Neuroshell 2 is commercially available from Ward Systems.

Genome sequence data provide a framework for predicting potential microbial activities; however, the proteome content of the cell dictates its response to its environment. Microbiology is witnessing a major initiative to elucidate the nature of the proteome of large numbers of species. The tool driving the proteomic revolution is matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. During the analysis process, proteins are ionized and separated on the basis of their mass-to-charge ratios, which results in a characteristic mass-spectral profile. Because of the dynamic nature of the cell and the large number of external parameters that could influence its mass-spectral profile, considerable work was needed initially to optimize sample analysis and obtain consistent and reproducible results. For many anaerobes that grow poorly or are nonreactive in most diagnostic systems, proteome analysis is likely to have a major impact on microbial diagnosis and the delineation of centers of diversity associated with infections.

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Surface enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF MS) has been applied in large numbers of oncological studies but the microbiological field has not been extensively explored to date. This paper describes the application of SELDI-TOF MS in concert with a multi-layer perceptron artificial neural network (ANN) with a back propagation algorithm for the identification of Neisseria gonorrhoeae. N. gonorrhoeae, the aetiological agent of gonorrhoea, is the second most common sexually transmitted disease in the UK and USA. Analysis of over 350 strains of N. gonorrhoeae and closely related species by SELDI-TOF MS facilitated the design of an ANN model and revealed 20 ion peak descriptors of positive, negative and secondary nature that were paramount for the identification of the pathogen. The model performed with over 96 % efficiency when based on these 20 ion peak descriptors and exhibited a sensitivity of 95 . 7 % and a specificity of 97 . 1 %, with an area under the curve value of 0 . 996. The technology has the potential to link several ANN models for a comprehensive rapid identification platform for clinically important pathogens.