Japanese encephalitis (JE) virus principally infects neuron systems of animals and causes severe encephalitis. The mechanism by which the virus enters the central nervous system (CNS) from the circulatory system remains elusive. In this study, electron-microscopic techniques have been used to determine these sequential events in the suckling mouse brain. The results indicate that (1) endocytosis is employed when JE virus is transported across the cerebral blood vessels (CBV) and breaches the blood-brain barrier (BBB). (2) Uncoated vesicles, which may be caveolae, and coated vesicles are involved in the endocytic and transcytotic vesicles of capillary endothelium and pericytes. (3) The JE virus is transported in endocytic vesicles across the endothelial cells and pericytes. (4) Endocytosis and transportation of JE virus in pericytes seems to be the same as that in endothelial cells. (5) The interaction of the viral envelope and cell membrane of endothelial cells and pericytes plays an important role in the endocytosis. This study elucidates the infectious processes of JE virus entering the CNS from the circulatory system in the mouse brain.

This study is the first to demonstrate that the pathway regulated by the sensor kinase BfmS is associated with biofilm formation, adherence to biotic surfaces, serum resistance, and antibiotic susceptibility, which may be associated with the release of Omps in A. baumannii.

Periodontitis is an inflammatory disease involving complex interactions between oral microorganisms and the host immune response. Understanding the structure of the microbiota community associated with periodontitis is essential for improving classifications and diagnoses of various types of periodontal diseases and will facilitate clinical decision-making. In this study, we used a 16S rRNA metagenomics approach to investigate and compare the compositions of the microbiota communities from 76 subgingival plagues samples, including 26 from healthy individuals and 50 from patients with periodontitis. Furthermore, we propose a novel feature selection algorithm for selecting features with more information from many variables with a combination of these features and machine learning methods were used to construct prediction models for predicting the health status of patients with periodontal disease. We identified a total of 12 phyla, 124 genera, and 355 species and observed differences between health- and periodontitis-associated bacterial communities at all phylogenetic levels. We discovered that the genera Porphyromonas, Treponema, Tannerella, Filifactor, and Aggregatibacter were more abundant in patients with periodontal disease, whereas Streptococcus, Haemophilus, Capnocytophaga, Gemella, Campylobacter, and Granulicatella were found at higher levels in healthy controls. Using our feature selection algorithm, random forests performed better in terms of predictive power than other methods and consumed the least amount of computational time.