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Monitoring kidney-transplant patients using metabolomics and dynamic modeling
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Nuclear Magnetic Resonance Centre of Siena University, Siena, Italy.
Nuclear Magnetic Resonance Centre of Siena University, Siena, Italy.
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2009 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 98, no 1, 45-50 p.Article in journal (Refereed) Published
Abstract [en]

A kidney transplant provides the only hope for a normal life for patients with end-stage renal disease, i.e., kidney failure. Unfortunately, the lack of available organs leaves some patients on the waiting list for years. In addition, the post-transplant treatment is extremely important for the final outcome of the surgery, since immune responses, drug toxicity and other complications pose a real and present threat to the patient. In this article, we describe a novel strategy for monitoring kidney transplanted patients for immune responses and adverse drug effects in their early recovery. Nineteen patients were followed for two weeks after renal transplantation, two of them experienced problems related to kidney function, both of whom were correctly identified by means of nuclear magnetic resonance spectroscopic analysis of urine samples and multivariate data analysis.

Place, publisher, year, edition, pages
Elsevier B.V. , 2009. Vol. 98, no 1, 45-50 p.
Keyword [en]
Nuclear Magnetic Resonance (NMR) Spectroscopy, Chemometrics; Metabolomics, Kidney transplant, Dynamic modeling, Orthogonal Projections to Latent Structures (OPLS)
National Category
Chemical Sciences
Identifiers
URN: urn:nbn:se:umu:diva-26561DOI: 10.1016/j.chemolab.2009.04.013OAI: oai:DiVA.org:umu-26561DiVA: diva2:272454
Available from: 2009-10-15 Created: 2009-10-15 Last updated: 2017-12-12
In thesis
1. Improving interpretation by orthogonal variation: Multivariate analysis of spectroscopic data
Open this publication in new window or tab >>Improving interpretation by orthogonal variation: Multivariate analysis of spectroscopic data
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The desire to use the tools and concepts of chemometrics when studying problems in the life sciences, especially biology and medicine, has prompted chemometricians to shift their focus away from their field‘s traditional emphasis on model predictivity and towards the more contemporary objective of optimizing information exchange via model interpretation. The complex data structures that are captured by modern advanced analytical instruments open up new possibilities for extracting information from complex data sets. This in turn imposes higher demands on the quality of data and the modeling techniques used.

The introduction of the concept of orthogonal variation in the late 1990‘s led to a shift of focus within chemometrics; the information gained from analysis of orthogonal structures complements that obtained from the predictive structures that were the discipline‘s previous focus. OPLS, which was introduced in the beginning of 2000‘s, refined this view by formalizing the model structure and the separation of orthogonal variations. Orthogonal variation stems from experimental/analytical issues such as time trends, process drift, storage, sample handling, and instrumental differences, or from inherent properties of the sample such as age, gender, genetics, and environmental influence.

The usefulness and versatility of OPLS has been demonstrated in over 500 citations, mainly in the fields of metabolomics and transcriptomics but also in NIR, UV and FTIR spectroscopy. In all cases, the predictive precision of OPLS is identical to that of PLS, but OPLS is superior when it comes to the interpretation of both predictive and orthogonal variation. Thus, OPLS models the same data structures but provides increased scope for interpretation, making it more suitable for contemporary applications in the life sciences.

This thesis discusses four different research projects, including analyses of NIR, FTIR and NMR spectroscopic data. The discussion includes comparisons of OPLS and PLS models of complex datasets in which experimental variation conceals and confounds relevant information. The PLS and OPLS methods are discussed in detail. In addition, the thesis describes new OPLS-based methods developed to accommodate hyperspectral images for supervised modeling. Proper handling of orthogonal structures revealed the weaknesses in the analytical chains examined. In all of the studies described, the orthogonal structures were used to validate the quality of the generated models as well as gaining new knowledge. These aspects are crucial in order to enhance the information exchange from both past and future studies.

Place, publisher, year, edition, pages
Umeå: Kemiska institutionen, Umeå universitet, 2011. 62 p.
Keyword
OPLS, PLS, Multivariate Analysis, Orthogonal variation, Chemometrics, hyperspectral images, FTIR, NIR, spectroscopy
National Category
Chemical Sciences
Identifiers
urn:nbn:se:umu:diva-43476 (URN)978-91-7459-207-8 (ISBN)
Public defence
2011-06-01, KBC-huset, KB3A9, Umeå universitet, Umeå, 10:00 (English)
Opponent
Supervisors
Available from: 2011-05-11 Created: 2011-05-02 Last updated: 2011-05-30Bibliographically approved

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