Susceptibility of hamsters to clostridium difficile isolates of differing toxinotype

Clostridium difficile is the most commonly associated cause of antibiotic associated disease (AAD), which caused ~21,000 cases of AAD in 2011 in the U.K. alone. The golden Syrian hamster model of CDI is an acute model displaying many of the clinical features of C. difficile disease. Using this model we characterised three clinical strains of C. difficile, all differing in toxinotype; CD1342 (PaLoc negative), M68 (toxinotype VIII) and BI-7 (toxinotype III). The naturally occurring non-toxic strain colonised all hamsters within 1-day post challenge (d.p.c.) with high-levels of spores being shed in the faeces of animals that appeared well throughout the entire experiment. However, some changes including increased neutrophil influx and unclotted red blood cells were observed at early time points despite the fact that the known C. difficile toxins (TcdA, TcdB and CDT) are absent from the genome. In contrast, hamsters challenged with strain M68 resulted in a 45% mortality rate, with those that survived challenge remaining highly colonised. It is currently unclear why some hamsters survive infection, as bacterial and toxin levels and histology scores were similar to those culled at a similar time-point. Hamsters challenged with strain BI-7 resulted in a rapid fatal infection in 100% of the hamsters approximately 26 hr post challenge. Severe caecal pathology, including transmural neutrophil infiltrates and extensive submucosal damage correlated with high levels of toxin measured in gut filtrates ex vivo. These data describes the infection kinetics and disease outcomes of 3 clinical C. difficile isolates differing in toxin carriage and provides additional insights to the role of each toxin in disease progression

Diurnal Regulation of Lipid Metabolism and Applications of Circadian Lipidomics

AbstractThe circadian timing system plays a key role in orchestrating lipid metabolism. In concert with the solar cycle, the circadian system ensures that daily rhythms in lipid absorption, storage, and transport are temporally coordinated with rest-activity and feeding cycles. At the cellular level, genes involved in lipid synthesis and fatty acid oxidation are rhythmically activated and repressed by core clock proteins in a tissue-specific manner. Consequently, loss of clock gene function or misalignment of circadian rhythms with feeding cycles (e.g., in shift work) results in impaired lipid homeostasis. Herein, we review recent progress in circadian rhythms research using lipidomics, i.e., large-scale profiling of lipid metabolites, to characterize circadian-regulated lipid pathways in mammals. In mice, novel regulatory circuits involved in fatty acid metabolism have been identified in adipose tissue, liver, and muscle. Extensive diversity in circadian regulation of plasma lipids has also been revealed in humans using lipidomics and other metabolomics approaches. In future studies, lipidomics platforms will be increasingly used to better understand the effects of genetic variation, shift work, food intake, and drugs on circadian-regulated lipid pathways and metabolic health

Effective extraction of ventricles and myocardium objects from cardiac magnetic resonance images with a multi-task learning U-net.

Accurate extraction of semantic objects such as ventricles and myocardium from magnetic resonance (MR) images is one essential but very challenging task for the diagnosis of the cardiac diseases. To tackle this problem, in this paper, an automatic end-to-end supervised deep learning framework is proposed, using a multi-task learning based U-Net (MTL-UNet). Specifically, an edge extraction module and a fusion-based module are introduced for effectively capturing the contextual information such as continuous edges and consistent spatial patterns in terms of intensity and texture features. With a weighted triple loss including the dice loss, the cross-entropy loss and the edge loss, the accuracy of object segmentation and extraction has been effectively improved. Extensive experiments on the publicly available ACDC 2017 dataset have validated the efficacy and efficiency of the proposed MTL-UNet model