Electronic nose in discrimination of children with uncontrolled asthma

A Review on Long COVID Screening: Challenges and Perspectives Focusing on Exhaled Breath Gas Sensing

Long COVID (LC) is a great global health concern, affecting individuals recovering from SARS-CoV-2 infection. The persistent and varied symptoms across multiple organs complicate diagnosis and management, and an incomplete understanding of the condition hinders advancements in therapeutics. Current diagnostic methods face challenges related to standardization and completeness. To overcome this, new technologies such as sensor-based electronic noses are being explored for LC assessment, offering a noninvasive screening approach via volatile organic compounds (VOC) sensing in exhaled breath. Although specific LC-associated VOCs have not been fully characterized, insights from COVID-19 research suggest their potential as biomarkers. Additionally, AI-driven chemometrics are promising in identifying and predicting outcomes; despite challenges, AI-driven technologies hold the potential to enhance LC evaluation, providing rapid and accurate diagnostics for improved patient care and outcomes. This review underscores the importance of emerging and sensing technologies and comprehensive diagnostic strategies to address screening and treatment challenges in the face of LC.

Present and Future of Emerging Catalysts in Gas Sensors for Breath Analysis

To rationalize the noninvasive disease diagnosis by breath analysis, developing a high-performance gas sensor with exceptional sensitivity and selectivity is important to detect trace biomarkers in complex exhaled breath under harsh conditions. Among the various technological innovations, catalyst design and synthesis techniques are the foremost challenges, because gas sensing properties are predominantly determined by surface chemical reactions governed by catalytic activities. Conventional nanoparticle-based catalysts, with their simple structural features, have technical limitations in achieving the requirement for accurate breath analysis. Innovative strategies have been pursued to synthesize unconventional catalyst types with enhanced catalytic capabilities. This Perspective provides a comprehensive overview of recent advancements in catalyst technology for chemiresistive-type gas sensors used in breath analysis. It discusses various emerging catalysts, such as doping catalysts, single-atom catalysts (SACs), bimetallic alloy catalysts, high-entropy alloy (HEA) catalysts, exsolution catalysts, and catalytic filter membranes, along with their unique chemical activation mechanisms that enhance gas sensing properties for detecting target biomarkers in exhaled breath. The review also explores novel strategies for catalyst design, including computational prediction, advanced synthesis techniques, and the integration of sensor arrays with artificial intelligence (AI) to improve diagnostic reliability. By highlighting the crucial role of these emerging catalysts, this review provides valuable insights into the catalytic, synthetic, and analytical aspects that are essential for advancing breath analysis technology.

Early Diagnosis of Bronchopulmonary Dysplasia with E-Nose: A Pilot Study in Preterm Infants

Bronchopulmonary dysplasia (BPD) is the most common respiratory disease in preterm and is still associated with increased mortality and morbidity. The great interest lies in identifying early biomarkers that can predict the development of BPD. This pilot study explores the potential of e-nose for the early identification of BPD risk in premature infants by analyzing volatile organic compounds (VOCs) in the exhaled breath condensate (EBC). Fourteen mechanically ventilated very preterm infants were included in this study. The clinical parameters and EBC were collected within the first 24 h of life. The discriminative ability of breath prints between preterms who did and did not develop BPD was investigated using pattern recognition, a machine learning algorithm, and standard statistical methods. We found that e-nose probes can significantly predict the outcome of "no-BPD" vs. "BPD". Specifically, a subset of probes (S18, S24, S14, and S6) were found to be significantly predictive, with an AUC of 0.87, 0.89, 0.82, 0.8, and p = 0.019, 0.009, 0.043, 0.047, respectively. The e-nose is an easy-to-use, handheld, non-invasive electronic device that quickly samples breath. Our preliminary study has shown that it has the potential for early prediction of BPD in preterms.

Association between individual sensor behavior of an electronic nose and airways inflammation in children with asthma: a pilot study at alpine altitude climate

BACKGROUND

Markers of airway inflammation can be helpful in the management of childhood asthma. Residential activities, such as intensive asthma camps at alpine altitude climate (AAC), can help reduce bronchial inflammation in patients who fail to achieve optimal control of the disease. Analysis of volatile organic compounds (VOCs) can be obtained using electronic devices such as e-Noses. We aimed to identify alterations in urinary e-Nose sensors among children with asthma participating in an intensive camp at AAC and to investigate associations between urinary e-Nose analysis and airway inflammation.

METHODS

We analyzed data collected in children with asthma recruited between July and September 2020. All children were born and resided at altitudes below 600 m asl. Urinary VOCs (measured using the Cyranose 320® VOC analyzer), Fractional exhaled Nitric Oxide (FeNO) and spirometry were evaluated upon children's arrival at the Istituto Pio XII, Misurina (BL), Italy, at 1756 m asl (T0), and after 7 (T1) and 15 days (T2) of stay.

RESULTS

Twenty-two patients (68.2% males; median age: 14.5 years) were enrolled. From T0 to T1 and T2, the negative trend for FeNO was significant (p < .001). Significant associations were observed between e-Nose sensors S7 (p = .002), S12 (p = .013), S16 (p = .027), S17 (p = .017), S22 (p = .029), S29 (p = .021), S31 (p = .009) and ΔFeNO at T0-T1. ΔFeNO at T0-T2 was significantly associated with S17 (p = .015), S19 (p = .004), S21 (p = .020), S24 (p = .012), S25 (p = .018), S26 (p = .008), S27 (p = .002), S29 (p = .007), S30 (p = .013).

CONCLUSIONS

We showed that a decrease in FeNO levels after a short sojourn at AAC is associated with behaviors of individual urinary e-Nose sensors in children with asthma.

Multiwavelength Photoacoustic Breath Analysis Sensor for the Diagnosis of Lung Diseases: COPD and Asthma

Breath analysis is emerging as a universal diagnostic method for clinical applications. The possibility of breath analysis is being explored vigorously using different analytical techniques. We have designed and assembled a multiwavelength UV photoacoustic spectroscopy (PAS) sensor for the said application. To optimize laser wavelength for sample excitation, photoacoustic signals from disease and normal conditions are recorded with different laser excitations (213, 266, 355, and 532 nm) on exhaled breath samples. Principal component analysis (PCA) of the PA signals has shown that 213, 266, and 355 nm laser excitations are suitable for breath analysis, with reliable descriptive statistics obtained for 266 nm laser. The study has, therefore, been extended for breath samples collected from asthma, chronic obstructive pulmonary disease (COPD), and normal subjects, using 266 nm laser excitation. PCA of the PA data shows good classification among asthma, COPD, and normal subjects. Match/No-match study performed with asthma, COPD, and normal calibration set has demonstrated the potential of using this method for diagnostic application. Sensitivity and specificity are observed as 88 and 89%, respectively. The area under the curve of the ROC curve is found to be 0.948, which justifies the diagnostic capability of the device for lung diseases. The same samples were studied using a commercial E-Nose, and the measurement outcome strongly supports the PAS results.

Research progress of electronic nose technology in exhaled breath disease analysis

Exhaled breath analysis has attracted considerable attention as a noninvasive and portable health diagnosis method due to numerous advantages, such as convenience, safety, simplicity, and avoidance of discomfort. Based on many studies, exhaled breath analysis is a promising medical detection technology capable of diagnosing different diseases by analyzing the concentration, type and other characteristics of specific gases. In the existing gas analysis technology, the electronic nose (eNose) analysis method has great advantages of high sensitivity, rapid response, real-time monitoring, ease of use and portability. Herein, this review is intended to provide an overview of the application of human exhaled breath components in disease diagnosis, existing breath testing technologies and the development and research status of electronic nose technology. In the electronic nose technology section, the three aspects of sensors, algorithms and existing systems are summarized in detail. Moreover, the related challenges and limitations involved in the abovementioned technologies are also discussed. Finally, the conclusion and perspective of eNose technology are presented.

Exhaled volatile organic compounds for diagnosis and monitoring of asthma

The asthmatic inflammatory process results in the generation of volatile organic compounds (VOCs), which are subsequently secreted by the airways. The study of these elements through gas chromatography-mass spectrometry (GC-MS), which can identify individual molecules with a discriminatory capacity of over 85%, and electronic-Nose (e-NOSE), which is able to perform a quick onboard pattern-recognition analysis of VOCs, has allowed new prospects for non-invasive analysis of the disease in an "omics" approach. In this review, we aim to collect and compare the progress made in VOCs analysis using the two methods and their instrumental characteristics. Studies have described the potential of GC-MS and e-NOSE in a multitude of relevant aspects of the disease in both children and adults, as well as differential diagnosis between asthma and other conditions such as wheezing, cystic fibrosis, COPD, allergic rhinitis and last but not least, the accuracy of these methods compared to other diagnostic tools such as lung function, FeNO and eosinophil count. Due to significant limitations of both methods, it is still necessary to improve and standardize techniques. Currently, e-NOSE appears to be the most promising aid in clinical practice, whereas GC-MS, as the gold standard for the structural analysis of molecules, remains an essential tool in terms of research for further studies on the pathophysiologic pathways of the asthmatic inflammatory process. In conclusion, the study of VOCs through GC-MS and e-NOSE appears to hold promise for the non-invasive diagnosis, assessment, and monitoring of asthma, as well as for further research studies on the disease.

Breath Prints for Diagnosing Asthma in Children

Electronic nose (e-nose) is a new technology applied for the identification of volatile organic compounds (VOC) in breath air. Measuring VOC in exhaled breath can adequately identify airway inflammation, especially in asthma. Its noninvasive character makes e-nose an attractive technology applicable in pediatrics. We hypothesized that an electronic nose could discriminate the breath prints of patients with asthma from controls. A cross-sectional study was conducted and included 35 pediatric patients. Eleven cases and seven controls formed the two training models (models A and B). Another nine cases and eight controls formed the external validation group. Exhaled breath samples were analyzed using Cyranose 320, Smith Detections, Pasadena, CA, USA. The discriminative ability of breath prints was investigated by principal component analysis (PCA) and canonical discriminative analysis (CDA). Cross-validation accuracy (CVA) was calculated. For the external validation step, accuracy, sensitivity and specificity were calculated. Duplicate sampling of exhaled breath was obtained for ten patients. E-nose was able to discriminate between the controls and asthmatic patient group with a CVA of 63.63% and an M-distance of 3.13 for model A and a CVA of 90% and an M-distance of 5.55 for model B in the internal validation step. In the second step of external validation, accuracy, sensitivity and specificity were 64%, 77% and 50%, respectively, for model A, and 58%, 66% and 50%, respectively, for model B. Between paired breath sample fingerprints, there were no significant differences. An electronic nose can discriminate pediatric patients with asthma from controls, but the accuracy obtained in the external validation was lower than the CVA obtained in the internal validation step.

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Background

Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on long-term management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap.

Methods

We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm.

Results

Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms.

Discussion

In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.

Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.

Review of the algorithms used in exhaled breath analysis for the detection of diabetes

Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as Support Vector Machines, k-Nearest Neighbours and various variations of Neural Networks for the detection of diabetes in patient samples and simulated artificial breath samples.

Chemometric analysis of the global pattern of volatile organic compounds in the exhaled breath of patients with COVID-19, post-COVID and healthy subjects. Proof of concept for post-COVID assessment

The objective of this research was to evaluate the application of an electronic nose and chemometric analysis to discriminate volatile organic compounds between patients with COVID-19, post-COVID syndrome and controls in exhaled breath samples. A cross-sectional study was performed on 102 exhaled breath samples, 42 with COVID-19, 30 with the post-COVID syndrome and 30 control subjects. Breath-print analysis was performed by the Cyranose 320 electronic nose with 32 sensors. Group data were evaluated by Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA), and Support Vector Machine (SVM), and the test's diagnostic power was evaluated through a Receiver Operaring Characteristic curve(ROC curve). The results of the chemometric analysis indicate in the PCA a 97.6% (PC₁ = 95.9%, PC₂ = 1.0%, PC₃ = 0.7%) of explanation of the variability between the groups by means of 3 PCs, the CDA presents a 100% of correct classification of the study groups, SVM a 99.4% of correct classification, finally the PLS-DA indicates an observable separation between the groups and the 12 sensors that were related. The sensitivity, specificity of post-COVID vs. controls value reached 97.6% (87.4%–99.9%) and 100% (88.4%–100%) respectively, according to the ROC curve. As a perspective, we consider that this technology, due to its simplicity, low cost and portability, can support strategies for the identification and follow-up of post-COVID patients. The proposed classification model provides the basis for evaluating post-COVID patients; therefore, further studies are required to enable the implementation of this technology to support clinical management and mitigation of effects.

Assessing Gut Microbiota in an Infant with Congenital Propionic Acidemia before and after Probiotic Supplementation

Propionic Acidemia (PA) is a rare inherited metabolic disorder caused by the enzymatic block of propionyl-CoA carboxylase with the consequent accumulation of propionic acid, which is toxic for the brain and cardiac cells. Since a considerable amount of propionate is produced by intestinal bacteria, interest arose in the attempt to reduce propionate-producing bacteria through a monthly antibiotic treatment of metronidazole. In the present study, we investigated the gut microbiota structure of an infant diagnosed at 4 days of life through Expanded Newborn Screening (NBS) and treated the child following international guidelines with a special low-protein diet, specific medications and strict biochemical monitoring. Microbiota composition was assessed during the first month of life, and the presence of Bacteroides fragilis, known to be associated with propionate production, was effectively decreased by metronidazole treatment. After five antibiotic therapy cycles, at 4 months of age, the infant was supplemented with a daily mixture of three bifidobacterial strains, known not to be propionate producers. The supplementation increased the population of bifidobacteria, with Bifidobacterium breve as the dominating species; Ruminococcus gnavus, an acetate and formate producer, was also identified. Metabarcoding analysis, compared with low coverage whole metagenome sequencing, proved to capture all the microbial biodiversity and could be the elected tool for fast and cost-effective monitoring protocols to be implemented in the follow up of rare metabolic disorders such as PA. Data obtained could be a possible starting point to set up tailored microbiota modification treatment studies in the attempt to improve the quality of life of people affected by propionic acidemia.

The smell of lung disease: a review of the current status of electronic nose technology

There is a need for timely, accurate diagnosis, and personalised management in lung diseases. Exhaled breath reflects inflammatory and metabolic processes in the human body, especially in the lungs. The analysis of exhaled breath using electronic nose (eNose) technology has gained increasing attention in the past years. This technique has great potential to be used in clinical practice as a real-time non-invasive diagnostic tool, and for monitoring disease course and therapeutic effects. To date, multiple eNoses have been developed and evaluated in clinical studies across a wide spectrum of lung diseases, mainly for diagnostic purposes. Heterogeneity in study design, analysis techniques, and differences between eNose devices currently hamper generalization and comparison of study results. Moreover, many pilot studies have been performed, while validation and implementation studies are scarce. These studies are needed before implementation in clinical practice can be realised. This review summarises the technical aspects of available eNose devices and the available evidence for clinical application of eNose technology in different lung diseases. Furthermore, recommendations for future research to pave the way for clinical implementation of eNose technology are provided.

Discrimination of COPD and lung cancer from controls through breath analysis using a self-developed e-nose

This work details the application of a metal oxide semiconductor (MOS) sensor based electronic nose (e-nose) system in the discrimination of lung cancer and chronic obstructive pulmonary disease (COPD) from healthy controls. The sensor array integrated with supervised classification algorithms was able to detect and classify exhaled breath samples from healthy controls, patients with COPD, and lung cancer by recognizing the amount of volatile organic compounds present in it. This paper details the e-nose design, participant selection, sampling methods, and data analysis. The clinical feasibility of the system was checked in 32 lung cancer patients, 38 COPD patients, and 72 healthy controls including smokers and non-smokers. One of the advantages of the equipment design was portability and robustness since the system was conditioned with elements that allowed its easy movement. In the discrimination of lung cancer from controls, the k-nearest neighbors gave an acceptable accuracy, sensitivity, and specificity of 91.3%, 84.4%, and 94.4% respectively. The support vector machine gave better results for COPD discrimination from controls with 90.9% accuracy, 81.6% sensitivity, and 95.8% specificity. Even though the attained results were good, further examinations are essential to enhance the sensor array system, investigate the long-run reproducibility, repeatability, and enlarge its relevancy.

E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing

Conducting polymers (CPs) are extensively studied due to their high versatility and electrical properties, as well as their high environmental stability. Based on the above, their applications as electronic devices are promoted and constitute an interesting matter of research. This review summarizes their application in common electronic devices and their implementation in electronic tongues and noses systems (E-tongues and E-noses, respectively). The monitoring of diverse factors with these devices by multivariate calibration methods for different applications is also included. Lastly, a critical discussion about the enclosed analytical potential of several conducting polymer-based devices in electronic systems reported in literature will be offered.

A review on human body fluids for the diagnosis of viral infections: scope for rapid detection of COVID-19

Introduction: The unprecedented outbreaks of corona virus disease of 2019 (COVID-19) have highlighted the necessity of readily available, reliable, precise, and faster techniques for its detection. Nasopharyngeal swab has been the gold standard for the diagnosis of COVID-19. However, it is not an ideal screening procedure for massive screening as it implicates the patient's stay in the hospital or at home until diagnosis, thus causing crowding of the specimen at the diagnostic centers. Present study deal with the exploration of potential application of different body fluids using certain highly objective techniques (Optical and e-Nose) for faster detection of molecular markers thereby diagnosing viral infections.Areas covered: This report presents an evaluation of different body fluids, and their advantages for the rapid detection of COVID-19, coupled with highly sensitive optical techniques for the detection of molecular biomarkers.Expert opinion: Tears, saliva, and breath samples can provide valuable information about viral infections. Our brief review strongly recommends the application of saliva/tears and exhaled breath as clinical samples using technics such as high-performance liquid chromatography-laser-induced fluorescence, photoacoustic spectroscopy, and e-Nose, respectively, for the fast diagnosis of viral infections.

Potential of the Electronic Nose for the Detection of Respiratory Diseases with and without Infection

Respiratory tract infections are common, and when affecting the lower airways and lungs, can result in significant morbidity and mortality. There is an unfilled need for simple, non-invasive tools that can be used to screen for such infections at the clinical point of care. The electronic nose (eNose) is a novel technology that detects volatile organic compounds (VOCs). Early studies have shown that certain diseases and infections can result in characteristic changes in VOC profiles in the exhaled breath. This review summarizes current knowledge on breath analysis by the electronic nose and its potential for the detection of respiratory diseases with and without infection.