Antimicrobial resistance increased over an 8-year period in Enterobacteriaceae cultured from canine urine samples.

OBJECTIVES: The aims of the present study were to describe the prevalence of positive urinary bacterial culture in dogs, to identify the most commonly isolated microorganisms and to analyse changes in antimicrobial susceptibility patterns over time. MATERIAL AND METHODS: A retrospective case series was performed using culture and susceptibility results from canine urine samples collected between January 2010 and December 2017. The presence or absence of infection, identity of the bacterium with heaviest growth, and susceptibility profile were recorded for each sample. Trends in the frequency of positive culture and antimicrobial resistance were assessed by Poisson regression modelling. Prevalence rate ratio and 95% confidence interval were reported for resistance to each antimicrobial. RESULTS: A positive urine culture was documented in 771 (22.5%) of 3420 samples. Escherichia coli was the most commonly isolated microorganism. There was no significant increase in the frequency of positive bacterial culture over the study period (prevalence rate ratio 0.98; 95% confidence interval: 0.92 to 1.0). Overall, there was an increase in antimicrobial resistance within Enterobacteriaceae from 5.2 to 35.6%. The prevalence of multidrug-resistant bacteria varied from year to year throughout the study period. However, the Poisson regression model identified a significant increase in the frequency of multidrug-resistant Enterobacteriaceae over this period, averaging approximately 22% per year (prevalence rate ratio 1.22, 95% confidence interval: 1.06 to 1.42). CLINICAL SIGNIFICANCE: The significant increase in antimicrobial resistance observed in this study is concerning and may have implications for veterinary and public health. Appropriate measures, such as antibiotic stewardship programmes, should be implemented to address increasing antimicrobial resistance.

Altered dynamics may drift pathological fibrillization in membraneless organelles.

Protein phase transition can generate non-membrane bound cellular compartments, which can convert from liquid-like to solid-like states. While the molecular driving forces of phase separation have been largely understood, much less is known about the mechanisms of material-state conversion. We apply a recently developed algorithm to describe the weak interaction network of multivalent motifs, and simulate the effect of pathological mutations. We demonstrate that linker dynamics is critical to the material-state of biomolecular condensates. We show that linker flexibility/mobility is a major regulator of the weak, heterogeneous meshwork of multivalent motifs, which promotes phase transition and maintains a liquid-like state. Decreasing linker dynamics increases the propensity of amyloid-like fragments via hampering the motif-exchange and reorganization of the weak interaction network. In contrast, increasing linker mobility may compensate rigidifying mutations, suggesting that the meshwork of weak, variable interactions may provide a rescue mechanism from aggregation. Motif affinity, on the other hand, has a moderate impact on fibrillization. Here we demonstrate that the fuzzy framework provides an efficient approach to handle the intricate organization of membraneless organelles, and could also be applicable to screen for pathological effects of mutations.

Classifier based on support vector machine for JET plasma configurations.

The last flux surface can be used to identify the plasma configuration of discharges. For automated recognition of JET configurations, a learning system based on support vector machines has been developed. Each configuration is described by 12 geometrical parameters. A multiclass system has been developed by means of the one-versus-the-rest approach. Results with eight simultaneous classes (plasma configurations) show a success rate close to 100%.