Sniffing Chronic Renal Failure in Rat Model by an Array of Random Networks of Single-Walled Carbon Nanotubes

In this study, we use an experimental model of bilateral nephrectomy in rats to identify an advanced, yet simple nanoscale-based approach to discriminate between exhaled breath of healthy states and of chronic renal failure (CRF) states. Gas chromatography/mass spectroscopy (GC-MS) in conjugation with solid-phase microextraction (SPME) of healthy and CRF breath, collected directly from the trachea of the rats, identified 15 common volatile organic compounds (VOCs) in all samples of healthy and CRF states and 27 VOCs that appear in CRF but not in healthy states. Online breath analysis via an array of chemiresistive random network of single-walled carbon nanotubes (SWCNTs) coated with organic materials showed excellent discrimination between the various breath states. Stepwise discriminate analysis showed that enhanced discrimination capacity could be achieved by decreasing the humidity prior to their analysis with the sensors' array. Furthermore, the analysis showed the adequacy of using representative simulated VOCs to imitate the breath of healthy and CRF states and, therefore, to train the sensors' array the pertinent breath signatures. The excellent discrimination between the various breath states obtained in this study provides expectations for future capabilities for diagnosis, detection, and screening various stages of kidney disease, especially in the early stages of the disease, where it is possible to control blood pressure and protein intake to slow the progression.