Decrease of Pneumococcal Community-Acquired Pneumonia Hospitalization and Associated Complications in Children after the Implementation of the 13-Valent Pneumococcal Conjugate Vaccine (PCV13) in Taiwan.

The impact of the 13-valent pneumococcal conjugate vaccine (PCV13) on overall community-acquired pneumonia (CAP) and disease severity still needs thorough evaluation. In this study, we retrieve both pneumococcal CAP (P-CAP) and unspecific CAP (U-CAP) inpatient data from the Taiwan National Health Insurance Database (NHID) between 2005 and 2016. The interrupted time-series (ITS) analysis was performed to compare the incidence trend before and after the implementation of PCV13. After PCV13 implementation, there is a significant decreasing trend of P-CAP hospitalization, especially in children <1 year, 2-5 years, adults aged 19-65 years, 66 years, or older (all p value < 0.05). This corresponds to a 59% reduction in children <1 year, 47% in children aged 2-5 years, 39% in adult aged 19-65 years, and 41% in elderly aged 66 years or older. The intensive care rate (6.8% to 3.9%), severe pneumonia cases (21.7 to 14.5 episodes per 100,000 children-years), and the need for invasive procedures (4.3% to 2.0%) decreased in children aged 2-5 years (p value < 0.0001) with P-CAP. This PCV13 implementation program in Taiwan not only reduced the incidence of P-CAP, but also attenuated disease severity, especially in children aged 2-5 years.

Studies of Coumarin Derivatives for Constitutive Androstane Receptor (CAR) Activation.

Constitutive androstane receptor (CAR) activation has found to ameliorate diabetes in animal models. However, no CAR agonists are available clinically. Therefore, a safe and effective CAR activator would be an alternative option. In this study, sixty courmarin derivatives either synthesized or purified from Artemisia capillaris were screened for CAR activation activity. Chemical modifications were on position 5,6,7,8 with mono-, di-, tri-, or tetra-substitutions. Among all the compounds subjected for in vitro CAR activation screening, 6,7-diprenoxycoumarin was the most effective and was selected for further preclinical studies. Chemical modification on the 6 position and unsaturated chains were generally beneficial. Electron-withdrawn groups as well as long unsaturated chains were hazardous to the activity. Mechanism of action studies showed that CAR activation of 6,7-diprenoxycoumarin might be through the inhibition of EGFR signaling and upregulating PP2Ac methylation. To sum up, modification mimicking natural occurring coumarins shed light on CAR studies and the established screening system provides a rapid method for the discovery and development of CAR activators. In addition, one CAR activator, scoparone, did showed anti-diabetes effect in db/db mice without elevation of insulin levels.

Constructing and validating readability models: the method of integrating multilevel linguistic features with machine learning.

Multilevel linguistic features have been proposed for discourse analysis, but there have been few applications of multilevel linguistic features to readability models and also few validations of such models. Most traditional readability formulae are based on generalized linear models (GLMs; e.g., discriminant analysis and multiple regression), but these models have to comply with certain statistical assumptions about data properties and include all of the data in formulae construction without pruning the outliers in advance. The use of such readability formulae tends to produce a low text classification accuracy, while using a support vector machine (SVM) in machine learning can enhance the classification outcome. The present study constructed readability models by integrating multilevel linguistic features with SVM, which is more appropriate for text classification. Taking the Chinese language as an example, this study developed 31 linguistic features as the predicting variables at the word, semantic, syntax, and cohesion levels, with grade levels of texts as the criterion variable. The study compared four types of readability models by integrating unilevel and multilevel linguistic features with GLMs and an SVM. The results indicate that adopting a multilevel approach in readability analysis provides a better representation of the complexities of both texts and the reading comprehension process.