The Fractional exhaled Nitric Oxide (FeNO)- test as add-on test in the diagnostic work-up of asthma: a study protocol.

BACKGROUND: Asthma is a common disease characterized by chronic inflammation of the lower airways, bronchial hyperactivity, and (reversible) airway obstruction. The Global Initiative of Asthma Guideline recommends a flowchart to diagnose asthma with first-step spirometry with reversibility and a bronchial challenge test (BPT) with histamine or methacholine as a second step [1]. The BPT is considered burdensome, time-consuming for patients and staff, can cause side effects, and is expensive. In addition, this test strongly encumbers lung function capacity. Elevated Nitric Oxide (NO) is associated with airway eosinophilic inflammation in asthma patients and can be measured in exhaled air with the Fractional exhaled (Fe) NO-test. This low-burden FeNO-test could be used as an 'add-on' test in asthma diagnostics [2, 3]. METHODS AND ANALYSIS: This multi-center prospective study (Trial number: NCT06230458) compares the 'standard asthma diagnostic work-up' (spirometry with reversibility and BPT) to the 'new asthma diagnostics work-up' (FeNO-test as an intermediate step between the spirometry with reversibility and the BPT), intending to determine the impact of the FeNO-based strategy, in terms of the number of avoided BPTs, cost-effectiveness and reduced burden to the patient and health care. The cost reduction of incorporating the FeNO-test in the new diagnostic algorithm will be established by the number of theoretically avoided BPT. The decrease in burden will be studied by calculating differences in the Visual Analogue Scale (VAS) -score and Asthma Quality of Life Questionnaire (AQLQ) -score after the BPT and FeNO-test with an independent T-test. The accuracy of the FeNO-test will be calculated by comparing the FeNO-test outcomes to the (gold standard) BPTs outcomes in terms of sensitivity and specificity. The intention is to include 171 patients. ETHICS AND DISSEMINATION: The local medical ethics committee approved the proposed study and is considered a low-burden and risk-low study. The local medical ethics committee registration number: R23.005. STRENGTHS AND LIMITATIONS OF THIS STUDY: Strengths: This is the first study that investigates the value of the FeNO-test (cut off ≥ 50 ppb) as an add-on test, to determine the impact of the FeNO-based strategy, in terms of the number of avoided BPTs, cost-effectiveness, and reduced burden on the patient and health care. LIMITATIONS: High FeNO levels may also be observed in other diseases such as eosinophilic chronic bronchitis and allergic rhinitis. The FeNO-test can be used to rule in a diagnosis of asthma with confidence, however, due to the poor sensitivity it is not suitable to rule out asthma.

Colorectal Cancer Diagnosis through Breath Test Using a Portable Breath Analyzer-Preliminary Data.

Screening methods available for colorectal cancer (CRC) to date are burdened by poor reliability and low patient adherence and compliance. An altered pattern of volatile organic compounds (VOCs) in exhaled breath has been proposed as a non-invasive potential diagnostic tool for distinguishing CRC patients from healthy controls (HC). The aim of this study was to evaluate the reliability of an innovative portable device containing a micro-gas chromatograph in enabling rapid, on-site CRC diagnosis through analysis of patients' exhaled breath. In this prospective trial, breath samples were collected in a tertiary referral center of colorectal surgery, and analysis of the chromatograms was performed by the Biomedical Engineering Department. The breath of patients with CRC and HC was collected into Tedlar bags through a Nafion filter and mouthpiece with a one-way valve. The breath samples were analyzed by an automated portable gas chromatography device. Relevant volatile biomarkers and discriminant chromatographic peaks were identified through machine learning, linear discriminant analysis and principal component analysis. A total of 68 subjects, 36 patients affected by histologically proven CRC with no evidence of metastases and 32 HC with negative colonoscopies, were enrolled. After testing a training set (18 CRC and 18 HC) and a testing set (18 CRC and 14 HC), an overall specificity of 87.5%, sensitivity of 94.4% and accuracy of 91.2% in identifying CRC patients was found based on three VOCs. Breath biopsy may represent a promising non-invasive method of discriminating CRC patients from HC.

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.

Distinguishing thrombotic thrombocytopenic purpura from primary immune thrombocytopenia accompanied by anemia using the carbon monoxide breath test.

OBJECTIVES: Thrombotic thrombocytopenic purpura (TTP) is a rare but life-threatening hematological disorder. Early differentiation between TTP and primary immune thrombocytopenia (ITP) accompanied by anemia is crucial to initiate an appropriate therapeutic strategy. The objective of this study was to evaluate the predictive value of red blood cell lifespan (RBCLS), determined using the carbon monoxide breath test, in the differential diagnosis of these two diseases. METHODS: We conducted a retrospective analysis of 23 patients with TTP and 32 patients with ITP accompanied by anemia. RBCLS measurements were compared and evaluated between these two patient groups. RESULTS: TTP patients had a significantly shorter mean RBCLS (20 ± 8 days) than patients with ITP accompanied by anemia (77 ± 22 days, P < 0.001) and healthy controls (114 ± 25 days, P < 0.001). In TTP patients, RBCLS showed a significant negative correlation with reticulocyte percentage and lactic dehydrogenase levels (P < 0.001). When using a standard baseline of 75 days, RBCLS demonstrated a sensitivity of 100% and specificity of 53.1% in identifying TTP. The diagnostic accuracy could reach 93% by excluding the impact of gastrointestinal bleeding. By employing the Receiver Operator Characteristics (ROC) curve, the area under the curve for RBCLS was 0.985 (95% CI: 0-1, P < 0.01) in predicting TTP, with an optimal cut-off value of 32 days, and sensitivity and specificity of 95.7% and 96.9%, respectively. CONCLUSIONS: Our study proposes a simple and accessible method for evaluating RBCLS to differentiate between TTP and ITP accompanied by anemia.

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.

Exhaled nitric oxide levels in COPD patients who use electronic cigarettes.

Emerging data from clinical studies have shown pro-inflammatory effects associated with e-cigarette use. Fractional exhaled nitric oxide (FeNO) is a biomarker of pulmonary type 2 (T2) inflammation. The effect of chronic e-cigarette use on FeNO is unclear. The aim of this study was to compare FeNO levels in COPD ex-smokers who use e-cigarettes (COPDE + e-cig) to COPDE ex-smokers (COPDE) and COPD current smokers (COPDS). FeNO levels were significantly higher in COPDE + e-cig (median 16.2 ppb) and COPDE (median 18.0 ppb) compared to COPDS (median 7.6 ppb) (p = 0.0003 and p < 0.0001 respectively). There was no difference in FeNO levels between COPDE + e-cig compared to COPDE (p > 0.9). The importance of our results is that electronic cigarette use does not alter the interpretation of FeNO results, and so does not interfere with the use of FeNO as a practical biomarker of T2 inflammation, unlike current cigarette smoking in COPD. Whilst the effect of electronic cigarette use on FeNO levels is not the same as cigarette smoke, this cannot be taken as evidence that electronic cigarettes are harmless. These differential pulmonary effects can be attributed to differences in the chemical composition of the two products.

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.

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.

Exhaled breath and urinary volatile organic compounds (VOCs) for cancer diagnoses, and microbial-related VOC metabolic pathway analysis: a systematic review and meta-analysis.

BACKGROUND: The gradual evolution of the detection and quantification of volatile organic compounds (VOCs) has been instrumental in cancer diagnosis. The primary objective of this study was to assess the diagnostic potential of exhaled breath and urinary VOCs in cancer detection. As VOCs are indicative of tumor and human metabolism, our work also sought to investigate the metabolic pathways linked to the development of cancerous tumors. MATERIALS AND METHODS: An electronic search was performed in the PubMed database. Original studies on VOCs within exhaled breath and urine for cancer detection with a control group were included. A meta-analysis was conducted using a bivariate model to assess the sensitivity and specificity of the VOCs for cancer detection. Fagan's nomogram was designed to leverage the findings from our diagnostic analysis for the purpose of estimating the likelihood of cancer in patients. Ultimately, MetOrigin was employed to conduct an analysis of the metabolic pathways associated with VOCs in relation to both human and/or microbiota. RESULTS: The pooled sensitivity, specificity and the area under the curve for cancer screening utilizing exhaled breath and urinary VOCs were determined to be 0.89, 0.88, and 0.95, respectively. A pretest probability of 51% can be considered as the threshold for diagnosing cancers with VOCs. As the estimated pretest probability of cancer exceeds 51%, it becomes more appropriate to emphasize the 'ruling in' approach. Conversely, when the estimated pretest probability of cancer falls below 51%, it is more suitable to emphasize the 'ruling out' approach. A total of 14, 14, 6, and 7 microbiota-related VOCs were identified in relation to lung, colorectal, breast, and liver cancers, respectively. The enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in the aforementioned tumor types. CONCLUSIONS: The analysis of exhaled breath and urinary VOCs showed promise for cancer screening. In addition, the enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in four tumor types, namely lung, colorectum, breast and liver. These findings hold significant implications for the prospective clinical application of multiomics correlation in disease management and the exploration of potential therapeutic targets.

Wearable sampling of proteins from human exhaled aerosols for nano-liquid chromatography-tandem mass spectrometry analysis: A pilot study.

RATIONALE: Human exhaled breath usually contains unique proteins that may provide clues to characterize individual physiological activities and many diseases. However, the concentration of exhaled proteins in exhaled breath is extremely low and usually does not reach the detection limits of all online breath mass spectrometry instruments. Therefore, developing a new breath sampler for collecting and characterizing exhaled proteins is important. METHODS: In this study, a new mask-based wearable sampler was developed by fixing metal materials into the inner surface of the KN95 mask. Human exhaled proteins could be directly adsorbed onto the metal material while wearing the mask. After sampling, the collected proteins were eluted, digested, and identified using nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS). RESULTS: The adsorption of exhaled proteins was evaluated, showing that modified gold foil is an effective material for collecting exhaled proteins. Various endogenous proteins were successfully identified from exhaled breath, many of which can be potential biomarkers for disease diagnosis. CONCLUSIONS: By coupling the newly developed mask sampler with nano-LC-MS/MS, human exhaled proteins were successfully collected and identified. Our results show that the mask sampler is wearable, simple, and convenient, and the method is noninvasive for investigating disease diagnosis and human health.

Detection ofClostridioides difficileinfection by assessment of exhaled breath volatile organic compounds.

Clostridioides difficileinfection (CDI) is the leading cause of hospital-acquired infective diarrhea. Current methods for diagnosing CDI have limitations; enzyme immunoassays for toxin have low sensitivity andClostridioides difficilepolymerase chain reaction cannot differentiate infection from colonization. An ideal diagnostic test that incorporates microbial factors, host factors, and host-microbe interaction might characterize true infection. Assessing volatile organic compounds (VOCs) in exhaled breath may be a useful test for identifying CDI. To identify a wide selection of VOCs in exhaled breath, we used thermal desorption-gas chromatography-mass spectrometry to study breath samples from 17 patients with CDI. Age- and sex-matched patients with diarrhea and negativeC.difficiletesting (no CDI) were used as controls. Of the 65 VOCs tested, 9 were used to build a quadratic discriminant model that showed a final cross-validated accuracy of 74%, a sensitivity of 71%, a specificity of 76%, and a receiver operating characteristic area under the curve of 0.72. If these findings are proven by larger studies, breath VOC analysis may be a helpful adjunctive diagnostic test for CDI.

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.

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.

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.

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.

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.

Metal and oxidative potential exposure through particle inhalation and oxidative stress biomarkers: a 2-week pilot prospective study among Parisian subway workers.

OBJECTIVE: In this pilot study on subway workers, we explored the relationships between particle exposure and oxidative stress biomarkers in exhaled breath condensate (EBC) and urine to identify the most relevant biomarkers for a large-scale study in this field. METHODS: We constructed a comprehensive occupational exposure assessment among subway workers in three distinct jobs over 10 working days, measuring daily concentrations of particulate matter (PM), their metal content and oxidative potential (OP). Individual pre- and post-shift EBC and urine samples were collected daily. Three oxidative stress biomarkers were measured in these matrices: malondialdehyde (MDA), 8-hydroxy-2'deoxyguanosine (8-OHdG) and 8-isoprostane. The association between each effect biomarker and exposure variables was estimated by multivariable multilevel mixed-effect models with and without lag times. RESULTS: The OP was positively associated with Fe and Mn, but not associated with any effect biomarkers. Concentration changes of effect biomarkers in EBC and urine were associated with transition metals in PM (Cu and Zn) and furthermore with specific metals in EBC (Ba, Co, Cr and Mn) and in urine (Ba, Cu, Co, Mo, Ni, Ti and Zn). The direction of these associations was both metal- and time-dependent. Associations between Cu or Zn and MDAEBC generally reached statistical significance after a delayed time of 12 or 24 h after exposure. Changes in metal concentrations in EBC and urine were associated with MDA and 8-OHdG concentrations the same day. CONCLUSION: Associations between MDA in both EBC and urine gave opposite response for subway particles containing Zn versus Cu. This diverting Zn and Cu pattern was also observed for 8-OHdG and urinary concentrations of these two metals. Overall, MDA and 8-OHdG responses were sensitive for same-day metal exposures in both matrices. We recommend MDA and 8-OHdG in large field studies to account for oxidative stress originating from metals in inhaled particulate matter.

Lung cancer detection in perioperative patients' exhaled breath with nanomechanical sensor array.

INTRODUCTION: Breath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning. METHODS: Patients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning. RESULTS: Sixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively. CONCLUSION: Breath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer.

The value of bronchodilator response in FEV1 and FeNO for differentiating between chronic respiratory diseases: an observational study.

BACKGROUND: There is no uniform standard for a strongly positive bronchodilation test (BDT) result. In addition, the role of bronchodilator response in differentiating between asthma, chronic obstructive pulmonary disease (COPD), and asthma-COPD overlap (ACO) in patients with a positive BDT result is unclear. We explored a simplified standard of a strongly positive BDT result and whether bronchodilator response combined with fractional exhaled nitric oxide (FeNO) can differentiate between asthma, COPD, and ACO in patients with a positive BDT result. METHODS: Three standards of a strongly positive BDT result, which were, respectively, defined as post-bronchodilator forced expiratory volume in 1-s responses (ΔFEV1) increasing by at least 400 mL + 15% (standard I), 400 mL (standard II), or 15% (standard III), were analyzed in asthma, COPD, and ACO patients with a positive BDT result. Receiver operating characteristic curves were used to determine the optimal values of ΔFEV1 and FeNO. Finally, the accuracy of prediction was verified by a validation study. RESULTS: The rates of a strongly positive BDT result and the characteristics between standards I and II were consistent; however, those for standard III was different. ΔFEV1 ≥ 345 mL could predict ACO diagnosis in COPD patients with a positive BDT result (area under the curve [AUC]: 0.881; 95% confidence interval [CI] 0.83-0.94), with a sensitivity and specificity of 90.0% and 91.2%, respectively, in the validation study. When ΔFEV1 was < 315 mL combined with FeNO < 28.5 parts per billion, patients with a positive BDT result were more likely to have pure COPD (AUC: 0.774; 95% CI 0.72-0.83). CONCLUSION: The simplified standard II can replace standard I. ΔFEV1 and FeNO are helpful in differentiating between asthma, COPD, and ACO in patients with a positive BDT result.