Invasive infections caused by Corynebacterium diphtheriae in vaccinated and non-vaccinated individuals have been reported increasingly. In this study we used multilocus sequence typing (MLST) to study genetic relationships between six invasive strains of this bacterium isolated solely in the urban area of Rio de Janeiro, Brazil, during a 10-year period. Of note, all the strains rendered negative results in PCR reactions for the tox gene, and four strains presented an atypical sucrose-fermenting ability. Five strains represented new sequence types. MLST results did not support the hypothesis that invasive (sucrose-positive) strains of C. diphtheriae are part of a single clonal complex. Instead, one of the main findings of the study was that such strains can be normally found in clonal complexes with strains related to non-invasive disease. Comparative analyses with C. diphtheriae isolated in different countries provided further information on the geographical circulation of some sequence types.

Objective: The tcdC gene of C. difficile negatively regulates the production of toxins A and B. Mutations within this gene may result in loss of the negative regulatory role, potentially leading to hyperproduction of toxins and increased virulence. This hypervirulent strain caused an epidemic of C. difficile-associated disease in Quebec in 2002 and has been seen in increasing numbers in the western provinces, including BC. The objective of this study was to produce a baseline of the molecular and phenotypic profile of strains from participating centres to complement future infection control surveillance programs for C. difficile infection in BC. Methods: Over a one month period in March 2008, all stool specimens positive for C. difficile testing were cultured at participating laboratories or forwarded to the reference laboratory (BCCDC) for culture. Methods were standardized across participating laboratories. C. difficile was identified by colony morphology, Gram-stain, aerotolerance and C. difficile latex agglutination kit. The presence of the tcdC gene deletion was determined for each isolate using fragment analysis PCR. A fingerprint pattern was generated using pulsed-field gel electrophoresis (PFGE) and assigned PFGE pattern numbers and NAP type using the Canadian C. difficile database. Results: The total number of specimens received over the month of March 2008 from all participating sites was 414. Of these, 368 were culture confirmed cases of C. difficile. The tcdC gene mutation was detected in 189 (51.4%) isolates and the remaining 179 (48.6%) were wildtype strains with no mutation. Of those detecting a mutation, 145 (76.7%) displayed a mutation at ( 18)( 1) in the tcdC gene, associated with NAP1 PFGE designation and the remaining 29 (15.3%) showed a mutation other than at the ( 18)( 1) region. 156 (42.4%) of the total cases matched the NAP1 PFGE designation. Less common were PFGE patterns with NAP2 designation (9.5%) and NAP4 designation (7.8%). Conclusions: The prevalence of the NAP1 strain of C. difficile in the province of BC was 42% for March 2008. Of the C. difficile isolates recovered, the majority contained mutation at ( 18)( 1), with 15.3% exhibiting mutations at other sites within PaLoc. This study provides a baseline for infection control and public health functions in BC. O30 The use of a multi locus sequence typing scheme to characterize Canadian isolates of Corynebacterium diphtheriae K. Bernard1 *, S. Schindle2, T. Burdz1, D. Wiebe1, A. Reimer, C. Dowson3, F. Bolt3, A. Baldwin3. 1National Microbiology Laboratory, Winnipeg, Canada, 2University of Manitoba, Winnipeg, Canada, 3University of Warwick, Warwick, United Kingdom

This paper demonstrates the application of chemical headspace analysis to the problem of classifying the presence of bacteria in biomedical samples by using computational tools. Blood and urine samples of disparate forms were analysed using a Cyrano Sciences C320 electronic nose together with an Agilent 4440 Chemosensor. The high dimensional data sets resulting from these devices present computational problems for parameter estimation of discriminant models. A variety of data reduction and pattern recognition techniques were employed in an attempt to optimise the classification process. A 100% successful classification rate for the blood data from the Agilent 4440 was achieved by combining a Sammon mapping with a radial basis function neural network. In comparison a successful classification rate of 80% was achieved for the urine data from the C320 which were analysed using a novel nonlinear time series model.

Gas sensor analysis of blood samples is shown to be an effective method to screen for bacterial pathogens. In this work. various strains of Staphylococcus Aureus are cultured in a blood medium. The data are analysed using normalisation techniques, Principal Components Analysis (PCA) and Radial Basis Function (RBF) networks trained using the Support Vector Method (SVM). Good separation of data is reported and a classification rate of 100% was obtained.