Background Brucellosis is a major public health problem that seriously affects developing countries and could cause significant economic losses to the livestock industry and great harm to human health. Reasonable prediction of the incidence is of great significance in controlling brucellosis and taking preventive measures. Methods Our human brucellosis incidence data were extracted from Shanxi Provincial Center for Disease Control and Prevention. We used seasonal-trend decomposition using Loess (STL) and monthplot to analyse the seasonal characteristics of human brucellosis in Shanxi Province from 2007 to 2017. The autoregressive integrated moving average (ARIMA) model, a combined model of ARIMA and the back propagation neural network (ARIMA-BPNN), and a combined model of ARIMA and the Elman recurrent neural network (ARIMA-ERNN) were established separately to make predictions and identify the best model. Additionally, the mean squared error (MAE), mean absolute error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the performance of the model. Results We observed that the time series of human brucellosis in Shanxi Province increased from 2007 to 2014 but decreased from 2015 to 2017. It had obvious seasonal characteristics, with the peak lasting from March to July every year. The best fitting and prediction effect was the ARIMA-ERNN model. Compared with those of the ARIMA model, the MAE, MSE and MAPE of the ARIMA-ERNN model decreased by 18.65, 31.48 and 64.35%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 60.19, 75.30 and 64.35%, respectively. Second, compared with those of ARIMA-BPNN, the MAE, MSE and MAPE of ARIMA-ERNN decreased by 9.60, 15.73 and 11.58%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 31.63, 45.79 and 29.59%, respectively. Conclusions The time series of human brucellosis in Shanxi Province from 2007 to 2017 showed obvious seasonal characteristics. The fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA-BPNN and ARIMA models. This will provide some theoretical support for the prediction of infectious diseases and will be beneficial to public health decision making.

In recent years, due to their structural diversity, adjustability, versatility, and excellent electrochemical properties, organic compounds with nitrogen‐containing groups (OCNs) have become some of the most promising organic electrode materials. The nitrogen‐containing groups acting as electrochemical active sites include carbon–nitrogen groups, nitrogen–nitrogen groups, nitrogen–oxygen groups in OCNs, and nitrogen‐containing groups in covalent organic frameworks. The molecular structure regulation of OCNs with nitrogen‐containing groups acting as electrochemical active centers can suppress dissolution in electrolytes, increase electronic conductivity, and improve the kinetics of redox reactions. The kinetics behavior and electrochemical characteristics of OCN electrode materials in alkali metal rechargeable batteries with organic electrolytes are reviewed, and the related relationships between the structure and electrochemical properties of OCNs are the core of this review. Herein, the electrochemical reaction mechanisms and the strategies to improve the electrochemical activity of nitrogen‐containing groups in OCNs are clarified, and the conjugate molecular structure of OCNs is shown to be an important direction for improvement. These results will have implications for research on electrode materials and provide more choices for rechargeable batteries. Moreover, this work will guide the study of more efficient OCNs that can be used as electrode materials.

The optimal initial treatment strategy in nonesmall-cell lung cancer patients initially diagnosed with 1 to 3 synchronous brain metastases remains unknown. A total of 252 patients were enrolled onto our study to conduct a comprehensive analysis. Patients in the allelocal therapy (LT) group had a longer median overall survival than patients in the part-LT group and non-LT group. LT to both lung and brain lesions deserves consideration as an initial treatment strategy. Background: The treatment options for newly diagnosed nonesmall-cell lung cancer (NSCLC) patients with 1 to 3 synchronous brain metastases (BM) remain controversial. The current study aimed to comprehensively analyze the characteristics, local treatment paradigms, and survival outcomes in these populations. Patients and Methods: A total of 252 NSCLC patients initially diagnosed with 1 to 3 synchronous brain-only metastases were enrolled onto this study. Local therapy (LT) to primary lung tumors (PLT) and BM included surgery, radiotherapy, or both. Median overall survival (mOS) was measured among patients who received LT to both PLT and BM (all-LT group), patients who were treated with LT to either PLT or BM (part-LT group), and patients who did not receive any LT (non-LT group). Results:The mOS for all-LT (n ¼ 70), part-LT (n ¼ 113), and non-LT (n ¼ 69) groups was 33.2, 18.5, and 16.8 months, respectively (P ¼ .002). The OS rates at 5 years for the all-LT, part-LT, and non-LT groups were 25.5%, 16.2%, and 0, respectively. Multivariable analysis revealed that all-LT versus non-LT, pretreatment Karnofsky performance status > 70, histology of adenocarcinoma, thoracic stage I-II, EGFR mutation, ALK positive, and second-line systemic therapies were independent prognostic factors for improved mOS. Conclusions: The current study showed that LT for both PLT and BM is associated with superior OS in appropriately selected NSCLC patients initially diagnosed with 1 to 3 synchronous BM. Prospective trials are urgently needed to confirm this finding.

Lung cancer is one of the most common causes of brain metastases and is always associated with poor prognosis. We investigated the immunophenotypes of primary lung tumors and paired brain metastases, as well as immunophenotypes in the synchronous group (patients with brain metastases upon initial diagnosis) and metachronous group (patients developed brain metastases during the course of their disease). RNA sequencing of eighty-six samples from primary lung tumors and paired brain metastases of 43 patients was conducted to analyze the tumor immune microenvironment. Our data revealed that matched brain metastases compared with primary lung tumors exhibited reduced tumor infiltrating lymphocytes (TILs), a higher fraction of neutrophils infiltration, decreased scores of immune-related signatures, and a lower proportion of tumor microenvironment immune type I (high PD-L1/high CD8A) tumors. Additionally, we found a poor correlation of PD-L1 expression between paired brain metastases and primary lung tumors. In addition, gene set enrichment analysis (GSEA) showed that some gene sets associated with the immune response were enriched in the metachronous group, while other gene sets associated with differentiation and metastasis were enriched in the synchronous group in the primary lung tumors. Moreover, the tumor immune microenvironment between paired brain metastases and primary lung tumors displayed more differences in the metachronous group than in the synchronous group. Our work illustrates that brain metastatic tumors are more immunosuppressed than primary lung tumors, which may help guide immunotherapeutic strategies for NSCLC brain metastases.

Background Diabetes Mellitus (DM) has become the third chronic non-communicable disease that hits patients after tumors, cardiovascular and cerebrovascular diseases, and has become one of the major public health problems in the world. Therefore, it is of great importance to identify individuals at high risk for DM in order to establish prevention strategies for DM. Methods Aiming at the problem of high-dimensional feature space and high feature redundancy of medical data, as well as the problem of data imbalance often faced. This study explored different supervised classifiers, combined with SVM-SMOTE and two feature dimensionality reduction methods (Logistic stepwise regression and LAASO) to classify the diabetes survey sample data with unbalanced categories and complex related factors. Analysis and discussion of the classification results of 4 supervised classifiers based on 4 data processing methods. Five indicators including Accuracy, Precision, Recall, F1-Score and AUC are selected as the key indicators to evaluate the performance of the classification model. Results According to the result, Random Forest Classifier combining SVM-SMOTE resampling technology and LASSO feature screening method (Accuracy = 0.890, Precision = 0.869, Recall = 0.919, F1-Score = 0.893, AUC = 0.948) proved the best way to tell those at high risk of DM. Besides, the combined algorithm helps enhance the classification performance for prediction of high-risk people of DM. Also, age, region, heart rate, hypertension, hyperlipidemia and BMI are the top six most critical characteristic variables affecting diabetes. Conclusions The Random Forest Classifier combining with SVM-SMOTE and LASSO feature reduction method perform best in identifying high-risk people of DM from individuals. And the combined method proposed in the study would be a good tool for early screening of DM.