Conventional and molecular methods used in the detection and subtyping of Yersinia enterocolitica in food.

Yersinia enterocolitica is an important foodborne pathogen, but the prevalence in food is underestimated due to drawbacks in the detection methods. Problems arise from the low concentration of pathogenic strains present in food samples, similarities with other Enterobacteriaceae and Y. enterocolitica-like species and the heterogeneity of Y. enterocolitica as it comprises both pathogenic and non-pathogenic isolates. New rapid, cost-effective and more sensitive culture media and molecular techniques have been developed to overcome the drawbacks of conventional culture methods. Recent molecular subtyping methods have been applied to Y. enterocolitica strains to track infection sources and to investigate phylogenetic relationships between different Yersinia strains. Further application of modern subtyping tools such as WGS in a variety of bioserotypes, and comparison with other members of the genus will help to better understanding of the virulence determinants of pathogenic Y. enterocolitica, its mechanisms to cope in the host environments, and can contribute to the development of more specific detection and typing strategies.

In vitro antimicrobial activity of five essential oils on multidrug resistant Gram-negative clinical isolates.

AIM/BACKGROUND: The emergence of drug-resistant pathogens has drawn attention on medicinal plants for potential antimicrobial properties. The objective of the present study was the investigation of the antimicrobial activity of five plant essential oils on multidrug resistant Gram-negative bacteria. MATERIALS AND METHODS: Basil, chamomile blue, origanum, thyme, and tea tree oil were tested against clinical isolates of Acinetobacter baumannii (n = 6), Escherichia coli (n = 4), Klebsiella pneumoniae (n = 7), and Pseudomonas aeruginosa (n = 5) using the broth macrodilution method. RESULTS: The tested essential oils produced variable antibacterial effect, while Chamomile blue oil demonstrated no antibacterial activity. Origanum, Thyme, and Basil oils were ineffective on P. aeruginosa isolates. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration values ranged from 0.12% to 1.50% (v/v) for tea tree oil, 0.25-4% (v/v) for origanum and thyme oil, 0.50% to >4% for basil oil and >4% for chamomile blue oil. Compared to literature data on reference strains, the reported MIC values were different by 2SD, denoting less successful antimicrobial activity against multidrug resistant isolates. CONCLUSIONS: The antimicrobial activities of the essential oils are influenced by the strain origin (wild, reference, drug sensitive, or resistant) and it should be taken into consideration whenever investigating the plants' potential for developing new antimicrobials.

Cardiac electrophysiology numerical models using symmetric multiprocessing (SMP).

Multi-dimensional electrophysiological models have been introduced to investigate electrical propagation in tissue level, based on cell-dynamics models. The models include a set of non-linear differential equations which describe the dynamics of cell and tissue excitation. However, as models evolve, it is inevitable that proper and powerful tools need to be introduced in order to reproduce the detailed and thus computationally intensive simulations. To build such tools, several computational methodologies need to be adopted regarding efficiency and reliability. On the other hand improvements apply to the hardware too. State of the art computers, even personal computers, tend to make use of multiple core Central Processing Units. Unfortunately the aforementioned methodologies follow sequential logic, resulting to low efficiency of the working platform. In this work we present the performance bottleneck in symmetric multiprocessing (SMP) for simulations of propagation phenomena in cardiac tissue electrophysiological models. We demonstrate the scalability and efficacy of the different methodologies used in the discretisation scheme and message passing in SMP.

A computational framework for the analysis of biological models.

We present a computational framework for the visualization of large datasets produced from simulated multidimensional biological cell and tissue models. In this work we present a subsystem of a complete biological simulation system called BioPSiS (Biological Process Simulation System). The host system consists of special subsystems which monitor the simulation process and visualize its results. The mathematical computations in BioPSis can be performed either in a simple processing unit or in a distributed unit. The functionality of the subsystem presented here provides an interactive tool. In particular this intuitive visualization subsystem facilitates the analysis of the simulation results as well as the evaluation of the computational efficiency. Case studies and experiments demonstrate the efficacy of the proposed approach in terms of its ease to use, as well as its various features such as the capability to extract complex information from the output data for further study.