Breath Samples Research Articles

Non-invasive multiple cancer screening using trained detection canines and artificial intelligence: a prospective double-blind study.

The specificity and sensitivity of a simple non-invasive multi-cancer screening method in detecting breast, lung, prostate, and colorectal cancer in breath samples were evaluated in a double-blind study. Breath samples of 1386 participants (59.7% males, median age 56.0 years) who underwent screening for cancer using gold-standard screening methods, or a biopsy for a suspected malignancy were collected. The samples were analyzed using a bio-hybrid platform comprising trained detection canines and artificial intelligence tools. According to cancer screening/biopsy results, 1048 (75.6%) were negative for cancer and 338 (24.4%) were positive. Among the 338 positive samples, 261 (77.2%) were positive for one of the four cancer types that the bio-hybrid platform was trained to detect, with an overall sensitivity and specificity of 93.9% (95% confidence interval [CI] 90.3-96.2%) and 94.3% (95% CI 92.7%-95.5%), respectively. The sensitivity of each cancer type was similar; breast: 95.0% (95% CI87.8-98.0%), lung: 95.0% (95% CI87.8-98.0%), colorectal: 90.0% (95% CI74.4-96.5%), prostate: 93.0% (95% CI84.6-97.0%). The sensitivity of 14 other malignant tumors that the bio-hybrid platform was not trained to detect, but identified, was 81.8% (95% CI 71.8%-88.8%). Early cancer (0-2) detection sensitivity was 94.8% (95% CI 91.0%-97.1%). This bio-hybrid multi-cancer screening platform demonstrated high sensitivity and specificity and enables early-stage cancer detection.

Development of a new breath collection method for analyzing volatile organic compounds from intubated mouse models

Abstract A new pre-clinical method for capturing breath samples from intubated mice is presented. This method significantly reduces background levels, allowing more accurate measurements of VOCs originating from the breath (“on-breath”) as opposed to background contamination. The method was developed by integrating industry-standard volatile-capturing sorbent tubes with respiratory mechanics measurement equipment (flexiVent®), resulting in a mouse breath sample that can be transported and analyzed by TD-GC-MS and other central lab technologies. Using the methodology, the discrimination between on-breath VOCs from background compounds provides a cleaner dataset, which can accelerate the validation of VOCs identified from mouse models and their translation to clinical trials. Three metrics were developed to identify on-breath VOCs, with 22 identified using Type 1 (50% of the breath samples exceeding three standard deviations above the mean signal of the system blanks), 34 with Type 2 (p-value ≤ 0.05 between paired breath and blank samples), and 61 with Type 3 (ROC-AUC value ≥ 0.8 to differentiate between breath and blank samples). The number of compounds seen at elevated levels on mouse breath was quantified and compared to the levels seen on human breath samples to compare methodologies.

Study protocol: the ‘Endoscope CRC’ cohort, a prospective biobank study on the development and evaluation of diagnostic and prognostic biomarker profiles for colorectal cancer and premalignant lesions

IntroductionEarly detection of colorectal cancer (CRC) and clinically relevant (advanced) adenomas leads to a significant reduction of CRC-related mortality and morbidity. However, the faecal immunochemical test (FIT) suffers from a...

Exhaled Breath Volatile Organic Compounds in Pregnancy: A Pilot Study.

This study aimed to assess the volatile organic compounds (VOC)s in breath samples collected non-invasively from pregnant women during pregnancy and postpartum and compare it to non-pregnant controls. This pilot study included 50 subjects: ten pregnant patients in their first trimester, ten in second trimester, ten in third trimester, ten in the first postpartum week, and ten non-pregnant subjects as a control. We collected exhaled breath from subjects who reported to be healthy and free of any respiratory symptoms. Clinical and demographic data were collected. The samples were analyzed for VOCs using a selected-ion flow-tube mass spectrometer (SIFT-MS). The VOCs monitored were twenty-two compounds selected for their common presence in exhaled breath. During pregnancy and postpartum period, there were differences in five compounds mainly: 2-propanol, acrylonitrile, 1-nonene, 2-nonene, and hydrogen sulfide. Significant differences in VOCs were identified during each trimester compared to controls. Volatile organic compounds could be measured safely and noninvasively in pregnant women. VOCs differed significantly among non-pregnant, pregnant women and postpartum period. The utilization of this novel assay to identify fetal conditions or identifying women at risk premature delivery should be further investigated in future studies.

Noninvasive, Multimodal Inflammatory Biomarker Discovery for Systemic Inflammation (NOVA Study): Protocol for a Cross-Sectional Study.

Prolonged systemic inflammation is recognized as a major contributor to the development of various chronic inflammatory diseases. Daily measurements of inflammatory biomarkers can significantly improve disease monitoring of systemic inflammation, thus contributing to reducing the burden on patients and the health care system. There exists, however, no scalable, cost-efficient, and noninvasive biomarker for remote assessment of systemic inflammation. To this end, we propose a novel, multimodal, and noninvasive approach for measuring inflammatory biomarkers. This study aimed to evaluate the relationship between the levels of inflammatory biomarkers in serum (gold standard) and those measured noninvasively in urine, sweat, saliva, exhaled breath, stool, and core body temperature in patients with systemic inflammation. This study is a single-center, cross-sectional study and includes a total of 20 participants (10 patients with systemic inflammation and 10 control patients). Eligible participants provide serum, urine, sweat, saliva, exhaled breath, and stool samples for biomarker analyses. Core body temperature is measured using a sensor. The primary end point is the level of C-reactive protein (CRP). The secondary end points are interleukin (IL)-1β, IL-6, IL-8, IL-10, and tumor necrosis factor-α levels. The tertiary end points are fractional exhaled nitric oxide, calprotectin, and core body temperature. Samples will be collected in 2 batches, enabling preliminary analysis of the first batch (patients 1-5 from each group). The full analysis will include both batches. CRP and cytokine levels will be measured using enzyme-linked immunosorbent assay and electrochemiluminescence immunoassay. For statistical analysis, the Shapiro-Wilk test will be used to evaluate the normality of the distribution in each variable. We will perform the 2-tailed t test or Wilcoxon rank sum test to compare the levels of inflammatory biomarkers between patients with systemic inflammations and control patients. Pearson and Spearman correlation coefficients will assess the relationship between inflammatory biomarkers from noninvasive methods and serum biomarkers. Using all-subset regression analysis, we will determine the combination of noninvasive methods yielding the highest predictive accuracy for serum CRP levels. Participants' preferences for sampling methods will be assessed through a questionnaire. The study received ethics approval from the independent research ethics committee of Canton Zurich on October 28, 2022. A total of 20 participants participated in the study measurements. Data collection started on February 22, 2023, and was completed on September 22, 2023. Participants were on average 52.8 (SD 14.4; range 24-82) years of age, and 70% (14/20) of them were women. The analysis results reporting findings are expected to be published in 2025. This study aims to evaluate the feasibility of noninvasive, multimodal assessment of inflammatory biomarkers in patients with systemic inflammation. Promising results could lead to the creation of noninvasive and potentially digital biomarkers for systemic inflammation, enabling continuous monitoring and early diagnosis of inflammatory activity in a remote setting. DERR1-10.2196/62877.

Photoacoustic Trace-Analysis of Breath Isoprene and Acetone via Interband- and Quantum Cascade Lasers

This research presents two laser-based photoacoustic approaches for analyzing exhaled breath isoprene and acetone. The integration of a PTR-ToF-MS as a reference device ensured the reliability and accuracy of the photoacoustic systems that is based on an ICL for isoprene and a QCL for acetone detection. The calibration yielded limits of detection of 26.9 ppbV and 1.7 ppbV, respectively, and corresponding normalized noise equivalent absorption coefficients (NNEAs) of 5.0E-9 Wcm-1Hz-0.5 and 4.9E-9 Wcm-1Hz-0.5. Laboratory as well as real breath sample measurements from alveolar breath revealed a robust system performance, with only one outlier within the static isoprene measurements. However, discrepancies emerged under dynamic breath sampling conditions, emphasizing the need for further optimization. Especially by knowing the dynamic nature and endogenous origin of exhaled isoprene our findings highlight the potential of breath analysis for non-invasive physio-metabolic and pathophysiological monitoring towards point-of-care devices.

A clinical study to detect COVID-19 infection from a non-invasive breath sample

Abstract Objectives Rapid diagnosis of coronavirus disease 2019 (COVID-19) is important to the control of SARS-CoV-2. The objective of this study was to assess the feasibility of diagnosing COVID-19 infection from a breath sample using a rapid, non-invasive point of care breath test that does not require off-site analysis. This could increase the accessibility of testing and reduce the discomfort of current swabbing techniques. Methods In this prospective observational study, samples of expired air from adults diagnosed with COVID-19 and controls were collected, analysed with gas chromatography combined with ion mobility spectrometry (BreathSpec© GC-IMS, G.A.S mbH) and also with machine learning (ML) biomarker analysis (MLBA, Ancon Technologies Ltd.). Results 330 participants who tested negative or positive for COVID-19 by RT-PCR were enrolled in the study. In an ML analysis of data collected, the MLBA algorithms enable distinction between COVID-19 and non-COVID-19 subjects with an accuracy of 94·1%. Conclusions This study indicates that patients with COVID-19 can be quickly identified at point of use. Development and validation of this method may allow rapid circa 10-minute diagnosis of COVID-19 both now and in future seasons. It may also offer an alternative tool for the detection of other viral and microbial infections.

Usability of volatile organic compounds from exhaled breath compared to those from ruminal fluid, serum, urine, and milk to identify diet-specific metabolite profiles in lactating dairy cows

To investigate dietary influences on the volatilome, the volatile subcategory of the metabolome, we performed a comparative untargeted volatilome analysis of exhaled breath, ruminal fluid, serum, urine, and milk from lactating Holstein cows fed different diets. Thirty-two cows (79.4 ± 31.3 d in milk [DIM], 30.6 ± 4.83 kg milk/d) were assigned to 4 diets. The experiment lasted 16 weeks. Throughout the experiment, half of the animals were fed a hay-based diet (HAY; n = 16), and the other half were fed a silage-based diet (SIL; n = 16). In experimental wk 5 to 12, half of the animals in each group received the control concentrate (CON), and the other half was fed with the CON supplemented with a blend of essential oils (EXP). We hypothesized that the basal diet and the essential oils influence the VOC profiles of the cows through potential changes in ruminal fermentation, digestion, and metabolism (hypothesis 1). Furthermore, we hypothesized that the potential effects of essential oils would have a delayed onset and a carryover effect (hypothesis 2). Every 4 experimental weeks, i.e., in W4, W8, W12 and W16, samples of exhaled breath, ruminal fluid, serum, urine, milk, and feed were collected for dynamic headspace extraction and gas chromatographic analysis of volatile organic compounds (VOC) in their gaseous phase. Milk yield, milk composition, body weight, and feed intake were recorded regularly. Linear mixed models and multivariate and univariate data analyses were performed. The total DMI and basal diet intake was similar between HAY and SIL cows. However, SIL cows consumed less of the concentrate, neutral detergent fiber, and water-soluble carbohydrates (WSC) and more starch than HAY cows. The SIL cows had a higher milk production than the HAY cows. No effect was found regarding the concentrate type on feed intake or milk production. Irrespective of diet, 2,957 VOC were detected in the gaseous phase of serum; 2,771 in exhaled breath; 1,016 in urine; 1,001 in milk; and 921 in ruminal fluid. Across the experimental wk 4, 8, 12 and 16, the basal diet altered the VOC profiles of ruminal fluid, urine, and exhaled breath but not those of serum and milk. The concentrate type affected only the VOC profiles of the exhaled breath. Most diet-influenced VOC in the affected biological matrices were identified as dietary components. The experimental week influenced the VOC profiles of all matrices, especially those of exhaled breath. The VOC profile of exhaled breath strongly correlated with that of urine, followed by that of ruminal fluid, milk, and serum. This study provides the first description of diet- and time-specific VOC profiles from the biological matrices of dairy cows. The identified discriminatory VOC seem suitable as markers to discriminate between HAY and SIL cows. Exhaled breath may be a promising, sensitive, and less invasive tool to follow diet- and time-related metabolic changes.

Assessing Data Fusion in Sensory Devices for Enhanced Prostate Cancer Detection Accuracy

The combination of an electronic nose and an electronic tongue represents a significant advance in the pursuit of effective detection methods for prostate cancer, a widespread form of cancer affecting men across the globe. These cutting-edge devices, collectively called “E-Senses”, use data fusion to identify distinct chemical compounds in exhaled breath and urine samples, potentially improving existing diagnostic techniques. This study combined the information from two sensory perception devices to detect prostate cancer in biological samples (breath and urine). To achieve this, data from patients diagnosed with the disease and from control individuals were collected using a gas sensor array and chemical electrodes. The signals were subjected to data preprocessing algorithms to prepare them for analysis. Following this, the datasets for each device were individually analyzed and subsequently merged to enhance the classification results. The data fusion was assessed and it successfully improved the accuracy of detecting prostate-related conditions and distinguishing healthy patients, achieving the highest success rate possible (100%) in classification through machine learning methods, outperforming the results obtained from individual electronic devices.

Point-of-care breath sample analysis by semiconductor-based E-Nose technology discriminates non-infected subjects from SARS-CoV-2 pneumonia patients: a multi-analyst experiment.

Metal oxide sensor-based electronic nose (E-Nose) technology provides an easy to use method for breath analysis by detection of volatile organic compound (VOC)-induced changes of electrical conductivity. Resulting signal patterns are then analyzed by machine learning (ML) algorithms. This study aimed to establish breath analysis by E-Nose technology as a diagnostic tool for severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pneumonia within a multi-analyst experiment. Breath samples of 126 subjects with (n=63) or withoutSARS-CoV-2 pneumonia (n=63) were collected using the ReCIVA® Breath Sampler, enriched and stored on Tenax sorption tubes, and analyzed using an E-Nose unit with 10 sensors. ML approaches were applied by three independent data analyst teams and included a wide range of classifiers, hyperparameters, training modes, and subsets of training data. Within the multi-analyst experiment, all teams successfully classified individuals as infected or uninfected with an averaged area under the curve (AUC) larger than 90% and misclassification error lower than 19%, and identified the same sensor as most relevant to classification success. This new method using VOC enrichment and E-Nose analysis combined with ML can yield results similar to polymerase chain reaction (PCR) detection and superior to point-of-care (POC) antigen testing. Reducing the sensor set to the most relevant sensor may prove interesting for developing targeted POC testing.

An evaluation of autonomic and gastrointestinal symptoms, and gastric emptying, in patients with systemic sclerosis.

Assessment of gastrointestinal and autonomic symptoms in patients with systemic sclerosis, and possible associations with gastric emptying rate. Participant and patient disease-related characteristics were collected. Gastrointestinal and autonomic symptoms were assessed by the UCLA-SCTC GIT 2.0 and COMPASS-31 questionnaires, respectively. Potentially confounding gastrointestinal medications were discontinued where possible. Gastric emptying was assessed using a non-radioactive 13C sodium acetate isotope, end-expiratory breath samples collected at baseline and then serial timepoints up to 120 min. In total, 49 participants were studied: 17 with systemic sclerosis with variable gastrointestinal involvement, and healthy matched (n = 17) and non-matched controls (n = 15), the last to control for the impact of age rather than disease on gastric emptying and autonomic function. The total mean (range) UCLA GIT 2.0 questionnaire for patients with systemic sclerosis was 0.63 (0.0-1.5) and for both healthy matched and non-matched controls was 0.04 (0.0-0.2), and was higher in patients with systemic sclerosis across all domains. The total mean (range) COMPASS-31 score for patients with systemic sclerosis patients was 32.2 (0.0-54.9) and for healthy matched- and non-matched controls: 7.45 (0.0-24.9) and 4.25 (0.0-2.1), respectively, again higher for patients with systemic sclerosis across all domains. No association was observed between patients' UCLA GIT 2.0 total score (s = -0.039, p = 0.38), total COMPASS 31 score (s = -0.108, p = 0.68), or COMPASS-31 GI domain (s = -0.051, p = 0.85) and gastric emptying rates. Gastrointestinal and autonomic symptoms are overrepresented in patients with systemic sclerosis but did not associate with gastric emptying rates.

Volatile Organic Compounds in Exhaled Breath, Blood, and Urine Detected in Patients with Thyroid Carcinoma Using Gas Chromatography-Ion Mobility Spectrometry- A Pilot Study.

Background
Distinguishing between malignant and benign thyroid nodules remains a significant challenge for clinicians and researchers globally. The use of volatile organic compounds (VOCs) has emerged as a novel approach in cancer diagnosis. This prospective pilot study aims to identify VOCs in exhaled breath, blood, and urine that can differentiate benign from malignant thyroid nodules using gas chromatography-ion mobility spectrometry (GC-IMS).
Methods
Patients with thyroid nodules scheduled for surgery were enrolled at the Maastricht University Medical Center (MUMC+). Breath samples were analyzed using a BreathSpec GC-IMS machine (G.A.S. Dortmund, Germany), specifically designed for breath analysis. All blood and urine samples were analyzed with a separate GC-IMS device, the FlavourSpec® (G.A.S., Dortmund, Germany).
Results 
In this proof-of-concept study, 70 patients undergoing thyroid surgery at MUMC+ were consecutively included. Of these patients, 29 were confirmed to have thyroid cancer after surgical resection. The overall analysis did not reveal statistically significant differences in VOCs in breath, urine and blood, between patients with benign and malignant thyroid cancer. 
Conclusion
This proof-of-concept study demonstrated that GC-IMS was unable to adequately distinguish between the VOC profiles of malignant and benign thyroid nodules. However, this study had a small sample size and future larger studies are needed to investigate the potential of using VOCs to distinguish between benign and malignant thyroid nodules. Furthermore, future research should focus on investigating potential confounders that affect patient VOC profiles.
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Advancing Breathomics through Accurate Discrimination of Endogenous from Exogenous Volatiles in Breath.

Breathomics, a growing field in exposure monitoring and clinical diagnostics, has faced accuracy challenges due to unclear contributing factors. This study aims to enhance the potential of breathomics in various frontiers by categorizing exhaled volatile organic compounds (VOCs) as endogenous or exogenous. Analyzing ambient air and breath samples from 271 volunteers via TD-GC × GC-TOF MS/FID, we identify and quantify 50 common VOCs in exhaled breath. Advanced quantitative structure-property relationships and compartment models are employed to obtain VOCs kinetic parameters. This in-depth approach allows us to accurately determine the alveolar concentration of VOCs and further discern their origins, facilitating personalized application of breathomics in exposure assessment and disease diagnosis. Our findings demonstrate that prolonged external exposure turns humans into secondary pollutant sources. Analysis of endogenous VOCs reveals that internal exposure poses more significant health risks than external. Moreover, by correcting environmental backgrounds, we improve the accuracy of gastrointestinal disease diagnostic models by 15-25%. This advancement in identifying VOC origins via compartmental models promises to elevate the clinical relevance of breathomics, marking a leap forward in exposure assessment and precision medicine.

Discovery and analysis of the relationship between organic components in exhaled breath and bronchiectasis

The prevalence of patients with bronchiectasis (BE) has been rising in recent years, which increases the substantial burden on the family and society. Exploring a convenient, effective, and low-cost screening tool for the diagnosis of BE is urgent. We expect to identify the accuracy (ACC) of breath biomarkers (BBs) for the diagnosis of BE through breathomics testing and explore the association between BBs and clinical features of BE. Exhaled breath samples were collected and detected by high-pressure photon ionization time-of-flight mass spectrometry in a cross-sectional study. Exhaled breath samples were from 215 patients with BE and 295 control individuals. The potential BBs were selected via the machine learning (ML) method. The overall performance was assessed for the BBs-based BE detection model. The significant BBs between different subgroups such as the severity of BE, acute or stable stage, combined with hemoptysis or not, with or without nontuberculous mycobacterium (NTM),P. aeruginosa(P.a) isolation or not, and the BBs related to the number of involved lung lobes and lung function were discovered and analyzed. The top ten BBs based ML model achieved an area under the curve of 0.940, sensitivity of 90.7%, specificity of 85%, and ACC of 87.4% in BE diagnosis. Except for the top ten BBs, other BBs were found also related to the severity, acute/stable status, hemoptysis or not, NTM infection,P.aisolation, the number of involved lobes, and three lung functional parameters in BE patients. BBs-based BE detection model showed good ACC for diagnosis. BBs have a close relationship with the clinical features of BE. The breath test method may provide a new strategy for BE screening and personalized management.

Lactose Breath Test: Possible Strategies to Optimize Test Performance, Accuracy, and Clinical Impact.

Lactose malabsorption (LM) refers to the incomplete absorption of lactose in the small intestine, resulting in the arrival of ingested lactose in the colon, which can give rise to symptoms defined as lactose intolerance (LI). The lactose breath test (LBT), thanks to its low cost, availability, and noninvasiveness, is the most used diagnostic method. However, the LBT is a tedious tool, requiring prolonged involvement of patients, qualified staff, and infrastructure, of which the most time-consuming factor is the frequency and number of breath samples needed. Objectives: To simplify the current LBT methodology, compliant with the current guidelines' statements, by reducing the test duration or the number of breath samples, without compromising the test's accuracy. Methods: The results of the standard LBT were compared with two simplified tests: a "shortened" test, lasting three hours, with samples taken every 30 min; and a "five-sample" test, lasting four hours, with samples taken every hour. Patients were stratified into three grades of malabsorption (mild, moderate, severe) based on the amount of gas exhaled. A clinical severity score was introduced to assess the clinical relevance of LI using a specific questionnaire. Results: Among the 543 patients enrolled (F 71.5%, mean age 43.7 ± 17.6 yrs), 60.4% (328/543) tested positive for LM. A total of 70.5% (383/543) presented LI, with 32.1% of those being true intolerants (LI without LM). The shortened test demonstrated an accuracy of 93.9%, with a sensitivity of 89.9% and a false negative rate of 10.1% (33/328). The five-sample test showed higher accuracy and sensitivity than the shortened test (96.5% and 94.2%, respectively; p = 0.03) with a false negative rate of 5.8% (19/328). Of the 19 false negatives in the five-sample test, 95% (18/19) were categorized as mild malabsorbents. No statistical correlation was found between the clinical severity score and LBT results. Conclusions: The five-sample test, involving hourly breath measurements, is a reliable option for simplifying the LBT without significantly reducing the procedure's sensitivity.

Behavioral responses of Bigg's and Southern Resident killer whales (Orcinus orca) to uncrewed aerial vehicle‐based breath sample collection

Behavioral responses of Bigg's and Southern Resident killer whales (<i>Orcinus orca</i>) to uncrewed aerial vehicle‐based breath sample collection

Development and validation of the Thai Halitosis Associated Life-Quality Test (T-HALT): an evaluation of psychometric properties.

Halitosis appears to have significant impacts on quality of life, necessitating reliable assessment tools. The Halitosis Associated Life-Quality Test (HALT) has been validated in various populations, but not among Thai people. While HALT provides a valuable foundation, there is a need for a culturally adapted and expanded instrument for the Thai context. Consequently, this study aimed to develop and validate a comprehensive questionnaire for assessing halitosis-related quality of life in Thai populations, incorporating a Thai version of HALT (T-HALT) as a core component. This cross-sectional study involved 200 dental patients at Mahidol University. The original HALT was translated into Thai using forward-backward translation. Cultural adaptation and psychometric properties of T-HALT were evaluated through multiple approaches. Content validity was ensured through expert reviews, while face validity was assessed by patient feedback. Reliability was examined via test-retest and internal consistency measures. Criterion and discriminant validity was evaluated by correlating T-HALT scores with self-perceived halitosis and volatile sulfur compound (VSC) measurements, respectively. VSCs were quantified using the OralChroma™ device, which analyzes breath samples collected directly from patients' mouths. Construct validity was assessed through exploratory (EFA) and confirmatory factor analysis (CFA), providing insights into the questionnaire's underlying structure. T-HALT demonstrated excellent internal consistency (Cronbach's alphas = 0.940-0.943) and test-retest reliability (ICC = 0.886). Criterion validity was supported by a significant correlation between T-HALT scores and self-perceived halitosis (r = 0.503, P < 0.001). Discriminant validity was confirmed by the absence of a significant correlation between T-HALT scores and VSC levels (r = 0.071, P = 0.32). EFA revealed a four-factor structure, which was subsequently confirmed by CFA. However, Items 1 and 7 were excluded due to poor standardized factor loadings. T-HALT demonstrates good reliability and validity for assessing halitosis-related quality of life in Thai populations. It performs well as a unidimensional measure, but its multidimensional application requires modifications. Future research should validate a modified version excluding Items 1 and 7 across diverse Thai populations, potentially enhancing its cultural specificity.

Sensor technology advancement enhancing exhaled breath portability: Device set up and pilot test in the longitudinal study of lung cancer

User-friendliness of exhaled breath sampling procedure and portability of the collected samples are strategic key-points for disease monitoring in unstructured environments. These issues have been addressed by collecting exhaled breath on adsorbing cartridges; many samplers on cartridge have been used in clinical experiments through exhalate collection, including our patented Pneumopipe (European patent 12425057.2). Increasing the operative functionality of the sampling device enhances collection effectiveness and the representativity of the specimen. These features become of paramount importance when a minimal amount of disease needs to be detected with preventive approach in screening and follow-up. The primary endpoint was aimed at achieving Pneumopipe optimization (Pneumopipe II). The secondary endpoint was to investigate through a pilot longitudinal study focused on monitoring recurrence after lung cancer surgical resection and the evolution of the breath fingerprint before and after surgery. A quartz crystal-based gas sensor array, named BIONOTE-V was used to measure the breath fingerprint. Cross-validated Partial Least Square Discriminant Analysis and Principal component analysis were used to study the time evolution of the breath fingerprint before and after surgery. Thirty-five patients had the breath fingerprint collected before and after surgery, with fifteen having at least two more measurements for each timepoint (at least two before and two after surgery). (Primary endpoint) At isoprene lab tests, Pneumopipe has shown to preserve its effectiveness in collecting volatile mixture in a representative sample. (Secondary endpoint) Cancer recurrence was observed in 8 patients. Postoperative breath fingerprint detected lung cancer recurrence with an accuracy of 91 %, a sensitivity of 75 %, a specificity of 96 %, a PPV of 86 % and a NPV of 93 %. Effectiveness of exhalate collection and portability of the sampler are preserved with Pneumopipe II.

The optimization of low volume-SPME method for volatilomics analysis of exhaled breath condensate

The analysis of breath can provide insight into the metabolic state of the body. However, the collection, transport, and storage of breath samples present certain challenges. The collection of exhaled breath condensate (EBC) emerges as a promising method for breath analysis. Despite this, the volume of EBC is relatively low, necessitating a sensitive method capable of extracting analytes from a small sample volumes. Therefore, this study aimed to develop and optimize a robust and green solid-phase extraction method for the analysing volatile organic compounds (VOCs) of various polarities in a low-volume EBC using a statistical design of experiment (DoE).Based on the screening experiments and DoE, the optimal conditions were determined to be 15 min equilibration time, and 20 min of extraction at 44 °C using DVB/CAR/PDMS fibre in direct immersion (DI) mode. The method was validated for linearity, precision and accuracy, yielding satisfactory results. The optimized method was applied to quantify the concentration of selected VOCs in real samples collected from a healthy subject, as well as after consumption of coffee and smoking of an electronic cigarette. Many of the selected compounds are associated with diseases and are challenging to detect in specimens from a healthy subject, but this method can measure them as potential disease markers.The proposed method enables the analysis of VOCs is very low-volume samples (200 µL), which is particularly important for studies involving body fluids. Notably, the applied approach demonstrated that different VOCs, representing various chemical classes and polarities, can be detected and quantified.

Breath Analysis: Identification of Potential Volatile Biomarkers for Non-Invasive Diagnosis of Chronic Kidney Disease (CKD).

Recently, volatile organic compound (VOC) determination in exhaled breath has seen growing interest due to its promising potential in early diagnosis of several pathological conditions, including chronic kidney disease (CKD). Therefore, this study aimed to identify the breath VOC pattern providing an accurate, reproducible and fast CKD diagnosis at early stages of disease. A cross-sectional observational study was carried out, enrolling a total of 30 subjects matched for age and gender. More specifically, the breath samples were collected from (a) 10 patients with end-stage kidney disease (ESKD) before undergoing hemodialysis treatment (DIAL); (b) 10 patients with mild-moderate CKD (G) including 3 patients in stage G2 with mild albuminuria, and 7 patients in stage G3 and (c) 10 healthy controls (CTRL). For each volunteer, an end-tidal exhaled breath sample and an ambient air sample (AA) were collected at the same time on two sorbent tubes by an automated sampling system and analyzed by Thermal Desorption-Gas Chromatography-Mass Spectrometry. A total of 110 VOCs were detected in breath samples but only 42 showed significatively different levels with respect to AA. Nonparametric tests, such as Wilcoxon/Kruskal-Wallis tests, allowed us to identify the most weighting variables able to discriminate between AA, DIAL, G and CTRL breath samples. A promising multivariate data mining approach incorporating only selected variables (showing p-values lower than 0.05), such as nonanal, pentane, acetophenone, pentanone, undecane, butanedione, ethyl hexanol and benzene, was developed and cross-validated, providing a prediction accuracy equal to 87% and 100% in identifying patients with both mild-moderate CKD (G) and ESKD (DIAL), respectively.