases”, “early warning” and “prediction” were the most frequently used keywords. Research on big data and COVID-19 epidemic prevention and control is the current research frontier. Conclusion There is little cooperation among authors and between institutions in the field of early warning of infectious diseases in China. Using big data to early warning of infectious diseases and improving the ability of early warning of COVID-19 are the main research directions and trends at present.
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Objective:To evaluate the value of quantitative analysis of the relative signal intensity (SI) of liver gadolinium disodium enhanced MRI in the grading of liver fibrosis.Methods:From January 2018 to October 2020, the relevant data of 131 patients who underwent gadoxetate disodium enhanced MRI examination were retrospectively analyzed in Henan Provincial People′s Hospital. All patients had histopathological results. According to the Laennec grading system of liver fibrosis, the patients were classified in F0-F1 (27 cases), F2 (19 cases), F3 (17 cases) and F4 (68 cases). The signal intensity of the liver, erector spinae and spleen were measured before and after the enhancement; and 5 post-contrast relative SI parameters were calculated, including the relative enhancement (RE), liver-to-muscle contrast ratio (LMC), liver-to-spleen contrast ratio (LSC), LMC increase rate, LSC increase rate. The differences of 5 post-contrast relative SI parameters among the different fibrosis grades were compared using one-way analysis of variance. The receiver operating characteristic (ROC) curves were drawn to evaluate the diagnostic efficacy of 5 post-contrast relative SI parameters in the diagnosis of clinically significant liver fibrosis (F2-F4), advanced liver fibrosis (F3-F4) and liver cirrhosis (F4).Results:The differences of RE, LMC, LSC, LMC increase rate, LSC increase rate among different liver fibrosis grades were statistically significant (all P<0.001). With the increasing of the degree of liver fibrosis, the RE, LMC increase rate and LSC increase rate showed decreased. ROC results showed that the area under the curve (AUC) of RE, LMC increase rate, LSC increase rate in diagnosing liver fibrosis in all levels were greater than those of LMC and LSC. The AUC values of RE, LMC increase rate, LSC increase rate in the diagnosis of significant fibrosis (F2-F4) were 0.89, 0.86, 0.83, with the sensitivity as 81.7%, 71.2%, 81.7%, and the specificity as 96.3%, 85.2%, and 74.1%, respectively. The AUC values of RE, LMC increase rate, LSC increase rate in the diagnosis of advanced liver fibrosis (F3-F4) were 0.93, 0.88, 0.86, with the sensitivity as 84.7%, 72.9%, 91.8%, and the specificity as 91.3%, 87.0 %, 71.7%; and the AUC values for diagnosing liver cirrhosis (F4) were 0.92, 0.86, 0.85, with the sensitivity as 82.4%, 76.5%, 92.7%, and the specificity as 88.9%, 81.0%, 65.1%, respectively. Conclusion:Gadoxetate disodium enhanced MRI relative SI parameters including RE, LMC increase rate and LSC increase rate might be used as a useful imaging marker in liver fibrosis grading.
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Objective@#To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. @*Materials and Methods@#Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. @*Results@#Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825–0.910) in the training cohort and 0.890 (0.844–0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. @*Conclusion@#The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.