Analysis of exhaled breath to identify critically ill patients with ventilator-associated pneumonia.

Ventilator-associated pneumonia commonly occurs in critically ill patients. Clinical suspicion results in overuse of antibiotics, which in turn promotes antimicrobial resistance. Detection of volatile organic compounds in the exhaled breath of critically ill patients might allow earlier detection of pneumonia and avoid unnecessary antibiotic prescription. We report a proof of concept study for non-invasive diagnosis of ventilator-associated pneumonia in intensive care (the BRAVo study). Mechanically ventilated critically ill patients commenced on antibiotics for clinical suspicion of ventilator-associated pneumonia were recruited within the first 24 h of treatment. Paired exhaled breath and respiratory tract samples were collected. Exhaled breath was captured on sorbent tubes and then analysed using thermal desorption gas chromatography-mass spectrometry to detect volatile organic compounds. Microbiological culture of a pathogenic bacteria in respiratory tract samples provided confirmation of ventilator-associated pneumonia. Univariable and multivariable analyses of volatile organic compounds were performed to identify potential biomarkers for a 'rule-out' test. Ninety-six participants were enrolled in the trial, with exhaled breath available from 92. Of all compounds tested, the four highest performing candidate biomarkers were benzene, cyclohexanone, pentanol and undecanal with area under the receiver operating characteristic curve ranging from 0.67 to 0.77 and negative predictive values from 85% to 88%. Identified volatile organic compounds in the exhaled breath of mechanically ventilated critically ill patients show promise as a useful non-invasive 'rule-out' test for ventilator-associated pneumonia.

Rapid through-container detection of fake spirits and methanol quantification with handheld Raman spectroscopy.

The spirits drinks industry is of significant global economic importance and a major employer worldwide, and the ability to ensure product authenticity and maintain consumer confidence in these high-value products is absolutely essential. Spirit drinks counterfeiting is a worldwide problem, with counterfeiting and adulteration of spirit drinks taking many forms, such as substitution, stretching with lower-grade products, or creation of counterfeits with industrial, surrogate, or locally produced alcohols. Methanol for example, which has been used as a substitute alcohol for ethanol, has a high toxicity in humans. The counterfeiting of spirit drinks is consequently one of the few leading reported types of food fraud which can be directly and unequivocally linked to food safety and health concerns. Here, for the first time, we use handheld Raman spectroscopy with excitation in the near IR (1064 nm) for the through-container differentiation of multiple spirit drinks, detection of multiple chemical markers of counterfeit alcohol, and for the quantification of methanol. We established the limits of detection (LOD) of methanol in the analysed samples from four different spirit types (between 0.23-0.39%), which were considerably lower than a quoted maximum tolerable concentration (MTC) of 2% (v/v) methanol for humans in a 40% alcohol by volume (ABV) spirit drink, and even lower than the general EU limit for naturally occurring methanol in fruit spirits of 0.5% v/v (10 g methanol per L ethanol). We believe that Raman spectroscopy has considerable practicable potential for the rapid in situ through-container detection of counterfeit spirits drinks, as well as for the analysis and protection of other beverages and liquid samples.

Implementation of Fourier transform infrared spectroscopy for the rapid typing of uropathogenic Escherichia coli.

In this paper, we demonstrate that Fourier transform infrared (FT-IR) spectroscopy is able to discriminate rapidly between uropathogenic Escherichia coli (UPEC) of key lineages with only relatively simple sample preparation. A total of 95 bacteria from six different epidemiologically important multilocus sequence types (ST10, ST69, ST95, ST73, ST127 and ST131) were used in this project and principal component-discriminant function analysis (PC-DFA) of these samples produced clear separate clustering of isolates, based on the ST. Analysis of data using partial least squares-discriminant analysis (PLS-DA), incorporating cross-validation, indicated a high prediction accuracy of 91.19% for ST131. These results suggest that FT-IR spectroscopy could be a useful method for the rapid identification of members of important UPEC STs.

Fourier transform infrared spectroscopy and multivariate analysis for the detection and quantification of different milk species.

The authenticity of milk and milk products is important and has extended health, cultural, and financial implications. Current analytical methods for the detection of milk adulteration are slow, laborious, and therefore impractical for use in routine milk screening by the dairy industry. Fourier transform infrared (FT-IR) spectroscopy is a rapid biochemical fingerprinting technique that could be used to reduce this sample analysis period significantly. To test this hypothesis we investigated 3 types of milk: cow, goat, and sheep milk. From these, 4 mixtures were prepared. The first 3 were binary mixtures of sheep and cow milk, goat and cow milk, or sheep and goat milk; in all mixtures the mixtures contained between 0 and 100% of each milk in increments of 5%. The fourth combination was a tertiary mixture containing sheep, cow, and goat milk also in increments of 5%. Analysis by FT-IR spectroscopy in combination with multivariate statistical methods, including partial least squares (PLS) regression and nonlinear kernel partial least squares (KPLS) regression, were used for multivariate calibration to quantify the different levels of adulterated milk. The FT-IR spectra showed a reasonably good predictive value for the binary mixtures, with an error level of 6.5 to 8% when analyzed using PLS. The results improved and excellent predictions were achieved (only 4-6% error) when KPLS was employed. Excellent predictions were achieved by both PLS and KPLS with errors of 3.4 to 4.9% and 3.9 to 6.4%, respectively, when the tertiary mixtures were analyzed. We believe that these results show that FT-IR spectroscopy has excellent potential for use in the dairy industry as a rapid method of detection and quantification in milk adulteration.

Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics.

The chemical identification of mass spectrometric signals in metabolomic applications is important to provide conversion of analytical data to biological knowledge about metabolic pathways. The complexity of electrospray mass spectrometric data acquired from a range of samples (serum, urine, yeast intracellular extracts, yeast metabolic footprints, placental tissue metabolic footprints) has been investigated and has defined the frequency of different ion types routinely detected. Although some ion types were expected (protonated and deprotonated peaks, isotope peaks, multiply charged peaks) others were not expected (sodium formate adduct ions). In parallel, the Manchester Metabolomics Database (MMD) has been constructed with data from genome scale metabolic reconstructions, HMDB, KEGG, Lipid Maps, BioCyc and DrugBank to provide knowledge on 42,687 endogenous and exogenous metabolite species. The combination of accurate mass data for a large collection of metabolites, theoretical isotope abundance data and knowledge of the different ion types detected provided a greater number of electrospray mass spectrometric signals which were putatively identified and with greater confidence in the samples studied. To provide definitive identification metabolite-specific mass spectral libraries for UPLC-MS and GC-MS have been constructed for 1,065 commercially available authentic standards. The MMD data are available at http://dbkgroup.org/MMD/.

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.

Discrimination of aerobic endospore-forming bacteria via electrospray-lonization mass spectrometry of whole cell suspensions.

Direct injection electrospray ionization mass spectrometry (ESI-MS) without prior analyte separation was investigated for the analysis of whole cell suspensions of bacteria. Thirty-six strains of aerobic endospore-forming bacteria, consisting of six Bacillus species and one Brevibacillus species, were studied

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Variable selection in discriminant partial least-squares analysis.

Variable selection enhances the understanding and interpretability of multivariate classification models. A new chemometric method based on the selection of the most important variables in discriminant partial least-squares (VS-DPLS) analysis is described. The suggested method is a simple extension of DPLS where a small number of elements in the weight vector w is retained for each factor. The optimal number of DPLS factors is determined by cross-validation. The new algorithm is applied to four different high-dimensional spectral data sets with excellent results. Spectral profiles from Fourier transform infrared spectroscopy and pyrolysis mass spectrometry are used. To investigate the uniqueness of the selected variables an iterative VS-DPLS procedure is performed. At each iteration, the previously found selected variables are removed to see if a new VS-DPLS classification model can be constructed using a different set of variables. In this manner, it is possible to determine regions rather than individual variables that are important for a successful classification.

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.

Genetic programming: a novel method for the quantitative analysis of pyrolysis mass spectral data.

A technique for the analysis of multivariate data by genetic programming (GP) is described, with particular reference to the quantitative analysis of orange juice adulteration data collected by pyrolysis mass spectrometry (PyMS). The dimensionality of the input space was reduced by ranking variables according to product moment correlation or mutual information with the outputs. The GP technique as described gives predictive errors equivalent to, if not better than, more widespread methods such as partial least squares and artificial neural networks but additionally can provide a means for easing the interpretation of the correlation between input and output variables. The described application demonstrates that by using the GP method for analyzing PyMS data the adulteration of orange juice with 10% sucrose solution can be quantified reliably over a 0-20% range with an RMS error in the estimate of ∼1%.