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This study aimed to develop a simple experimental system utilising bacterial cells to investigate the dose responses resulting from exposures to static magnetic flux densities ranging from 0.05 to 0.5 T on viability, bacterial metabolism and levels of DNA damage in Streptococcus pyogenes. Exposure of S. pyogenes to a field of 0.3 T at 24 degrees C under anaerobic conditions resulted in a significant (P < 0.05) decrease in growth rate, with an increased mean generation time of 199 +/- 6 min compared to the control cells at 165 +/- 6 min (P < 0.05). Conversely, exposure to magnetic fields of 0.5 T significantly accelerated the growth rate at 24 degrees C compared to control cells, with a decreased mean generation time of 147 +/- 4 min (P < 0.05). The patterns of metabolite release from cells incubated in phosphate buffered saline (PBS) at 24 degrees C and exposed to different magnetic flux densities (0.05-0.5 T) were significantly (P < 0.05) altered, compared to non-exposed controls. Concentrations of metabolites, with the exception of aspartic acid (r = 0.44), were not linearly correlated with magnetic flux density, with all other r < 0.20. Instead, "window" effects were observed, with 0.25-0.3 T eliciting the maximal release of the majority of metabolites, suggesting that magnetic fields of these strengths had significant impacts on metabolic homeostasis in S. pyogenes. The exposure of cells to 0.3 T was also found to significantly reduce the yield of 8-hydroxyguanine in extracted DNA compared to controls, suggesting some possible anti-oxidant protection to S. pyogenes at this field strength.

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The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information.Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.