Nanomedicine: Nanotechnology, Biology, and Medicine xx (2014) xxx – xxx

Sensor arrays based on nanoparticles for early detection of kidney injury by breath samples Morad K. Nakhleh, MSc a , Haitham Amal, MSc a , Hoda Awad, BSc b , A'laa Gharra, BSc a , Niroz Abu-Saleh, MSc b , Raneen Jeries, BSc a , Hossam Haick, PhD a,⁎, Zaid Abassi, PhD b, c a

Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion–Israel Institute of Technology, Haifa, Israel b Department of Physiology and Biophysics, Faculty of Medicine, Technion–Israel Institute of Technology, Haifa, Israel c Research Unit, Rambam Health Campus, Hiafa, Israel Received 24 December 2013; accepted 11 June 2014

Abstract The outcomes of acute kidney injury (AKI) could be severe and even lethal, if not diagnosed in its early stages and treated appropriately. Blood and urine biomarkers, currently in use as indicators for kidney function, are either inaccurate in various cases or not timely. We report on dramatic changes in exhaled breath composition, associated with kidney dysfunction after ischemic insult in rat models. Gas chromatography linked mass spectrometry examination of breath samples indicated significant elevations in the concentration of three exhaled volatile organic compounds, two to six hours after AKI was surgically induced. Relying on these findings, we introduce an array of sensors, based on organiclayer capped gold nanoparticles, sensitive to odor changes. The ability of the array to detect AKI via breath testing was examined and scored a sensitivity of 96%, only one hour after disease induction. © 2014 Elsevier Inc. All rights reserved. Key words: Acute kidney injury; Detection; Gold nanoparticles; Breath test; Volatile organic compound

Acute kidney injury (AKI) is a common clinical problem affecting about 2% to 7% of hospitalized patients and 5% to 10% of critically ill patients. 1,2 AKI occurs in response to a large number of insults that damage the nephron with adverse functional consequences such as metabolic acidosis, hyperkalemia, abnormal body fluid balance and adverse effects on other organ systems. 3,4 Renal ischemia/reperfusion (I/R) injury is the leading cause for intrinsic AKI. 2–5 The mechanisms underlying renal I/R injury are complex, including, decreased oxygen delivery, depletion of cellular energy stores and accumulation of toxic metabolites. 2–4 However, reperfusion of ischemic tissue, being necessary as a reparative mechanism, has been shown to exacerbate acute ischemic injury through the generation of

Support: This study was supported by a NOFAR grant. Disclosure: No coauthor declares any conflict of interest. The authors acknowledge Dr. Yoav Broza and Dr. Ulrike Tisch (both from Technion-IIT, Haifa, Israel) for helpful discussion and script review. The authors also acknowledge Dr. Viki Kloper for her assistance in the preparation of the graphical images. ⁎ Corresponding author. E-mail address: [email protected] (H. Haick). 1549-9634/© 2014 Elsevier Inc. All rights reserved.

reactive oxygen and nitrogen species. 2–4,6,7 Despite the advances in medicine in general and critical care medicine in particular, AKI is still associated with high morbidity, causing an overall in-hospital mortality of approximately 20% and more than 50% in Intensive Care Unit (ICU) patients. 8,9 Urinalysis and urine volume are invaluable tools for the diagnosis of kidney dysfunction. However, both tools suffer from limited sensitivity and specificity. 10 Specifically, a reduction in urine output (oliguria) usually denotes more significant AKI than when urine output is maintained. However, preserved and even enhanced urine output is noted in AKI of various etiologies including I/R AKI. Sodium excretion suffers from a few shortcomings, which could be altered by rhabdomylosis, radio contrast or sepsis-induced AKI. 10 Other criteria for diagnosis and classification of AKI are highly dependent on changes in serum creatinine (SCr). 1,2 However, rapid, major loss of kidney function could be associated with small changes in SCr levels, particularly in the early phases of AKI, so this could cause a delay in the diagnosis and intervention. 11 Biomarkers such as neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) were also suggested for AKI detection. 12–14 NGAL increased in urine very early after renal


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FIGURE 1. Effect of ischemia–reperfusion injury (I/R) for 45 min on: (A) urine flow (V), (B) sodium excretion (UNaV), (C) fractional excretion of sodium (FENa), and (D) mean arterial blood pressure (MAP) at different time points: baseline (T0); and, one, two, three and four hours after I/R (T1-T4). (#) represents P-value b0.05 when comparing between the two groups at the same time point, whereas (*) represents P-value b0.05 when comparing the value at specific time point versus the baseline (T0) of the same group.

ischemia in mouse and rat models, 15,16 and in children with AKI after cardiopulmonary bypass. 12,14,17 However, NGAL levels in plasma and urine are known to rise in various clinical settings including inflammatory and infective conditions. 13,14 Therefore, there is an urgent need for alternative/complementary rapid and accurate for early detection of AKI. Several mass spectrometry studies over the past two decades have shown that the spectrum of exhaled volatile organic compounds (VOCs) is remarkably altered as a result of disease, 18–44 including kidney dysfunction. 35,36,44–47 This could be due to the accumulation of toxins in the blood, production of ischemic tissue and its surrounding, or due to metabolic changes of disease pathway byproducts. 34–36,38 A more innovative method in the field of breath testing uses cross-reactive nanomaterial-based sensor arrays in conjugation with pattern-recognition algorithms. 18–20,23,48 These nanomaterial-based sensor arrays are more likely to become a clinical and laboratory diagnostic tool because they are small in size, easy to use, and inexpensive. Furthermore, they respond rapidly and differently to small changes in concentration and provide a consistent output that is specific to a given exposure. When not in contact with the analyte, these sensors return to their baseline state rapidly.

The advantages of these sensors have led to promising outcomes in several fields, including detection of cancer 18–20,22,23,25–31,49–52 and various non-cancerous diseases. 32–36,53–55 Recently, we have demonstrated the ability of these sensor arrays to differentiate between healthy states and induced end stage renal disease (ESRD) in rats via breath samples with an accuracy of over 95%, using a model of bilateral nephrectomy. 36 Moreover, we have also shown discrimination between various stages of kidney dysfunction in patients with chronic kidney disease (CKD) 35 and that the efficacy of hemodialysis treatment could be evaluated by gold nanoparticles based sensors. 34 The present study is designed to examine whether AKI-induced changes in the VOC concentrations are detectable at an early stage and whether these changes can be identified using a nanoparticle-based sensor array.

Materials and methods Study design Studies were conducted on 16 male Sprague Dawley rats (Harlan Laboratories, Jerusalem), weighing 300-350 g, that were

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FIGURE 2. Effect of ischemia-reperfusion injury (I/R) on: (A) glomerular filtration rate (GFR) and (B) urine NGAL levels at the different time points: baseline (T0); and, one, two, three and four hours after I/R (T1-T4). (#) represents P-value b0.05 when comparing between the two groups at the same time point, whereas (*) represents P-value b0.05 when comparing the value at specific time point versus the baseline (T0) of the same group.

maintained on standard rat chow (0.5% NaCl) and water ad libitum. The investigation was conducted according to the guidelines of the Animal Use and Care Committee, Technion– Israel Institute of Technology. AKI was surgically induced in 8 of the animals, while the remaining 8 animals served as a sham operated control group. AKI induction, blood and urine sampling All animals (n = 16) were housed under standardized conditions for 2–3 days. Following an overnight fast, the animals were anaesthetized with inactin anesthesia (100 mg/kg, IP) and placed on a controlled heating (thermo-regulated) table (37 °C). Polyethylene tubes (PE50) were jugular vein for blood sampling and infusion of 0.9% normal saline (0.9% NaCl) at a 1.5 ml/h rate. After that, an upper abdominal midline incision was made and renal blood vessels were isolated bilaterally. The area was covered with saline-soaked gauze to minimize dehydration. Baseline (T0) breath and blood samples were collected at this stage. Then, AKI was surgically induced in only 8 of the animals. In this surgery, we removed the right kidney (nephrectomy) and immediately after that, we occluded the left renal artery, occluded for 45 min. At the


FIGURE 3. Effect of I/R for 45 min on: (A) serum NGAL, (B) serum creatinine. (#) represents P-value b0.05 when comparing between the two groups at the same time point, whereas (*) represents P-value b0.05 when comparing the value at specific time point versus the baseline (T0) of the same group.

end of the 45-min period, we allowed a reperfusion for 15 min (so called T1). Breath and blood samples were obtained at T0 and T1 as well as 2 h, 4 h, and 6 h after the nephrectomy process (denoted as T2, T4 and T6, respectively). Rats that underwent the same procedure with the exception of right nephrectomy and left renal ischemia served as controls. Two rats (one AKI and the other sham operated) were examined per day. The experiments were conducted over a period of 4 weeks. Blood samples were centrifuged and the separated serum was stored at − 20 °C. Serum samples were analyzed for NGAL and creatinine. In an additional group of animals, a catheter (PE50) was inserted into the bladder for urine collection; saline was infused at a rate of 1% of body weight per hour (~ 3.0 ml/h) throughout the experiment. Urine samples were collected at T0, T1, T2, and T4. Blood and urine were chemically analyzed in order to evaluate kidney function using conventional biomarkers such as Inulin clearance, urine NGAL, urine flow and sodium excretion. For further details regarding the sampling and analysis procedure, see Supplementary Materials (Sections 1.1. and 1.2.). Breath collection Tracheo-tubes were inserted into all rats after anesthesia. A custom-made airway system was used (Supplementary Figure S1-A),


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Table 1 Number of eligible samples contributed to GC/MS and sensor array analysis, after exculsion of damaged samples.

Table 2 VOCs with significant change in concentration, at least at one time point when compared to its baseline measurment.

Analysis method


GC-MS Sensor Array

Time point Group








7 6 8 7

7 6 7 6

8 7 8 7

5 5 7 6

8 3 7 6

35 27 37 32

The samples were collected from 8 AKI and 8 sham operated rats. Baseline: after abdominal insection. T1: one hour after nephrectomy and renal ischemia, T2, T4 and T6, respectively.

Retention Main m/z Time of P-value for P-value for time (min) significant AKI group SHAM group change

Ethanol 2.499 Acetone 2.736 2-Pentanone 7.006

31 43 43

T2 T6 T6

0.0253 0.0223 0.0009

0.2012 0.3625 0.4580

T2: 2 h after after nephrectomy and renal ischemia. T6: 6 h after nephrectomy and renal ischemia.

consisting of two one-way valves. The first valve opens when the rats applied negative chest pressure, as they attempt to inhale, directing ambient air into the lungs. The second valve remains closed during inhalation and is activated when the rats exhale, directing the exhaled breath into 1 liter Tedlar bags. Samples were simultaneously collected from AKI and sham operated rats at T0, T1, T2, T4 and T6. Two bags were obtained at each time point and 700 ml from the contents of each bag were pumped (100 ml/min flow rate) into a Tenax TA/Carboxen-1018 glass adsorbent tube (Sigma Aldrich Ltd.). All samples were analyzed within three months from collection. Ambient air samples (700 ml) were also collected on a daily basis, monitoring the environmental settings during the experiment.

electrode was 20 μm. The GNP sensors used in this study responded rapidly and reversibly when exposed to typical VOCs in the breath. 58,59 Additionally, we have confirmed that the GNP sensors have a very low response to water. 60 Therefore, the effect of the high, varying air humidity in the room and the high, varying humidity levels in exhaled breath is insignificant. After fabrication of the devices, each sensor went through a characterization procedure, in which it was exposed to several concentrations of compounds, usually found in breath, (e.g. isopropyl alcohol, 2-ethyl-hexanol, water vapor and others), in a range of tens of parts per billion (ppbs)—several parts per million (ppms). The information obtained in these experiments allowed us to choose the most suitable sensors array to be used for breath samples analysis.

Quantitative chemical analysis of breath samples

Breath analysis using the sensor arrays

Gas chromatography linked with mass spectrometry (GC/ MS) was used for the chemical analysis of the breath samples. Statistical analysis was performed for the identified compounds, described in the Supplementary Material (Section 1.3.).

Breath samples were exposed to a reservoir of GNP-based sensors. The exposure resulted in a fully reversible change in resistance of the sensors, which was recorded. Extracted sensing features and used discriminant factor analysis (DFA) to choose the minimal number of sensors required in order to achieve best discrimination between two groups. The detailed procedure is described in Supplementary Material (Sections 1.4. and 1.5.).

Description of the nanoparticles-based sensor array A sensor array is an artificial system that simulates the different stages of the human olfactory system; it consists of an array of chemical sensors for VOC detection, with amplification of the sensor signal responses, and an algorithm for pattern recognition. 18,19,48 The nanomaterial-based sensor array that was used to analyze the breath samples contained cross-reactive, chemically diverse chemiresistors that were based on organically stabilized spherical gold nanoparticles (GNPs, core diameter: 3–4 nm). The chemical diversity of the sensors was achieved through three different organic functionalities: dodecanthiol, octadecanethiol, and 4-chlorobenzenemethanethiol. The organic ligands of the GNPs provided broadly cross-selective absorption sites for the breath VOCs. 56,57 The GNPs were synthesized as described in Refs. 58,59 and dispersed in chloroform. Chemiresistive layers were formed by drop casting the solution onto semi-circular microelectronic transducers, until a resistance of several MΩ was reached. The device was dried for 2 h at ambient temperature and then baked overnight at 50 °C in a vacuum oven. The microelectronic transducers consisted of ten pairs of circular interdigitated (ID) gold electrodes on silicon with 300 nm thermal oxide (Silicon Quest International, Nevada, US). The outer diameter of the circular electrode area was 3 mm. The gap between two adjacent electrodes as well as the width of each

Statistical analysis Statistical significance was assessed for repeated measurements by one-way analysis of variance (ANOVA) or by two-way ANOVA, as appropriate. The Dunnett test and Tukey's multiple comparisons test were used for data point comparisons in each group. P-values of b 0.05 were considered statistically significant. Statistical tests were performed by SAS JMP, Version 10.0.

Results Impact of I/R on kidney function and urine NGAL The changes in urine flow, urinary sodium excretion, fractional sodium excretion, mean arterial pressure and GFR following renal I/R are depicted in Figure 1 and 2, A. Rats that were subject to I/R exhibited a substantial increase in urine flow and in sodium excretion, both absolute and fractional, throughout the experiment. In parallel, GFR significantly declined following I/R, most remarkably at T1 and throughout the entire experiment, although it gradually improved over time. Even 4 h after I/R, the kidney continued to display significant

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activation of vasoconstrictory neurohormonal systems, fluid loss from the open abdominal cavity, or insufficient saline infusion. It should be pointed out that the V and UNaV baseline values of the controls were lower than those obtained in AKI rats. However, the FENa, which is considered a better indicator of AKI than UNaV or urine flow, was comparable for the two experimental groups (Figure 1). In line with the decrease in GFR, urinary excretion of NGAL was gradually enhanced in I/R and reached statistical significance within 2 h and lasted for 4 h after the renal insult (Figure 2, B). In contrast, no significant change in urinary NGAL was found in sham control rats. Impact of I/R on serum NGAL and creatinine As expected, rats that were subject to I/R exhibited a higher serum level of NGAL throughout the entire monitoring period. Although the increase was remarkable, starting one hour after I/R induction, statistical significance was observed in the first 4 h after I/R induction (Figure 3, A). A mild increase in circulatory NGAL was also noticed in sham control rats, where it reached statistical significance at T6. In addition, rats with I/R displayed a gradual increase in SCr, which reached statistical significance after 2 h (Figure 3, B). Chemical analysis of the breath samples by GC-MS

FIGURE 4. Breath VOCs affected by AKI progression. Average concentration and SEM of (A) Ethanol, (B) acetone and (C) 2-pentanone, at different time point measurements. (*P b 0.05 single measurement vs. baseline).

hypo-filtration in the AKI group. On the other hand, we noticed a mild decline in GFR along with a slight increase in V and UNaV in the control group too, yet these changes did not reach statistical significance (Figures 1 and 2, A). These alterations in kidney function of the sham operated rats may stem from

After excluding damaged tubes (shattered glass or leaky), a total of 62 breath samples, collected from AKI rats and sham operated rats at five different time points during the procedure (Table 1), were analyzed. We tested 12 identified VOCs that were present in at least 80% of the samples, including ethanol, acetone, 1-dodecyl-aziridine, 2-pentanone, 1-butanol, 5-methyl 2-hexanone, 2,5-dimethyloctane, 2,2,4,6,6-pentamethyl-heptane, hexanal, benzoic acid, 1-eicosanol and 5-methyl-tridecane. We noticed significant changes in the concentrations of three VOCs (P-value b 0.05) in the AKI group. Ethanol levels, for instance, increased two hours after AKI induction, and then returned to normal levels at T4, followed by a significant increase at T6. The levels of acetone and 2-pentonane increased significantly 6 h after I/R (Table 2 and Figure 4). We have carefully examined the possibility that the changes observed were influenced by environmental effects: ambient air samples that were collected time of the experiments and were analyzed in the same way; we examined the abundance of breath VOCs in them. While 2-pentonane was not detected in any of the room samples, acetone appeared once. Ethanol was abundant in 4 of 8 samples, with average concentration of 187 ±165 ppb. Concerning 2-pentanone and acetone concentrations, we observed a remarkable variation in the concentrations of one animal, affecting the average and standard deviation. This could be due to several reasons: (i) an exogenous contamination during this animal specific procedure; (ii) metabolic reaction to anesthetic drug or surgical procedure; and/or (iii) technical defect during breath sampling of this specific animal. Analysis of exhaled breath samples using sensor arrays VOC patterns of AKI states were derived from the normalized sensing features of three sensors of the sensor array as described in


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FIGURE 5. Statistical analysis for the responses of the sensors array. (A) First canonical value (CV1): DFA model was built using AKI group samples. (B) First canonical value (CV1): DFA model was built applied on sham group samples. (C) Receiver operating characteristic (ROC) curve analysis for DFA model. (D) Receiver operating characteristic (ROC) curve analysis for DFA model upon. Sham samples (E): average canonical value of each group at every time-point (error bars for SEM). (F) Individual responses of each sample. • (A and C) The boxes represent the 95% confidence intervals of the CV1 values, corresponding to 1.96 × SEM.

the Supplementary Materials (Sections 1.4. and 1.5.). When using a combination of three independent sensing features, extracted from a differently modified GNP sensor (Supplementary Figure S1 and Supplementary Table S1), we could derive a powerful

discriminative model capable of sensing AKI and monitoring its progression, starting one hour post-surgery (T1). Figure 5, A shows the first canonical value (CV1) of the DFA model for AKI. As seen in the Figure, the post-surgery states (T1–T6) formed a

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Table 3 Classification success of the DFA model estimated by leave-one-out cross validation. Group


Target group

Sensitivity (TP)

Specificity (TN)


ROC curve area

AKI (n = 37)

Baseline (n = 8)

T1-T6 (n = 29)


Baseline (n = 7)

T1-T6 (n = 25)

75% (6) 42% (3)


SHAM (n = 32)

96% (28) 60% (15)



well-defined cluster that was well-separated from the cluster of the pre-surgery baseline states (T0) of the same animals. Leave-oneout cross-validation showed that the model accurately classified 28 of 29 breath samples from post-surgery AKI rats, scoring a sensitivity of 96%, and 6 of 8 per-surgery (baseline) samples, setting a specificity of 75% and total diagnostic accuracy of 92% (Table 3). Moreover, a receiver operating characteristic (ROC) curve analysis was applied to the diagnostic model, examining the binary state classification ability: the area under curve (AUC) was 89% (Figure 5, C), showing high classification ability. When applying the same model to the samples collected from the sham operated rats before and after surgery, we observed that the DFA clusters overlapped completely (Figure 5, B). Leave-one-out cross-validation yielded a classification accuracy of only 56% (18 of 32), i.e., random classification, and an AUC of 60% was obtained in the ROC curve analysis (Table 3 and Figure 5, D). These results confirm that the derived DFA model is indeed sensitive only to AKI, and not to other post-surgery injury, or to environmental fluctuations at the site of the breath sample collection during the measurement period. Otherwise, similar trend would also be observed for the sham-operated rats, since on each day of the experiment, one AKI and one sham-operated rat were prepared and analyzed. Figure 5, E shows the average of the CV1 values that were obtained for all the breath samples from the AKI rats and the sham operated rats, and Figure 5, F displays the actual values for each sample. Prior to the procedure, at T0, all the animals shared the same baseline; their CV1 values overlapped completely and no significant difference was observed between the two groups. The CV1 values of the AKI rats changed dramatically immediately after I/R, whereas the CV1 values of the sham operated controls show only a sub-significant shift towards lower numbers. For all time-points after surgery up to T6, the difference in average CV1 between AKI rats and controls was significant. Discussion Blood and urine markers Our findings suggest that the sensor array responded immediately to changes in the exhaled VOC profile that were associated with AKI, within not more than one hour from the occurrence of the injury. The response of the sensor array to AKI states was found to be faster than urinary and circulatory NGAL, which started to increase, only after 2 h and 6 h, respectively, from the AKI induction. Likewise, SCr was significantly elevated only after 2 h from the AKI. These findings are of special interest in light of the poor accuracy of the classic biomarkers in detecting AKI. 61–63 Kidney injury is usually diagnosed by laboratory analysis of

a patient's blood and urine, whereby elevated SCr and BUN, together with insufficient urinary output, are typical characteristics. 1,2 Despite its widespread use, SCr suffers from numerous limitations. Baseline values of SCr are not normally available for patients suspected of having AKI. Moreover, changes in SCr usually lag behind the rapid alterations in GFR that characterize AKI. Up to 60% of kidney function may be lost before SCr even begins to rise and can take several days to reach a new steady state. 61–63 NGAL and KIM-1 are the most prominent representatives of these biomarkers. Experimental and clinical studies revealed that circulatory and urinary NGAL levels increased as early as 2 h after the development of AKI. 12–17 In line with these findings, our results also demonstrated elevated urinary NGAL levels 2 h after the kidney I/R insult. Concerning the serum levels of NGAL, we noticed a gradual increase during the follow-up period; however, the difference from the baseline reached statistical significance only 6 h after the AKI induction. Despite NGAL superiority over SCr, NGAL measurements may be influenced by a number of coexisting variables, including chronic kidney disease, chronic hypertension, systemic infections, inflammatory conditions, anemia, hypoxia and malignancies. 64 Thus, the moderate increase in serum and urinary NGAL in the control group a few hours after the initiation of the experiment could be attributed to possible systemic infection due to the surgical procedure. Alternatively, the increase in SCr even in the control group might stem from inadequate fluid administration in these relatively long experiments. Dehydration is known to cause pre-renal failure, which is associated with mild to moderate increases in SCr and urinary and Serum NGAL levels. 65 Chemical analysis for AKI breath samples While the effects of AKI on the body fluid and electrolyte homeostasis are well known, little is known about its impact on the composition of the exhaled breath either in humans or experimental models of the disease. This issue is interesting for two reasons: (i) It is well-established that CKD affects the composition of gases found in exhaled breath, and these VOCs were used to detect the disease in patients 35,36,66 and rats 36; (ii) AKI triggers a cascade of extra-renal abnormalities, including cardiac, neuronal, hepatic dysfunctions along enhanced inflammatory responses affecting lung homeostasis. 67 AKI significantly affects lung physiology by altering the homeostasis of fluid balance, acid–base balance, metabolism, vascular tone and the status of the inflammatory/oxidative stress systems. All these are assumed to modify gaseous metabolites found in the exhaled breath during AKI. Indeed, chemical analysis of the breath composition indicated a remarkable increase of three


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oxygen-containing compounds; acetone, 2-pentanone and ethanol. Aldehydes, in general, and ethanol, in particular, might originate by alcohol metabolism in the body. However, in our case, it is more likely to be produced by reduction of hydroperoxide by cytochrome p-450, as secondary product of lipid peroxidation process in the kidney. 21 As mentioned earlier, we have used an ischemic model to induce acute tubular necrosis–ischemic AKI. Specifically, we blocked blood supply for 45 min to the remnant kidney, after removing the first one. During this period, cells suffer from a lack of oxygen and nutrients. However, with circulation restoration, we find an excessive production of reactive oxygen species by inflammatory cells, stealing electrons from the lipids in cell membrane, damaging the surrounding tissue, all as part of the reperfusion injury process. 21,68 The ketones (acetone and 2-pentanoneare) are known to be elevated in ketonemic patients, for example, in the cases of prolonged fasting or uncontrolled diabetes, in which an extreme lipolysis process occurs. Similar conditions could be found in severe and rapid AKI, due to an imbalance of blood glucose homeostasis. Moreover, it is well known that ketone-formation could be secondary to lipid peroxidation and fatty acids oxidation. 21,68 However, further in-vitro studies are required in order to determine the origin of these compounds. Moreover, blood glucose and insulin levels should also be monitored in order to examine ketonomic effect of AKI. Although the ethanol levels change two hours after AKI, we don't suggest it as an optimal biomarker for early detection of AKI, mainly because the multiple possible sources of ethanol, such as alcohol consumption or production by GI tract bacteria. 69 Consistent with previous publications in the field of kidney failure, ethanol, acetone and 2-pentanone, were found in both breath and fecal samples.. 38,40,66 Ethanol was found to be lower in both breath and feces of chronic kidney disease inducted in the rat model. Acetone was lower in the feces but higher in the breath of CKD rats when compared to control group. It was also reported that 2-pentanone levels were higher in the breath of CKD rats. 66 Sensor array for AKI detection As indicated earlier in the introduction the GC/MS analysis has several disadvantages, including the complicity of operation, the high cost and the need of sample treatment prior to analysis which might insert a bias to it or cause a data loss. 24The described sensor array constitutes a more robust and more sensitive detection system than GC/MS, mainly because the sensors monitor the collective changes in the overall breath-VOC composition, rather than examining each compounds' concentration. Moreover, it could be used for online point-of-care analysis, which would be helpful especially among mechanically ventilated patients. Several types 42,70,71 of chemical sensors were tested as potential tools to monitor kidney function, and some scored relatively high correlation to GFR for accuracy of kidney dysfunction detection. However, some types require high temperatures (N 200 °C) in order to analyze the samples 71; others use blood samples, which require invasive procedures and could be hazardous. 70 In few instances sensors 39,43 were used for

the selective sensing of breath ammonia levels. Some of them showed correlation between blood and breath ammonia levels; moreover, breath ammonia was significantly decreased after hemodialysis. 39 However, ammonia is not a stable biomarker for kidney diseases since ammonia levels are easily altered and not specific to kidney disease. Ammoni was also found elevated in Helicobacter pylori infection, 43 encephalopathy, 43 and pulmonary artery hypertension. 37 In contrast to these studies, our approach relies on the detection of a VOC mixtures for detection of AKI conditions, thus decreasing the chances to find similar patterns in other confounding diseases and/or environmental conditions. At present, physicians rely on ranges of healthy population as the normal baseline. The same concept could be applied to our system, in which we establish a database of breath samples from numerous healthy subjects as a baseline reference for future comparisons when AKI is suspected. As a result, we are able to track their behavior with regard to VOCs and normalize the results in a way that prevents baseline and response drift. The method could also be translated to real world clinical conditions by simple daily exposure of the sensors to specific mixture of compounds. Such an array of three sensors could be easily adapted as a reusable kit for fast testing. The patient would breathe into an opening at one end of the device, after removing a sterile seal. Mounted on an electronic chip for signal readout and data storage, the sensors' readout and the software for the data analysis could be loaded to any computer, and the data could be analyzed on-site within few minutes.

Conclusions The results of the current study show that AKI is characterized by very rapid metabolic alterations in blood stream, as expressed in the concentrations of VOCs present in exhaled air, just one hour after disease induction. We present a simple, non-invasive breath test with a nanoparticle-based sensor array that is capable of early detection of AKI. The sensor array can be used as a complementary tool for early diagnosis and monitoring AKI progression. Further studies are needed to explore whether this approach is reliable in early detection of clinical AKI. Appendix A. Supplementary data Supplementary data to this article can be found online at

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Sensor arrays based on nanoparticles for early detection of kidney injury by breath samples.

The outcomes of acute kidney injury (AKI) could be severe and even lethal, if not diagnosed in its early stages and treated appropriately. Blood and u...
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