Distinguishing normal from rejecting renal allografts: application of a three—stage classification strategy to MR and IR spectra of urine

Somorjai, Ray L.; Dolenko, Brion; Nikulin, A; Nickerson, Peter; Rush, David; Shaw, Anthony; Glogowski, Miriam; Rendell, Jennifer M; Deslauriers, Roxanne

doi:10.1016/s0924-2031(01)00148-5

Cited by 44 publications

(37 citation statements)

References 17 publications

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“…For classifier robustness and reliability, it is desirable that the number of spectra per species group in the training set be 5 to 10 times larger than the number of independent variables (19). Such large data sets are rare in the published literature and usually difficult to acquire, especially if the derived classifier is to be validated against a test set independent of the training set.…”

Section: Discussionmentioning

confidence: 99%

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Identification ofEnterococcus, Streptococcus, andStaphylococcusby Multivariate Analysis of Proton Magnetic Resonance Spectroscopic Data from Plate Cultures

Bourne

Himmelreich

Sharma³

et al. 2001

J Clin Microbiol

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A new fingerprinting technique with the potential for rapid identification of bacteria was developed by combining proton magnetic resonance spectroscopy ( 1 H MRS) with multivariate statistical analysis. This resulted in an objective identification strategy for common clinical isolates belonging to the bacterial species Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecalis, Streptococcus pneumoniae, Streptococcus pyogenes, Streptococcus agalactiae, and the Streptococcus milleri group. Duplicate cultures of 104 different isolates were examined one or more times using 1 H MRS. A total of 312 cultures were examined. An optimized classifier was developed using a bootstrapping process and a seven-group linear discriminant analysis to provide objective classification of the spectra. Identification of isolates was based on consistent high-probability classification of spectra from duplicate cultures and achieved 92% agreement with conventional methods of identification. Fewer than 1% of isolates were identified incorrectly. Identification of the remaining 7% of isolates was defined as indeterminate.In both clinical and industrial laboratories, methods for identification of microorganisms have historically been based on multiple phenotypic characters, including morphological features and a range of biochemical reactions. These tests are often time-consuming and/or relatively expensive in their application, and some are imprecise. Recently, alternative methods have been investigated in an attempt to develop a single, rapid method for characterization and identification of microorganisms. These have included Fourier transform infrared spectroscopy (11,14), pyrolysis mass spectrometry (12), electrospray ionization mass spectrometry (7), UV resonance Raman spectroscopy (15), and protein electrophoresis (16). While reports of these techniques suggest the possibility of rapid and reliable identification of some groups of microorganisms, most have been tested with small data sets. With the exception of Fourier transform infrared spectroscopy, they are destructive techniques which analyze cellular decomposition products. All have the limitation that they do not directly yield information about the biochemistry of the intact viable organism.In contrast, magnetic resonance spectroscopy (MRS) of viable cells can provide information on a large range of metabolites. Biological applications of MRS most commonly exploit the noninvasive nature of the technique to study aspects of cellular biochemistry in living systems (6). However, not all applications of MRS require or include identification of the metabolites contributing to the MR spectrum. Pattern recognition techniques, which detect gross spectral characteristics associated with a priori-defined classes (such as pathological conditions), have been successfully applied to MRS of both tissues and body fluids. Accurate and reliable classifiers based on multivariate analyses of 1 H MR spectroscopic data have been developed and validated for objective diagnosis of t...

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Section: Discussionmentioning

confidence: 99%

“…An optimized seven-group classifier was developed based on the bootstrap method (2) modified and renamed the robust bootstrap method by Somorjai et al (19). Starting with all 312 spectra, we randomly selected half the spectra from each species group and used this training set to train the seven-group classifier (LDA).…”

Section: Methodsmentioning

confidence: 99%

Identification ofEnterococcus, Streptococcus, andStaphylococcusby Multivariate Analysis of Proton Magnetic Resonance Spectroscopic Data from Plate Cultures

Bourne

Himmelreich

Sharma³

et al. 2001

J Clin Microbiol

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“…1 H NMR spectra of CSF from controls and animals with confirmed S. pneumoniae and C. neoformans meningitis (obtained three to eight days after infection) were used to develop three pairwise classifiers (for S. pneumoniae versus C. neoformans; C. neoformans versus control and S. pneumoniae versus control) as described previously [16,42,43]. In brief: magnitude NMR spectra were normalized to the total integral between 0.35 to 4.0 ppm, which contains 1500 data points.…”

Section: Statistical Classification Strategymentioning

confidence: 99%

“…Class assignment was called crisp if class assignment probabilities were .66%. Software developed in-house was used for all steps of the statistical classification (IBD, NRC Canada, Winnipeg) as described before [34,42,43].…”

Section: Statistical Classification Strategymentioning

confidence: 99%

Near-Infrared Spectroscopy-Derived Tissue Oxygen Saturation in Battlefield Injuries: A Case Series Report

2016

Spectroscopy

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Bacterial meningitis is an acute disease with high mortality that is reduced by early treatment. Identification of the causative microorganism by culture is sensitive but slow. Large volumes of cerebrospinal fluid (CSF) are required to maximise sensitivity and establish a provisional diagnosis.We have utilised nuclear magnetic resonance (NMR) spectroscopy to rapidly characterise the biochemical profile of CSF from normal rats and animals with pneumococcal or cryptococcal meningitis. Use of a miniaturised capillary NMR system overcame limitations caused by small CSF volumes and low metabolite concentrations. The analysis of the complex NMR spectroscopic data by a supervised statistical classification strategy included major, minor and unidentified metabolites.Reproducible spectral profiles were generated within less than three minutes, and revealed differences in the relative amounts of glucose, lactate, citrate, amino acid residues, acetate and polyols in the three groups. Contributions from microbial metabolism and inflammatory cells were evident. The computerised statistical classification strategy is based on both major metabolites and minor, partially unidentified metabolites. This data analysis proved highly specific for diagnosis (100% specificity in the final validation set), provided those with visible blood contamination were excluded from analysis; 6-8% of samples were classified as indeterminate.This proof of principle study suggests that a rapid etiologic diagnosis of meningitis is possible without prior culture. The method can be fully automated and avoids delays due to processing and selective identification of specific pathogens that are inherent in DNA-based techniques.

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“…It is generally accepted by the pattern recognition community that robust classifier development requires 5-10 samples per feature [1,2]. Hence, some form of feature selection/extraction provides a natural way to address this problem [3,4] . Feature selection/extraction is especially desirable in disease profiling applications when using biomedical spectra [5,6], for which the main interest lies in identifying discriminatory spectral regions (adjacent spectral intensities).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised feature dimension reduction for classification of MR spectra

Baumgartner

Somorjai

Bowman

et al. 2004

Magnetic Resonance Imaging

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We present an unsupervised feature dimension reduction method for the classification of magnetic resonance spectra. The technique preserves spectral information, important for disease profiling. We propose to use this technique as a preprocessing step for computationally demanding wrapper-based feature subset selection. We show that the classification accuracy on an independent test set can be sustained while achieving considerable feature reduction. Our method is applicable to other classification techniques, such as neural networks, support vector machines, etc.

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Distinguishing normal from rejecting renal allografts: application of a three—stage classification strategy to MR and IR spectra of urine

Cited by 44 publications

References 17 publications

Identification ofEnterococcus, Streptococcus, andStaphylococcusby Multivariate Analysis of Proton Magnetic Resonance Spectroscopic Data from Plate Cultures

Identification ofEnterococcus, Streptococcus, andStaphylococcusby Multivariate Analysis of Proton Magnetic Resonance Spectroscopic Data from Plate Cultures

Near-Infrared Spectroscopy-Derived Tissue Oxygen Saturation in Battlefield Injuries: A Case Series Report

Unsupervised feature dimension reduction for classification of MR spectra

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