Biomedical subjects

Roger M Jarvis

Publications and source records attributed to Roger M Jarvis.

5 recordsLinked to original sources

PubMed2006

Surface-enhanced Raman scattering for the rapid discrimination of bacteria.

Raman spectroscopy is attracting interest for the rapid identification of bacteria and fungi and is now becoming accepted as a potentially powerful whole-organism fingerprinting technique. However, the Raman effect is so weak that collection times are lengthy, and this insensitivity means that bacteria must be cultured to gain enough biomass, which therefore limits its usefulness in clinical laboratories where high-throughput analyses are needed. The Raman effect can fortunately be greatly enhanced (by some 10(3)-10(6)-fold) if the molecules are attached to, or microscopically close to, a suitably roughened surface; a technique known as surface-enhanced Raman scattering (SERS). In this study we investigated SERS, employing an aggregated silver colloid substrate, for the analysis of a closely related group of bacteria belonging to the genus Bacillus. Each spectrum took only 20 s to collect and highly reproducible data were generated. The multivariate statistical technique of principal components-discriminant function analysis (PC-DFA) was used to group these bacteria based on their SERS fingerprints. The resultant ordination plots showed that the SERS spectra were highly discriminatory and gave accurate identification at the strain level. In addition, Bacillus species also undergo sporulation, and we demonstrate that SERS peaks that could be attributed to the dipicolinic acid biomarker, could be readily generated from Bacillus spores.

Bacillus↗

PubMed2004

Genetic algorithm optimization for pre-processing and variable selection of spectroscopic data.

MOTIVATION: The major difficulties relating to mathematical modelling of spectroscopic data are inconsistencies in spectral reproducibility and the black box nature of the modelling techniques. For the analysis of biological samples the first problem is due to biological, experimental and machine variability which can lead to sample size differences and unavoidable baseline shifts. Consequently, there is often a requirement for mathematical correction(s) to be made to the raw data if the best possible model is to be formed. The second problem prevents interpretation of the results since the variables that most contribute to the analysis are not easily revealed; as a result, the opportunity to obtain new knowledge from such data is lost. METHODS: We used genetic algorithms (GAs) to select spectral pre-processing steps for Fourier transform infrared (FT-IR) spectroscopic data. We demonstrate a novel approach for the selection of important discriminatory variables by GA from FT-IR spectra for multi-class identification by discriminant function analysis (DFA). RESULTS: The GA selects sensible pre-processing steps from a total of approximately 10(10) possible mathematical transformations. Application of these algorithms results in a 16% reduction in the model error when compared against the raw data model. GA-DFA recovers six variables from the full set of 882 spectral variables against which a satisfactory DFA model can be formed; thus inferences can be made as to the biochemical differences that are reflected by these spectral bands.

Algorithms↗

PubMed2004

Surface-enhanced Raman spectroscopy for bacterial discrimination utilizing a scanning electron microscope with a Raman spectroscopy interface.

Surface-enhanced Raman scattering (SERS) utilizing colloidal silver has already been shown to provide a rapid means of generating "whole-organism fingerprints" for use in bacterial identification and discrimination. However, one of the main drawbacks of the technique for the analysis of microbiological samples with optical Raman microspectroscopy has been the inability to acquire pre-emptively a region of the sample matrix where both the SERS substrate and biomass are both present. In this study, we introduce a Raman interface for scanning electron microscopy (SEM) and demonstrate the application of this technology to the reproducible and targeted collection of bacterial SERS spectra. In secondary electron mode, the SEM images clearly reveal regions of the sample matrix where the sodium borohydride-reduced silver colloidal particles are present, Stokes spectra collected from these regions are rich in vibrational bands, whereas spectra taken from other areas of the sample elicit a strong fluorescence response. Replicate SERS spectra were collected from two bacterial strains and show excellent reproducibility both by visual inspection and as demonstrated by principal components analysis on the whole SERS spectra.

Bacillus subtilis↗

PubMed2004

Ultra-violet resonance Raman spectroscopy for the rapid discrimination of urinary tract infection bacteria.

The ability to identify pathogenic organisms rapidly provides significant benefits to clinicians; in particular, with respect to best prescription practices and tracking of recurrent infections. Conventional bioassays require 3-5 days before identification of an organism can be made, thus compromising the effectiveness with which patients can be treated for bacterial infections. We analysed 20 clinical isolates of urinary tract infections (UTI) by ultra-violet resonance Raman (UVRR) spectroscopy, utilising 244 nm excitation delivering approximately 0.1 mW laser power at the sample, with typical spectral collection times of 120 s. UVRR results in resonance-enhanced Raman signals for certain chromophoric segments of macromolecules, intensifying those selected bands above what would otherwise be observed for a normal Raman experiment. Utilising the whole-organism 'fingerprints' obtained by UVRR we were able to discriminate successfully between UTI pathogens using chemometric cluster analyses. This work demonstrates significant improvements in the speed with which spectra can be obtained by Raman spectroscopic techniques for the discrimination of clinical bacterial samples.

Algorithms↗

PubMed2004

Discrimination of bacteria using surface-enhanced Raman spectroscopy.

Raman spectroscopy has recently been shown to be a potentially powerful whole-organism fingerprinting technique and is attracting interest within microbial systematics for the rapid identification of bacteria and fungi. However, while the Raman effect is so weak that only approximately 1 in 10(8) incident photons are Raman scattered (so that collection times are in the order of minutes), it can be greatly enhanced (by some 10(3)-10(6)-fold) if the molecules are attached to, or microscopically close to, a suitably roughened surface, a technique known as surface-enhanced Raman scattering (SERS). In this study, SERS, employing an aggregated silver colloid substrate, was used to analyze a collection of clinical bacterial isolates associated with urinary tract infections. While each spectrum took 10 s to collect, to acquire reproducible data, 50 spectra were collected making the spectral acquisition times per bacterium approximately 8 min. The multivariate statistical techniques of discriminant function analysis (DFA) and hierarchical cluster analysis (HCA) were applied in order to group these organisms based on their spectral fingerprints. The resultant ordination plots and dendrograms showed correct groupings for these organisms, including discrimination to strain level for a sample group of Escherichia coli, which was validated by projection of test spectra into DFA and HCA space. We believe this to be the first report showing bacterial discrimination using SERS.

Escherichia coli↗