MRS-based Metabolomics in Cancer Research.

Metabolomics is a relatively new technique that is gaining importance very rapidly. MRS-based metabolomics, in particular, is becoming a useful tool in the study of body fluids, tissue biopsies and whole organisms. Advances in analytical techniques and data analysis methods have opened a new opportunity for such technology to contribute in the field of diagnostics.

Feasibility of infrared spectroscopy with pattern recognition techniques to identify a subpopulation of mares at risk of producing foals diagnosed with failure of transfer of passive immunity.

Objective: To assess the feasibility of a serum-based test using infrared spectroscopy to identify a subpopulation of mares at risk of producing foals susceptible to failure of passive transfer of immunity (FPT) because of mare-associated factors.

Materials And Methods: Serum was collected from post-parturient mares (n = 126) and their foals at 24-72 h of age. A radial immunodiffusion IgG test was used to determine each foal's serum IgG concentration.

Vertebrate-like regeneration in the invertebrate chordate amphioxus.

An important question in biology is why some animals are able to regenerate, whereas others are not. The basal chordate amphioxus is uniquely positioned to address the evolution of regeneration. We report here the high regeneration potential of the European amphioxus Branchiostoma lanceolatum.

Class proximity measures--dissimilarity-based classification and display of high-dimensional data.

For two-class problems, we introduce and construct mappings of high-dimensional instances into dissimilarity (distance)-based Class-Proximity Planes. The Class Proximity Projections are extensions of our earlier relative distance plane mapping, and thus provide a more general and unified approach to the simultaneous classification and visualization of many-feature datasets. The mappings display all L-dimensional instances in two-dimensional coordinate systems, whose two axes represent the two distances of the instances to various pre-defined proximity measures of the two classes.

Deriving biomedical diagnostics from NMR spectroscopic data.

Biomedical spectroscopic experiments generate large volumes of data. For accurate, robust diagnostic tools the data must be analyzed for only a few characteristic observations per subject, and a large number of subjects must be studied. We describe here two of the current data analytic approaches applied to this problem: SIMCA (principal component analysis, partial least squares), and the statistical classification strategy (SCS).

Creating robust, reliable, clinically relevant classifiers from spectroscopic data.

I describe in detail the intimately connected feature extraction and classifier development stages of the data-driven Statistical Classification Strategy (SCS) and compare them with current practice used in MR spectroscopy. We initially created the SCS for the analysis of MR and IR spectra of biofluids and tissues, and subsequently extended it to analyze biomedical data in general. I focus on explaining how to extract discriminatory spectral features and create robust classifiers that can reliably discriminate diseases and disease states.

MR metabolomics of fecal extracts: applications in the study of bowel diseases.

NMR-based metabolomics is becoming a useful tool in the study of body fluids and has a strong potential to contribute to disease diagnosis. While applications on urine and serum have been the focus to date, there are a number of other body fluids that are readily available and could potentially be used for metabolomics-based disease diagnosis. One such body fluid is stool or fecal extract.

QASI, an international quality management system for CD4 T-cell enumeration focused to make a global difference.

Background: A significant worldwide mobilization effort to treat people with HIV disease began in 2003. Most guidelines for initiating antiretroviral therapy require reliable and reproducible CD4 T-cell counting. Therefore, any effort that improves global availability of quality managed assessment schemes for CD4 T-cell enumeration is a positive achievement for the clinical management of AIDS on a worldwide scale.

Potential of mid-infrared spectroscopy to aid the triage of patients with acute chest pain.

A total of 1,429 serum samples from 389 consecutive patients with acute chest pain were analyzed with the goal to aid the rapid diagnosis of acute myocardial infarction. To the best of our knowledge this is the largest and most comprehensive study on mid-infrared spectroscopy in cardiology. We were able to identify those signatures in the mid-infrared spectra of the samples, which were specific to either acute myocardial infarction or chest pain of other origin (angina pectoris, oesophagitis, etc).

Rapid etiological classification of meningitis by NMR spectroscopy based on metabolite profiles and host response.

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.

Detecting colorectal cancer by 1H magnetic resonance spectroscopy of fecal extracts.

Colorectal cancer is one of the most common cancers in the western world. Its early detection has been found to improve the prognosis of the patient, providing a wide window of opportunity for successful therapeutic interventions. However, current diagnostic techniques all have some limitations; there is a need to develop a better technique for routine screening purposes.

Metabolomic biomarkers in a model of asthma exacerbation: urine nuclear magnetic resonance.

Rationale: Airway obstruction in patients with asthma is associated with airway dysfunction and inflammation. Objective measurements including sputum analysis can guide therapy, but this is often not possible in typical clinical settings. Metabolomics is the study of molecules generated by metabolic pathways.

Statistical classification strategy for proton magnetic resonance spectra of soft tissue sarcoma: an exploratory study with potential clinical utility.

Purpose: Histological grading is currently one of the best predictors of tumor behavior and outcome in soft tissue sarcoma. However, occasionally there is significant disagreement even among expert pathologists. An alternative method that gives more reliable and non-subjective diagnostic information is needed.

Disease pattern recognition in infrared spectra of human sera with diabetes mellitus as an example.

To benefit from the full information content of the mid-IR spectra of human sera, we directly related the overall shape of the spectra to the donors' disease states. For this approach of disease pattern recognition we applied cluster analysis and discriminant analysis to the example of the disease states diabetes type 1, diabetes type 2, and healthy. In a binary, supervised classification of any pair of these disease states we achieved specificities and sensitivities of approximately 80% within our data set.

Pattern recognition approaches for classifying proteomic mass spectra of biofluids.

The statistical classification strategy we have developed for magnetic resonance, infrared, and Raman spectra for the analysis of biomedical data is discussed, particularly as it applies to proteomic mass spectra. A general discussion of the current use of pattern recognition methods is given, with caveats and suggestions relevant for clinical applicability.

A pattern recognition application framework for biomedical datasets.

Scopira facilitates the development of high-performance applications by providing many useful subsystems, flexible and efficient data models, low-level tools such as memory management and serialization, GUI constructs, high-level visualization modules, and the ability to implement parallel algorithms with MPI. Scopira plug-in extensions have been developed to enable Matlab scripts to easily call any Scopira module, thus facilitating the migration of prototypes to highly efficient C++ applications. Scopira is continuously under development and future capabilities will include the ability to develop distributed programs using agents, applicable to grid-computing data mining applications.

Direct classification of high-dimensional data in low-dimensional projected feature spaces--comparison of several classification methodologies.

Previously, we introduced a distance (similarity)-based mapping for the visualization of high-dimensional patterns and their relative relationships. The mapping preserves exactly the original distances from all points to any two reference patterns in a special two-dimensional coordinate system, the relative distance plane (RDP). We extend the RDP mapping's applicability from visualization to classification.

Magnetic resonance spectroscopy detects biochemical changes in the brain associated with chronic low back pain: a preliminary report.

Magnetic resonance (MR) spectroscopy is a noninvasive technique that can be used to detect and measure the concentration of metabolites and neurotransmitters in the brain and other organs. We used in vivo (1)H MR spectroscopy in subjects with low back pain compared with control subjects to detect alterations in biochemistry in three brain regions associated with pain processing. A pattern recognition approach was used to determine whether it was possible to discriminate accurately subjects with low back pain from control subjects based on MR spectroscopy.

Identification of signatures in biomedical spectra using domain knowledge.

Objective: Demonstrate that incorporating domain knowledge into feature selection methods helps identify interpretable features with predictive capability comparable to a state-of-the-art classifier.

Methods: Two feature selection methods, one using a genetic algorithm (GA) the other a L(1)-norm support vector machine (SVM), were investigated on three real-world biomedical magnetic resonance (MR) spectral datasets of increasing difficulty. Consensus sets of the feature sets obtained by the two methods were also assessed.

Melanoma metastases in regional lymph nodes are accurately detected by proton magnetic resonance spectroscopy of fine-needle aspirate biopsy samples.

Background: Nonsurgical assessment of sentinel nodes (SNs) would offer advantages over surgical SN excision by reducing morbidity and costs. Proton magnetic resonance spectroscopy (MRS) of fine-needle aspirate biopsy (FNAB) specimens identifies melanoma lymph node metastases. This study was undertaken to determine the accuracy of the MRS method and thereby establish a basis for the future development of a nonsurgical technique for assessing SNs.

A rapid screening test to distinguish between Candida albicans and Candida dubliniensis using NMR spectroscopy.

Nuclear magnetic resonance (NMR) spectroscopy combined with a statistical classification strategy (SCS) successfully distinguished between Candida albicans and Candida dubliniensis. 96% of the isolates from an independent test set were identified correctly. This proves that this rapid approach is a valuable method for the identification and chemotaxonomic characterisation of closely related taxa.

Diagnosis of cerebral cryptococcoma using a computerized analysis of 1H NMR spectra in an animal model.

Viable cryptococci load in biopsy material from an animal model of cerebral cryptococcoma were correlated with 1H NMR spectra and metabolite profiles. A statistical classification strategy was applied to distinguish among high-resolution 1H NMR spectra acquired from cryptococcomas, glioblastomas, and normal brain tissue. The overall classification accuracy was 100% when a genetic-algorithm-based optimal region selection preceded the development of linear discriminant analysis-based classifiers.

Identification of Staphylococcus aureus brain abscesses: rat and human studies with 1H MR spectroscopy.

Purpose: To determine the feasibility of a statistical classification strategy (SCS) and the identity of metabolites of bacterial and host origins that potentially contributed to the most discriminatory regions of magnetic resonance (MR) spectra from Staphylococcus aureus abscesses of biopsy material from controls, gliomas, and staphylococcal abscesses.

Materials And Methods: Human and animal study received ethics committee approval, and informed patient consent was obtained. A rat model of S aureus brain abscess was developed.

Determination of grade and receptor status from the primary breast lesion by magnetic resonance spectroscopy.

Magnetic resonance spectra (MRS) from fine needle aspiration biopsies (FNAB) from primary breast lesions were analysed using a pattern recognition method, Statistical Classification Strategy, to assess tumor grade and oestrogen receptor (ER) and progesterone receptor (PgR) status. Grade 1 and 2 breast cancers were separated from grade 3 cancers with a sensitivity and specificity of 96% and 95%, respectively. The ER status was predicted with a sensitivity of 91% and a specificity of 90%, and the PgR status with a sensitivity of 91% and a specificity of 86%.

Molecular classification of scrapie strains in mice using gene expression profiling.

Transmissible spongiform encephalopathy strains demonstrate specific prion characteristics, each with specific incubation times, and strain-specific patterns of deposition of the misfolded isoform of prion, PrPSc, in the brains of infected individuals. Different biochemical properties, including glycosylation profiles and the degree of proteinase resistance, have been shown to be strain-specific. However, no relationship between these properties and the phenotypic differences in the subsequent diseases has as yet been determined.