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Biomedical subjects

R Goodacre

Publications and source records attributed to R Goodacre.

At least 19 recordsLinked to original sources
PubMed2004

The rapid identification of Acinetobacter species using Fourier transform infrared spectroscopy.

AIMS: Fourier transform infrared (FT-IR) was used to analyse a selection of Acinetobacter isolates in order to determine if this approach could discriminate readily between the known genomic species of this genus and environmental isolates from activated sludge. METHODS AND RESULTS: FT-IR spectroscopy is a rapid whole-organism fingerprinting method, typically taking only 10 s per sample, and generates 'holistic' biochemical profiles (or 'fingerprints') from biological materials. The cluster analysis produced by FT-IR was compared with previous polyphasic taxonomic studies on these isolates and with 16S-23S rDNA intergenic spacer region (ISR) fingerprinting presented in this paper. FT-IR and 16S-23S rDNA ISR analyses together indicate that some of the Acinetobacter genomic species are particularly heterogeneous and poorly defined, making characterization of the unknown environmental isolates with the genomic species difficult. CONCLUSIONS: Whilst the characterization of the isolates from activated sludge revealed by FT-IR and 16S-23S rDNA ISR were not directly comparable, the dendrogram produced from FT-IR data did correlate well with the outcomes of the other polyphasic taxonomic work. SIGNIFICANCE AND IMPACT OF THE STUDY: We believe it would be advantageous to pursue this approach further and establish a comprehensive database of taxonomically well-defined Acinetobacter species to aid the identification of unknown strains. In this instance, FT-IR may provide the rapid identification method eagerly sought for the routine identification of Acinetobacter isolates from a wide range of environmental sources.

Acinetobacter↗
PubMed2001

Degeneration of solventogenic Clostridium strains monitored by Fourier transform infrared spectroscopy of bacterial cells.

Strain degeneration in solventogenic clostridia is a known problem in the technical acetone-butanol fermentation bioprocess, especially in the continuous process mode. Clostridial strain degeneration was studied by Fourier transform infrared (FT-IR) spectroscopy of the bacterial cells. Degenerative variant formation in two strains, Clostridium beijerinckii NCIMB 8052 and Clostridium species AA332, was detected spectroscopically. Colonies on solid media were sampled, or assayed directly in situ by IR microscopy. It has previously been shown that the distinctive acidogenic and solventogenic physiological phases of Clostridium acetobutylicum in liquid medium can be discriminated by FT-IR spectroscopy. This was confirmed here for C. beijerinckii NCIMB 8052. The proportion of degenerate cells in a mixed population in liquid medium could be quantified, as the spectral features change in different ways during the normal growth cycle of wild type organisms and degenerate variants in batch culture. This opens a new perspective for physiology-based process monitoring and control, especially of the continuous acetone-butanol fermentation.

Acetone↗
PubMed2001

Laparoscopic versus open bowel resection for Crohn's disease.

BACKGROUND: Laparoscopic bowel resection is an alternative to open surgery for patients with Crohn's disease requiring surgical resection. The present report describes a seven-year experience with the laparoscopic treatment of Crohn's disease compared with the open technique in a tertiary Canadian centre. PATIENTS AND METHODS: A retrospective analysis of 61 consecutive patients undergoing elective resection for Crohn's disease was carried out between October 1992 and June 1999. This analysis included 32 laparoscopic resections (mean age 33 years) and 29 open resections (mean age 42 years). Patient demographics were compared, as well as short and long term outcomes after surgery (mean follow-up 39 months). RESULTS: Patients in the laparoscopic group were younger and had fewer previous bowel surgeries than patients who had open resections. Indications for surgery and operative times were similar between the groups. Patients who underwent laparoscopic resections required fewer doses of narcotic analgesics. The resumption of bowel function after surgery, and tolerance of a clear liquid and solid diet was quicker in the laparoscopic group. Patients who underwent laparoscopic resections had significantly shorter hospital stays than those who underwent open resections. Fifteen patients (48.4%) in the laparoscopic group experienced recurrence of disease compared with 13 patients (44.8%) in the open group. In both groups, the most common site of recurrence was at the anastomosis. The disease-free interval was the same length for both groups (23.9+/-17.3 months for the laparoscopic resection patients compared with 23.9+/-20.2 months for the open resection patients; P=1.00). CONCLUSIONS: Laparoscopic resection for Crohn's disease can be performed safely and effectively. Quicker resumption of oral feeds, less postoperative pain and earlier discharge from hospital are advantages of the laparoscopic method. No differences in the recurrence rate or the disease-free interval were noted.

Adolescent↗
PubMed2000

Detection of the dipicolinic acid biomarker in Bacillus spores using Curie-point pyrolysis mass spectrometry and Fourier transform infrared spectroscopy.

Thirty-six strains of aerobic endospore-forming bacteria confirmed by polyphasic taxonomic methods to belong to Bacillus amyloliquefaciens, Bacillus cereus, Bacillus licheniformis, Bacillus megaterium, Bacillus subtilis (including Bacillus niger and Bacillus globigii), Bacillus sphaericus, and Brevi laterosporus were grown axenically on nutrient agar, and vegetative and sporulated biomasses were analyzed by Curie-point pyrolysis mass spectrometry (PyMS) and diffuse reflectance-absorbance Fourier-transform infrared spectroscopy (FT-IR). Chemometric methods based on rule induction and genetic programming were used to determine the physiological state (vegetative cells or spores) correctly, and these methods produced mathematical rules which could be simply interpreted in biochemical terms. For PyMS it was found that m/z 105 was characteristic and is a pyridine ketonium ion (C6H3ON+) obtained from the pyrolysis of dipicolinic acid (pyridine-2,6-dicarboxylic acid; DPA), a substance found in spores but not in vegetative cells; this was confirmed using pyrolysis-gas chromatography/mass spectrometry. In addition, a pyridine ring vibration at 1447-1439 cm-1 from DPA was found to be highly characteristic of spores in FT-IR analysis. Thus, although the original data sets recorded hundreds of spectral variables from whole cells simultaneously, a simple biomarker can be used for the rapid and unequivocal detection of spores of these organisms.

Bacillus↗
PubMed2000

Rapid analysis of high-dimensional bioprocesses using multivariate spectroscopies and advanced chemometrics.

There are an increasing number of instrumental methods for obtaining data from biochemical processes, many of which now provide information on many (indeed many hundreds) of variables simultaneously. The wealth of data that these methods provide, however, is useless without the means to extract the required information. As instruments advance, and the quantity of data produced increases, the fields of bioinformatics and chemometrics have consequently grown greatly in importance. The chemometric methods nowadays available are both powerful and dangerous, and there are many issues to be considered when using statistical analyses on data for which there are numerous measurements (which often exceed the number of samples). It is not difficult to carry out statistical analysis on multivariate data in such a way that the results appear much more impressive than they really are. The authors present some of the methods that we have developed and exploited in Aberystwyth for gathering highly multivariate data from bioprocesses, and some techniques of sound multivariate statistical analyses (and of related methods based on neural and evolutionary computing) which can ensure that the results will stand up to the most rigorous scrutiny.

Algorithms↗
PubMed2000

Fourier transform infrared spectroscopy of follicular fluids from large and small antral follicles.

Fourier transform infrared spectroscopy (FTIR) was used to obtain 'biochemical fingerprints' for the constitution of follicular fluids from large and small antral luteinized follicles (n = 54 pairs). All samples gave reproducible characteristic biological infrared absorption spectra, with recognizable amide I protein vibrations and acyl vibrations from fatty acids. Discriminant function analysis of the first derivative FTIR spectra, together with hierarchical cluster analysis used to construct a dendrogram, showed fluid from large follicles formed a homogeneous closely related cluster, whilst that from small follicles was distinct from the large, and heterogeneous in nature. The large follicle fluids showed closer biochemical similarity to each other than to the corresponding fluid taken from small matched follicles. An artificial neural network was trained and following validation with an independent test set, successfully distinguished follicular fluids from large and small follicles. The sex steroid concentrations in the fluids from large and small follicles were significantly different. These results show that fluid from large follicles is distinct in biochemical nature from that from small follicles, but the degree of homogeneity implies size-specific changes take place. These may have consequences for the developmental potential of the oocyte.

Discriminant Analysis↗
PubMed1999

Rapid analysis of the expression of heterologous proteins in Escherichia coli using pyrolysis mass spectrometry and Fourier transform infrared spectroscopy with chemometrics: application to alpha 2-interferon production.

Cell pastes and supernatant Escherichia coli samples, taken from an industrial bioprocess overproducing recombinant alpha 2 IFN were analysed using pyrolysis mass spectrometry (PyMS) and diffuse reflectance-absorbance Fourier transform infrared spectroscopy (FT-IR). PyMS and FT-IR are physico-chemical methods which measure predominantly the bond strengths of molecules and the vibrations of bonds within functional groups, respectively. They therefore give quantitative information about the total biochemical composition of the bioprocess sample. The interpretation of these hyperspectral data, in terms of the quantity of alpha 2 IFN in the cell pastes and supernatant samples was possible only after the application of the 'supervised learning' methods of artificial neural networks (ANNs) and partial least squares (PLS) regression. Both PyMS and FT-IR are novel, rapid and economical methods for the screening and the quantitative analysis of complex biological bioprocess over producing recombinant proteins. Models established using either spectral data set had a similarly satisfactory predictive ability. This shows that whole-reaction mixture spectral methods, which measure all molecules simultaneously, do contain enough information to allow their quantification when the entire spectra are used as the inputs to methods based on supervised learning. Moreover, this is the first study where FT-IR in the mid-IR range has been used to quantify the expression of a heterologous protein directly from fermentation broths and the first study to compare the abilities of PyMS and FT-IR for the quantitative analyses of an industrial bioprocess.

Chemistry Techniques, Analytical↗
PubMed1998

The deconvolution of pyrolysis mass spectra using genetic programming: application to the identification of some Eubacterium species.

Pyrolysis mass spectrometry was used to produce complex biochemical fingerprints of Eubacterium exiguum, E. infirmum, E. tardum and E. timidum. To examine the relationship between these organisms the spectra were clustered by canonical variates analysis, and four clusters, one for each species, were observed. In an earlier study we trained artificial neural networks to identify these clinical isolates successfully; however, the information used by the neural network was not accessible from this so-called 'black box' technique. To allow the deconvolution of such complex spectra (in terms of which masses were important for discrimination) it was necessary to develop a system that itself produces 'rules' that are readily comprehensible. We here exploit the evolutionary computational technique of genetic programming; this rapidly and automatically produced simple mathematical functions that were also able to classify organisms to each of the four bacterial groups correctly and unambiguously. Since the rules used only a very limited set of masses, from a search space some 50 orders of magnitude greater than the dimensionality actually necessary, visual discrimination of the organisms on the basis of these spectral masses alone was also then possible.

Artificial Intelligence↗
PubMed1998

Differentiation of brewing yeast strains by pyrolysis mass spectrometry and Fourier transform infrared spectroscopy.

Two rapid spectroscopic approaches for whole-organism fingerprinting--pyrolysis mass spectrometry (PyMS) and Fourier transform infrared spectroscopy (FT-IR)--were used to analyse 22 production brewery Saccharomyces cerevisiae strains. Multivariate discriminant analysis of the spectral data was then performed to observe relationships between the 22 isolates. Upon visual inspection of the cluster analyses, similar differentiation of the strains was observed for both approaches. Moreover, these phenetic classifications were found to be very similar to those previously obtained using genotypic studies of the same brewing yeasts. Both spectroscopic techniques are rapid (typically 2 min for PyMS and 10 s for FT-IR) and were shown to be capable of the successful discrimination of both ale and lager yeasts. We believe that these whole-organism fingerprinting methods could find application in brewery quality control laboratories.

Beer↗
PubMed1998

Discrimination between methicillin-resistant and methicillin-susceptible Staphylococcus aureus using pyrolysis mass spectrometry and artificial neural networks.

Curie-point pyrolysis mass spectra were obtained from 15 methicillin-resistant and 22 methicillin-susceptible Staphylococcus aureus strains. Cluster analysis showed that the major source of variation between the pyrolysis mass spectra resulted from the phage group of the bacteria, not their resistance or susceptibility to methicillin. By contrast, artificial neural networks could be trained to recognize those aspects of the pyrolysis mass spectra that differentiated methicillin-resistant from methicillin-sensitive strains. The trained neural network could then use pyrolysis mass spectral data to assess whether an unknown strain was resistant to methicillin. These results give the first demonstration that the combination of pyrolysis mass spectrometry with neural networks can provide a very rapid and accurate antibiotic susceptibility testing technique.

Cluster Analysis↗
PubMed1998

Rapid differentiation of closely related Candida species and strains by pyrolysis-mass spectrometry and Fourier transform-infrared spectroscopy.

Two rapid spectroscopic approaches for whole-organism fingerprinting of pyrolysis-mass spectrometry (PyMS) and Fourier transform-infrared spectroscopy (FT-IR) were used to analyze a group of 29 clinical and reference Candida isolates. These strains had been identified by conventional means as belonging to one of the three species Candida albicans, C. dubliniensis (previously reported as atypical C. albicans), and C. stellatoidea (which is also closely related to C. albicans). To observe the relationships of the 29 isolates as judged by PyMS and FT-IR, the spectral data were clustered by discriminant analysis. On visual inspection of the cluster analyses from both methods, three distinct clusters, which were discrete for each of the Candida species, could be seen. Moreover, these phenetic classifications were found to be very similar to those obtained by genotypic studies which examined the HinfI restriction enzyme digestion patterns of genomic DNA and by use of the 27A C. albicans-specific probe. Both spectroscopic techniques are rapid (typically, 2 min for PyMS and 10 s for FT-IR) and were shown to be capable of successfully discriminating between closely related isolates of C. albicans, C. dubliniensis, and C. stellatoidea. We believe that these whole-organism fingerprinting methods could provide opportunities for automation in clinical microbial laboratories, improving turnaround times and the use of resources.

Candida↗
PubMed1997

Rapid quantitative analysis of binary mixtures of Escherichia coli strains using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks.

Pyrolysis mass spectrometry (PyMS) and multivariate calibration were used to show the high degree of relatedness between Escherichia coli HB101 and E. coli UB5201. Next, binary mixtures of these two phenotypically closely related E. coli strains were prepared and subjected to PyMS. Fully interconnected feedforward artificial neural networks (ANNs) were used to analyse the pyrolysis mass spectra to obtain quantitative information representative of level of E. coli UB5201 in E. coli HB101. The ANNs exploited were trained using the standard back propagation algorithm, and the nodes used sigmoidal squashing functions. Accurate quantitative information was obtained for mixtures with > 3% E. coli UB5201 in E. coli HB101. To remove noise from the pyrolysis mass spectra and so lower the limit of detection, the spectra were reduced using principal components analysis (PCA) and the first 13 principal components used to train ANNs. These PCA-ANNs allowed accurate estimates at levels as low as 1% E. coli UB5201 in E. coli HB101 to be predicted. In terms of bacterial numbers, it was shown that the limit of detection of PyMS in conjunction with ANNs was 3 x 10(4) E. coli UB5201 cells in 1.6 x 10(7) E. coli HB101 cells. It may be concluded that PyMS with ANNs provides a powerful and rapid method for the quantification of mixtures of closely related bacterial strains.

Bacteriological Techniques↗
PubMed1996

Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbance Fourier transform infrared spectroscopy and artificial neural networks.

Diffuse reflectance-absorbance Fourier transform infrared spectroscopy (FT-IR) was used to analyse 19 hospital isolates which had been identified by conventional means to one Enterococcus faecalis, E. faecium, Streptococcus bovis, S. mitis, S. pneumoniae, or S. pyogenes. Principal components analysis of the FT-IR spectra showed that this 'unsupervised' learning method failed to form six separable clusters (one of each species) and thus could not be used to identify these bacteria base on their FT-IR spectra. By contrast, artificial neural networks (ANNs) could be trained by 'supervised' learning (using the back-propagation algorithm) with the principal components scores of derivatised spectra to recognise the strains from their FT-IR spectra. These results demonstrate that the combination of FT-IR and ANNs provides a rapid, novel and accurate bacterial identification technique.

Algorithms↗
PubMed1996

Pyrolysis mass spectrometry and its applications in biotechnology.

Pyrolysis mass spectrometry is a rapid and high-resolution method for the analysis of otherwise non-volatile material and has been widely applied for discriminating between closely related microbial strains. Recent advances in statistical and neural network methods based on supervised learning have now permitted exploitation of pyrolysis mass spectrometry in the quantitative analysis of many diverse samples of biotechnological interest; the technique may thus be regarded as an 'anything-sensor'.

Journal Article↗
PubMed1996

Correction of mass spectral drift using artificial neural networks.

For pyrolysis mass spectrometry (PyMS) to be used for the routine identification of microorganisms, for quantifying determinands in biological and biotechnological systems, and in the production of useful mass spectral libraries, it is paramount that newly acquired spectra be compared to those previously collected. Neural network and other multivariate calibration models have been used to relate mass spectra to the biological features of interest. As commonly observed, however, mass spectral fingerprints showed a lack of long-term reproducibility, due to instrumental drift in the mass spectrometer; when identical materials were analyzed by PyMS at dates from 4 to 20 months apart, neural network models produced at earlier times could not be used to give accurate estimates of determinand concentrations or bacterial identities. Neural networks, however, can be used to correct for pyrolysis mass spectrometer instrumental drift itself, so that neural network or other multivariate calibration models created using previously collected data can be used to give accurate estimates of determinand concentration or the nature of bacteria (or, indeed, other materials) from newly acquired pyrolysis mass spectra. This approach is not limited solely to pyrolysis mass spectrometry but is generally applicable to any analytical tool which is prone to instrumental drift, such as IR, ESR, NMR and other spectroscopies, and gas and liquid chromatography, as well as other types of mass spectrometry.

Ampicillin↗
PubMed1996

Identification and discrimination of oral asaccharolytic Eubacterium spp. by pyrolysis mass spectrometry and artificial neural networks.

Curie-point pyrolysis mass spectra were obtained from 29 oral asaccharolytic Eubacterium strains and 6 abscess isolates previously identified as Peptostreptococcus heliotrinreducens. Pyrolysis mass spectrometry (PyMS) with cluster analysis was able to clarify the taxonomic position of this group of organisms. Artificial neural networks (ANNS) were then trained by supervised learning (with the back-propagation algorithm) to recognize the strains from their pyrolysis mass spectra; all Eubacterium strains were correctly identified, and the abscess isolates were identified as un-named Eubacterium taxon C2 and were distinct from the type strain of P. heliotrinreducens. These results demonstrate that the combination of PyMS and ANNs provides a rapid and accurate identification technique.

Bacterial Typing Techniques↗
PubMed1996

Sub-species discrimination, using pyrolysis mass spectrometry and self-organising neural networks, of Propionibacterium acnes isolated from normal human skin.

Curie-point pyrolysis mass spectra were obtained from 30 Propionibacterium acnes strains isolated from the foreheads of six healthy humans. Multivariate analyses and Kohonen artificial neural networks (KANNs), employing unsupervised learning, were used successfully to discriminate between the P.acnes isolates from different individual hosts. The classification of the isolates by KANNs was compared with the more classical multivariate techniques of canonical variates analysis and hierarchical cluster analysis and found to give similar groupings. The combination of pyrolysis mass spectrometry with these numerical methods also showed that more than one strain of P.acnes had been isolated from three of the human hosts.

Adult↗