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).

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.

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.

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.

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.

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.

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.

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%.

Rapid identification of Candida species by using nuclear magnetic resonance spectroscopy and a statistical classification strategy.

Nuclear magnetic resonance (NMR) spectra were acquired from suspensions of clinically important yeast species of the genus Candida to characterize the relationship between metabolite profiles and species identification. Major metabolites were identified by using two-dimensional correlation NMR spectroscopy. One-dimensional proton NMR spectra were analyzed by using a staged statistical classification strategy.

Pathology of Barrett's esophagus by proton magnetic resonance spectroscopy and a statistical classification strategy.

Background: Barrett's esophagus is thought to be a precursor of adenocarcinoma. The incidence of adenocarcinoma of the lower esophagus in the Western world is rising and accounts for more than 40% of esophageal carcinomas in males. It is not possible to identify which Barrett's patients are at high risk of developing malignancy.

Pathology of hepatocellular carcinoma and its precursors using proton magnetic resonance spectroscopy and a statistical classification strategy.

Aim: Apply the statistical classification strategy (SCS) to magnetic resonance spectroscopy (MRS) data from liver biopsies and test its potential to discriminate between normal liver, cirrhotic nodules and nodules of hepatocellular carcinoma with a high degree of accuracy.

Methods: Liver tissue specimens from 54 patients undergoing either partial (hemi) or total hepatectomy were analysed by one-dimensional proton MRS at 8.5 Tesla.

An ex vivo study exploring the diagnostic potential of 1H magnetic resonance spectroscopy in squamous cell carcinoma of the head and neck region.

Background: Definitive diagnosis of head and neck cancer is generally made by histopathologic evaluation. Management and prognosis largely depend on accurate and timely diagnosis. We have explored the use of (1)H magnetic resonance spectroscopy in search of a better or complementary diagnostic technique.