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.

Stenlund, H., Madsen, R., Vivi, A., Calderisi, M., Lundstedt, T., Tassini, M., et al. (2009). Monitoring kidney-transplant patients using metabolomics and dynamic modeling. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 98(1), 45-50 [10.1016/j.chemolab.2009.04.013].

Monitoring kidney-transplant patients using metabolomics and dynamic modeling

Vivi, A.;Calderisi, M.;Tassini, M.;Carmellini, M.;
2009

Abstract

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.
Stenlund, H., Madsen, R., Vivi, A., Calderisi, M., Lundstedt, T., Tassini, M., et al. (2009). Monitoring kidney-transplant patients using metabolomics and dynamic modeling. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 98(1), 45-50 [10.1016/j.chemolab.2009.04.013].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/8649