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This paper evaluates the general equilibrium trade and welfare effects of the Belt and Road Initiative (BRI). We estimate the partial trade effect of the BRI using the synthetic control method. By feeding the implied trade cost reduction into counterfactual equations, we simulate the general equilibrium trade and welfare effects of the BRI based on the structural gravity model. We find that the BRI has greatly increased China's and BRI members' trade and welfare, with little impact on non‐BRI members. [ABSTRACT FROM AUTHOR]

With the rapid development of imaging technology and comprehensive treatment in modern medicine, the early diagnosis rate of breast cancer is constantly improving, and the prognosis is also improving; As breast cancer patients survive longer, the risk of developing second primary cancers increases. Since both breast and thyroid are Hormone receptor sensitive organs, which are regulated by hypothalamus pituitary target gland endocrine axis, changes in body endocrine status may lead to the occurrence of these two diseases in succession or simultaneously. This study extracted clinical data and survival outcomes of breast cancer patients registered in the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2019. After matching the case and controls with propensity scores, the selected patients were randomly split into training and test datasets at a ratio of 7:3. Univariate and multivariate COX proportional regression analysis is used to determine independent risk factors for secondary thyroid cancer and construct a column chart prediction model. Age, ethnicity, whether radiotherapy, tumor primary location, N stage, M stage were identified by Cox regression as independent factors affecting secondary thyroid cancer in patients with breast cancer patients, and a risk factor nomogram was established to predict patients' 3 and 5 year survival probabilities. The AUC values for 3 and 5 years in the training set were 0.713, 0.707, and the c-index was 0.693 (95% CI 0.67144, 0.71456), and the AUC values for 3 and 5 years in the validation set were 0.681, 0.681, and the c-index was 0.673 (95% CI 0.64164, 0.70436), respectively., (© 2024. The Author(s).)

Background: Osteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a need to develop a method to identify those at high risk for osteoporosis., Objective: This study aimed to develop a machine learning algorithm to effectively identify people with low bone density, using readily available demographic and blood biochemical data., Methods: Using NHANES 2017-2020 data, participants over 50 years old with complete femoral neck BMD data were selected. This cohort was randomly divided into training (70%) and test (30%) sets. Lasso regression selected variables for inclusion in six machine learning models built on the training data: logistic regression (LR), support vector machine (SVM), gradient boosting machine (GBM), naive Bayes (NB), artificial neural network (ANN) and random forest (RF). NHANES data from the 2013-2014 cycle was used as an external validation set input into the models to verify their generalizability. Model discrimination was assessed via AUC, accuracy, sensitivity, specificity, precision and F1 score. Calibration curves evaluated goodness-of-fit. Decision curves determined clinical utility. The SHAP framework analyzed variable importance., Results: A total of 3,545 participants were included in the internal validation set of this study, of whom 1870 had normal bone density and 1,675 had low bone density Lasso regression selected 19 variables. In the test set, AUC was 0.785 (LR), 0.780 (SVM), 0.775 (GBM), 0.729 (NB), 0.771 (ANN), and 0.768 (RF). The LR model has the best discrimination and a better calibration curve fit, the best clinical net benefit for the decision curve, and it also reflects good predictive power in the external validation dataset The top variables in the LR model were: age, BMI, gender, creatine phosphokinase, total cholesterol and alkaline phosphatase., Conclusion: The machine learning model demonstrated effective classification of low BMD using blood biomarkers. This could aid clinical decision making for osteoporosis prevention and management., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xu, Chen, Yao, Wu, Cui, Wang, Diao, Jin, Hong and Li.)