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Monolayer-capped gold nanoparticles for disease detection from breath
The recognition of volatile organic compounds in breath samples is a promising approach for noninvasive safe diagnosis of disease. Spectrometry and spectroscopy methods used for breath analysis suffer from suboptimal accuracy, are expensive and are unsuitable for diagnostics. This article presents a concise review on arrays of monolayer-capped gold nanoparticle (GNP) sensors in conjugation with pattern recognition methods for cost-effective, fast and high-throughput point-of-care diagnostic results from exhaled breath samples. The article starts with a general introduction to the rationale and advantages of breath analysis as well as with a presentation of the utility of monolayer-capped GNP sensors in this field. The article continues with a presentation of the main fabrication and operation principles of these GNP sensors and concludes with selected examples regarding their utility in different fields of medicine, particularly in neurology, infectiology, respiratory medicine and oncology.
Morad K Nakhleh‡,1, Yoav Y Broza‡,1 & Hossam Haick*,1 Department of Chemical Engineering & Russell Berrie Nanotechnology Institute Technion – Israel Institute of Technology, Haifa 3200003, Israel *Author for correspondence: Tel.: +972 4829 3087 Fax: +972 778 871 880 [email protected] ‡ Authors contributed equally 1
Keywords: breath • detection • gold nanoparticle • sensor
Breath analysis A promising approach for the screening, diagnosis and follow-up of disease conditions relies on the detection of volatile organic compounds (VOCs) that appear in exhaled breath [1–6] . The rationale behind this approach rests on the fact that the human volatolome (i.e., a compendium of VOCs with relatively low molecular weight, emanating from the human body) expresses distinct and immediate changes when pathological processes occur and alter the body’s biochemistry [1–6] . The volatolome could be affected via one or a combination of the following processes: oxidative stress, CYP450, liver enzymes, carbohydrate metabolism (glycolysis or gluconeogenesis pathways), lipid metabolism and others [1–2,7] . Each disease has its own volatolome (i.e., VOC) pattern and, therefore, would enable discrimination between various disease states [3,8] . Once generated, the disease-related VOCs migrate throughout the tissue and/or are stored in fat compartments and are then
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released into the bloodstream [1,2] . When approaching the bronchial circulatory tree, VOCs with low solubility in blood (i.e., having a low blood-to-air partition coefficient) diffuse via gas exchange occurring at the alveoli level [1,2] . By contrast, VOCs that are more soluble in the blood have a higher chance of diffusing throughout the bronchial tree tissue [1,2] . Collectively, these VOCs alter the exhaled air composition, giving it a unique breathprint [1–3] . Hence, the shifts in the composition of exhaled air could be considered a ‘mirror reflection’ of the chemical changes occurring in the blood’s chemistry and even beyond, in the functioning organ (Figure 1) . Mass spectrometry analysis has shown that breath samples contain hundreds of VOCs at concentrations that range between a few parts per billion (ppb) and hundreds of parts per trillion [7,9] . Out of this range of compounds, it has been very difficult to identify unique and stable VOCs that exist in disease state, but do not exist in control (or healthy) states
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walled carbon nanotubes [21–25] or silicon nanowires [3,26] . The current article focuses on the utility of films of monolayer-capped gold nanoparticles (GNPs) for the detection and classification of breath VOCs of a wide variety of diseases.
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Figure 1. Links between volatile organic compounds, various organs in the human body and exhaled breath samples. (Figure 2A & B) .
Instead, it has been found that most diseases are characterized by VOC mixtures (or patterns) that have distinct compositions than those mixtures from related control groups (e.g., healthy states) or from other diseases (Figure 2B–D) [1,4,6–8,10–14] . Spectrometry techniques are powerful tools for detecting breath VOCs. However, to date, the use of these techniques has been impeded by the need for expensive equipment, the high levels of expertise required to operate such instruments and the need for preconcentration techniques [1,10] . Chemical sensor matrices based on nanomaterials are more likely to become clinical and laboratory diagnostic tools because they are significantly smaller, easier-to-use and less expensive than spectrometry techniques [1,3–4,6,15] . In general, an ideal sensor for breath VOC analysis should be sensitive at very low concentrations of VOCs in the presence of water vapor, because the clinical samples are very humid [3,16–17] . Furthermore, it should respond rapidly and reversibly to different compounds, yet consistently respond to repeated exposures to the same compound. When evacuating the VOC, the sensor should return rapidly to its baseline state. Common examples of transducers based on nanomaterials include field effect transistors based on single-walled carbon nanotubes [18] or nanowires of various materials [15,17,19] , nanoporous chemioptical materials [20] , and chemiresistors based on random networks of single-
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General principles of GNP sensors Sensors based on monolayer-capped GNPs offer the advantages of (sub-)ppb detection limits for the VOCs of interest, a wide dynamic range, ambient room operation, tolerance for background molecules (particularly varying humidity levels, since exhaled breath is fully humidified), reasonable dimensions and low cost, among others [6] . These advantages could be attributed to the fact that the chemical and physical properties of monolayer-capped GNPs can be accurately tailored to obtain the desired sensitivity and selectivity for a particular sensing application. Moreover, it is possible to relatively freely vary the particles’ sizes and shapes and, as a result, the constituent surface-to-volume ratio [27,28] . Using monolayer-capped GNPs with controlled shapes enables the manipulation of their properties with greater versatility than can be achieved by controlling any of their other characteristic features [27,28] . For sensing applications in particular, this grants control over the interparticle distance and makes it possible to obtain nearly uniform nanopores in the composite films [27–31] . As a result, effective control over the surface properties and, consequently, over the interaction ‘quality’ with the analyte molecules can be achieved. The most common configuration of monolayercapped GNP sensors for breath analysis is based on a chemiresistor platform. The production of these sensors starts with a two-step precursor synthesis, in which the specific organic coatings of the GNPs are determined [3–4,6] . Synthesis is followed by the assembly of thin films of monolayer-capped GNPs between adjacent microelectrodes and a series of characterization tests, in which the sensors are exposed to various concentrations of separate VOCs as well as mixtures of VOCs that are usually found in human breath [1,3,11] . Features such as the repeatability, sensitivity, limitof-detection and signal-to-noise on exposure to the various VOCs are documented. Moreover, response to humidity and long-term baseline stability/drift are also taken into account [16,32] . Sensors that show the best sensitivity towards simulated breath (individual) VOCs and simulated breath samples [11] are chosen for use in real clinical studies [1,3–4] . In the monolayer-capped GNP chemiresistive films, the sorption of VOCs is achieved by the organic film component and the electric conductivity is achieved by the inorganic nanomaterials, mainly a single metal or an alloy of two or more metals (Figure 3) [6,28] . In
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Monolayer-capped gold nanoparticles for disease detection from breath
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PDISM (*) p-value 25 mmHg). It consists of a group of disorders characterized by pulmonary vasoconstriction and/or progressive pulmonary vascular remodeling that leads to right heart failure and, ultimately, death. The pulmonary hypertension diagnostic gold standard requires invasive right heart catheterization. This costly procedure has significant associated risks and is thus unsuitable for widespread screening. To obtain a noninvasive and low-cost tool for the early and accurate diagnosis of pulmonary hypertension within very short time frames, samples of alveolar breath from 22 pulmonary hypertension patients and 23 healthy volunteers were analyzed by monolayer-capped GNP sensor arrays (Table 1) . A subset of the samples (65–75%) was used as a training set and the remainder (25–35%) was used for the blind analysis [49] . From the available sensors, three sets/combinations of sensors were used to classify the samples. The first set of six sensors correctly identified 100% of the pulmonary artery hypertension cases, with a total accuracy of 92% [49] . A second classifier was acquired by sensing the features of three sensors, which correctly distinguished between idiopathic pulmonary artery hypertension patients and others suffering from heritable pulmonary artery hypertension with an accuracy of 87%.
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The third DFA model was sensitive to the severity of the disease, with an accuracy of 91%. The effects of some potentially important confounding factors, such as smoking habits, age, sex and residence area, were examined. The analysis indicated that none of the three DFA models presented were affected by any of the tested factors. Still the cohort size of the study was relatively small; therefore, a larger cohort is needed to validate the results [49] . Tuberculosis
Modern methods fordiagnosis of active tuberculosis (TB), such as Xpert ® (Cepheid, CA, US) and IFN-γ release assays are very accurate and time saving [26] . Nevertheless, thesetechniques are not available for use in developing countries, where sputum microscopy and culturing, characterized with diagnostic sensitivity of 50–60%, still the main methods used for diagnosis of active TB [50] . TB-related VOCs, emitted from infected lung tissue via exhaled breath and/or from Mycobacterium tuberculosis cultures, have been reported in the literature. However, none of them have been effective as biomarkers for disease detection [12,51–52] . Nakhleh et al. have conducted a case–control study in which they used monolayer-capped GNP sensors for detecting the collective VOC spectrum in the breath of TB patients [43] . The researchers analyzed a training set of 138 samples (collected from patients with culture-proven TB and from patients with suspected TB yet ruled out by microbiological tests and healthy controls) in order to choose the most sensitive sensors for TB. The results were then validated with 60 unlabeled blind samples. Dodecanethiol-coated GNP sensor (Table 1) demonstrated an excellent discrimination ability when comparing TB-positive results with a control group; in the training phase, it correctly classified 121 out of 138 training samples with a receiver operating area under the curve of 94.8%. Applying the same threshold to validation set classification, the sensor scored 90% sensitivity, 93% specificity and 92% accuracy (Figure 4D) . Since this was a one-sensor analysis, moving the threshold to a specificity of 96% (i.e., very low rates of false-positive results), exhibited positive predictive value of 93% in the blind test, indicating a potentially promising TB rule-in test. Furthermore, moving the threshold to achieve 95% sensitivity (i.e., very low rates of false-negative results), exhibited negative predictive value of 94%, raising the possibility of using the test for ruling out TB infectionThe reported sensor showed no sensitivity towards common confounding factors, such as smoking habits, HIV infection and the location of the breath sampling site (Figure 4E)
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Monolayer-capped gold nanoparticles for disease detection from breath
[43] .
In yet another aspect of TB infections, Peled et al. have reported on the ability of an array of monolayer-capped GNP sensors to discriminate between breath samples of cows infected with bovine TB and other healthy cows with a sensitivity of 72% and a specificity of 100% [53] . Cancer diseases Lung cancer
Lung cancer is the leading cause of cancer mortality, causing more than 1 million deaths worldwide every year [54] . Early diagnosis and classification is crucial for increasing survival chances [54] . However, the technologies used today could be inaccurate in some cases, are unavailable in many medical facilities and require relatively large amounts of tissue for analysis, as in invasive biopsy procedures [7] . Many research groups have reported the existence of lung cancer-related VOCs. Analysis of the headspace of cancer cells, blood samples and exhaled breath has revealed dozens of compounds with altered concentrations in lung cancer-related samples [1,7] . Moreover, some of them were correlated with specific lung cancer subtypes [44] , and others were associated with gene mutations known to increase the risk of lung cancer [55,56] . In a series of studies, Haick and coworkers haves shown that these molecular differences are reflected in breath samples and therefore can be detected by monolayer-capped GNP sensor arrays (Table 1) [1,8,11,44,45,55,56] . GNP sensor analysis of breath samples exhibited discrimination between the early stages (I and II) and advanced stages (III and IV) of lung cancer (88% accuracy), between benign and malignant tumors (88% accuracy; Figure 4H ), between small-cell lung cancer and non-small-cell lung cancer (93% accuracy) and between adenocarcinoma and squamous cell carcinoma (88% accuracy) [11,44] . In a complementary study, the authors have tested the sensor array ability to discriminate between breath VOCs that characterize healthy states and the four most widespread cancer states in the developed world: lung, breast, colorectal and prostate cancers. The results confirmed a sharp separation of all five subpopulations, with minimal/negligible overlap between the clusters (Figure 4G) [8] . Many confounding factors (e.g., geographical location, age, gender, ethnicity and smoking habits, among others) were successfully neutralized using advanced hardware and software algorithms [8] . Nevertheless, this series of projects focused on the early detection of lung cancer, and therefore was unable to develop a personalized diagnosis through exhaled breath. Using a wider spectrum of GNP sensors might provide more useful information, including measuring tumor response to
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therapy through the necrosis signature and disease genetic profile and its heterogeneity. Assuming that lung cancer cells are the origin of some of the exhaled VOCs, a case study was carried out to assess the feasibility to follow-up early-stage lung cancer (stages Ia, Ib and IIa) before and 3 weeks after tumor resection [45] . An array of spherical monolayer-capped GNP sensors and monolayer-capped cubic platinum nanoparticle sensors distinguished between pre- and post-surgery lung cancer states (80% accuracy), as well as between presurgery lung cancer and benign states (94% accuracy; Figure 4I ). By contrast, the same sensor array could not distinguish between pre- and post-surgery benign states (59% accuracy) or between lung cancer and benign states after surgery [45] . Head & neck cancer
Head & neck cancer refers to a group of diverse tumors arising in the region of the head and neck, including various anatomic structures such as the craniofacial bones, soft tissues, salivary glands, skin and mucosal membranes [57] . The number of new head and neck cancer cases accounts for approximately 5% of all newly diagnosed adult malignancies and was responsible for 1–2% of all cancer-related mortalities [54] . The diagnosis of head and neck cancer is not trivial and requires specialist settings. A general medical evaluation has to be performed, including a thorough head and neck examination by one or more physicians, followed by contrast-enhanced computed tomography and/or MRI and biopsies. The ability of monolayer-capped GNP sensors to sense the breathprint of head and neck cancer patients and to discriminate between these patients and healthy subjects or between the head and neck cancer patients and lung cancer patients was tested in a cross-sectional study including 87 volunteers [58] . An array of five monolayer-capped GNP sensors (Table 1) was used. Signal processing of the five sensors showed no or little overlap when exposed to the breath samples from the study subgroups. Moreover, three different pattern recognition methods were applied to the data, establishing three different classifiers. The first and second models precisely categorized all 16 head and neck cancer patients and 20 lung cancer patients when compared with healthy subjects, scoring 100% sensitivity. The third model precisely discriminated between all lung cancer and head and neck cancer patients with 100% accuracy. In a complementary analysis, mass spectrometry analysis showed major differences in the concentrations of more than 13 different VOCs between the three subgroups [58] . In a more recent study, similar sensor
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Review Nakhleh, Broza & Haick arrays provided accurate predictive models for distinguishing head and neck squamous cell carcinoma (HNSCC) from healthy states, HNSCC from benign tumors and benign tumors from healthy states [59] . Two additional predictive models allowed classification of HNSCC according to site and stage. Patient classification was possible irrespective of the gender and tobacco consumption of the individual participants, which constitute important confounding as well as risk factors for HNSCC [59] . Gastric cancer
Gastric cancer is one of the most common causes of death from cancer worldwide, and most of the cases occur in developing countries [54] . Unspecific clinical symptoms and the lack of defined risk factors often delay the diagnosis of the disease, leading to extremely poor prognosis and high rates of recurrence. Earlier diagnosis substantially improves the prognosis: 95% of patients with cancer that is confined to the inner lining of the stomach wall will survive longer than 5 years. Gastric endoscopy and biopsy is the gold standard method for gastric cancer diagnosis, and although it is a relatively accurate method, it is invasive, risky and expensive, so might not be available for the general population as a screening tool [20] . Breath testing could be an ideal tool to fill this gap, as it is noninvasive and relatively inexpensive. Xu et al. have tested VOC spectrum alterations in gastric cancer patients compared with confounding gastric conditions, including ulcers and other less severe conditions [14] . They used an array of monolayer-capped GNP sensors for the identification and distinction of cancer and noncancer gastric conditions (Table 1) . Blind analysis of 25% of the cohort size indicated excellent discriminative power of the sensor array, with 96% accuracy (Figure 4F) . Moreover, the gastric cancer cases were divided into two separate clusters – early- and late-stage disease – with 90% accuracy. A third classifier was capable of separating the noncancerous conditions into two subgroups (ulcers and other conditions) with 83% sensitivity and accuracy [14] . Breast cancer
Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancer-related death in women [54] . The diagnosis of breast cancer is mostly performed using imaging techniques, such as mammography, ultrasound and MRI, in combination with biopsies [60] . However, the accuracy of imagining techniques is not optimal, and their ability to distinguish between benign breast diseases and invasive
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breast cancer lacks accuracy [60] . With this in mind, Shuster et al. examined the ability of six spherical GNPs (core size of 3–6 nm) with different capping ligands and benzyl mercaptan-capped cubic platinum nanoparticle for identifying and distinguishing between benign breast conditions, cancer lesions and healthy states [61] . Pattern recognition analysis of the output signal demonstrated that two out of the seven sensors (spherical octadecylamine-capped GNPs and cubic benzyl mercaptan-capped platinum nanoparticles) could appropriately categorize healthy women, benign conditions and cancer lesions, with an overall accuracy of 86% [61] . Conclusion & future perspective Arrays of monolayer-capped GNPs in conjugation with pattern recognition methods provide promising approaches for diagnostic breath testing. Their small size and the ability to engineer their surfaces with a wide variety of organic ligands make them highly sensitive to very low concentrations of VOCs (down to several ppb) as well as sensitive to delicate changes in an exhaled VOC profile. The suitability of this sensing approach for breath analysis has been successfully demonstrated in different fields of medicine, particularly in neurology, infectiology, respiratory medicine and oncology. Many confounding factors (e.g., geographical location, age, gender, ethnicity and smoking habit, among others) were successfully neutralized by tailoring the organic ligand or morphology of the GNPs and/or by using advanced hardware and software algorithms. Overall, monolayer-capped GNP sensors for breath analysis act as a solid basis for a potential noninvasive, accurate point-of-care diagnostic tool. In many cases, it could replace the multiple invasive, risky and expensive examinations that patients must undergo in order to characterize their illness. Nevertheless, further large-cohort studies should be conducted in order to validate the initial clinical results. Financial & competing interests disclosure The research leading to some of these results has received funding from the FP7’s ERC grant under DIAG-CANCER (grant agreement no. 256639). The authors gratefully acknowledge V Kloper for her assistance in the preparation of the presented figures. This assistance was funded from FP7’s ERC grant under DIAG-CANCER (grant agreement no. 256639). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.
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Executive summary • The recognition of exhaled volatile organic compounds, originating in infected tissue, offers a promising approach for the noninvasive diagnosis of disease. • Arrays of crossreactive sensors in conjugation with pattern recognition algorithms could be trained to detect diseases that have unique volatile organic compound signatures from exhaled breath. • Monolayer-capped gold nanoparticles are ideal components of sensor arrays because they are versatile, easy to fabricate and can be easily integrated into existing sensing platforms. • The suitability of this sensing approach for breath analysis has been successfully demonstrated in different fields of medicine, particularly in neurology, infectiology, respiratory medicine and oncology.
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Monolayer-capped gold nanoparticles for disease detection from breath
The recognition of volatile organic compounds in breath samples is a promising approach for noninvasive safe diagnosis of disease. Spectrometry and spectroscopy methods used for breath analysis suffer from suboptimal accuracy, are expensive and are unsuitable for diagnostics. This article presents a concise review on arrays of monolayer-capped gold nanoparticle (GNP) sensors in conjugation with pattern recognition methods for cost-effective, fast and high-throughput point-of-care diagnostic results from exhaled breath samples. The article starts with a general introduction to the rationale and advantages of breath analysis as well as with a presentation of the utility of monolayer-capped GNP sensors in this field. The article continues with a presentation of the main fabrication and operation principles of these GNP sensors and concludes with selected examples regarding their utility in different fields of medicine, particularly in neurology, infectiology, respiratory medicine and oncology.
Morad K Nakhleh‡,1, Yoav Y Broza‡,1 & Hossam Haick*,1 Department of Chemical Engineering & Russell Berrie Nanotechnology Institute Technion – Israel Institute of Technology, Haifa 3200003, Israel *Author for correspondence: Tel.: +972 4829 3087 Fax: +972 778 871 880 [email protected] ‡ Authors contributed equally 1
Keywords: breath • detection • gold nanoparticle • sensor
Breath analysis A promising approach for the screening, diagnosis and follow-up of disease conditions relies on the detection of volatile organic compounds (VOCs) that appear in exhaled breath [1–6] . The rationale behind this approach rests on the fact that the human volatolome (i.e., a compendium of VOCs with relatively low molecular weight, emanating from the human body) expresses distinct and immediate changes when pathological processes occur and alter the body’s biochemistry [1–6] . The volatolome could be affected via one or a combination of the following processes: oxidative stress, CYP450, liver enzymes, carbohydrate metabolism (glycolysis or gluconeogenesis pathways), lipid metabolism and others [1–2,7] . Each disease has its own volatolome (i.e., VOC) pattern and, therefore, would enable discrimination between various disease states [3,8] . Once generated, the disease-related VOCs migrate throughout the tissue and/or are stored in fat compartments and are then
10.2217/NNM.14.121 © 2014 Future Medicine Ltd
released into the bloodstream [1,2] . When approaching the bronchial circulatory tree, VOCs with low solubility in blood (i.e., having a low blood-to-air partition coefficient) diffuse via gas exchange occurring at the alveoli level [1,2] . By contrast, VOCs that are more soluble in the blood have a higher chance of diffusing throughout the bronchial tree tissue [1,2] . Collectively, these VOCs alter the exhaled air composition, giving it a unique breathprint [1–3] . Hence, the shifts in the composition of exhaled air could be considered a ‘mirror reflection’ of the chemical changes occurring in the blood’s chemistry and even beyond, in the functioning organ (Figure 1) . Mass spectrometry analysis has shown that breath samples contain hundreds of VOCs at concentrations that range between a few parts per billion (ppb) and hundreds of parts per trillion [7,9] . Out of this range of compounds, it has been very difficult to identify unique and stable VOCs that exist in disease state, but do not exist in control (or healthy) states
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walled carbon nanotubes [21–25] or silicon nanowires [3,26] . The current article focuses on the utility of films of monolayer-capped gold nanoparticles (GNPs) for the detection and classification of breath VOCs of a wide variety of diseases.
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Figure 1. Links between volatile organic compounds, various organs in the human body and exhaled breath samples. (Figure 2A & B) .
Instead, it has been found that most diseases are characterized by VOC mixtures (or patterns) that have distinct compositions than those mixtures from related control groups (e.g., healthy states) or from other diseases (Figure 2B–D) [1,4,6–8,10–14] . Spectrometry techniques are powerful tools for detecting breath VOCs. However, to date, the use of these techniques has been impeded by the need for expensive equipment, the high levels of expertise required to operate such instruments and the need for preconcentration techniques [1,10] . Chemical sensor matrices based on nanomaterials are more likely to become clinical and laboratory diagnostic tools because they are significantly smaller, easier-to-use and less expensive than spectrometry techniques [1,3–4,6,15] . In general, an ideal sensor for breath VOC analysis should be sensitive at very low concentrations of VOCs in the presence of water vapor, because the clinical samples are very humid [3,16–17] . Furthermore, it should respond rapidly and reversibly to different compounds, yet consistently respond to repeated exposures to the same compound. When evacuating the VOC, the sensor should return rapidly to its baseline state. Common examples of transducers based on nanomaterials include field effect transistors based on single-walled carbon nanotubes [18] or nanowires of various materials [15,17,19] , nanoporous chemioptical materials [20] , and chemiresistors based on random networks of single-
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General principles of GNP sensors Sensors based on monolayer-capped GNPs offer the advantages of (sub-)ppb detection limits for the VOCs of interest, a wide dynamic range, ambient room operation, tolerance for background molecules (particularly varying humidity levels, since exhaled breath is fully humidified), reasonable dimensions and low cost, among others [6] . These advantages could be attributed to the fact that the chemical and physical properties of monolayer-capped GNPs can be accurately tailored to obtain the desired sensitivity and selectivity for a particular sensing application. Moreover, it is possible to relatively freely vary the particles’ sizes and shapes and, as a result, the constituent surface-to-volume ratio [27,28] . Using monolayer-capped GNPs with controlled shapes enables the manipulation of their properties with greater versatility than can be achieved by controlling any of their other characteristic features [27,28] . For sensing applications in particular, this grants control over the interparticle distance and makes it possible to obtain nearly uniform nanopores in the composite films [27–31] . As a result, effective control over the surface properties and, consequently, over the interaction ‘quality’ with the analyte molecules can be achieved. The most common configuration of monolayercapped GNP sensors for breath analysis is based on a chemiresistor platform. The production of these sensors starts with a two-step precursor synthesis, in which the specific organic coatings of the GNPs are determined [3–4,6] . Synthesis is followed by the assembly of thin films of monolayer-capped GNPs between adjacent microelectrodes and a series of characterization tests, in which the sensors are exposed to various concentrations of separate VOCs as well as mixtures of VOCs that are usually found in human breath [1,3,11] . Features such as the repeatability, sensitivity, limitof-detection and signal-to-noise on exposure to the various VOCs are documented. Moreover, response to humidity and long-term baseline stability/drift are also taken into account [16,32] . Sensors that show the best sensitivity towards simulated breath (individual) VOCs and simulated breath samples [11] are chosen for use in real clinical studies [1,3–4] . In the monolayer-capped GNP chemiresistive films, the sorption of VOCs is achieved by the organic film component and the electric conductivity is achieved by the inorganic nanomaterials, mainly a single metal or an alloy of two or more metals (Figure 3) [6,28] . In
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Monolayer-capped gold nanoparticles for disease detection from breath
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PDISM (*) p-value 25 mmHg). It consists of a group of disorders characterized by pulmonary vasoconstriction and/or progressive pulmonary vascular remodeling that leads to right heart failure and, ultimately, death. The pulmonary hypertension diagnostic gold standard requires invasive right heart catheterization. This costly procedure has significant associated risks and is thus unsuitable for widespread screening. To obtain a noninvasive and low-cost tool for the early and accurate diagnosis of pulmonary hypertension within very short time frames, samples of alveolar breath from 22 pulmonary hypertension patients and 23 healthy volunteers were analyzed by monolayer-capped GNP sensor arrays (Table 1) . A subset of the samples (65–75%) was used as a training set and the remainder (25–35%) was used for the blind analysis [49] . From the available sensors, three sets/combinations of sensors were used to classify the samples. The first set of six sensors correctly identified 100% of the pulmonary artery hypertension cases, with a total accuracy of 92% [49] . A second classifier was acquired by sensing the features of three sensors, which correctly distinguished between idiopathic pulmonary artery hypertension patients and others suffering from heritable pulmonary artery hypertension with an accuracy of 87%.
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The third DFA model was sensitive to the severity of the disease, with an accuracy of 91%. The effects of some potentially important confounding factors, such as smoking habits, age, sex and residence area, were examined. The analysis indicated that none of the three DFA models presented were affected by any of the tested factors. Still the cohort size of the study was relatively small; therefore, a larger cohort is needed to validate the results [49] . Tuberculosis
Modern methods fordiagnosis of active tuberculosis (TB), such as Xpert ® (Cepheid, CA, US) and IFN-γ release assays are very accurate and time saving [26] . Nevertheless, thesetechniques are not available for use in developing countries, where sputum microscopy and culturing, characterized with diagnostic sensitivity of 50–60%, still the main methods used for diagnosis of active TB [50] . TB-related VOCs, emitted from infected lung tissue via exhaled breath and/or from Mycobacterium tuberculosis cultures, have been reported in the literature. However, none of them have been effective as biomarkers for disease detection [12,51–52] . Nakhleh et al. have conducted a case–control study in which they used monolayer-capped GNP sensors for detecting the collective VOC spectrum in the breath of TB patients [43] . The researchers analyzed a training set of 138 samples (collected from patients with culture-proven TB and from patients with suspected TB yet ruled out by microbiological tests and healthy controls) in order to choose the most sensitive sensors for TB. The results were then validated with 60 unlabeled blind samples. Dodecanethiol-coated GNP sensor (Table 1) demonstrated an excellent discrimination ability when comparing TB-positive results with a control group; in the training phase, it correctly classified 121 out of 138 training samples with a receiver operating area under the curve of 94.8%. Applying the same threshold to validation set classification, the sensor scored 90% sensitivity, 93% specificity and 92% accuracy (Figure 4D) . Since this was a one-sensor analysis, moving the threshold to a specificity of 96% (i.e., very low rates of false-positive results), exhibited positive predictive value of 93% in the blind test, indicating a potentially promising TB rule-in test. Furthermore, moving the threshold to achieve 95% sensitivity (i.e., very low rates of false-negative results), exhibited negative predictive value of 94%, raising the possibility of using the test for ruling out TB infectionThe reported sensor showed no sensitivity towards common confounding factors, such as smoking habits, HIV infection and the location of the breath sampling site (Figure 4E)
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Monolayer-capped gold nanoparticles for disease detection from breath
[43] .
In yet another aspect of TB infections, Peled et al. have reported on the ability of an array of monolayer-capped GNP sensors to discriminate between breath samples of cows infected with bovine TB and other healthy cows with a sensitivity of 72% and a specificity of 100% [53] . Cancer diseases Lung cancer
Lung cancer is the leading cause of cancer mortality, causing more than 1 million deaths worldwide every year [54] . Early diagnosis and classification is crucial for increasing survival chances [54] . However, the technologies used today could be inaccurate in some cases, are unavailable in many medical facilities and require relatively large amounts of tissue for analysis, as in invasive biopsy procedures [7] . Many research groups have reported the existence of lung cancer-related VOCs. Analysis of the headspace of cancer cells, blood samples and exhaled breath has revealed dozens of compounds with altered concentrations in lung cancer-related samples [1,7] . Moreover, some of them were correlated with specific lung cancer subtypes [44] , and others were associated with gene mutations known to increase the risk of lung cancer [55,56] . In a series of studies, Haick and coworkers haves shown that these molecular differences are reflected in breath samples and therefore can be detected by monolayer-capped GNP sensor arrays (Table 1) [1,8,11,44,45,55,56] . GNP sensor analysis of breath samples exhibited discrimination between the early stages (I and II) and advanced stages (III and IV) of lung cancer (88% accuracy), between benign and malignant tumors (88% accuracy; Figure 4H ), between small-cell lung cancer and non-small-cell lung cancer (93% accuracy) and between adenocarcinoma and squamous cell carcinoma (88% accuracy) [11,44] . In a complementary study, the authors have tested the sensor array ability to discriminate between breath VOCs that characterize healthy states and the four most widespread cancer states in the developed world: lung, breast, colorectal and prostate cancers. The results confirmed a sharp separation of all five subpopulations, with minimal/negligible overlap between the clusters (Figure 4G) [8] . Many confounding factors (e.g., geographical location, age, gender, ethnicity and smoking habits, among others) were successfully neutralized using advanced hardware and software algorithms [8] . Nevertheless, this series of projects focused on the early detection of lung cancer, and therefore was unable to develop a personalized diagnosis through exhaled breath. Using a wider spectrum of GNP sensors might provide more useful information, including measuring tumor response to
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therapy through the necrosis signature and disease genetic profile and its heterogeneity. Assuming that lung cancer cells are the origin of some of the exhaled VOCs, a case study was carried out to assess the feasibility to follow-up early-stage lung cancer (stages Ia, Ib and IIa) before and 3 weeks after tumor resection [45] . An array of spherical monolayer-capped GNP sensors and monolayer-capped cubic platinum nanoparticle sensors distinguished between pre- and post-surgery lung cancer states (80% accuracy), as well as between presurgery lung cancer and benign states (94% accuracy; Figure 4I ). By contrast, the same sensor array could not distinguish between pre- and post-surgery benign states (59% accuracy) or between lung cancer and benign states after surgery [45] . Head & neck cancer
Head & neck cancer refers to a group of diverse tumors arising in the region of the head and neck, including various anatomic structures such as the craniofacial bones, soft tissues, salivary glands, skin and mucosal membranes [57] . The number of new head and neck cancer cases accounts for approximately 5% of all newly diagnosed adult malignancies and was responsible for 1–2% of all cancer-related mortalities [54] . The diagnosis of head and neck cancer is not trivial and requires specialist settings. A general medical evaluation has to be performed, including a thorough head and neck examination by one or more physicians, followed by contrast-enhanced computed tomography and/or MRI and biopsies. The ability of monolayer-capped GNP sensors to sense the breathprint of head and neck cancer patients and to discriminate between these patients and healthy subjects or between the head and neck cancer patients and lung cancer patients was tested in a cross-sectional study including 87 volunteers [58] . An array of five monolayer-capped GNP sensors (Table 1) was used. Signal processing of the five sensors showed no or little overlap when exposed to the breath samples from the study subgroups. Moreover, three different pattern recognition methods were applied to the data, establishing three different classifiers. The first and second models precisely categorized all 16 head and neck cancer patients and 20 lung cancer patients when compared with healthy subjects, scoring 100% sensitivity. The third model precisely discriminated between all lung cancer and head and neck cancer patients with 100% accuracy. In a complementary analysis, mass spectrometry analysis showed major differences in the concentrations of more than 13 different VOCs between the three subgroups [58] . In a more recent study, similar sensor
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Review Nakhleh, Broza & Haick arrays provided accurate predictive models for distinguishing head and neck squamous cell carcinoma (HNSCC) from healthy states, HNSCC from benign tumors and benign tumors from healthy states [59] . Two additional predictive models allowed classification of HNSCC according to site and stage. Patient classification was possible irrespective of the gender and tobacco consumption of the individual participants, which constitute important confounding as well as risk factors for HNSCC [59] . Gastric cancer
Gastric cancer is one of the most common causes of death from cancer worldwide, and most of the cases occur in developing countries [54] . Unspecific clinical symptoms and the lack of defined risk factors often delay the diagnosis of the disease, leading to extremely poor prognosis and high rates of recurrence. Earlier diagnosis substantially improves the prognosis: 95% of patients with cancer that is confined to the inner lining of the stomach wall will survive longer than 5 years. Gastric endoscopy and biopsy is the gold standard method for gastric cancer diagnosis, and although it is a relatively accurate method, it is invasive, risky and expensive, so might not be available for the general population as a screening tool [20] . Breath testing could be an ideal tool to fill this gap, as it is noninvasive and relatively inexpensive. Xu et al. have tested VOC spectrum alterations in gastric cancer patients compared with confounding gastric conditions, including ulcers and other less severe conditions [14] . They used an array of monolayer-capped GNP sensors for the identification and distinction of cancer and noncancer gastric conditions (Table 1) . Blind analysis of 25% of the cohort size indicated excellent discriminative power of the sensor array, with 96% accuracy (Figure 4F) . Moreover, the gastric cancer cases were divided into two separate clusters – early- and late-stage disease – with 90% accuracy. A third classifier was capable of separating the noncancerous conditions into two subgroups (ulcers and other conditions) with 83% sensitivity and accuracy [14] . Breast cancer
Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancer-related death in women [54] . The diagnosis of breast cancer is mostly performed using imaging techniques, such as mammography, ultrasound and MRI, in combination with biopsies [60] . However, the accuracy of imagining techniques is not optimal, and their ability to distinguish between benign breast diseases and invasive
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breast cancer lacks accuracy [60] . With this in mind, Shuster et al. examined the ability of six spherical GNPs (core size of 3–6 nm) with different capping ligands and benzyl mercaptan-capped cubic platinum nanoparticle for identifying and distinguishing between benign breast conditions, cancer lesions and healthy states [61] . Pattern recognition analysis of the output signal demonstrated that two out of the seven sensors (spherical octadecylamine-capped GNPs and cubic benzyl mercaptan-capped platinum nanoparticles) could appropriately categorize healthy women, benign conditions and cancer lesions, with an overall accuracy of 86% [61] . Conclusion & future perspective Arrays of monolayer-capped GNPs in conjugation with pattern recognition methods provide promising approaches for diagnostic breath testing. Their small size and the ability to engineer their surfaces with a wide variety of organic ligands make them highly sensitive to very low concentrations of VOCs (down to several ppb) as well as sensitive to delicate changes in an exhaled VOC profile. The suitability of this sensing approach for breath analysis has been successfully demonstrated in different fields of medicine, particularly in neurology, infectiology, respiratory medicine and oncology. Many confounding factors (e.g., geographical location, age, gender, ethnicity and smoking habit, among others) were successfully neutralized by tailoring the organic ligand or morphology of the GNPs and/or by using advanced hardware and software algorithms. Overall, monolayer-capped GNP sensors for breath analysis act as a solid basis for a potential noninvasive, accurate point-of-care diagnostic tool. In many cases, it could replace the multiple invasive, risky and expensive examinations that patients must undergo in order to characterize their illness. Nevertheless, further large-cohort studies should be conducted in order to validate the initial clinical results. Financial & competing interests disclosure The research leading to some of these results has received funding from the FP7’s ERC grant under DIAG-CANCER (grant agreement no. 256639). The authors gratefully acknowledge V Kloper for her assistance in the preparation of the presented figures. This assistance was funded from FP7’s ERC grant under DIAG-CANCER (grant agreement no. 256639). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.
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Monolayer-capped gold nanoparticles for disease detection from breath
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Executive summary • The recognition of exhaled volatile organic compounds, originating in infected tissue, offers a promising approach for the noninvasive diagnosis of disease. • Arrays of crossreactive sensors in conjugation with pattern recognition algorithms could be trained to detect diseases that have unique volatile organic compound signatures from exhaled breath. • Monolayer-capped gold nanoparticles are ideal components of sensor arrays because they are versatile, easy to fabricate and can be easily integrated into existing sensing platforms. • The suitability of this sensing approach for breath analysis has been successfully demonstrated in different fields of medicine, particularly in neurology, infectiology, respiratory medicine and oncology.
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