|M.Sc Student||Marom Ophir|
|Subject||Detection of Abnormal Renal Function by Measurement of|
Volatile Organic Compounds in Exhaled Breath
|Department||Department of Chemical Engineering||Supervisor||Professor Hossam Haick|
|Full Thesis text|
Chronic kidney disease (CKD) is characterized by a progressive loss in renal function over a period of months or years. CKD is present in about 10% of the population in western countries. In CKD the loss in renal function is irreversible and the available medical treatment enables only stabilizing or slowing the disease progression. The common diagnostic technique today is an estimation of Glomerular Filtration Rate (eGFR) calculated as a function of serum creatinine levels. Early detection of CKD is critical in preventing patients from reaching advance stages of the disease, and consequently, improving their quality of life. A total loss of renal function requires patients to receive renal replacement therapy (RRT) such as kidney transplant or dialysis. Dialysis sessions are normally prescribed three times a week, four hours at a time. Adequate dialysis treatment improves patients' quality of life and life span. Improving current dialysis online monitoring technology could enable refining length of dialysis sessions which will improve quality of treatment.
In this study, a novel method for identifying abnormal renal function was explored for two characteristic clinical conditions. Organically functionalized nanoparticle-based sensors were prepared and used for analyzing breath samples from CKD patients and healthy controls. In the first project, when identifying CKD and disease progression, a support vector machine analysis of the signals of two or three sensors yielded good distinction between early-stage CKD and healthy controls and between stage-4 and stage-5 CKD states. Moreover, the response of a single sensor and eGFR values were found to show similar trend when correlated to the serum creatinine levels. Gas chromotograpy/mass spectrometry (GC/MS) analysis of breath samples has revealed a systematic change in the presence of several VOCs as a function of CKD severity. A metabolic mechanism supporting these results has been proposed. In the second project, nanoparticle-based sensors were used to analyze breath samples of patients undergoing hemodialysis therapy for monitoring purposes. Signal analysis enabled extraction of relevant sensors, presenting trends in responses characteristic of clinical changes derived from hemodialysis session. A blind test was performed with accuracy of 84% for detection of hemodialysis patients. GC-MS analysis of pre- and post-hemodialysis breath samples has revealed a number of characteristic VOCs. These VOCs presented different trends in concentration as a function of hemodialysis treatment. The results obtained by GC-MS analysis reinforce different trends in the response of various sensors, providing an insight on the clinical changes induced by hemodialysis.