To date, 4 unique enamelin gene (ENAM) defects have been identified in kindreds with amelogenesis imperfecta. To improve our understanding of the roles of enamelin in normal enamel formation, and to gain information related to possible genotype/phenotype correlations, we have identified 2 ENAM mutations in kindreds with hypoplastic ADAI, 1 novel (g.4806A>C, IVS6-2A>C) and 1 previously identified (g.8344delG), and have characterized the resulting enamel phenotypes. The IVS6-2A>C mutation caused a severe enamel phenotype in the proband, exhibiting horizontal grooves of severely hypoplastic enamel. The affected mother had several shallow hypoplastic horizontal grooves in the lower anterior teeth. In the case of the g.8344delG mutation, the phenotype was generalized hypoplastic enamel with shallow horizontal grooves in the middle 1/3 of the anterior teeth. In general, mutations in the human enamelin gene cause hypoplastic enamel, often with horizontal grooves, but the severity of the enamel defects is variable, even among individuals with the same mutation.

We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks.