Profiling the fecal microbiome and its modulators across the lifespan in the Netherlands.

Defining what constitutes a healthy microbiome throughout our lives remains an ongoing challenge. Understanding to what extent host and environmental factors can influence it has been the primary motivation for large population studies worldwide. Here, we describe the fecal microbiome of 3,746 individuals (0-87 years of age) in a nationwide study in the Netherlands, in association with extensive questionnaires.

AlGaN-Based Deep Ultraviolet Light-Emitting Diodes with Thermally Oxidized Al Ga O Sidewalls.

AlGaN and GaN sidewalls were turned into Al Ga O and GaO, respectively, by thermal oxidation to improve the optoelectrical characteristics of deep ultraviolet (DUV) light-emitting diodes (LEDs). The thermally oxidized GaO is a single crystal with nanosized voids homogenously distributed inside the layer. Two oxidized Al Ga O layers were observed on the sidewall of the AlGaN layer in transmission electron microscopy images.

High glucose enhances lipopolysaccharide-induced inflammation in cultured BV2 microglial cell line.

Introduction: Diabetes mellitus emerges as a global health crisis and is related to the development of neurodegenerative diseases. Microglia, a population of macrophages-like cells, govern immune defense in the central nervous system. Activated microglia are known to play active roles in the pathogenesis of neurodegenerative diseases.

Cost impact of procalcitonin-guided decision making on duration of antibiotic therapy for suspected early-onset sepsis in neonates.

Backgrounds: The large, international, randomized controlled NeoPInS trial showed that procalcitonin (PCT)-guided decision making was superior to standard care in reducing the duration of antibiotic therapy and hospitalization in neonates suspected of early-onset sepsis (EOS), without increased adverse events. This study aimed to perform a cost-minimization study of the NeoPInS trial, comparing health care costs of standard care and PCT-guided decision making based on the NeoPInS algorithm, and to analyze subgroups based on country, risk category and gestational age.

Methods: Data from the NeoPInS trial in neonates born after 34 weeks of gestational age with suspected EOS in the first 72 h of life requiring antibiotic therapy were used.

Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis.

Background: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk factors (RFs), clinical signs and biomarkers and to develop a prediction model for culture-proven EOS. We hypothesized that the contribution to diagnostic accuracy of biomarkers is higher than of RFs or clinical signs.