Monitoring kidney-transplant patients using metabolomics and dynamic modeling
| Autor: | Hans Stenlund, Marco Calderisi, Johan Trygg, Antonio Vivi, Rasmus Bødker Madsen, Maria Tassini, Mario Carmellini, Torbjörn Lundstedt |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
medicine.medical_specialty
Multivariate analysis Orthogonal Projections to Latent Structures (OPLS) Renal function Disease Kidney transplant Analytical Chemistry Nuclear magnetic resonance Immune system Internal medicine medicine Metabolomics Chemometrics Nuclear Magnetic Resonance (NMR) Spectroscopy Drug toxicity Spectroscopy Kidney business.industry Process Chemistry and Technology Computer Science Applications Transplantation medicine.anatomical_structure Chemometrics Dynamic modeling Kidney transplant Metabolomics Nuclear Magnetic Resonance (NMR) Spectroscopy Orthogonal Projections to Latent Structures (OPLS) business Software Dynamic modeling |
| Zdroj: | Chemometrics and Intelligent Laboratory Systems. 98:45-50 |
| ISSN: | 0169-7439 |
| DOI: | 10.1016/j.chemolab.2009.04.013 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect