SERS-based AI diagnosis of lung and gastric cancer via exhaled breath

Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.

Determination of the two-compartment model parameters of exhaled HCN by fast negative photoionization mass spectrometry

Breath exhaled hydrogen cyanide (HCN) has been identified to be associated with several respiratory diseases. Accurately distinguishing the concentration and release rate of different HCN sources is of great value in clinical research. However, there are still significant challenges due to the high adsorption and low concentration characteristics of exhaled HCN. In this study, a two-compartment kinetic model method based on negative photoionization mass spectrometry was developed to simultaneously determine the kinetic parameters including concentrations and release rates in the airways and alveoli. The influences of the sampling line diameter, length, and temperature on the response time of the sampling system were studied and optimized, achieving a response time of 0.2 s. The negative influence of oral cavity-released HCN was reduced by employing a strategy based on anatomical lung volume calculation. The calibration for HCN in the dynamic range of 0.5-100 ppbv and limit of detection (LOD) at 0.3 ppbv were achieved. Subsequently, the experiments of smoking, short-term passive smoking, and intake of bitter almonds were performed to examine the influences of endogenous and exogenous factors on the dynamic parameters of the model method. The results indicate that compared with steady-state concentration measurements, the kinetic parameters obtained using this model method can accurately and significantly reflect the changes in different HCN sources, highlighting its potential for HCN-related disease research.

Quantifying exhaled acetone and isoprene through solid phase microextraction and gas chromatography-mass spectrometry

BACKGROUND

Acetone, isoprene, and other volatile organic compounds (VOCs) in exhaled breath have been shown to be biomarkers for many medical conditions. Researchers use different techniques for VOC detection, including solid phase microextraction (SPME), to preconcentrate volatile analytes prior to instrumental analysis by gas chromatography-mass spectrometry (GC-MS). These techniques include a previously developed method to detect VOCs in breath directly using SPME, but it is uncommon for studies to quantify exhaled volatiles because it can be time consuming due to the need of many external/internal standards, and there is no standardized or widely accepted method. The objective of this study was to develop an accessible method to quantify acetone and isoprene in breath by SPME GC-MS.

RESULTS

A system was developed to mimic human exhalation and expose VOCs to a SPME fiber in the gas phase at known concentrations. VOCs were bubbled/diluted with dry air at a fixed flow rate, duration, and volume that was comparable to a previously developed breath sampling method. Identification of acetone and isoprene through GC-MS was verified using standards and observing overlaps in chromatographic retention/mass spectral fragmentation. Calibration curves were developed for these two analytes, which showed a high degree of linear correlation. Acetone and isoprene displayed limits of detection/quantification equal to 12 ppb/37 ppb and 73 ppb/222 ppb respectively. Quantification results in healthy breath samples (n = 15) showed acetone concentrations spanned between 71 ppb and 294 ppb, and isoprene varied between 170 ppb and 990 ppb. Both concentration ranges for acetone and isoprene in this study overlap with those reported in existing literature.

SIGNIFICANCE

Results indicate the development of a system to quantify acetone and isoprene in breath that can be adapted to diverse sampling methods and instrumental analyses beyond SPME GC-MS.

Breath of Health: spectroscopy-based breath test for the detection of SARS-CoV-2

BACKGROUND

Nucleic acid amplification tests (NAAT) are considered the gold standard for COVID-19 diagnosis. These tests require professional manpower and equipment, long processing and swab sampling which is unpleasant to the patients. Several volatile organic compounds (VOCs) have been identified in the breath of COVID-19 patients. Detection of these VOCs using a breath test could help rapidly identify COVID-19 patients.

OBJECTIVE

Assess the accuracy of 'Breath of Health' (BOH) COVID-19 Fourier-transform infra-red (FTIR) Spectroscopy-based breath test.

METHODS

Breath samples from patients with or without symptoms suggestive for COVID-19 who had NAAT results were collected using Tedlar bags and were blindly analysed using BOH FTIR spectroscopy. BOH Measures several VOCs simultaneously and differentiating positive and negative results. BOH results were compared to NAAT results as gold standard.

RESULTS

Breath samples from 531 patients were analysed. The sensitivity of BOH breath test was found to be 79.5% and specificity was 87.2%. Positive predictive value (PPV) was 74.7% and negative predictive value (NPV) 90.0%. Calculated accuracy rate was 84.8% and area under the curve 0.834. Subgroup analysis revealed that the NPV of patients without respiratory symptoms was superior over the NPV of symptomatic patients (94.7% vs 80.7%, P-value < 0.0001) and PPV of patients with respiratory symptoms outranks the PPV of individuals without symptoms (85.3% vs 69.2%, P-value 0.0196).

CONCLUSION

We found BOH COVID-19 breath test to be a patient-friendly, rapid, non-invasive diagnostic test with high accuracy rate and NPV that could efficiently rule out COVID-19 especially among individuals with low pre-test probability.

Alternative electrolyte solutions for untargeted breath metabolomics using secondary-electrospray ionization high-resolution mass spectrometry

RATIONALE

Secondary-electrospray ionization (SESI) coupled with high-resolution mass spectrometry is a powerful tool for the discovery of biomarkers in exhaled breath. A primary electrospray consisting of aqueous formic acid (FA) is currently used to charge the volatile organic compounds in breath. To investigate whether alternate electrospray compositions could enable different metabolite coverage and sensitivities, the electrospray dopants NaI and AgNO3 were tested.

METHODS

In a proof-of-principle manner, the exhaled breath of one subject was analyzed repeatedly with different electrospray solutions and with the help of a spectral stitching technique. Capillary diameter and position were optimized to achieve proper detection of exhaled breath. The detected features were then compared using formula annotation. Using an evaporation-based gas standard system, the signal response of the different solutions was probed.

RESULTS

Principal component analysis revealed a substantial difference in features detected with AgNO3 . With silver, more sulfur-containing features and more unsaturated hydrocarbon compounds were detected. Furthermore, more primary amines were potentially ionized, as indicated by van Krewelen diagrams. In total, twice as many features were unique to AgNO3 than for other electrospray dopants. Using gas standards at known concentrations, the high sensitivity of FA as a dopant was demonstrated but also indicated alternate sensitivities of the other electrospray solutions.

CONCLUSIONS

This work demonstrated the potential of AgNO3 as a complementary dopant for further biomarker discovery in SESI-based breath analysis.

Establishing breath as a biomarker platform-take home messages from the Breath Biopsy Conference 2023

The annual Breath Biopsy Conference hosted by Owlstone Medical gathers together the leading experts, early career researchers, and physicians working with breath as a biomarker platform for clinical purposes. The current topics in breath research are discussed and presented, and an overarching topical theme is identified and discussed as part of an expert panel to close the conference. The profiling of normal breath composition and the establishment of standards for analyzing breath compared to background signal were two important topics that were major focuses of this conference, as well as important innovative progress that has been made since last year, including the development of a non-invasive breath test for lung cancer and liver disease. This meeting report offers an overview of the key take-home messages from the various presentations, posters, and discussions from the conference.

Effect of Chronic Ethanol Consumption on Exogenous Glucose Metabolism in Rats Using [1-13C], [2-13C], and [3-13C]glucose Breath Tests

The C3 carbon of glucose molecules becomes the C1 carbon of pyruvate molecules during glycolysis, and the C1 and C2 carbons of glucose molecules are metabolized in the tricarboxylic acid (TCA) cycle. Utilizing this position-dependent metabolism of C atoms in glucose molecules, [1-13C], [2-13C], and [3-13C]glucose breath tests are used to evaluate glucose metabolism. However, the effects of chronic ethanol consumption remain incompletely understood. Therefore, we evaluated glucose metabolism in ethanol-fed rats using [1-13C], [2-13C], and [3-13C]glucose breath tests. Ethanol-fed (ERs) and control rats (CRs) (n = 8 each) were used in this study, and ERs were prepared by replacing drinking water with a 16% ethanol solution. We administered 100 mg/kg of [1-13C], [2-13C], or [3-13C]glucose to rats and collected expired air (at 10-min intervals for 180 min). We compared the 13CO2 levels (Δ13CO2, ‰) of breath measured by IR isotope ratio spectrometry and area under the curve (AUC) values of the 13CO2 levels-time curve between ERs and CRs. 13CO2 levels and AUCs after administration of [1-13C]glucose and [2-13C]glucose were lower in ERs than in CRs. Conversely, the AUC for the [3-13C]glucose breath test showed no significant differences between ERs and CRs, although 13CO2 levels during the 110-120 min interval were significantly high in ERs. These findings indicate that chronic ethanol consumption diminishes glucose oxidation without concomitantly reducing glycolysis. Our study demonstrates the utility of 13C-labeled glucose breath tests as noninvasive and repeatable methods for evaluating glucose metabolism in various subjects, including those with alcoholism or diabetes.

Enhancing diagnosis of benign lesions and lung cancer through ensemble text and breath analysis: a retrospective cohort study

Early diagnosis of lung cancer (LC) can significantly reduce its mortality rate. Considering the limitations of the high false positive rate and reliance on radiologists' experience in computed tomography (CT)-based diagnosis, a multi-modal early LC screening model that combines radiology with other non-invasive, rapid detection methods is warranted. A high-resolution, multi-modal, and low-differentiation LC screening strategy named ensemble text and breath analysis (ETBA) is proposed that ensembles radiology report text analysis and breath analysis. In total, 231 samples (140 LC patients and 91 benign lesions [BL] patients) were screened using proton transfer reaction-time of flight-mass spectrometry and CT screening. Participants were randomly assigned to a training set and a validation set (4:1) with stratification. The report section of the radiology reports was used to train a text analysis (TA) model with a natural language processing algorithm. Twenty-two volatile organic compounds (VOCs) in the exhaled breath and the prediction results of the TA model were used as predictors to develop the ETBA model using an extreme gradient boosting algorithm. A breath analysis model was developed based on the 22 VOCs. The BA and TA models were compared with the ETBA model. The ETBA model achieved a sensitivity of 94.3%, a specificity of 77.3%, and an accuracy of 87.7% with the validation set. The radiologist diagnosis performance with the validation set had a sensitivity of 74.3%, a specificity of 59.1%, and an accuracy of 68.1%. High sensitivity and specificity were obtained by the ETBA model compared with radiologist diagnosis. The ETBA model has the potential to provide sensitivity and specificity in CT screening of LC. This approach is rapid, non-invasive, multi-dimensional, and accurate for LC and BL diagnosis.

Optimization of volatile organic compounds sampling from dairy cow exhaled breath using polymer-based solid-phase extraction cartridges for gas chromatographic analysis

We explored appropriate technical setups for the detection of volatile organic compounds (VOCs) from exhaled cow breath by comparing six different polymer-based solid-phase extraction (SPE) cartridges currently on the market for gas chromatography/mass spectrometry (GC-MS) screening. Exhaled breath was sampled at a single timepoint from five lactating dairy cows using six different SPE cartridges (Bond Elut ENV (ENV); Chromabond HRX (HRX); Chromabond HRP (HRP); Chromabond HLB (HLB); Chromabond HR-XCW (XCW) and Chromabond HR-XAW (XAW)). The trapped VOCs were analyzed by dynamic headspace vacuum in-tube extraction GC-MS (DHS-V-ITEX-GC-MS). Depending on the SPE cartridge, we detected 1174-1312 VOCs per cartridge. Most VOCs were alkenes, alkanes, esters, ketones, alcohols, aldehydes, amines, nitriles, ethers, amides, carboxylic acids, alkynes, azoles, terpenes, pyridines, or sulfur-containing compounds. The six SPE cartridges differed in their specificity for the chemical compounds, with the XAW cartridge showing the best specificity for ketones. The greatest differences between the tested SPE cartridges appeared in the detection of specific VOCs. In total, 176 different VOCs were detected with a match factor >80%. The greatest number of specific VOCs was captured by XAW (149), followed by ENV (118), HLB (117), HRP (115), HRX (114), and XCW (114). We conclude that the tested SPE cartridges are suitable for VOC sampling from exhaled cow breath, but the SPE cartridge choice enormously affects the detected chemical groups and the number of detected VOCs. Therefore, an appropriate SPE adsorbent cartridge should be selected according to our proposed inclusion criteria. For targeted metabolomics approaches, the SPE cartridge choice depends on the VOCs or chemical compound groups of interest based on our provided VOC list. For untargeted approaches without information on the animals' metabolic condition, we suggest using multi-sorbent SPE cartridges or multiple cartridges per animal.

Alveolar nitric oxide concentration plays an important role in identifying cough variant asthma and assessing asthma control in children

OBJECTIVE

To study the value of alveolar nitric oxide concentration (CaNO) in the identification and disease control of cough variant asthma.

METHODS

A retrospective study was conducted on cough variant asthma (CVA-Group), nonasthmatic cough (NAC-Group) and healthy control children (C-Group) aged 5-12 years. The exhaled nitric oxide and spirometry test results of the three groups were collected and compared.

RESULTS

A total of 410 children were included in this study, including 190 in the CVA-Group, 183 in the NAC-Group, and 37 in the C-Group. The CaNO values of the CVA-Group [11.40 ppb (8.48-14.25)] were significantly higher than those of the NAC-Group and C-Group (all p values <.05). The MMEF %pred values of the CVA-Group [63.65 (56.28-73.58)] were significantly lower than those of the NAC-Group and C-Group (all p values <.05). FeNO50, JawNO and other spirometry indices (FVC %pred, FEV1%pred, FEV1/FVC %pred) showed no significant difference among the three groups. ROC curve analysis showed that the optimal cutoff point value of CaNO was 9.45 ppb, corresponding to 0.816 sensitivity and 0.736 specificity. Spearman correlation analysis showed a significant negative correlation between the CaNO measurement and CVA control score.

CONCLUSIONS

CaNO can not only help identify CVA early in children aged 5-12 years with chronic cough but is also significantly negatively correlated with the CVA control score.

Exhaled breath condensate contains extracellular vesicles (EVs) that carry miRNA cargos of lung tissue origin that can be selectively purified and analyzed

Lung diseases, including lung cancer, are rising causes of global mortality. Despite novel imaging technologies and the development of biomarker assays, the detection of lung cancer remains a significant challenge. However, the lung communicates directly with the external environment and releases aerosolized droplets during normal tidal respiration, which can be collected, stored and analzsed as exhaled breath condensate (EBC). A few studies have suggested that EBC contains extracellular vesicles (EVs) whose microRNA (miRNA) cargos may be useful for evaluating different lung conditions, but the cellular origin of these EVs remains unknown. In this study, we used nanoparticle tracking, transmission electron microscopy, Western blot analyses and super resolution nanoimaging (ONi) to detect and validate the identity of exhaled EVs (exh-EVs). Using our customizable antibody-purification assay, EV-CATCHER, we initially determined that exh-EVs can be selectively enriched from EBC using antibodies against three tetraspanins (CD9, CD63 and CD81). Using ONi we also revealed that some exh-EVs harbour lung-specific proteins expressed in bronchiolar Clara cells (Clara Cell Secretory Protein [CCSP]) and Alveolar Type II cells (Surfactant protein C [SFTPC]). When conducting miRNA next generation sequencing (NGS) of airway samples collected at five different anatomic levels (i.e., mouth rinse, mouth wash, bronchial brush, bronchoalveolar lavage [BAL] and EBC) from 18 subjects, we determined that miRNA profiles of exh-EVs clustered closely to those of BAL EVs but not to those of other airway samples. When comparing the miRNA profiles of EVs purified from matched BAL and EBC samples with our three tetraspanins EV-CATCHER assay, we captured significant miRNA expression differences associated with smoking, asthma and lung tumor status of our subjects, which were also reproducibly detected in EVs selectively purified with our anti-CCSP/SFTPC EV-CATCHER assay from the same samples, but that confirmed their lung tissue origin. Our findings underscore that enriching exh-EV subpopulations from EBC allows non-invasive sampling of EVs produced by lung tissues.

Comparison of jejunal aspirate culture and methane and hydrogen breath test in the diagnosis of small intestinal bacterial overgrowth

BACKGROUND

Small intestinal bacterial overgrowth (SIBO) is still difficult to diagnose. Quantitative culture of small intestine aspirate is recommended to be the gold standard. The methane and hydrogen breath tests are easily repeatable, sufficiently sensitive and highly specific for SIBO diagnosis. Our goal is to contrast the diagnostic value of the breath tests with jejunal aspiration cultures.

METHODS

40 adult outpatients (age < 60) were enrolled in our study. Randomly, within 2 days, both the methane and the hydrogen breath test and jejunal aspiration culture were performed on each patient and the results of both tests were evaluated and contrasted.

RESULTS

The jejunal culture was positive (105CFU / mL) in 14/40(35%) subjects, the lactulose breath test (LBT) was positive in 18/40 (45%) subjects, and the glucose breath test (GBT) was positive in 12/40 (30%). The GBT showed good agreement (κ = 0.659) and LBT showed poor agreement (κ = 0.588) with the jejunal aspirate culture. The sensitivity, specificity, positive and negative predictive values of LBT/GBT were 85.7/71.4%,76.9/92.3%, 66.6/83.3% and 90.9/85.7%, respectively.

CONCLUSIONS

35% of patients with suspected SIBO are identified using jejunal aspirate cultures. For the identification of SIBO, GBT is more specific than LBT, but has a lower sensitivity. In individuals with suspected SIBO, the breath test should be initially due to its good agreement with the jejunal aspirate culture.

Mixed Potential Type Isoprene Sensor for the Application in Real-Time Monitoring of Biomarker Gases

Monitoring of isoprene in exhaled breath is expected to provide a noninvasive and painless method for dynamic monitoring of physiological and metabolic states during exercise. However, for real-time and portable detection of isoprene, gas sensors have become the best choice for gas detection technology, which are crucial to achieving the goal of anytime, anywhere, human-centered healthcare in the future. Here, we first report a mixed potential type isoprene sensor based on a Gd2Zr2O7 solid electrolyte and a CdSb2O6 sensing electrode, which enables sensitive detection for isoprene with sensitivities of -21.2 mV/ppm and -65.8 mV/decade in the range of 0.05-1 and 1-100 ppm. The sensing behavior of the sensor follows the mixed potential sensing mechanism and was further verified by the electrochemical polarization curves. The significant differentiation between the sensor response to exhaled breath of healthy individuals and simulated breath containing different concentrations of isoprene demonstrates the potential of the sensor for the detection of isoprene in exhaled breath. Simultaneously, monitoring of isoprene during exercise signifies the feasibility of the sensor in dynamic monitoring of physiological indicators, which is not only of great significance for optimizing training and guiding therapeutic exercise intervention in sporting scenarios but also expected to help further reveal the interaction between exercise, muscle, and organ metabolism in medicine.

Ultrasensitive Online NO Sensor Based on a Distributed Parallel Self-Regulating Neural Network and Ultraviolet Differential Optical Absorption Spectroscopy for Exhaled Breath Diagnosis

The concentration of fractional exhaled nitric oxide (FeNO) is closely related to human respiratory inflammation, and the detection of its concentration plays a key role in aiding diagnosing inflammatory airway diseases. In this paper, we report a gas sensor system based on a distributed parallel self-regulating neural network (DPSRNN) model combined with ultraviolet differential optical absorption spectroscopy for detecting ppb-level FeNO concentrations. The noise signals in the spectrum are eliminated by discrete wavelet transform. The DPSRNN model is then built based on the separated multipeak characteristic absorption structure of the UV absorption spectrum of NO. Furthermore, a distributed parallel network structure is built based on each absorption feature region, which is given self-regulating weights and finally trained by a unified model structure. The final self-regulating weights obtained by the model indicate that each absorption feature region contributes a different weight to the concentration prediction. Compared with the regular convolutional neural network model structure, the proposed model has better performance by considering the effect of separated characteristic absorptions in the spectrum on the concentration and breaking the habit of bringing the spectrum as a whole into the model training in previous related studies. Lab-based results show that the sensor system can stably achieve high-precision detection of NO (2.59-750.66 ppb) with a mean absolute error of 0.17 ppb and a measurement accuracy of 0.84%, which is the best result to date. More interestingly, the proposed sensor system is capable of achieving high-precision online detection of FeNO, as confirmed by the exhaled breath analysis.

Volatilomic profiles of gastric juice in gastric cancer patients

Volatilomics is a powerful tool capable of providing novel biomarkers for the diagnosis of gastric cancer. The main objective of this study was to characterize the volatilomic signatures of gastric juice in order to identify potential alterations induced by gastric cancer. Gas chromatography with mass spectrometric detection, coupled with headspace solid phase microextraction as the pre-concentration technique, was used to identify volatile organic compounds (VOCs) released by gastric juice samples collected from 78 gastric cancer patients and two cohorts of controls (80 and 96 subjects) from four different locations (Latvia, Ukraine, Brazil, and Colombia). 1440 distinct compounds were identified in samples obtained from patients and 1422 in samples provided by controls. However, only 6% of the VOCs exhibited an incidence higher than 20%. Amongst the volatiles emitted, 18 showed differences in their headspace concentrations above gastric juice of cancer patients and controls. Ten of these (1-propanol, 2,3-butanedione, 2-pentanone, benzeneacetaldehyde, 3-methylbutanal, butylated hydroxytoluene, 2-pentyl-furan, 2-ethylhexanal, 2-methylpropanal and phenol) appeared at significantly higher levels in the headspace of the gastric juice samples obtained from patients; whereas, eight species showed lower abundance in patients than found in controls. Given that the difference in the volatilomic signatures can be explained by cancer-related changes in the activity of certain enzymes or pathways, the former set can be considered potential biomarkers for gastric cancer, which may assist in developing non-invasive breath tests for the diagnosis of this disease. Further studies are required to elucidate further the mechanisms that underlie the changes in the volatilomic profile as a result of gastric cancer.

Simultaneous real-time detection of fractional exhaled nitric oxide and end-tidal carbon dioxide by quantum cascade laser absorption spectroscopy

The simultaneous detection of fractional exhaled nitric oxide (FeNO) and end-tidal carbon dioxide (ETCO2) is of great importance for the distinguishing and diagnosis of asthma and chronic obstructive pulmonary disease (COPD), providing more comprehensive information on respiratory disorders. This work demonstrates a simultaneous ETCO2 and FeNO detection system based on quantum cascade laser absorption spectroscopy (QCLAS) technology was presented. The system employs wavelength modulation spectroscopy (WMS) technology and the Herriott multi-pass cell, achieving a detection limit of 2.82 ppb for nitric oxide (NO) and 0.05 % for carbon dioxide (CO2). Real-time exhalation measurements were performed on volunteers with varying ETCO2 and FeNO levels, and the results of the test can accurately distinguish whether the corresponding volunteer was healthy, had asthma or COPD. The effect of exhalation flow rate on the concentration of the two gases was explored. A range of expiratory flow rates were tested in the flow rate interval from 1 to 4 L/min, and there was always an inverse relationship between expiratory flow rate and FeNO concentration, but flow rate changes did not affect ETCO2 concentration. The results indicate that this detection system can simultaneously and effectively measure ETCO2 and FeNO concentrations in real-time.

New perspectives on 'Breathomics': metabolomic profiling of non-volatile organic compounds in exhaled breath using DI-FT-ICR-MS

Breath analysis offers tremendous potential for diagnostic approaches, since it allows for easy and non-invasive sample collection. "Breathomics" as one major research field comprehensively analyses the metabolomic profile of exhaled breath providing insights into various (patho)physiological processes. Recent research, however, primarily focuses on volatile compounds. This is the first study that evaluates the non-volatile organic compounds (nVOCs) in breath following an untargeted metabolomic approach. Herein, we developed an innovative method utilizing a filter-based device for metabolite extraction. Breath samples of 101 healthy volunteers (female n = 50) were analysed using DI-FT-ICR-MS and biostatistically evaluated. The characterisation of the non-volatile core breathome identified more than 1100 metabolites including various amino acids, organic and fatty acids and conjugates thereof, carbohydrates as well as diverse hydrophilic and lipophilic nVOCs. The data shows gender-specific differences in metabolic patterns with 570 significant metabolites. Male and female metabolomic profiles of breath were distinguished by a random forest approach with an out-of-bag error of 0.0099. Additionally, the study examines how oral contraceptives and various lifestyle factors, like alcohol consumption, affect the non-volatile breathome. In conclusion, the successful application of a filter-based device combined with metabolomics-analyses delineate a non-volatile breathprint laying the foundation for discovering clinical biomarkers in exhaled breath.

Signal-correction errors in the EasyOne Pro LAB multiple-breath washout device significantly impact outcomes in children and adults

Multiple-breath washout (MBW) is an established technique to assess functional residual capacity (FRC) and ventilation inhomogeneity in the lung. Indirect calculation of nitrogen concentration requires accurate measurement of gas concentrations. To investigate the accuracy of the CO2 concentration and molar mass (MM) values used for the indirect calculation of nitrogen concentration in a commercial MBW device [EasyOne Pro LAB (EOPL), ndd Medizintechnik AG, Switzerland] and its impact on outcomes. We used high-precision gas mixtures to evaluate CO2 and MM sensor output in vivo and in vitro. We developed updated algorithms to correct observed errors and assessed the impact on MBW outcomes and FRC measurement accuracy compared with body plethysmography. The respiratory exchange ratio (RER)-based adjustment of the measured CO2 signal used in the EOPL led to an overestimated CO2 signal (range -0.1% to 1.0%). In addition, an uncorrected dependence on humidity was identified. These combined effects resulted in an overestimation of expired nitrogen concentrations (range -0.7% to 2.6%), and consequently MBW outcomes. Corrected algorithms reduced the mean (SD) cumulative expired volume by 15.8% (9.7%), FRC by 6.6% (3.0%), and lung clearance index by 9.9% (7.6%). Differences in FRC between the EOPL and body plethysmography further increased. Inadequate signal correction causes RER- and humidity-dependent expired nitrogen concentration errors and overestimation of test outcomes. Updated algorithms reduce average signal error, however, RER values far from the population average still cause measurement errors. Despite improved signal accuracy, the updated algorithm increased the difference in FRC between the EOPL and body plethysmography.NEW & NOTEWORTHY We investigated the accuracy of the molar mass (MM) and CO2 sensors of a commercial multiple-breath washout device (ndd Medizintechnik AG, Switzerland). We identified humidity and respiratory exchange ratio-dependent errors that in most measurements resulted in an overestimation of expired nitrogen concentrations, and consequently, MBW results. Functional residual capacity and lung clearance index decreased by 6.6% and 9.9%, respectively. Despite improved signal accuracy, the difference in FRC between the EOPL and body plethysmography increased.

Exhaled breath condensate analysis in horses: A scoping review

Exhaled breath condensate (EBC) collection is a non-invasive sampling method that provides valuable information regarding the health status of the respiratory system by measuring inflammatory mediators, such as pH, hydrogen peroxide, and leukotriene B4. This scoping review aimed to provide an update on the collection and analysis of EBC in horses. A systematic search of three electronic databases, PubMed, Google Scholar, Science Direct, identified 40,978 articles, of which 1590 duplicates were excluded. Moreover, 39,388 articles were excluded because of irrelevance to this review, such as studies on other species, studies on respiratory exhalation, reviews, and theses. Finally, we evaluated 14 articles in this review. Our review revealed significant differences in the collection, storage, and processing of EBC samples, emphasizing the need for standardizing the technique and using specific equipment to improve the interpretation of the results.

Prediction of response to neoadjuvant chemo-immunotherapy in patients with esophageal squamous cell carcinoma by a rapid breath test

BACKGROUND

Neoadjuvant chemo-immunotherapy combination has shown remarkable advances in the management of esophageal squamous cell carcinoma (ESCC). However, the identification of a reliable biomarker for predicting the response to this chemo-immunotherapy regimen remains elusive. While computed tomography (CT) is widely utilized for response evaluation, its inherent limitations in terms of accuracy are well recognized. Therefore, in this study, we present a novel technique to predict the response of ESCC patients before receiving chemo-immunotherapy by testing volatile organic compounds (VOCs) in exhaled breath.

METHODS

This study employed a prospective-specimen-collection, retrospective-blinded-evaluation design. Patients' baseline breath samples were collected and analyzed using high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). Subsequently, patients were categorized as responders or non-responders based on the evaluation of therapeutic response using pathology (for patients who underwent surgery) or CT images (for patients who did not receive surgery).

RESULTS

A total of 133 patients were included in this study, with 91 responders who achieved either a complete response (CR) or a partial response (PR), and 42 non-responders who had stable disease (SD) or progressive disease (PD). Among 83 participants who underwent both evaluations with CT and pathology, the paired t-test revealed significant differences between the two methods (p < 0.05). For the breath test prediction model using breath test data from all participants, the validation set demonstrated mean area under the curve (AUC) of 0.86 ± 0.06. For 83 patients with pathological reports, the breath test achieved mean AUC of 0.845 ± 0.123.

CONCLUSIONS

Since CT has inherent weakness in hollow organ assessment and no other ideal biomarker has been found, our study provided a noninvasive, feasible, and inexpensive tool that could precisely predict ESCC patients' response to neoadjuvant chemo-immunotherapy combination using breath test based on HPPI-TOFMS.

Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose

Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm's achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.

A Clinical Breathomics Dataset

This study entailed a comprehensive GC‒MS analysis conducted on 121 patient samples to generate a clinical breathomics dataset. Breath molecules, indicative of diverse conditions such as psychological and pathological states and the microbiome, were of particular interest due to their non-invasive nature. The highlighted noninvasive approach for detecting these breath molecules significantly enhances diagnostic and monitoring capacities. This dataset cataloged volatile organic compounds (VOCs) from the breath of individuals with asthma, bronchiectasis, and chronic obstructive pulmonary disease. Uniform and consistent sample collection protocols were strictly adhered to during the accumulation of this extensive dataset, ensuring its reliability. It encapsulates extensive human clinical breath molecule data pertinent to three specific diseases. This consequential clinical breathomics dataset is a crucial resource for researchers and clinicians in identifying and exploring important compounds within the patient's breath, thereby augmenting future diagnostic and therapeutic initiatives.

Urea breath test for Helicobacter pylori infection in adult dyspeptic patients: A meta-analysis of diagnostic test accuracy

BACKGROUND

Helicobacter pylori (H. pylori) infection has been well-established as a significant risk factor for several gastrointestinal disorders. The urea breath test (UBT) has emerged as a leading non-invasive method for detecting H. pylori. Despite numerous studies confirming its substantial accuracy, the reliability of UBT results is often compromised by inherent limitations. These findings underscore the need for a rigorous statistical synthesis to clarify and reconcile the diagnostic accuracy of the UBT for the diagnosis of H. pylori infection.

AIM

To determine and compare the diagnostic accuracy of 13C-UBT and 14C-UBT for H. pylori infection in adult patients with dyspepsia.

METHODS

We conducted an independent search of the PubMed/MEDLINE, EMBASE, and Cochrane Central databases until April 2022. Our search included diagnostic accuracy studies that evaluated at least one of the index tests (13C-UBT or 14C-UBT) against a reference standard. We used the QUADAS-2 tool to assess the methodological quality of the studies. We utilized the bivariate random-effects model to calculate sensitivity, specificity, positive and negative test likelihood ratios (LR+ and LR-), as well as the diagnostic odds ratio (DOR), and their 95% confidence intervals. We conducted subgroup analyses based on urea dosing, time after urea administration, and assessment technique. To investigate a possible threshold effect, we conducted Spearman correlation analysis, and we generated summary receiver operating characteristic (SROC) curves to assess heterogeneity. Finally, we visually inspected a funnel plot and used Egger's test to evaluate publication bias.

RESULTS

The titles and abstracts of 4621 studies were screened; 79 articles were retrieved and selected for full-text reading. Finally, 60 studies were included in the diagnostic test accuracy meta-analysis. Our analysis demonstrates superior diagnostic accuracy of 13C-UBT over 14C-UBT, indicated by higher sensitivity (96.60% vs 96.15%), specificity (96.93% vs 89.84%), likelihood ratios (LR+ 22.00 vs 10.10; LR- 0.05 vs 0.06), and area under the curve (AUC; 0.979 vs 0.968). Notably, 13C-UBT's DOR (586.47) significantly outperforms 14C-UBT (DOR 226.50), making it the preferred diagnostic tool for dyspeptic individuals with H. pylori infection. Correlation analysis revealed no threshold effect (13C-UBT: r = 0.48; 14C-UBT: r = -0.01), and SROC curves showed consistent accuracy. Both 13C-UBT and 14C-UBT showed high AUC values (13C-UBT 0.979; 14C-UBT 0.968) near 1.00, reinforcing their excellent accuracy and endorsing both as reliable diagnostic tools in clinical practice.

CONCLUSION

In summary, our study has demonstrated that 13C-UBT has been found to outperform the 14C-UBT, making it the preferred diagnostic approach. Additionally, our results emphasize the significance of carefully considering urea dosage, assessment timing, and measurement techniques for both tests to enhance diagnostic precision. Nevertheless, it is crucial for researchers and clinicians to evaluate the strengths and limitations of our findings before implementing them in practice.

Metabolic insights at the finish line: deciphering physiological changes in ultramarathon runners through breath VOC analysis

Exhaustive exercise can induce unique physiological responses in the lungs and other parts of the human body. The volatile organic compounds (VOCs) in exhaled breath are ideal for studying the effects of exhaustive exercise on the lungs due to the proximity of the breath matrix to the respiratory tract. As breath VOCs can originate from the bloodstream, changes in abundance should also indicate broader physiological effects of exhaustive exercise on the body. Currently, there is limited published data on the effects of exhaustive exercise on breath VOCs. Breath has great potential for biomarker analysis as it can be collected non-invasively, and capture real-time metabolic changes to better understand the effects of exhaustive exercise. In this study, we collected breath samples from a small group of elite runners participating in the 2019 Ultra-Trail du Mont Blanc ultra-marathon. The final analysis included matched paired samples collected before and after the race from 24 subjects. All 48 samples were analyzed using the Breath Biopsy Platform with GC-Orbitrap™ via thermal desorption gas chromatography-mass spectrometry. The Wilcoxon signed-rank test was used to determine whether VOC abundances differed between pre- and post-race breath samples (adjustedP-value < .05). We identified a total of 793 VOCs in the breath samples of elite runners. Of these, 63 showed significant differences between pre- and post-race samples after correction for multiple testing (12 decreased, 51 increased). The specific VOCs identified suggest the involvement of fatty acid oxidation, inflammation, and possible altered gut microbiome activity in response to exhaustive exercise. This study demonstrates significant changes in VOC abundance resulting from exhaustive exercise. Further investigation of VOC changes along with other physiological measurements can help improve our understanding of the effect of exhaustive exercise on the body and subsequent differences in VOCs in exhaled breath.

Breath volatile organic compounds for chronic kidney disease progression monitoring

Breath analysis may provide a convenient and non-invasive method for clinical monitoring of chronic kidney disease (CKD) progression. However, few breath volatile organic compounds (VOCs) indicating progression of CKD have been reported. In this study, we used gas chromatography-mass spectrometry (GC-MS) for untargeted detection of breath VOCs in stage 1, 3, and 5 CKD patients. The results showed that, the levels of breath 4-heptanone, n-octane, and n-dodecane gradually increased from CKD stage 1 to stage 5, and their increasing rates from CKD stage 3 to stage 5 were higher than those from CKD stage 1 to stage 3. Gender, smoking habits, age, and body mass index (BMI) had insignificant impact on the levels of the three breath VOCs. The accuracies of the polynomial support vector machine (SVM) and K-nearest neighbour (KNN) models based on 4-heptanone + n-octane + n-dodecane combination in distinguishing CKD stages 1, 3, and 5 were 76.3% and 72.8%, respectively. The combination of 4-heptanone + n-octane + n-dodecane was superior to any single component for monitoring CKD progression. These discoveries have valuable implications for long-term clinical monitoring of CKD and improving our understanding of CKD.

The optimization and comparison of two high-throughput faecal headspace sampling platforms: the microchamber/thermal extractor and hi-capacity sorptive extraction probes (HiSorb)

Disease detection and monitoring using volatile organic compounds (VOCs) is becoming increasingly popular. For a variety of (gastrointestinal) diseases the microbiome should be considered. As its output is to large extent volatile, faecal volatilomics carries great potential. One technical limitation is that current faecal headspace analysis requires specialized instrumentation which is costly and typically does not work in harmony with thermal desorption units often utilized in e.g. exhaled breath studies. This lack of harmonization hinders uptake of such analyses by the Volatilomics community. Therefore, this study optimized and compared two recently harmonized faecal headspace sampling platforms:High-capacity Sorptive extraction (HiSorb) probesand theMicrochamber thermal extractor (Microchamber). Statistical design of experiment was applied to find optimal sampling conditions by maximizing reproducibility, the number of VOCs detected, and between subject variation. To foster general applicability those factors were defined using semi-targeted as well as untargeted metabolic profiles. HiSorb probes were found to result in a faster sampling procedure, higher number of detected VOCs, and higher stability. The headspace collection using the Microchamber resulted in a lower number of detected VOCs, longer sampling times and decreased stability despite a smaller number of interfering VOCs and no background signals. Based on the observed profiles, recommendations are provided on pre-processing and study design when using either one of both platforms. Both can be used to perform faecal headspace collection, but altogether HiSorb is recommended.

Using mobile technology to influence alcohol-impaired driving risk perceptions and decisions

OBJECTIVE

Despite significant individual and societal risk, alcohol-impaired driving (AID) remains prevalent in the United States. Our aim was to determine whether breathalyzer-cued warning messages administered via mobile devices in the natural drinking environment could influence real-world AID cognitions and behaviors.

METHOD

One hundred twenty young adults (53% women; mean age = 24.7) completed 6 weeks of ecological momentary assessment (EMA) and provided breathalyzer samples using a BACtrack Mobile Pro linked to their mobile device. On mornings after drinking episodes, participants reported their driving activities from the previous evening (787 episodes). Participants were randomly assigned to receive warning messages if they reached a breath alcohol concentration (BrAC) ≥ .05, or no messages. Participants in the warnings condition reported their willingness to drive and perceived danger of driving at EMA prompts (1,541 reports).

RESULTS

We observed a significant effect of condition, such that the association between cumulative AID engagement and driving after reaching a BrAC of .05 was dampened among individuals in the warnings condition, compared to those in the no warnings condition. Receiving a warning message was associated with increased momentary perceived danger of driving and decreased willingness to drive.

CONCLUSIONS

We found that BrAC-cued warning messages reduced the probability of AID and willingness to drive while impaired, and increased the perceived danger of driving after drinking. These results serve as proof-of-concept for the use of mobile technology to deliver an adaptive just-in-time intervention to reduce the probability of AID. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Elevated glutathione in researchers exposed to engineered nanoparticles due to potential adaptation to oxidative stress

Aim: To find a practical biomonitoring method for researchers exposed to nanoparticles causing oxidative stress. Methods: In a continuation of a study in 2016-2018, biological samples (plasma, urine and exhaled breath condensate [EBC]) were collected in 2019-2020 from 43 researchers (13.8 ± 3.0 years of exposure) and 45 controls. Antioxidant status was assessed using glutathione (GSH) and ferric-reducing antioxidant power, while oxidative stress was measured as thiobarbituric acid reactive substances, all using spectrophotometric methods. Researchers' personal nanoparticle exposure was monitored. Results: Plasma GSH was elevated in researchers both before and after exposure (p < 0.01); postexposure plasma GSH correlated with nanoparticle exposure, and GSH in EBC increased. Conclusion: The results suggest adaptation to chronic exposure to nanoparticles, as monitored by plasma and EBC GSH.

A standardised nutritional drink as a test meal for the 13 C mixed triglyceride breath test for pancreatic exocrine insufficiency: A randomised, two-arm crossover comparative study

BACKGROUND

The 13 C mixed triglyceride breath test (13 C MTGT) is a diagnostic test for pancreatic exocrine insufficiency (PEI). It is poorly standardised with much heterogeneity of the test meal, the commonest being toast and butter. A standardised oral nutritional supplement that could be easily transported, stored and made up would be valuable for making this test accessible outside of specialist centres.

METHODS

A prospective, randomised, two-arm crossover study of different test meals was carried out in 14 healthy controls. The 13 C MTGT was performed in identical conditions on two separate days. Two test meals were given in random order, either standard (toast and butter) or novel (oral nutritional supplement), with 250 mg of 13 C-labelled mixed triglyceride incorporated. Breath samples were taken postprandially to calculate cumulative percentage dose recovery (cPDR) of 13 C at 6 h.

RESULTS

All 14 participants completed both arms of the study with no protocol deviations. The mean cPDR was 39.39% (standard deviation [SD] 5.19) for the standard test meal and 39.93% (SD 5.20) for the novel test meal. A one-way repeated measures analysis of variance (ANOVA) found no significant difference in cPDR between the two meals, F(1, 13) = 0.18, p = 0.68 (minimum detectable difference of 0.81 at 80% power).

CONCLUSION

This study demonstrates that a standardised oral nutritional supplement can be used without compromising 13 C recovery. Using this test meal provides a standardised dietary stimulus to the pancreas, avoiding possible variation in quantity of dietary components with other test meals. Further, the ease of use of this method would help establish the 13 C MTGT test more widely.

Digital image colorimetry for determination of ethanol in exhaled breath condensate

Aim: This study aimed to develop a colorimetric approach for quantifying ethanol using smartphone image analysis. Method: This research presents a straightforward smartphone-based colorimetric sensor that efficiently measures ethanol levels in exhaled breath condensate (EBC) samples. The process involved changing the acidic dichromate color in an ethanolic solution, followed by image analysis. Results: The results showed that this method was able to estimate ethanol concentrations in the range of 300-1500 and 1600-8000 μg ml-1 in EBC. Conclusion: This study was a follow-up study on the previous work published for the determination of ethanol in EBC samples and highlights the potential benefits of using digital images and smartphone applications for ethanol determination in biological samples.

Impact of Reanalysis of Nitrogen Multiple-Breath Washout on its Relationship with Chest Magnetic Resonance Imaging Findings in Clinically Stable and Pulmonary Exacerbated Children with Cystic Fibrosis

RATIONALE

Multiple-breath washout (MBW)-derived lung clearance index (LCI) detects lung disease in children with cystic fibrosis (CF). Correction of a cross-talk error in the software of the MBW device Exhalyzer D in a new software version has generated significant interest regarding its impact on previous MBW findings. Since LCI and chest magnetic resonance imaging (MRI) correlated before in CF children, this study aims to reassess previous MBW data after correction.

PATIENTS/METHODS

Reanalysis of the main findings from a previously published study comparing MBW and MRI in a pediatric CF cohort by reassessment of nitrogen (N2) MBW of 61 stable children with CF, 75 age-matched healthy controls (HC), and 15 CF children with pulmonary exacerbation (PEx) in the corrected software version.

RESULTS

The corrected LCI (N2LCIcor) decreased in the entire cohort (-17.0 (11.2)%), HC (-8.5 (8.2)%), stable CF children (-22.2 (11.1)%), and within the PEx group at baseline, at PEx and after antibiotic therapy (-21.5 (7.3)%; -22.5 (6.1)%; -21.4 (6.6)%; all P<0.01). N2LCIcor and N2LCIpre correlated with chest MRI scores in stable CF (r=0.70 to 0.84; all P<0.01) without a significant difference between N2LCIcor and N2LCIpre. Change in LCI from baseline to PEx and from PEx to after therapy decreased from N2LCIpre to N2LCIcor, but these changes remained significant (all P=0.001).

DISCUSSION/CONCLUSIONS

Our results indicate that N2LCIcor is significantly lower than N2LCIpre, but key results published in the original study demonstrating N2MBW and MRI as complementary methods for clinical surveillance in children with CF remain unaffected.

Combining Thermal Desorption with Selected Ion Flow Tube Mass Spectrometry for Analyses of Breath Volatile Organic Compounds

An instrument integrating thermal desorption (TD) to selected ion flow tube mass spectrometry (SIFT-MS) is presented, and its application to analyze volatile organic compounds (VOCs) in human breath is demonstrated for the first time. The rationale behind this development is the need to analyze breath samples in large-scale multicenter clinical projects involving thousands of patients recruited in different hospitals. Following adapted guidelines for validating analytical techniques, we developed and validated a targeted analytical method for 21 compounds of diverse chemical class, chosen for their clinical and biological relevance. Validation has been carried out by two independent laboratories, using calibration standards and real breath samples from healthy volunteers. The merging of SIFT-MS and TD integrates the rapid analytical capabilities of SIFT-MS with the capacity to collect breath samples across multiple hospitals. Thanks to these features, the novel instrument has the potential to be easily employed in clinical practice.

Breath Fingerprint of Colorectal Cancer Patients Based on the Gas Chromatography-Mass Spectrometry Analysis

The human body emits a multitude of volatile organic compounds (VOCs) via tissues and various bodily fluids or exhaled breath. These compounds collectively create a distinctive chemical profile, which can potentially be employed to identify changes in human metabolism associated with colorectal cancer (CRC) and, consequently, facilitate the diagnosis of this disease. The main goal of this study was to investigate and characterize the VOCs' chemical patterns associated with the breath of CRC patients and controls and identify potential expiratory markers of this disease. For this purpose, gas chromatography-mass spectrometry was applied. Collectively, 1656 distinct compounds were identified in the breath samples provided by 152 subjects. Twenty-two statistically significant VOCs (p-xylene; hexanal; 2-methyl-1,3-dioxolane; 2,2,4-trimethyl-1,3-pentanediol diisobutyrate; hexadecane; nonane; ethylbenzene; cyclohexanone; diethyl phthalate; 6-methyl-5-hepten-2-one; tetrahydro-2H-pyran-2-one; 2-butanone; benzaldehyde; dodecanal; benzothiazole; tetradecane; 1-dodecanol; 1-benzene; 3-methylcyclopentyl acetate; 1-nonene; toluene) were observed at higher concentrations in the exhaled breath of the CRC group. The elevated levels of these VOCs in CRC patients' breath suggest the potential for these compounds to serve as biomarkers for CRC.

Exhaled breath is found to be better than blood samples for determining propofol concentrations in the brain tissues of rats

The correlation between propofol concentration in exhaled breath (CE) and plasma (CP) has been well-established, but its applicability for estimating the concentration in brain tissues (CB) remains unknown. Given the impracticality of directly sampling human brain tissues, rats are commonly used as a pharmacokinetic model due to their similar drug-metabolizing processes to humans. In this study, we measuredCE,CP, andCBin mechanically ventilated rats injected with propofol. Exhaled breath samples from the rats were collected every 20 s and analyzed using our team's developed vacuum ultraviolet time-of-flight mass spectrometry. Additionally, femoral artery blood samples and brain tissue samples at different time points were collected and measured using high-performance liquid chromatography mass spectrometry. The results demonstrated that propofol concentration in exhaled breath exhibited stronger correlations with that in brain tissues compared to plasma levels, suggesting its potential suitability for reflecting anesthetic action sites' concentrations and anesthesia titration. Our study provides valuable animal data supporting future clinical applications.

Real-time breath analysis towards a healthy human breath profile

The direct analysis of molecules contained within human breath has had significant implications for clinical and diagnostic applications in recent decades. However, attempts to compare one study to another or to reproduce previous work are hampered by: variability between sampling methodologies, human phenotypic variability, complex interactions between compounds within breath, and confounding signals from comorbidities. Towards this end, we have endeavored to create an averaged healthy human 'profile' against which follow-on studies might be compared. Through the use of direct secondary electrospray ionization combined with a high-resolution mass spectrometry and in-house bioinformatics pipeline, we seek to curate an average healthy human profile for breath and use this model to distinguish differences inter- and intra-day for human volunteers. Breath samples were significantly different in PERMANOVA analysis and ANOSIM analysis based on Time of Day, Participant ID, Date of Sample, Sex of Participant, and Age of Participant (p< 0.001). Optimal binning analysis identify strong associations between specific features and variables. These include 227 breath features identified as unique identifiers for 28 of the 31 participants. Four signals were identified to be strongly associated with female participants and one with male participants. A total of 37 signals were identified to be strongly associated with the time-of-day samples were taken. Threshold indicator taxa analysis indicated a shift in significant breath features across the age gradient of participants with peak disruption of breath metabolites occurring at around age 32. Forty-eight features were identified after filtering from which a healthy human breath profile for all participants was created.

How well does your e-nose detect cancer? Application of artificial breath analysis for performance assessment

Comparing electronic nose (e-nose) performance is a challenging task because of a lack of standardised method. This paper proposes a method for defining and quantifying an indicator of the effectiveness of multi-sensor systems in detecting cancers by artificial breath analysis. To build this method, an evaluation of the performances of an array of metal oxide sensors built for use as a lung cancer screening tool was conducted. Breath from 20 healthy volunteers has been sampled in fluorinated ethylene propylene sampling bags. These healthy samples were analysed with and without the addition of nine volatile organic compound (VOC) cancer biomarkers, chosen from literature. The concentration of the VOC added was done in increasing amounts. The more VOC were added, the better the discrimination between 'healthy' samples (breath without additives) and 'cancer' samples (breath with additives) was. By determining at which level of concentration the e-nose fails to reliably discriminate between the two groups, we estimate its ability to well predict the presence of the disease or not in a realistic situation. In this work, a home-made e-nose is put to the test. The results underline that the biomarkers need to be about 5.3 times higher in concentration than in real breath for the home-made nose to tell the difference between groups with a sufficient confidence.

Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data

BACKGROUND

Breath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported.

OBJECTIVE

The objectives of this study were to assess the accuracy of electronic nose screening for lung cancer with imbalanced learning and to select the best mechanical learning algorithm.

METHODS

We conducted a case‒control study that included patients with lung cancer and healthy controls and analyzed metabolites in exhaled breath using a carbon nanotube sensor array. The study used five machine learning algorithms to build predictive models and a synthetic minority oversampling technique to address imbalanced data. The diagnostic accuracy of lung cancer was assessed using pathology reports as the gold standard.

RESULTS

We enrolled 190 subjects between 2020 and 2023. A total of 155 subjects were used in the final analysis, which included 111 lung cancer patients and 44 healthy controls. We randomly divided samples into one training set, one internal validation set, and one external validation set. In the external validation set, the summary sensitivity was 0.88 (95% CI 0.84-0.91), the summary specificity was 1.00 (95% CI 0.85-1.00), the AUC was 0.96 (95% CI 0.94-0.98), the pAUC was 0.92 (95% CI 0.89-0.96), and the DOR was 207.62 (95% CI 24.62-924.64).

CONCLUSION

Electronic nose screening for lung cancer is highly accurate. The support vector machine algorithm is more suitable for analyzing chemical sensor data from electronic noses.

Validation of the PyloPlus Urea Breath Test System in pediatric patients

IMPORTANCE

For the diagnosis and post-treatment monitoring of H. pylori infection, non-invasive testing methodologies improve patient comfort, particularly for children. Previously, only the BreathTek UBT had FDA approval for use in pediatric patients and required an adjustment calculation based on age, height, and weight of the patient. The purpose of this study was to evaluate the performance of the PyloPlus UBT assay in a pediatric population.

Accuracy of transdermal alcohol monitoring devices in a laboratory setting

The development of transdermal alcohol sensors (TASs) presents a new method to monitor alcohol consumption with the ability to objectively measure data 24/7. We aimed to evaluate the accuracy of two TASs (BACtrack Skyn and Smart Start BARE) in a laboratory setting. Thirty-two adults received a dose of ethanol 0.56 g/kg body weight as a 20% solution while wearing the two TASs and provided Breath Alcohol Concentration (BrAC) measurements for 3.5 h postalcohol consumption. Pearson's correlations and repeated measures analysis of variance tests were conducted on the peak, time-to-peak, and area under the curve data. Bland-Altman plots were derived. A time series analysis and cross-correlations were conducted to adjust for time lag. Both TASs were able to detect alcohol and increase within 20 min. BrAC peaked significantly quicker than Skyn and BARE. BrAC and Skyn peaks were negatively significantly correlated (r = -0.381, P = .035, n = 31), while Skyn and BARE peaks were positively significantly correlated (r = 0.380, P = .038, n = 30). Repeated measures analysis of variance found a significant difference between BrAC, Skyn, and BARE (F(1.946, 852.301) = 459.873, P < .001)). A time series analysis found when BrAC-Skyn and BrAC-BARE were adjusted for the delay to peak, and there was still a significant difference. Failure rates: 1.7% (Skyn) and 4.8% (BARE). Some evidence was obtained for TAS validity as both consistently detected alcohol. Failure rates and time lag show improvements in older device generations. However, neither TAS presented strong equivalence to the breathalyser even when the lag time was adjusted. With further testing and technology advancements, TAS could be a potential alcohol monitoring tool. Two of the newest TAS devices were worn in laboratory conditions for one afternoon to compare their accuracy of alcohol monitoring to a breathalyser. Findings suggest that the two TASs (BACtrack Skyn and SmartStart BARE) recorded significantly similar data postalcohol consumption, but not with the breathalyser.

Non-invasive detection of renal disease biomarkers through breath analysis

Breath biomarkers are substances found in exhaled breath that can be used for non-invasive diagnosis and monitoring of medical conditions, including kidney disease. Detection techniques include mass spectrometry (MS), gas chromatography (GC), and electrochemical sensors. Biosensors, such as GC-MS or electronic nose (e-nose) devices, can be used to detect volatile organic compounds (VOCs) in exhaled breath associated with metabolic changes in the body, including the kidneys. E-nose devices could provide an early indication of potential kidney problems through the detection of VOCs associated with kidney dysfunction. This review discusses the sources of breath biomarkers for monitoring renal disease during dialysis and different biosensor approaches for detecting exhaled breath biomarkers. The future of using various types of biosensor-based real-time breathing diagnosis for renal failure is also discussed.

Breath-based biosensors and system development for noninvasive detection of diabetes: A review

BACKGROUND AND AIMS

In recent years, noninvasive techniques are becoming conspicuous for diabetes detection. Sweat, tear, saliva, urine and breath-based methods showing prominent results in breath acetone detection which is considered as a biomarker of diabetes. A concrete relationship between breath acetone and BG helps in the development of devices for diabetes detection.

METHODS

The primary source for this study includes scholarly publications that primarily focus on the development of biosensors and systems for diabetes detection using acetone present in breath. Articles were analysed to examine various types of biosensors with their sensing materials to provide acetone detection limits. Recent noninvasive systems and products have been investigated and determine the relationship between breath acetone and BG levels.

RESULTS

Breath-based biosensor technologies are capable for diabetes detection. The acetone biosensor detection ranges from 100 ppb to 100 ppm, and it can applicable from room temperature to 400 °C. In healthy volunteers, acetone level ranges from 0.32 to 2.19 ppm, while patients with diabetes exhibit a wider range of 0.22-21 ppm depending on the biosensor, detection method, and clinical circumstances of patients and lab conditions.

CONCLUSION

This manuscript presents an extensive analysis of breath-based biosensors and their potential for detection of diabetes. Acetone detection methods are promising but unable to provide concrete correlation between breath acetone and blood glucose levels. The present study motivates the continued research and development of biosensors, and electronic devices to provide linear relationship of breath acetone and BG for noninvasive diabetes detection applications.

Reanalysis of N2-lung clearance index and the comparison to SF6-lung clearance index and magnetic resonance imaging

Since the finding and correction of an error in previous spiroware software versions commonly used with the Exhalyzer D for multiple-breath washout (MBW) analysis, there has been an ongoing discussion about its impact on MBW results. In this study, we reanalyzed previously published findings with the corrected spiroware version 3.3.1. In total, 31 infants and preschool children with cystic fibrosis (CF) (mean age 2.3 ± 0.8 years) and 20 healthy controls (mean age 2.3 ± 1.1 years) underwent consecutive sulfure hexafluoride (SF6)- and nitrogen (N2)-MBW. In addition, children with CF underwent chest magnetic resonance imaging (MRI) on the same day. After reanalysis of MBW data, the corrected N2-lung clearance index (LCI) decreased by 10-15% in both groups (P = 0.001) but remained significantly higher than the SF6-LCI (P<0.01). Diagnostic agreement between the MBW results remained moderate with a persistent correlation between SF6- and N2-MBW. The corrected upper limit of normal of the N2-LCI changed classification of nine children with CF, eight of which were within the normal range after correction. The correlation between the different LCI values and the chest MRI scores remained significant with strongest correlation with the MRI perfusion score. Consequently, the corrected N2-LCI is significantly lower than the previous N2-LCI, but key results published before are not affected by the reanalysis.

Is an intraoperative liver function assessment possible? Application of the 13C-methacetin-breath-test during major liver resections - a pilot study

BACKGROUND

As prevention of posthepatectomy-liver-failure is crucial, there is need of dynamic assessment of liver function, even intraoperatively. 13C-methacetin-breath-test estimates the organ's microsomal functional capacity. This is its first intraoperative evaluation in major liver surgery.

METHODS

30 patients planed for resection of ≥3 liver segments, between March-November 2019, were prospectively enrolled in this "single-center", pilot study. Using the 13C-methacetin-breath-test, liver function was assessed four times: preoperatively, intraoperatively before and after resection and postoperatively. The resulted maximum-liver-function-capacity (LiMAx)-values and delta-over-baseline (DOB)-curves were compared, further analyzed and correlated to respective liver volumes.

RESULTS

The intraoperative LiMAx-values before resection were mostly lower than the preoperative ones (-11.3% ± 28%). The intraoperative measurements after resection resulted to mostly higher values than the postoperative ones (42.35% ± 46.19%). Pharmacokinetically, an interference between the two intraoperative tests was observed. There was no strong correlation between residual liver volume and function with a percentual residual-LiMAx mostly lower than the percentual residual volume (-17.7% ± 4.1%).

CONCLUSIONS

Intraoperative application of the 13C-methacetin-breath-test during major liver resections seems to deliver lower values than the standard preoperative test. As multiple intraoperative tests interfere significantly to each other, a single intraoperative measurement is suggested. Multicentric standardized measurements could define the "normal" range for intraoperative measurements and control their predictive value.

ERS technical standard: Global Lung Function Initiative reference values for exhaled nitric oxide fraction (F ENO50 )

BACKGROUND

Elevated exhaled nitric oxide fraction at a flow rate of 50 mL·s-1 (F ENO50 ) is an important indicator of T-helper 2-driven airway inflammation and may aid clinicians in the diagnosis and monitoring of asthma. This study aimed to derive Global Lung Function Initiative reference equations and the upper limit of normal for F ENO50 .

METHODS

Available individual F ENO50 data were collated and harmonised using consensus-derived variables and definitions. Data collected from individuals who met the harmonised definition of "healthy" were analysed using the generalised additive models of location, scale and shape (GAMLSS) technique.

RESULTS

Data were retrospectively collated from 34 782 individuals from 34 sites in 15 countries, of whom 8022 met the definition of healthy (19 sites, 11 countries). Overall, height, age and sex only explained 12% of the between-subject variability of F ENO50 (R2=0.12). F ENO device was neccessary as a predictor of F ENO50 , such that the healthy range of values and the upper limit of normal varied depending on which device was used. The range of F ENO50 values observed in healthy individuals was also very wide, and the heterogeneity was partially explained by the device used. When analysing a subset of data in which F ENO50 was measured using the same device and a stricter definition of health (n=1027), between-site heterogeneity remained.

CONCLUSION

Available F ENO50 data collected from different sites using different protocols and devices were too variable to develop a single all-age reference equation. Further standardisation of F ENO devices and measurement are required before population reference values might be derived.

Serum interleukin-33 combined with FEF75% z-score and FeNO improves the diagnostic accuracy of asthma in children

OBJECTIVE

To investigate the diagnostic efficacy of serum IL-33 single indicator and combined indicators for asthma in children.

METHODS

132 children were initially diagnosed with asthma during acute exacerbation and 100 healthy children were included. Serum IL-33 concentration differences were compared between asthmatic and normal children. Correlations between IL-33 with pulmonary function parameters, FeNO, peripheral blood EOS counts and serum total IgE were analyzed in asthmatic children. ROC curves were used to assess IL-33 diagnostic efficacy and its combined indicators. To prevent overfitting of the predictive model, the hold-out cross-validation method was used.

RESULTS

(1) Serum IL-33 concentrations were significantly higher in children with asthma than in normal children (p < 0.001). (2) IL-33 concentration was negatively correlated with FVC z-score, FEV1 z-score and FEF75% z-score in asthmatic children (p < 0.05). (3) The area under the ROC curve of IL-33 was 0.821, which was higher than those of FeNO, FVC z-score, and FEV1 z-score. (4) Cross-validation of the combined indicators showed that IL-33 significantly improved asthma diagnostic efficacy. The combination of IL-33, FEF75% z-score, and FeNO showed the highest diagnostic efficacy, with the AUC, sensitivity, and specificity of the combined indicator being 0.954, 90.1%, and 89. 0%, respectively, and good extrapolation of the predictive model.

CONCLUSION

Serum IL-33 is higher in children with asthma and increases with the severity of pulmonary ventilation obstruction. A single indicator of serum IL-33 demonstrates moderate diagnostic accuracy, and its combination with FEF75% z-score and FeNO significantly improves the diagnostic accuracy in childhood asthma.

A novel coupling technique based on thermal desorption gas chromatography with mass spectrometry and ion mobility spectrometry for breath analysis

Exhaled breath analysis is evolving into an increasingly important non-invasive diagnostic tool. Volatile organic compounds (VOCs) in breath contain information about health status and are promising biomarkers for several diseases, including respiratory infections caused by bacteria. To monitor the composition of VOCs in breath or the emission of VOCs from bacteria, sensitive analytical techniques are required. Next to mass spectrometry, ion mobility spectrometry (IMS) is considered a promising analytical tool for detecting gaseous analytes in the parts per billion by volume to parts per trillion by volume range. This work presents a new, dual coupling of thermal desorption gas chromatography to a quadrupole mass spectrometer (MS) and an IMS by operating a simple splitter. Nearly identical retention times can be reached in the range of up to 30 min with slight deviations of 0.06 min-0.24 min. This enables the identification of unknown compounds in the IMS chromatogram using unambiguous mass spectral identification, as there are still no commercially available databases for IMS. It is also possible to discriminate one of the detectors using the splitter to improve detection limits. Using a test liquid mixture of seven ketones, namely 2-butanone, 2-pentanone, 2-hexanone, 2-heptanone, 2-octanone, 2-nonanone, and 2-decanone with a concentration of 0.01 g l-1reproducibilities ranging from 3.0% to 7.6% for MS and 2.2%-5.3%, for IMS were obtained, respectively. In order to test the system optimized here for the field of breath analysis, characteristic VOCs such as ethanol, isoprene, acetone, 2-propanol, and 1-propanol were successfully identified in exhaled air using the dual detector system due to the match of the corresponding IMS, and MS spectra. The presented results may be considered to be a starting point for the greater use of IMS in combination with MS within the medical field.

Photoacoustic Spectroscopy-Based Breath Analysis for SIBO

Breath hydrogen (H2) and methane (CH4) monitoring play an important role in the diagnosis of gastrointestinal disorders, such as lactose intolerance and small intestinal bacterial overgrowth (SIBO). In this paper, the photoacoustic spectroscopy method is used for H2 gas and CH4 gas detection. We present a novel approach for H2 gas concentration measurement, which is the linear relationship between the resonant frequency of breath carbon dioxide (CO2) and the H2 concentration in a resonant photoacoustic cell. Experimental results show that the minimum detectable limits of H2, CH4, and CO2 are calculated to be 8.86, 0.56, and 145.14 ppm, respectively, which can meet the requirements of breath diagnosis.

Impact of thermal desorption tubes on the variability of exhaled breath data

Due to the overall low abundance of volatile compounds in exhaled breath, it is necessary to preconcentrate the sample prior to traditional thermal desorption (TD) gas chromatography mass spectrometry analysis. While certain aspects of TD tubes, such as volatile storage, have been evaluated, many aspects remain uncharacterized. Two common TD tubes, Tenax TA and Biomonitoring 5TD tubes, were evaluated for background content and flow rate variability. The data illustrate that the Biomonitoring 5TD tubes have the highest number (23) and abundance of background contamination greater than 3x the mean noise when compared to Tenax TA (13) and empty tubes (9). Tentative identifications of the compounds in the background contamination experiment show that greater than 59% (16/27) of the compounds identified have been reported in the breath literature. The data illustrate the TD tube background abundance could account for more than 70% of the chromatographic signal from exhaled breath for these select compounds. Flow rate measurements of 200 Tenax TA and 200 Biomonitoring 5TD tubes show a large range in measured flow rates among the TD tubes (Tenax: 252.9-284.0 ml min-1, 5TD: 220.6-255.1 ml min-1). Finally, TD tubes of each type, Tenax TA and Biomonitoring 5TD, previously established to have high, medium, and low flow rates, show insignificant differences (p> 0.05) among the tubes of different flow rates, using both gas standards and an exhaled breath from a peppermint experiment. Collectively, these results establish overall background compounds attributed to each TD tube type tested. Additionally, while measured flow rate variability is present and plausibly impacts exhaled breath results, the data demonstrate no statistically significant difference was observed between tubes showing high, medium, and low flow rates from two separate sample types.

Detection of medically relevant volatile organic compounds with graphene field-effect transistors and separated by low-frequency spectral and time signatures

Exhaled human breath contains a mixture of gases including nitrogen, oxygen, carbon dioxide, water vapour and low molecular weight volatile organic compounds (VOCs). Different VOCs detected in human breath condensate have been recently related to several metabolic processes occurring inside body tissues in the pathological state, as candidate biomarkers for monitoring conditions such as lung injury, airway inflammation, immunity dysfunction, infection, and cancer. Current techniques for detecting these compounds include several types of mass spectroscopy, which are highly costly, time-consuming and dependent on trained personnel for sample analysis. The need for fast and label-free biosensors is paving the way towards the design of novel and portable electronic devices for point-of-care diagnosis with VOCs such as E-noses, and based on the measurement of signal signatures derived from their chemical composition. In this paper, we propose a device for VOC detection that was tested inside a controlled gas flow setup, resorting to graphene field-effect transistors (GFETs). Electrical measurements from graphene directly exposed to nitrogen plus VOC vapours involved cyclic measurements for the variation of graphene's resistance and low-frequency spectral noise in order to obtain distinctive signatures of the tested compounds in the time and frequency domains related, respectively, to Gutmann's theory for donor-acceptor chemical species and spectral sub-band analysis.

A pilot study of non-invasive diagnostic tools to detect Helicobacter pylori infection and peptic ulcer disease

Helicobacter pylori (H. pylori) infection can lead to various digestive system diseases, making accurate diagnosis crucial. However, not all available tests are equally non-invasive and sensitive. This study aimed to compare the efficacy of non-invasive and invasive diagnostic tools for H. pylori infection and assess their correlation with esophagogastroduodenoscopic (EGD) findings. The study utilized the Campylobacter-Like Organism (CLO) test, serum anti-HP IgG blood test, and C-13-urea breath test (UBT) to diagnose H. pylori infection. A total of 100 patients with peptic ulcer symptoms, including 45 males and 55 females, were recruited for the study. Symptomatic patients between the ages of 20-70, eligible for EGD examination, were enrolled. Each diagnostic test and any combination of two positive tests were considered the reference standard and compared against the other diagnostic methods. Additionally, the relationship between these diagnostic tests and EGD findings was evaluated. Among the participants, 74.0% were diagnosed with peptic ulcer disease through EGD. The UBT demonstrated the highest Youden's index, ranging from 58 to 100%, against all the non-invasive tests. The IgG blood test displayed the highest sensitivity at 100%, with a specificity of 60-70%. On the other hand, the CLO test exhibited the highest specificity at 100% and a sensitivity of 50-85%. Furthermore, only the CLO test showed a significant association with esophageal ulcers (p-value = 0.01). The IgG blood test holds promise as a primary screening tool due to its exceptional sensitivity. While the UBT is relatively expensive, its non-invasive nature and high sensitivity and specificity make it a potential standalone diagnostic test for H. pylori infection. Moreover, the noteworthy negative correlation between the CLO test and esophageal ulcers provides evidence of the differing effects of H. pylori infection on antral-predominant and corpus-predominant gastritis.