Optimization of a Breath Analysis Methodology to Potentially Diagnose Transplanted Kidney Rejection: A Preclinic Study
Chronic kidney disease (CKD) may result in end-stage renal disorder and an increased mortality rate. Presently, kidney transplantation represents the only definitive treatment to restore normal life expectancy. Nevertheless, there is an elevated risk of organ rejection in the short–medium term after...
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Veröffentlicht in: | Applied sciences 2023-02, Vol.13 (5), p.2852 |
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Sprache: | eng |
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Zusammenfassung: | Chronic kidney disease (CKD) may result in end-stage renal disorder and an increased mortality rate. Presently, kidney transplantation represents the only definitive treatment to restore normal life expectancy. Nevertheless, there is an elevated risk of organ rejection in the short–medium term after surgery. This preclinic study proposes the optimization of an innovative, fast, non-invasive, and cheap thermal desorption-gas chromatograph–mass spectrometry (TD-GC–MS) protocol, which provides simple monitoring of the respiratory profile of CKD-affected patients, before and during the first year after surgery, and aims to preemptively predict the rejection of the transplanted kidney. Twenty volatile organic compounds (VOCs), known as targets and, which are representative of the major classes of molecules discriminating between CKD-affected patients and healthy individuals, were selected from the literature, and employed to optimize the methodology. Calibration curves, linearity concentration ranges, the limit of detection (LOD), and the limit of quantification (LOQ) were estimated for the chosen molecules as well as the intraday and interday reproducibility of the method. The applicability of the TD-GC–MS developed approach was tested by analyzing the breath of healthy and pathological subjects using the ReCIVA® device. Sixty-seven molecules were identified, and between these, thirteen of the twenty selected compounds were quantified and were identified to have high prognostic values. |
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ISSN: | 2076-3417 2076-3417 |
DOI: | 10.3390/app13052852 |