Breath Analysis Research Articles

Au144 loaded hierarchical In2O3 nanospheres for effective detection of acetone in simulated exhalation

Loading noble metal nanoparticles (NPs) on metal oxides is an effective way to solve the challenge of low sensitivity and poor selectivity of semiconductor sensors in detecting acetone in breath. However, noble metal NPs synthesized by traditional methods suffer from problems including irregular grain shape and uneven grain size, leading to a decrease in gas sensing property. Herein, we loaded atomically precise Au144(SC2H4Ph)60 nanoclusters (NCs) on In2O3 nanospheres to construct Au-loaded In2O3 materials (xAu144/IO) for detecting acetone. The gas sensing properties of xAu144/IO were optimized, and the correlation between acetone responses and the size of Au NPs was studied. Among xAu144/IO, 3Au144/IO has higher responses to acetone with good selectivity, short response/recovery time, low detection limit, and excellent long-term stability. In addition, the dynamic responses to acetone exhibit the size dependence of gold nanoparticles. More attractively, the tests of simulated exhalation indicate that the sensor based on 3Au144/IO can distinguish diabetic patients from healthy people, which is further verified by detecting the breath of a diabetic. The strategy of loading atomically precise nanoclusters adopted in this work provides a new method for the design and preparation of highly selective acetone sensing materials for breath analysis.

Can all healthy adults use the current evidential breath alcohol analysers? An investigation using a large spirometry database.

People failing to give a specimen of breath at a police station are assumed to be deliberately obstructive and are charged with Failure to Provide under the Road Traffic Act 1988. However, spirometry records of 281,210 healthy individuals from UK BioBank showed that a significant minority cannot use existing evidential breath analysis machines. Women were three times more likely to be unable to use them than men (1.64% vs 0.54%) with the risk rising with age six-fold from those in their 40s (0.43%) to 2.7% in their 70s, with women more affected (0.65% to 3.8%). Short stature was a further risk factor: 2.6% of men and 3.8% of women below the 2nd percentile of height could not use the current machines, with almost one in ten elderly, short women unable to do so, while smokers aged 50+ were twice as likely as non-smokers of the same age to be unable to provide breath specimens.

Hybrid model with optimal features for non-invasive blood glucose monitoring from breath biomarkers

Analysis of exhaled breath is becoming more and more used as an additional diagnostic technique in medicine. Researchers must create unique algorithms for accurate data interpretation due to the sheer quantity of factors that must be considered. Therefore, a new NICBGM-based model from exhaled breath is introduced in this study. This work exploited median filtering (MF) for pre-processing. Then, “Improved Empirical Wavelet Functions (I-EWF), R-peak detection, QT intervals, PR intervals, Entropy-based feature, improved Discrete wavelet transform (DWT), Continuous Wavelet Transform (CWT), and short-time Fourier transformation (I-STFT)” are extracted. Further, optimal features are chosen, which are then put through a hybrid scheme that combines “Deep Max out (DMO) and Long Short-Term Memory (LSTM)”. Then, the mean is taken by DMO and LSTM to attain the fine result. Here, the Wild Beest Updated HGSO (WBU-HGSO) model is used to optimize the LSTM weights. The final step is an analysis that proves the superiority of the WBU-HGSO-based model.

Impact of breast density on classification of infrared spectroscopy for breath-based breast cancer screening.

e13558 Background: While mammography is currently the standard of care for breast cancer screening, dense breast tissue can significantly degrade results. Alternatively, infrared spectroscopy analysis of breath offers a highly sensitive method for identifying exhaled volatile organic compounds (VOCs), which may circumvent issues with breast density. Methods: Alveolar breath samples were collected onto Tenax TA sorbent tubes using a SohnoXB™ breath sampler. Using four desorb temperatures (75, 150, 225 and 300°C), absorption spectra were measured by infrared cavity ring-down spectroscopy (IR-CRDS), a technique for measuring absorption coefficients due to VOCs in exhaled breath. Missing values in the absorption spectra were backfilled using interpolation, and the spectrum was min-max normalized and quadratic detrended. First and second derivatives of the preprocessed absorption spectra were used as features for a support vector machine machine-learning model. Features were ranked based on minimum redundancy maximum relevance (mRMR). The top 20 ranked features were selected to limit the potential for overfitting and then optimized. Model performance was validated using non-nested leave-one-out cross-validation (LOOCV) and nested LOOCV to provide optimistic and pessimistic results, respectively. Results: Absorption spectra from 111 participants (71 positive, 40 control) were used. Of the positive subjects, 30 had low- and 31 had high-density breast tissue (measures missing for 10). Model performance is outlined. A subgroup analysis compared model performance for subjects with low- and high-density breast tissue for both non-nested and nested LOOCV models. Breast density data was not captured for control subjects thus they were not included in the subgroup analysis. Fisher’s exact test was performed to assess for significant difference between model performance for those with low- vs high-density breast tissue, resulting in a p-value of 1.00. Conclusions: Our results suggest that the classification of alveolar breath using IR-CRDS is a promising technique for the detection of breast cancer that is independent of breast density. Breath analysis may therefore become a new alternative or corroborative to mammography to support clinical decision-making. [Table: see text]

FRI-492 - Enhanced detection of cirrhosis and risk of advanced fibrosis with dynamic breath analysis

FRI-492 - Enhanced detection of cirrhosis and risk of advanced fibrosis with dynamic breath analysis

WS04.04 CFTR modulator triple combination treatment modifies exhaled breath of children with cystic fibrosis within a week

Objectives: The triple combination of CFTR modulators ivacaftor/tezacaftor/elexacaftor restores some CFTR activity in patients with rescuable mutations which may translate into an increase in respiratory function and quality of life. Yet, novel biomarkers are needed to evaluate treatment efficacy in patients with high basal lung function such as children. Our primary objective was to demonstrate the value of breath analysis to monitor short-term response to the triple combination.

Characterization of Breath Sensor at Different Frequencies in Outdoor Condition

This paper investigates a breath sensor device that is designed to detect the moisture in human bulk matrix in outdoor conditions. The human bulk matrix is rich in moisture and carbon dioxide apart from the 872 volatile organic compounds emanating from the human breath. Human breathing effort is a respiration process that involves inhalation and exhalation modes. The human bulk matrix is a product of the latter. Most research in human breath analysis is concentrating on both human bulk matrix and breathing pattern. The aim of this study is to characterize the fabricated breath sensor at different input wave frequencies in the outdoor environment. In this study, an outdoor experiment was carried out using the breath sensor device that is connected to the input waveform from the frequency generator and the output reading is captured using the oscilloscope. A single exhaled breath originated from the human subject is required to activate the breath sensor. This method is easier and simpler, and the output wave result is generated by an oscilloscope in real-time. The result indicates that the investigated breath sensor is suitable for clinical and healthcare monitoring. Early studies indicate that the breath sensor can diagnose diseases related to breathing problems such as sleep apnoea, asthma, and strokes.

Review of linear and nonlinear models in breath analysis by Cyranose 320

Analysis of volatile organic compounds (VOCs) in breath specimens has potential for point of care (POC) screening due to ease of sample collection. While the electronic nose (e-nose) is a standard VOC measure across a wide range of industries, it has not been adopted for POC screening in healthcare. One limitation of the e-nose is the absence of mathematical models of data analysis that yield easily interpreted findings at POC. The purposes of this review were to (1) examine the sensitivity/specificity results from studies that analyzed breath smellprints using the Cyranose 320, a widely used commercial e-nose, and (2) determine whether linear or nonlinear mathematical models are superior for analyzing Cyranose 320 breath smellprints. This systematic review was conducted according to the guidelines of the Preferred Reporting Items for Systematic Review and Meta-Analyses using keywords related to e-nose and breath. Twenty-two articles met the eligibility criteria. Two studies used a linear model while the rest used nonlinear models. The two studies that used a linear model had a smaller range for mean of sensitivity and higher mean (71.0%–96.0%; M = 83.5%) compared to the studies that used nonlinear models (46.9%–100%; M = 77.0%). Additionally, studies that used linear models had a smaller range for mean of specificity and higher mean (83.0%–91.5%; M = 87.2%) compared to studies that used nonlinear models (56.9%–94.0%; M = 76.9%). Linear models achieved smaller ranges for means of sensitivity and specificity compared to nonlinear models supporting additional investigations of their use for POC testing. Because our findings were derived from studies of heterogenous medical conditions, it is not known if they generalize to specific diagnoses.

The Role of Nano-Sensors in Breath Analysis for Early and Non-Invasive Disease Diagnosis

Early-stage, precise disease diagnosis and treatment has been a crucial topic of scientific discussion since time immemorial. When these factors are combined with experience and scientific knowledge, they can benefit not only the patient, but also, by extension, the entire health system. The development of rapidly growing novel technologies allows for accurate diagnosis and treatment of disease. Nanomedicine can contribute to exhaled breath analysis (EBA) for disease diagnosis, providing nanomaterials and improving sensing performance and detection sensitivity. Through EBA, gas-based nano-sensors might be applied for the detection of various essential diseases, since some of their metabolic products are detectable and measurable in the exhaled breath. The design and development of innovative nanomaterial-based sensor devices for the detection of specific biomarkers in breath samples has emerged as a promising research field for the non-invasive accurate diagnosis of several diseases. EBA would be an inexpensive and widely available commercial tool that could also be used as a disease self-test kit. Thus, it could guide patients to the proper specialty, bypassing those expensive tests, resulting, hence, in earlier diagnosis, treatment, and thus a better quality of life. In this review, some of the most prevalent types of sensors used in breath-sample analysis are presented in parallel with the common diseases that might be diagnosed through EBA, highlighting the impact of incorporating new technological achievements in the clinical routine.

On-Field Test of Tuberculosis Diagnosis through Exhaled Breath Analysis with a Gas Sensor Array

Tuberculosis (TB) is among the more frequent causes of death in many countries. For pulmonary TB, early diagnosis greatly increases the efficiency of therapies. Although highly sensitive tests based on nucleic acid amplification tests (NAATs) and loop-mediated isothermal amplification (TB-LAMP) are available, smear microscopy is still the most widespread diagnostics method in most low-middle-income countries, and the true positive rate of smear microscopy is lower than 65%. Thus, there is a need to increase the performance of low-cost diagnosis. For many years, the use of sensors to analyze the exhaled volatile organic compounds (VOCs) has been proposed as a promising alternative for the diagnosis of several diseases, including tuberculosis. In this paper, the diagnostic properties of an electronic nose (EN) based on sensor technology previously used to identify tuberculosis have been tested on-field in a Cameroon hospital. The EN analyzed the breath of a cohort of subjects including pulmonary TB patients (46), healthy controls (38), and TB suspects (16). Machine learning analysis of the sensor array data allows for the identification of the pulmonary TB group with respect to healthy controls with 88% accuracy, 90.8% sensitivity, 85.7% specificity, and 0.88 AUC. The model trained with TB and healthy controls maintains its performance when it is applied to symptomatic TB suspects with a negative TB-LAMP. These results encourage the investigation of electronic noses as an effective diagnostic method for future inclusion in clinical practice.

Impairment of Mesenteric Perfusion as a Marker of Major Bleeding in Trauma Patients.

The majority of potentially preventable mortality in trauma patients is related to bleeding; therefore, early recognition and effective treatment of hemorrhagic shock impose a cardinal challenge for trauma teams worldwide. The reduction in mesenteric perfusion (MP) is among the first compensatory responses to blood loss; however, there is no adequate tool for splanchnic hemodynamic monitoring in emergency patient care. In this narrative review, (i) methods based on flowmetry, CT imaging, video microscopy (VM), measurement of laboratory markers, spectroscopy, and tissue capnometry were critically analyzed with respect to their accessibility, and applicability, sensitivity, and specificity. (ii) Then, we demonstrated that derangement of MP is a promising diagnostic indicator of blood loss. (iii) Finally, we discussed a new diagnostic method for the evaluation of hemorrhage based on exhaled methane (CH4) measurement. Conclusions: Monitoring the MP is a feasible option for the evaluation of blood loss. There are a wide range of experimentally used methodologies; however, due to their practical limitations, only a fraction of them could be integrated into routine emergency trauma care. According to our comprehensive review, breath analysis, including exhaled CH4 measurement, would provide the possibility for continuous, non-invasive monitoring of blood loss.

Ternary Hybrid Materials for Highly Sensitive Acetone Sensing at Room Temperature

The performance of a conventional metal oxide sensor (MOX) is highly dependent on its high operating temperature. Many researchers have tried to solve the problem by exploring hybrid materials. On the other hand, ternary hybrid materials have emerged as a promising class of materials with unique properties and potential applications in various fields, be it environmental or medical, such as in breath analyzers for prediabetes analysis. This article focuses on the synthesis method, characterization, and application of ternary hybrid materials for room-temperature sensors, as well as recent advances and future developments in the field. The materials consist of three different components, metal oxide (Fe3O4), polymer (polyaniline) and carbon-based materials (reduced graphene oxide), which were synthesized using in-situ methods. Five samples were prepared in different ratios. The properties of these materials were characterized using techniques such as X-ray diffraction (XRD), Raman, scanning electron microscope (SEM) and transmission electron microscopy (TEM). The XRD and Raman analyses showed the existence of all the individual constituents in the hybrid sample. SEM and TEM also showed a strong interaction between the constituent materials as a hybrid nanocomposite. The response and recovery time were studied against 1, 10 and 100 ppm acetone. The results show that the sample with 10 wt%Fe3O4-PANI-RGO (S2_10) has a reaction and recovery time < 32 s against the above ppm and has the highest sensing response at room temperature.

Prospective Detection of Early Lung Cancer in Patients With COPD in Regular Care by Electronic Nose Analysis of Exhaled Breath

Patients with COPD are at high risk of lung cancer developing, but no validated predictive biomarkers have been reported to identify these patients. Molecular profiling of exhaled breath by electronic nose (eNose) technology may qualify for early detection of lung cancer in patients with COPD. Can eNose technology be used for prospective detection of early lung cancer in patients with COPD? BreathCloud is a real-world multicenter prospective follow-up study using diagnostic and monitoring visits in day-to-day clinical care of patients with a standardized diagnosis of asthma, COPD, or lung cancer. Breath profiles were collected at inclusion in duplicate by a metal-oxide semiconductor eNose positioned at the rear end of a pneumotachograph (SpiroNose; Breathomix). All patients with COPD were managed according to standard clinical care, and the incidence of clinically diagnosed lung cancer was prospectively monitored for 2 years. Data analysis involved advanced signal processing, ambient air correction, and statistics based on principal component (PC) analysis, linear discriminant analysis, and receiver operating characteristic analysis. Exhaled breath data from 682 patients with COPD and 211 patients with lung cancer were available. Thirty-seven patients with COPD (5.4%) demonstrated clinically manifest lung cancer within 2 years after inclusion. Principal components 1, 2, and 3 were significantly different between patients with COPD and those with lung cancer in both training and validation sets with areas under the receiver operating characteristic curve of 0.89 (95% CI, 0.83-0.95) and 0.86 (95% CI, 0.81-0.89). The same three PCs showed significant differences (P< .01) at baseline between patients with COPD who did and did not subsequently demonstrate lung cancer within 2 years, with a cross-validation value of 87%and an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.95). Exhaled breath analysis by eNose identified patients with COPD in whom lung cancer became clinically manifest within 2 years after inclusion. These results show that eNose assessment may detect early stages of lung cancer in patients with COPD.

Method for Determining the Dominant Type of Human Breathing Using Motion Capture and Machine Learning

Since the COVID-19 pandemic, the demand for respiratory rehabilitation has significantly increased. This makes developing home (remote) rehabilitation methods using modern technology essential. New techniques and tools, including wireless sensors and motion capture systems, have been developed to implement remote respiratory rehabilitation. Significant attention during respiratory rehabilitation is paid to the type of human breathing. Remote rehabilitation requires the development of automated methods of breath analysis. Most currently developed methods for analyzing breathing do not work with different types of breathing. These methods are either designed for one type (for example, diaphragmatic) or simply analyze the lungs’ condition. Developing methods of determining the types of human breathing is necessary for conducting remote respiratory rehabilitation efficiently. We propose a method of determining the type of breathing using wireless sensors with the motion capture system. To develop that method, spectral analysis and machine learning methods were used to detect the prevailing spectrum, the marker coordinates, and the prevailing frequency for different types of breathing. An algorithm for determining the type of human breathing is described. It is based on approximating the shape of graphs of distances between markers using sinusoidal waves. Based on the features of the resulting waves, we trained machine learning models to determine the types of breathing. After the first stage of training, we found that the maximum accuracy of machine learning models was below 0.63, which was too low to be reliably used in respiratory rehabilitation. Based on the analysis of the obtained accuracy, the training and running time of the models, and the error function, we choose the strategy of achieving higher accuracy by increasing the training and running time of the model and using a two-stage method, composed of two machine learning models, trained separately. The first model determines whether the breath is of the mixed type; if it does not predict the mixed type of breathing, the second model determines whether breathing is thoracic or abdominal. The highest accuracy achieved by the composite model was 0.81, which surpasses single models and is high enough for use in respiratory rehabilitation. Therefore, using three wireless sensors placed on the patient’s body and a two-stage algorithm using machine learning models, it was possible to determine the type of human breathing with high enough precision to conduct remote respiratory rehabilitation. The developed algorithm can be used in building rehabilitation applications.

Nasal nitric oxide in healthy Chinese children aged 6-18 years.

To obtain the normal values of fractional concentration of nasal nitric oxide in Chinese children aged 6-18 years, so as to provide reference for clinical diagnosis. 2,580 out of 3,200 children (1,359 males and 1,221 females), whom were included from 12 centers around China were taken tests, their height and weight were also recorded. Data were used to analyze the normal range and influencing factors of fractional concentration of nasal nitric oxide values. Data was measured using the Nano Coulomb Breath Analyzer (Sunvou-CA2122, Wuxi, China), according to the American Thoracic Society/European Respiratory Society (ATS/ERS) recommendations. We calculated the normal range and prediction equation of fractional concentration of nasal nitric oxide values in Chinese children aged 6-18 years. The mean FnNO values of Chinese aged 6-18 yrs was 454.5 ± 176.2 ppb, and 95% of them were in the range of 134.5-844.0 ppb. The prediction rule of FnNO values for Chinese children aged 6-11 yrs was: FnNO = 298.881 + 17.974 × age. And for children aged 12-18 yrs was: FnNO = 579.222-30.332 × (male = 0, female = 1)-5.503 × age. Sex and age were two significant predictors of FnNO values for Chinese children(aged 12-18 yrs). Hopefully this study can provide some reference value for clinical diagnosis in children.

Volatolomics analysis of exhaled breath and gastric-endoluminal gas for distinguishing early upper gastrointestinal cancer from benign

Exhaled breath and gastric-endoluminal gas (volatile products of diseased tissues) contain a large number of volatile organic compounds, which are valuable for early diagnosis of upper gastrointestinal (UGI) cancer. In this study, exhaled breath and gastric-endoluminal gas of patients with UGI cancer and benign disease were analyzed by gas chromatography-mass spectrometry (GC-MS) and ultraviolet photoionization time-of-flight mass spectrometry (UVP-TOFMS) to construct UGI cancer diagnostic models. Breath samples of 116 UGI cancer and 77 benign disease subjects and gastric-endoluminal gas samples of 114 UGI cancer and 76 benign disease subjects were collected. Machine learning (ML) algorithms were used to construct UGI cancer diagnostic models. Classification models based on exhaled breath for distinguishing UGI cancer from the benign group have area under the curve (AUC) of receiver operating characteristic curve values of 0.959 and 0.994 corresponding to GC-MS and UVP-TOFMS analysis, respectively. The AUC values of models based on gastric-endoluminal gas for UGI cancer and benign group classification are 0.935 and 0.929 corresponding to GC-MS and UVP-TOFMS analysis, respectively. This work indicates that volatolomics analysis of exhaled breath and gastric-endoluminal diseased tissues have great potential in early screening of UGI cancer. Moreover, gastric-endoluminal gas can be a means of gas biopsy to provide auxiliary information for the examination of tissue lesions during gastroscopy.

Emissions and uptake of volatiles by sampling components in breath analysis

The first and most crucial step in breath research is adequate sampling, which plays a pivotal role in quality assurance of breath datasets. In particular, the emissions or uptake of volatile organic compounds (VOCs) by sampling interface materials present a risk of disrupting breath gas samples. This study investigated emissions and uptake by three interface components, namely a silicon facemask, a reusable 3D-printed mouthpiece adapter, and a pulmonary function test filter compatible with the commercial Respiration Collector for In-Vitro Analysis (ReCIVA) breath sampling device. Emissions were examined before and after (hydro-)thermal treatment of the components, and uptake was assessed by exposing each material to 12 representative breath VOCs comprising alcohols, aldehydes, ketones, carboxylic acids, terpenes, sulphurous and nitrogenous compounds at different target concentration ranges (∼10 ppbV and ∼100 ppbV). Chemical analyses of VOCs were performed using proton transfer reaction-time-of-flight-mass spectrometry (PTR-TOFMS) with supporting analyses via thermal desorption comprehensive two-dimensional gas chromatography-TOFMS (TD-GC×GC-TOFMS). The filter exhibited the lowest overall emissions compared to the mask or adapter, which both had equivalently high emissions (albeit for different compounds). Treatment of the materials reduced the total VOC emissions by 62% in the mask, 89% in the filter and 99% in the adapter. Uptakes of compounds were lowest for the adapter and most pronounced in the mask. In particular, 1-butanol, acetone, 2-butanone, 1,8-cineole and dimethyl sulphide showed negligible uptake across all materials, whereas ethanol, nonanal, acetic acid, butanoic acid, limonene and indole exhibited marked losses. Knowledge of emissions and/or uptake by sampling components is key to reducing the likelihood of erroneous data interpretation, ultimately expediting progress in the field of breath test development.

Electronic nose based on multiple electrospinning nanofibers sensor array and application in gas classification

To mimic the human olfactory system, an electronic nose (E-nose, also known as artificial olfactory) has been proposed based on a multiple gas sensor array and a pattern recognition algorithm. Detection of volatile organic components (VOCs) has many potential applications in breath analysis, food quality estimation, and indoor and outdoor air quality monitoring, etc. In this study, a facile single-needle electrospinning technology was applied to develop the four different semiconductor metal oxide (MOS) nanofibers sensor arrays (SnO2, CuO, In2O3 and ZnO, respectively). The array shows a smooth surface and constant diameter of nanofiber (average of 150 nm) resulting in high sensitivity to multiple target analyte gases. Five human health related VOCs gases were measured by fabricated E-nose and different response patterns were obtained from four MOS nanofibers sensors. Combined with feature extraction from the response curves, a principal component analysis (PCA) algorithm was applied to reduce the dimension of feature matrix, Thus, the fabricated E-nose system successfully discriminated five different VOCs gases. Real-time and non-invasive gas monitoring by E-nose is very promising for application in human health monitoring, food monitoring, and other fields.

Room temperature sensing of primary alcohols via polyaniline/zirconium disulphide

Among the primary alcohols, ethanol is referred to as a heavy chemical due to its many applications in a variety of industries. Detection of primary alcohols can be deployed as a non-invasive method in medical diagnosis and safety measures in food processing companies. Zirconium disulphide is a novel 2D layered material with exotic features when in mono or few layers, which include fast electron transport, high carrier mobility and sizeable band gap. ZrS2 and PANI were fabricated using liquid exfoliation and chemical polymerization methods respectively. Functionalization of the conducting polyaniline with ZrS2 was done using facile sonication process. The sensor showed good sensitivities (43%, 58% and 104%) which were estimated from slopes of the linear fitted plots with fast response-recovery times of 8 s and 27 s (111 ppm); 12 s and 130 s (77 ppm); and 58 s and 88 s (58 ppm). Good reproducibility at three repeated measurements (111 ppm, 77 ppm and 58 ppm) was also observed for methanol, ethanol, and isopropanol vapours respectively. Meanwhile, the sensor displayed more linearity and sensitivity towards isopropanol compared to methanol and ethanol. The sensor showed good performance even at RH values close to 100% making it a potential alcohol breath analyser.

Breath analysis for detection of lung cancer with hybrid sensor-based electronic nose

Breath analysis for detection of lung cancer with hybrid sensor-based electronic nose