Efficacy, safety, and immunology of an inactivated alum-adjuvant enterovirus 71 vaccine in children in China: a multicentre, randomised, double-blind, placebo-controlled, phase 3 trial.

BACKGROUND: A vaccine for enterovirus 71 (EV71) is needed to address the high burden of disease associated with infection. We assessed the efficacy, safety, immunogenicity, antibody persistence, and immunological correlates of an inactivated alum-adjuvant EV71 vaccine. METHODS: We did a randomised, double-blind, placebo-controlled, phase 3 trial. Healthy children aged 6-35 months from four centres in China were randomly assigned (1:1) to receive vaccine or alum-adjuvant placebo at day 0 and 28, according to a randomisation list (block size 30) generated by an independent statistician. Investigators and participants and their guardians were masked to the assignment. Primary endpoints were EV71-associated hand, foot, and mouth disease (HFMD) and EV71-associated disease during the surveillance period from day 56 to month 14, analysed in the per-protocol population. This study is registered with ClinicalTrials.gov, number NCT01508247. FINDINGS: 10,245 participants were enrolled and assigned: 5120 to vaccine versus 5125 to placebo. 4907 (with three cases of EV71-associated HFMD and eight cases of EV71-associated disease) versus 4939 (with 30 cases of EV71-associated HFMD and 41 cases of EV71-associated disease) were included in the primary efficacy analysis. Vaccine efficacy was 90·0% (95% CI 67·1-96·9) against EV71-associated HFMD (p=0·0001) and 80·4% (95% CI 58·2-90·8) against EV71-associated disease (p<0·0001). Serious adverse events were reported by 62 of 5117 (1·2%) participants in the vaccine group versus 75 of 5123 (1·5%) in the placebo group (p=0·27). Adverse events occurred in 3644 (71·2%) versus 3603 (70·3%; p=0·33). INTERPRETATION: EV71 vaccine provides high efficacy, satisfactory safety, and sustained immunogenicity. FUNDING: China's 12-5 National Major Infectious Disease Program, Beijing Vigoo Biological.

[Soil Cadmium Prediction and Health Risk Assessment of an Oasis on the Eastern Edge of the Tarim Basin Based on Feature Optimization and Machine Learning].

Soil heavy metal pollution poses a serious threat to food security, human health, and soil ecosystems. Based on 644 soil samples collected from a typical oasis located at the eastern margin of the Tarim Basin, a series of models, namely, multiple linear regression （LR）, neural network （BP）, random forest （RF）, support vector machine （SVM）, and radial basis function （RBF）, were built to predict the soil heavy metal content. The optimal prediction result was obtained and utilized to analyze the spatial distribution features of heavy metal contamination and relevant health risks. The outcomes demonstrated that： ① The average Cd content in the study area was 0.14 mg·kg-1, which was 1.17 times the soil background value of Xinjiang, making it the primary factor of soil heavy metal contamination in the area. Additionally, the carcinogenicity risk coefficients of Cd for both adults and children were less than 10-4, indicating that there were no significant long-term health risks for humans in the area. ② The estimation accuracies of the five inversion models were compared, and the validation set of the RF model had an R2 value of 0.763 7, which was the highest among the five models. Additionally, the RMSE, MAE, and MBE of the RF model were the smallest among the five models. Therefore, the predicted values of the RF model were most consistent with the measured values of the soil Cd content. The predicted map of soil Cd distribution derived from the RF model coincided best with the interpolation map. ③ The RF model outperformed the other four models in predicting health risks associated with the soil Cd element for both adults and children, resulting in better prediction results. Comparatively, the predicted values of the LR model in the validation set varied greatly, leading to unreliable results. It was demonstrated that the RF was the best model for predicting soil Cd content and evaluating health risks in the study area, considering its superior generalization capability and anti-overfitting ability.