Researchers from Russia and Italy have proposed a compact sensor system that can implement the functionality of an electronic nose device and have developed a reproducible technology for its manufacture.
The device is a flexible electronics platform that can analyze exhaled air as well as identify pathologies of the respiratory tract and organs.
During the experiments, the device demonstrated high accuracy in determining patients with chronic obstructive pulmonary disease (COPD), an inflammatory disease of the respiratory tract, which increases the risk of complications when during COVID-19 infection.
Chronic obstructive pulmonary disease (COPD) develops in the bronchial mucosa in response to pathogenic external factors and leads to a negative change in the functions of the respiratory tract.
A person with COPD cannot receive the necessary oxygen because inhaled air flow is limited. COPD is commonly caused by gases and volatile particles such as dust, tobacco, cadmium and silicon particles, and others.
The methods for detecting this disease are complex and time consuming, which is inextricably linked to a threat to the patient’s health.
Conventional methods for breath analysis, such as gas chromatography and mass spectroscopy, are expensive and time-consuming, so new approaches are required that are notable for their low cost and speed of testing.
COPD is an urgent problem, as the disease may lead to the limitation of physical performance and disability of patients. It is important to note that people with COPD are most at risk for complications if they become infected with COVID-19.
“Malfunctioning of human organs causes a change in a number of processes in the metabolism, which affects the composition of exhaled air. Its analysis can be used to identify diseases of the respiratory system as well as other internal organs, such as the stomach,” explains Dr. Ivan Bobrinetskiy, project manager for the Russian Science Foundation grant, leading research associate of the National Research University of Electronic Technology.
“The proposed concept of the electronic nose allows for operational monitoring and preliminary detection of diseases in just a few minutes. At the same time, the sensors are reusable, and the basic data and the identification of possible pathologies of organs are transferred from the device to digital mode using methods of statistical data analysis, including the capabilities of artificial intelligence. “
The system is based on modified carbon nanotubes (CNTs), which allows the electronic nose to combine multiple properties. For example, flexible conductive films can be made from carbon nanotubes.
Such films are needed in order to provide the system with an electronic structure layer responsible for the operation of the device. “CNTs were synthesized by aerosol chemical vapor deposition and deposited in the form of thin transparent and conductive films.
This technology is highly reproducible, easily scalable and allows applying films of nanotubes to any surface,” said Albert Nasibulin, professor at the Skolkovo Institute of Science and Technology and the Russian Academy of Sciences.
The study of the effectiveness of the new system involved 12 patients with COPD and nine healthy individuals in accordance with the rules of clinical trials. Breath sampling was carried out in disposable polytetrafluoroethylene (PTFE) plastic bags made of a very inert material and containing a sensor matrix.
The subjects inhaled and inflated the bag as much as possible through a plastic straw. When the straw was removed, the packages were sealed. The sensor matrix inside the bag was in contact with exhaled air for about three minutes, so that all sensors could fully work and interact with the gas molecules that characterize the pathology.
Then the system was cleaned with dry air for the next study. Samples were collected from each participant with an interval of one hour.
Since the system detected all people with COPD, it can be argued that the device is effective. In the exhaled air, an increased concentration of nitrogen dioxide was detected. It should be noted that the gas content is less than one molecule per million molecules of the exhaled air, which indicates high sensitivity of the developed sensors.
The researchers have also successfully tested their system on gases that can characterize other diseases. The volatiles selected for this study (ammonia, nitrogen dioxide, sodium hypochlorite, water, benzene, hydrogen sulfide, acetone, ethanol and 2-propanol) are associated with specific diseases and can potentially be considered as their biomarkers.
Thus, the content of 2-propanol, benzene, ethanol and acetone in exhaled air is increased in people with lung cancer, while acetone is found in patients with diabetes.
A high concentration of ammonia in human breath is associated with liver or kidney diseases, and hydrogen sulfide has been proposed as a biomarker of asthma. The concentration of sodium hypochlorite is an increased content in exhaled air in children with bronchial asthma and cystic fibrosis.
Volatile organic compounds as biomarkers for respiratory diseases
A new frontier for fast, risk-free and potentially inexpensive diagnosis of respiratory diseases is based on volatile organic compounds (VOCs), i.e. organic compounds that have high vapor pressure at ambient conditions.
The rationale rests on the fact that VOCs show distinct and immediate changes when pathological conditions arise, altering the body’s biochemistry by one or a combination of the following processes: oxidative stress, cytochrome p450, liver enzymes, carbohydrate metabolism and lipid metabolism [2, 10].
Part of these VOCs, which appear both in normal and abnormal cells, are mixtures of distinctly different compositions . The remainder come from exclusively abnormal cells.
The particularly significant feature that can be exploited in this approach is that each disease has its own unique VOC pattern, and therefore the presence of one disease would not screen out others .
These VOCs can therefore be detected:
1) directly from the headspace (i.e. the mixture of VOCs trapped above the abnormal cells in a sealed vessel); or
2) in exhaled breath, blood or other body fluids, something highly dependent on their tissue–blood and blood–air partition coefficients .
Therefore, isolation and detection of VOCs in these body fluids can serve as a pathway for the early detection of respiratory and other diseases. The monitoring of respiratory diseases by breath analysis is noninvasive and breath sampling can be carried out without the need for specialist settings and specialist technical expertise.
Several spectrometry and spectroscopy techniques have been used to collect, detect and analyse exhaled VOCs of respiratory diseases [14–18]. Frequently used techniques include proton transfer reaction-mass spectrometry (MS), selected ion flow tube (SIFT)-MS, ion mobility spectrometry, laser spectroscopy and gas chromatography (GC), which is considered the most utilised method .
In GC techniques, the exhaled breath is collected and usually stored in inert bags or sorption tubes. After desorption, VOCs are assessed and analysed by GC which is usually followed by MS or flame ionisation detection .
VOCs are separated based on their chemical properties being consecutively ionised and separated by their mass-to-charge (m/z) ratio . While GC-MS-based techniques are powerful in detecting disease-related VOCs, they unfortunately require expensive equipment, high levels of expertise to operate the instruments, considerable time and effort for sampling and analysis, and a need for preconcentration techniques .
These approaches have been used to identify the VOCs distinctive of several respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, lung cancer, pulmonary arterial hypertension (PAH), obstructive sleep apnoea syndrome (OSAS), tuberculosis (TB), cystic fibrosis (CF) and pneumoconiosis. An updated list compiling the characteristic VOCs for these respiratory diseases is shown in table S1.
Sensor arrays for disease detection in exhaled breath
To overcome the challenges associated with spectroscopic and/or MS techniques for breath analysis of respiratory diseases, chemical sensors have been adopted. Detection of the disease-related VOCs from exhaled breath can be achieved using two main chemical sensing strategies.
The first is based on a selective mechanism in which a chemical sensor is designed to interact and detect the presence of a single compound in exhaled breath . This approach, though quite sensitive, is cumbersome due to the complex synthesis of separate and highly selective nanomaterials for the detection of each VOC, mainly with nonpolar targets. Moreover, there are currently no individual unique VOCs that are specific to any particular disease .
The second approach involves an array of broadly cross-reactive sensors along with methods of pattern recognition (figure 1a) [11, 21]. In contrast to the selective sensing method, the sensors array approach, bioinspired by the sense of smell, is capable of detecting a compendium of VOCs.
Each sensor in this approach responds to a range of VOCs which allows the sensing and analysis of individual components from a mixture of compounds . The underlying mechanism of this approach depends on the nature of the sensors (figure 1b); for example, chemiresistors change their electrical resistivity due to sorption of VOCs on the organic film, or by steric changes within the sensing layer affecting the charge transfer from/to the inorganic nanomaterial (figure 1b) .
Acoustic sensors detect changes in the propagation (velocity and amplitude) of acoustic waves through or on the surface of the sensor’s coating material due to sorption of VOCs . Colorimetric sensors are based on indicators, specifically chemoresponsive dyes, which chemically react and change colour on exposure to VOCs, thereby identifying the exposed species .
The most widely used approach for sensors array is possibly based on conductive polymers, which operate on electrical resistance changes from steady state induced by the attachment of VOCs to the sensor .
While polymer-based chemiresistors offer several advantages (e.g. low power consumption, small size, low operating temperature and low cost), their sensitivity depends on the type of coating; they also show a drift in baseline due to polymer instability [25, 26].
Nevertheless, selection of a sensors array type depends on the physical characteristics that are optimal for clinical purposes; sensor arrays with low recovery time are not optimal for screening purposes . Table 1 lists representative diseases and related types of sensor arrays used for their detection.
In the following sections, this table will be extended to discuss the use of sensor arrays conjugated with pattern recognition methods as diagnostic tools for different respiratory diseases.
Sensor arrays in the diagnosis of respiratory diseases
|Lung cancer||CBPC, MO, SWNTs, SiNW FET, MCMNPs, QMB, colourimetric||[27, 28, 29, 30, 31, 32, 33, 34, 35, 36]|
|COPD||CBPC, QMB, MO||[37, 38, 39, 40, 41, 42]|
|Asthma||CBPC, MO||[37, 43, 44, 45, 46, 47, 48]|
|OSAS||CBPC||[50, 51, 52]|
|CF||CBPC||[53, 54, 55]|
|TB||MO, SWNTs, MCMNPs,||[56, 57, 58, 59, 60, 61]|
COPD: chronic obstructive pulmonary disease; PAH: pulmonary arterial hypertension; OSAS: obstructive sleep apnoea syndrome; CF: cystic fibrosis; TB: tuberculosis. CBPC: carbon black polymer composite; MO: metal oxide; SWNTs: single-walled carbon nantotubes; SiNW FET: silicon nanowire field effect transistors; MCMNPs: monolayer-capped metal-coated nanoparticles; QMB: quartz microbalance.
Chronic obstructive pulmonary disease
COPD is characterised by oxidative stress and production of VOCs secreted by the lungs . Diagnosis is based on identifying characteristic symptoms and lung functioning parameters [23, 63]. Current diagnostic tools poorly reflect the severity and other distinctive features of the disease .
Nuclear magnetic resonance and GC-MS analysis of exhaled breath condensates (aerosolised nonvolatile particles contained in the fluid lining of the airway) and noncondensated exhaled breath concentrations of lactate, acetate, propionate, serine, proline and tyrosine are raised, but valine and lysine are lower than non-COPD controls [37, 45, 64–66].
Relying on these breath-print signatures, other research groups have developed sensors to detect COPD-related VOCs in exhaled breath. In a cross-sectional study of 100 patients with asthma and COPD, breath VOCs were analysed by a sensors array based on 32 derivatives of polymer/carbon black sensors .
Principle component analysis (PCA) of the sensing signals had 88% accuracy for distinguishing fixed asthma from COPD, and 83% for classic asthma, and the detection accuracy was not confounded by smoking status .
Since both COPD and asthma patients have chronic airway inflammation, the results might include overlapping features, in the sense that COPD patients could be misdiagnosed as asthmatics and vice versa.
Hence, it is essential to clearly discriminate COPD from asthma, especially in elderly people who have a higher probability of adverse reactions to different classes of inhaled agents or systemic corticosteroid [67, 68].
Thus, a combined system consisting of an array of quartz crystal microbalance (QMB) sensors coated with six derivatives of metal-based metalloporphyrins linked with a GC system found nine VOCs that were significantly correlated with COPD, of which two positively correlated with COPD and seven negatively correlated with COPD .
These results showed that the following were significantly increased in healthy subjects: limonene; butylated hydroxytoluene (BHT); 2-propanol; benzene, 1,3,5-tri-tert-butyl-; hexane, 3-ethyl-4-methyl-; hexyl ethylphosphono-fluoridate; and 1-pentene, 2,4,4-trimethyl. The alkanes decane and 6-ethyl-2-methyl decane were raised in COPD patients . Parallel to the GC-MS analysis, the cross-validated model provided the correct classification of 26 out of 27 COPD patients, and five out of seven control subjects, with an accuracy of 91% .
Symptoms of atopic asthma often begin in early childhood and mostly improve, or even disappear, at puberty, but can relapse later in life . Analysis of exhaled breath may, therefore, be used to assess inflammation and oxidative stress in the respiratory tract, thereby providing a diagnostic approach to the condition [69, 70].
The most common breath analysis approach is based on the detection and monitoring of exhaled nitric oxide fraction (FeNO) . Indeed, increased exhalation of nitric oxide (NO) as a result of interleukin (IL)-13-induced induction of NO synthase in the airway epithelium have been widely documented in asthmatic patients.
As a result, asthmatic patients exhale >30 ppbv of NO, whereas a healthy population exhales lower concentrations .
Portable NO-selective sensors are already routinely used to detect asthma, generally being sensitive to NO levels of <1 ppb, with a relatively rapid response time . Nevertheless, raised NO concentrations have been reported in other diseases, including hypertension, arthritis, lung diseases, bronchiectasis and CF, in addition to inflammatory bowel disease of the colon and small intestine .
Consequently, a pattern recognition approach of exhaled breath is more likely to be fruitful for predicting asthma than a single biomarker. Indeed, sensors array have given a higher degree of diagnostic accuracy for asthma than exhaled NO or lung function [14, 44, 73].
For example, a cross-sectional study with polymer/carbon black sensors array could distinguish between 30 patients with COPD, 20 patients with mild-to-severe asthma, 20 healthy smokers and 20 healthy nonsmokers .
The breath-prints of patients with COPD and asthmatics have been compared, and the data analysed by PCA and canonical linear discriminant analysis. Moreover, cross-validation with the leave-one-out method has been used to estimate the accuracy, showing that the polymer-based sensors could successfully discriminate patients with mild-to-severe asthma from those with COPD, healthy smokers and healthy nonsmokers with accuracies of 96%, 93% and 95%, respectively .
Similarly, polymer/carbon black sensors array successfully separated mild asthma from young controls; however, it failed to distinguish severe from mild asthmatics . Paredi et al.  have shown that the VOC profile can assess asthma severity and control. In addition to the collective analysis of breath-prints, raised ethane levels have been recorded in the breath of steroid-naïve asthmatics compared to subjects treated with steroids. Moreover, ethane was found to be higher in patients with severe asthma compared with patients with mild asthma, suggesting that ethane might be a preselected marker for the detection of asthma.
Lung cancer is typically asymptomatic in its early stages and, as such, most of the cases are diagnosed in later stages when treatment is no longer effective . The 5-year survival rate increases dramatically from 10% to 80% if the disease is detected at the early stages .
Numerous GC-MS and proton transfer reaction-MS studies have examined the profile of VOCs in lung cancer; >1000 trace VOCs have been found in the exhaled breath of lung cancer patients at concentrations ranging from parts per million by volume to parts per trillion by volume [6, 9, 15, 76–81].
Typical examples are isoprene, methanol, acetone and 2-propanol (appearing in all human breath samples), acetonitrile, furan, 2-methyl furan (primarily found in smokers) and many others .
Hence, efforts have been invested in analysing exhaled breath as a simple noninvasive method of the early detection of lung cancer [83, 84]. Relying on these findings, Di Natale et al.  reported 100% classification of patients with lung cancer versus healthy subjects by using eight QMB sensors coated with different metalloporphyrins.
Similarly, the same sensor array was used to detect lung cancer in a pool of 36 healthy controls, 28 patients with lung cancer and 28 patients with diverse lung diseases . The sensors response was analysed using PCA and discriminate analysis, combined with partial least square ; it could classify between the groups with 85%, 92.8% and 89.3% sensitivity, respectively.
Surface acoustic wave (SAW) sensors were used as a detector for the breath analysis of 42 volunteers, including 15 healthy subjects, 20 patients with lung cancer and seven patients with chronic bronchitis. This array was a SAW coated with a film of isobutylene regarded as the detector, with the other sensor being used as a reference.
After calibration of the sensors, breath samples were collected in Tedlar bags and absorbed on solid-phase microextraction fibres for preconcentration. The VOCs extracted from the thermal desorber column were absorbed on the SAW sensors.
Frequency response (Hz) and the corresponding retention times were recorded. Using artificial neural network analysis, the sensors could correctly diagnose patients with lung cancer from exhaled breath with 80% sensitivity and specificity .
Despite their sensitivity and good response time, SAW sensors are temperature sensitive, such that certain analyte compounds are affected by the different sensor coatings . Therefore, others have tried to verify the potential of these different types of nanoarray sensors to detect lung cancer.
For example, a colorimetric sensors array was designed for noninvasive lung cancer detection of exhaled breath of 49 patients with nonsmall cell lung cancer, 21 healthy volunteers and 73 patients with different pulmonary diseases, including COPD .
Each colorimetric sensors array was composed of 36 chemically sensitive spots with different sensitivities to volatile compounds. The data gave 73.3% sensitivity and 72.4% specificity for the diagnosis of lung cancer.
In a following study, breath samples were taken from 229 volunteers divided into a control group of individuals at high risk of developing lung cancer, subjects with intermediate lung nodules, and untreated patients with lung cancer validated by biopsy.
All groups were examined by a colorimetric sensors array and compared with breath signatures of eight binary groups for the identification and characterisation of lung cancer with high sensitivities and specificities.
The resulting model could discriminate between different lung cancer histologies with 90% sensitivity; however, prediction and differentiation between healthy volunteers and patients with lung cancer was less accurate (70%) . Arguably, the disposable features of the colorimetric sensors might be a limitation in real-world applications.
With these challenges in mind, chemiresistors based on monolayer-coated metal nanoparticles have been used to detect and monitor lung cancer as having advantages over other sensing techniques, such as: a larger surface-to-volume ratio of the sensors, operation at room temperature, lower detection limits for the VOCs of interest (sub-ppb), lower operating voltage, a wider dynamic range, faster response and recovery times, higher tolerance to humidity and compatibility with standard microelectronic industry [2, 30].
Using this approach, the group of Haick and co-workers. [31–33, 36] successfully discriminated early and late stages of lung cancer with 88% accuracy. Moreover, this sensors array could distinguish between small cell lung carcinoma and nonsmall cell carcinoma, as well as in differentiating between subhistologies of adenocarcinoma and squamous cell carcinoma with 93% and 88% accuracy, respectively.
Furthermore, in a study that included 144 breath samples from 39 patients with advanced lung cancer, one gold nanoparticle (GNP) sensor could differentiate between patients with lung cancer after surgery, as well as monitoring the response of patients to therapy with an accuracy of 59%.
Discriminant factor analysis of the collective responses from the nanoarray could monitor changes in tumour response during therapy and also indicate lack of any further response to therapy with a success rate of 85% . Using the same sensors array, these authors managed to detect lung cancer at early onset and monitor breath volatolomics after lung cancer resection. Moreover, patients with lung cancer and volunteers with benign nodules before and after surgery could clearly be differentiated by DFA maps .
Ultimately, the nanomaterial-based sensors array distinguished between pre-surgery and post-surgery lung cancer states yielding an 80% classification accuracy . In another study by the same group, exhaled breath was analysed in the diagnosis of epidermal growth factor receptor (EGFR) mutation in patients with lung cancer .
A nanomaterial-based sensors array composed of 40 cross-reactive, chemically diverse chemiresistors based on organically stabilised spherical GNPs, and single-walled carbon nanotubes, were used to discriminate patients with lung cancer who harboured the EGFR mutation from those with wild-type with an accuracy of 83%.
Nanoarray sensors also showed that patients with early lung cancer could be discriminated from patients with benign pulmonary nodules, with a sensitivity, specificity and accuracy of 75%, 93% and 87%, respectively (figure 2).
CF is characterised by inflammation and oxidative stress; thus, monitoring of airway inflammation and oxidative stress can be helpful in the diagnosis and monitoring of CF, especially since inflammation arises before clinical symptoms appear .
The currently available techniques for measuring inflammation and oxidative stress in the airways are bronchoscopy, bronchoalveolar lavage and biopsy; however, these techniques are too invasive for repeated routine use, especially in children .
The oxidative stress that accompanies inflammation in CF and other respiratory diseases leads to the formation of distinctive volatile substances in the breath, which has led to increasing interest in exhaled breath analysis for the detection of CF. FeNO is the most extensively studied marker in exhaled breath, and has been proved to be helpful clinically in some pulmonary diseases, including CF [6, 53]. N
evertheless, monitoring NO has several limitations, most noted in that FeNO is largely a marker of allergic inflammation, thereby limiting its use in nonallergic patients. Next to monitoring preselected unique markers, it is possible to assess the profiles of VOCs in exhaled air .
Indeed, the exhaled breath of 64 patients with CF and 21 with primary ciliary dyskinesia were analysed using the polymer composite-based sensors array along with PCA analysis .
A cross-validated ROC curve was constructed; breath profiles of patients with CF showed a significant difference from controls (p=0.001) and from patients with primary ciliary dyskinesia (p=0.005). The sensors’ response could also differentiate between CF and primary ciliary dyskinesia with or without a number of well-characterised chronic pulmonary infections .
Furthermore, the sensors array had the ability to discriminate between patients with CF suffering from chronic pulmonary Pseudomonas aeruginosa infection from those without a chronic pulmonary infection. However, the results indicated that it was impossible to detect any overall difference between chronically and nonchronically infected patients .
It was also impossible to differentiate nonchronically infected patients with CF from patients with CF having other chronic pulmonary infections with other pathogens, such as Achromobacter xylosoxidans, Stenotrophomonas maltophilia or a species of the Burkholderia cepacia complex. The authors explained these findings regarding possible bacteria-specific VOCs that patients with CF with chronic pulmonary infection emit in their exhaled breath .
McGrath et al.  found that patients with CF with an acute exacerbation had lower levels of exhaled isoprene compared with controls . Moreover, when these patients were treated with antibiotics, their isoprene levels increased to normal levels. Ethane levels were also raised in steroid-naïve patients with CF compared with steroid-treated patients . Overall, the data indicate that VOC profiling could be useful in assessing and following up exacerbations, and for rapid detection of P. aeruginosa in patients with CF.
More information: Sonia Freddi et al, Development of a Sensing Array for Human Breath Analysis Based on SWCNT Layers Functionalized with Semiconductor Organic Molecules, Advanced Healthcare Materials (2020). DOI: 10.1002/adhm.202000377
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