Glucose-fed microbiota alters C. elegans intestinal epithelium and increases susceptibility to multiple bacterial pathogens.

Overconsumption of dietary sugar can lead to many negative health effects including the development of Type 2 diabetes, metabolic syndrome, cardiovascular disease, and neurodegenerative disorders. Recently, the human intestinal microbiota, strongly associated with our overall health, has also been known to be affected by diet. However, mechanistic insight into the importance of the human intestinal microbiota and the effects of chronic sugar ingestion has not been possible largely due to the complexity of the human microbiome which contains hundreds of types of organisms.

SLIM-Based High-Resolution Ion Mobility Reveals New Structural Insights into Isomeric Vitamin D Metabolites and their Isotopologues.

Testing for vitamin D deficiency remains a high-volume clinical assay, much of which is done using mass spectrometry-based methods to alleviate challenges in selectivity associated with immunoassays. Ion mobility-mass spectrometry (IM-MS) has been proposed as a rapid alternative to traditional LC-MS/MS methods, but understanding the structural ensemble that contributes to the ion mobility behavior of this molecular class is critical. Herein we demonstrate the first application of high-resolution Structures for Lossless Ion Manipulations (SLIM) IM separations of several groups of isomeric vitamin D metabolites.

Trauma surgeons experience compassion fatigue: A major metropolitan area survey.

Introduction: Compassion fatigue (CF), the physical, emotional, and psychological impact of helping others, is composed of three domains: compassion satisfaction (CS), secondary traumatic stress (STS), and burnout (BO). Trauma surgeons (TSs) experience work-related stress resulting in high rates of CF, which can manifest as physical and psychological disorders. We hypothesized that TSs experience CF and there are potentially modifiable systemic factors to mitigate its symptoms.

A Mass Spectrometry-Machine Learning Approach for Detecting Volatile Organic Compound Emissions for Early Fire Detection.

Mass spectrometry in parallel with real-time machine learning techniques were paired in a novel application to detect and identify chemically specific, early indicators of fires and near-fire events involving a set of selected materials: Mylar, Teflon, and poly(methyl methacrylate) (PMMA). The volatile organic compounds emitted during the thermal decomposition of each of the three materials were characterized using a quadrupole mass spectrometer which scanned the 1-200 / range. CO, CHCHO, and CH were the main volatiles detected during Mylar thermal decomposition, while Teflon's thermal decomposition yielded CO and a set of fluorocarbon compounds including CF CF CF, CF CFO, and CFO.