Biomedical subjects

Olav M Kvalheim

Publications and source records attributed to Olav M Kvalheim.

3 recordsLinked to original sources

PubMed2004

Assigning environmental variables to observed biological changes.

A method for assigning environmental variables to observed biological changes in benthic communities is proposed. The approach requires biological and environmental sampling at the same sites. Additionally, a biological gradient or trend such as a change in observed species or a significant change in their relative abundances is necessary in order to connect the biological observations to the environmental measurements. Whether there is a statistical significant correspondence between the environmental measurements and the biological changes is tested after quantifying the biological changes by using the community disturbance index (CDI). Finally, the environmental variables that are most strongly associated with the biological changes are identified, and it is proposed that these are strong candidates as the pollutants responsible for the biological changes observed. However, this cannot be confirmed using the monitored data only. The approach is tested on data collected in monitoring surveys at the Ekofisk oil field in the North Sea. The results indicate the method is feasible for assigning environmental variables to observed biological changes.

Ecosystem↗

PubMed2003

Screening of biomarkers in rat urine using LC/electrospray ionization-MS and two-way data analysis.

Biofluids, like urine, form very complex matrixes containing a large number of potential biomarkers, that is, changes of endogenous metabolites in response to xenobiotic exposure. This paper describes a fast and sensitive method of screening biomarkers in rat urine. Biomarkers for phospholipidosis, induced by an antidepressant drug, were studied. Urine samples from rats exposed to citalopram were analyzed using solid-phase extraction (SPE) and liquid chromatography mass spectrometry (LC/MS) analysis detecting negative ions. A fast iterative method, called Gentle, was used for the automatic curve resolution, and metabolic fingerprints were obtained. After peak alignment principal component analysis (PCA) was performed for pattern recognition, PCA loadings were studied as a means of discovering potential biomarkers. In this study a number of potential biomarkers of phospholipidosis in rats are discussed. They are reported by their retention time and base peak, as their identification is not within the scope of the study. In addition to the fact that it was possible to differentiate control samples from dosed samples, the data were very easy to interpret, and signals from xenobiotic-related substances were easily removed without affecting the endogenous compounds. The proposed method is a complement or an alternative to NMR for metabolomic applications.

Animals↗

PubMed2002

Toxicological evaluation of complex mixtures by pattern recognition: correlating chemical fingerprints to mutagenicity.

We describe the use of pattern recognition and multivariate regression in the assessment of complex mixtures by correlating chemical fingerprints to the mutagenicity of the mixtures. Mixtures were 20 organic extracts of exhaust particles, each containing 102-170 individual compounds such as polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs, oxy-PAHs, and saturated hydrocarbons. Mixtures were characterized by full-scan GC-MS (gas chromatography-mass spectrometry). Data were resolved into peaks and spectra for individual compounds by an automated curve resolution procedure. Resolved chromatograms were integrated, resulting in a predictor matrix that was used as input to a principal component analysis to evaluate similarities between mixtures (i.e., classification). Furthermore, partial least-squares projections to latent structures were used to correlate the GC-MS data to mutagenicity, as measured in the Ames Salmonella assay (i.e., calibration). The best model (high r2 and Q2) identifies the variables that co-vary with the observed mutagenicity. These variables may subsequently be identified in more detail. Furthermore, the regression model can be used to predict mutagenicity from GC-MS chromatograms of other organic extracts. We emphasize that both chemical fingerprints as well as detailed data on composition can be used in pattern recognition.

Animals↗