Breath Analysis Research Articles

Breath Analysis via Surface Enhanced Raman Spectroscopy.

Breath analysis is increasingly recognized as a powerful noninvasive diagnostic technique, and a plethora of exhaled volatile biomarkers have been associated with various diseases. However, traditional analytical methodologies are not amenable to high-throughput diagnostic applications at the point of need. An optical spectroscopic technique, surface-enhanced Raman spectroscopy (SERS), mostly used in the research setting for liquid sample analysis, has recently been applied to breath-based diagnostics. This promising noninvasive diagnostic tool has been demonstrated for the identification of various diseases, including lung cancer, gastric cancer, and diabetes. The versatility of SERS has enabled the use of different diagnostic strategies and allowed for fast and accurate detection of small analytes in exhaled breath. In this review, we provide an overview of recent advances in SERS-based breath analysis, focusing on sensors for the detection of gases and volatile organic compounds (VOCs) in exhaled breath, and highlight generic strategies for sample preconcentration and methods for spectral analysis. We aim to provide an overview of the state of the art and inspiration for further SERS investigation of expiration.

A novel breath pattern model and analysis to minimize patient discomfort in medical ventilators

Typical waveforms used for the simulation of pressure and volume-controlled ventilation in medical ventilators have been extensively studied in the literature. The majority of simulation studies reported employ the step pattern or ramp pattern to model the pressure and flow variations in pressure/volume-controlled ventilation. It was observed that the above waveforms tend to add to the discomfort level of patients due to the presence of jerks in derivatives of pressure/flow variations; the pressure/flow variation of air and oxygen mixture should be smooth so that the patient discomfort is kept at a minimal level. To overcome the above-mentioned drawback, a careful study of the flow/pressure simulation using a cycloidal pattern during the inhalation and exhalation phases of the breath cycle was proposed and investigated in this work. Based on transient analysis of the pressure variation simulation, it was observed that the air and oxygen mixture delivered to the patient was relatively jerk-free due to the finite values of first and second-order derivatives of pressure/flow curves. Mathematical models of the proposed simulation study of the cycloidal pattern of flow variation in both pressure/volume-controlled ventilation, are formulated and presented for use by ventilator designers. A comparative study of the simulation of step, ramp and cycloidal profiles applied to the breath cycle in a typical pressure-controlled ventilation is carried out and a marginal decrease in tidal volumes was observed in the case of cycloidal profiles for a given set of ventilator settings and the results are discussed. A typical natural breath pattern of a healthy adult was experimentally measured using a CITRIX breath analyser and the above mathematical model for the volume-controlled ventilation was found to closely describe the natural breathing process, using statistical parameters. Thus, the proposed cycloidal profile of pressure/flow variations in medical ventilators will be a better alternative, when compared to the step/ramp profiles investigated in this work; further, the proposed cycloidal profile matches closely with the natural breath pattern, based on typical experimental studies.

Stability of volatile organic compounds in thermal desorption tubes and in solution

Exhaled breath volatile organic compounds (VOCs) are often collected and stored in sorbent tubes before thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) analysis. Information about the stability of VOCs during storage is needed to account for potential artifacts and monitor for losses. Additionally, information about the stability of VOC standards in solution is required to assess their performance as quality control and internal standards. We evaluated the stability of a standard mixture of 42 VOCs in dual-sorbent tubes containing Tenax® TA and Carbotrap 1TD over 60 d at commonly used storage conditions: room temperature (∼21 °C), 4 °C, and -80 °C. The same 42 VOCs were also evaluated for their stability in methanol over 60 d while stored at -20 °C. All samples were analyzed using TD-GC-MS. During storage, most VOCs were stable on sorbent after 60 d: 36/42 (86%), 39/42 (93%), and 41/42 (98%) had not statistically changed for room temperature, 4 °C and -80 °C, respectively, based on Spearman rank correlation coefficients and linear regression analysis. The isotopically labeled VOCs tested here are well-suited to serve as internal standards for pre-analysis or storage. Degradation of VOCs in solution was apparent after 60 d: 27/42 (64%) of VOCs had statistically decreased. The total VOC mixture had dropped to 90% of its original intensity after ∼22 d and a subset of VOCs typically used as internal standards dropped to 90% in ∼16 d. Analysts using similar mixtures should make a fresh solution at least every two weeks to ensure analytical accuracy. This study provides important insights into storage practices for both sorbent tubes and standard solutions, guiding analysts toward improved reliability and accuracy in exhaled breath analysis.

Early diagnosis of lung cancer using a sensor gas analysis complex: case report

Background. Currently, low-dose computed tomography (LDCT) is the only screening test that reduces the risk of death from lung cancer. However, there are a number of disadvantages, such as lack of widespread use, high cost, high false-positive rate and the need to conduct studies only in high-risk groups, which significantly limit mass screening. exhaled breath analysis, which uses sensitive breath sensors, is a promising method to improve early diagnosis of lung cancer. Cancer Research Institute of Tomsk National Research Medical Center together with Tomsk State University and Tomsk Polytechnic Research Institute has developed a gas analysis complex capable of analyzing the gas composition of exhaled air with remote sampling from bags. during the study, data obtained by digitizing signals from gas analysis system sensors and patient metadata are recorded in a database for subsequent automated processing and analysis using a neural network. Case description. A 48-year-old female patient with a long history of smoking came to the clinic of the Cancer Research Institute for consultation with suspected pathological infiltration around the celiac trunk detected by abdominal CT. As a clinical trial of the developed gas analytical complex for cancer detection, a sample of exhaled air was taken, and the comparison of the composition of volatile organic compounds (VOCs) with that in the control group (healthy individuals) revealed abnormalities characteristic of lung cancer. the patient underwent a chest CT scan, which revealed stage IIB peripheral cancer of the lower lobe of the left lung. the original sensor gas analysis complex, which has no analogues in Russia, was used for the first time in the detection of lung cancer. the data obtained allowed us to suspect the presence of lung tumor in the patient and perform radical surgical treatment. the composition of VOCs in exhaled air was assessed on day 10 after surgery, and no significant changes in the composition of exhaled air were observed. Conclusion. Machine learning algorithms are actively used to diagnose socially significant diseases. the platforms being developed based on arrays of chemical sensors with data analysis using a neural network are promising candidates for implementation in screening activities.

Application of Fourier transform infrared spectroscopy to exhaled breath analysis for detecting helicobacter pylori infection

Helicobacter pylori (H. pylori) is one of the most globally prevalent bacteria, closely associated with gastrointestinal diseases such as gastric ulcers and chronic gastritis. Current clinical methods primarily involve Carbon-13 and Carbon-14 urea breath test, both carrying potential safety risks. Fourier-transform infrared (FTIR) spectroscopy can detect human exhaled gases, which may change under disease conditions. This preliminary study aims to explore the application value of FTIR-based breath analysis in detecting H. pylori infection, providing theoretical basis and clinical references for new clinical detection methods. A cross-sectional survey was conducted from August 2021 to May 2022 at Renmin Hospital of Wuhan University. Breath samples were collected before and half an hour after ingesting unlabeled urea. Gas samples were analyzed using FTIR breath spectra. Individual exhalation spectral data after deducting baseline spectral data were used as the basis for the training and test sets through K-center clustering algorithm. Results: A total of 278 samples were collected (63 H. pylori infection cases, 215 healthy controls). There were no statistically significant differences in general data (age, gender, smoking history, alcohol consumption history, comorbidities, etc.) between the two groups. The predictive model was successfully established, with recognition rates of 94.12%, 98.39%, and 91.30% for the training set, test set, and validation set, respectively. Exhaled gas analysis based on Fourier-transform infrared spectroscopy has the potential to diagnose H. pylori infection.

Breath Analyzer for Real-Time Exercise Fat Burning Prediction: Oral and Alveolar Breath Insights with CNN.

The increasing prevalence of obesity and metabolic disorders has created a significant demand for personalized devices that can effectively monitor fat metabolism. In this study, we developed an advanced breath analyzer system designed to provide real-time monitoring of exercise-induced fat burning by analyzing volatile organic compounds (VOCs) present in both oral and alveolar breath. Acetone in exhaled breath and β-hydroxybutyric acid (BOHB) in the blood are both biomarkers closely linked to the metabolic fat burning process occurring in the liver, particularly after exercise. The breath analyzer utilizes a sensor array to detect VOC patterns, with the data analyzed using a one-dimensional convolutional neural network (1D CNN) for an accurate prediction of BOHB levels in the blood. We collected and analyzed 30 exhaled breath samples with our analyzer and blood samples for BOHB from participants before and after exercise. The results showed a strong correlation between sensor responses and BOHB levels, with Pearson correlation coefficients of 0.99 across different postexercise time points. The 1D CNN model effectively estimated BOHB concentrations, achieving Pearson coefficients of 0.96 for the training data set and 0.86 for the test data set. Additionally, our findings confirm that alveolar air samples, which contain metabolic byproducts from deeper in the lungs, offer more reliable data for fat burning analysis than oral air samples. This noninvasive, real-time breath monitoring tool offers a promising solution for individuals demanding to optimize their exercise routines and track metabolic health with high precision and accuracy.

PEDOT:PSS-Facilitated Directionally 3-D Assembled MXene-Based Aerogel for High-Performance Chemoresistive Sensing & Breath Analysis.

MXene has garnered growing interest in the field of electrochemistry, thanks to its unique electrical and surface characteristics. Nonetheless, significant challenges persist in realizing its full potential in chemoresistive sensing applications. In this study, a novel unidirectional freeze-casting approach for fabricating a Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS)-facilitated vertically aligned MXene-based aerogel with enhanced chemoresistive sensing properties was introduced. Firstly, the persistent challenge of poor gelation in MXene was addressed by formulating a nanohybrid of MXene and PEDOT:PSS, which acted as flexible conductive nanobinder. Employing a unique freeze-casting method, MXene flakes interconnected by PEDOT:PSS, were stabilized into a flexible, vertically aligned structure, leading to maximum surface exposure and enhanced robustness. The resulting 3-dimentional (3-D) aerogel exhibited a fast, heightened chemoresistive response of 7 to 50 parts per million (ppm) acetone and expanded the working range to between 10 parts per billion (ppb)-8000 ppm. Interfacial heterostructures formed between MXene and PEDOT:PSS, provided active sites, reduced activation energy, and enhanced selectivity. Modulated MXene bandgap, and its electron mobility further facilitated electron transfer, and enhanced signal strength. The sensor showed excellent biocompatibility and was also successfully employed as a breathalyzing tool, for on-demand alcohol consumption monitoring.

Shotgun metabolomic analysis of killer whale (Orcinus orca) exhaled breath condensate.

The ocean is facing many anthropogenic stressors caused from both pollution and climate change. These stressors are significantly impacting and changing the ocean's ecosystem, and as such, methods must continually be developed that can improve our ability to monitor the health of marine life. For cetaceans, the current practice for health assessments of individuals requires live capture and release, which is expensive, usually stressful, and for larger species impractical. In this study, we investigated the potential of exhaled breath condensate (EBC) samples to provide unique metabolomic profiles from healthy killer whales (Orcinus orca) of varying known age and sex. EBC collection is a non-invasive procedure that has potential for remote collection using unmanned aerial vehicles, thus improving our ability to understand physiologic parameters within wild populations while minimizing stress from collection procedures. However, descriptions of the available metabolome within EBC and its clinical significance within animals of known health and age must be described before this technique can be considered diagnostically useful. We describe normal variations of the metabolome across age and sex and provide evidence for the potential of this breath analysis method to become a valuable adjunctive tool for assessing the health of managed-care and free-ranging killer whales.

Molecular breath profile of acute COPD exacerbations

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) show high variability in individual susceptibility and promote disease progression; thus, accurate diagnosis and treatment is essential. Unravelling the molecular metabolic changes during AECOPD in breath could promote understanding of AECOPD and its treatment. Our objective was to investigate the metabolic breath profiles during AECOPD for biomarker detection. We conducted real-time breath analysis in patients with COPD during AECOPD and during subsequent stable phase. Molecular breath patterns were compared between AECOPD and stable phase by dimension reduction techniques and paired t-tests. Pathway enrichment analyses were performed to investigate underlying metabolic pathways. Partial least-squares discriminant analysis and XGboost were utilised to build a prediction model to differentiate AECOPD from stable state. 35 patients (60% male) with a mean age of 65 (10.2) yr with AECOPD were included. AECOPD could be predicted with a high sensitivity of 82.5% (95% confidence interval of 68.8%-93.8%) and an excellent discriminative power (AUC = 0.86). Metabolic changes in the linoleate, tyrosine, and tryptophan pathways during AECOPD were predominant. Significant metabolic changes occur during COPD exacerbations, predominantly in the linoleate, tyrosine, and tryptophan pathways, which are all linked to inflammation. Real-time exhaled breath analysis enables a good prediction of AECOPD compared to stable state and thus could enhance precision of AECOPD diagnosis and efficacy in clinical practice.

Wearable Temperature Sensor Enhanced Volatilomics Technique for Swift and Convenient Detection of Latrogenic Botulism.

Accurately assessing potential side effects following botulinum neurotoxin (BoNT) injection remains a formidable challenge. To address this issue, an innovative approach is developed that combines a wearable temperature sensor with a sophisticated volatilomics technique, aimed at facilitating the rapid and convenient prediction of potential physical discomfort related to latrogenic botulism. The investigation identifies five volatile organic compounds (VOCs)-acetone, styrene, ethanol, 2-pentanone, and n-butano-as promising markers indicative of BoNT poisoning. Specifically, a handheld breath analyzer, featuring a yttrium stabilized zirconia (YSZ)-based gas sensor array, alongside a wearable temperature sensor integrated with a bio-compatible methacrylated gelatin (GelMA) sensing film, are developed to simultaneously monitor breath signal variations and body temperature fluctuations. Preliminary animal testing validates the effectiveness of the integrated approach, achieving an accuracy exceeding 91.2% in early detection of physical discomfort associated with BoNT poisoning. These promising findings represent a significant advancement towards the early identification of BoNT-related issues, enabling timely intervention and improved management strategies.

Machine Learning Model Discriminate Ischemic Heart Disease Using Breathome Analysis.

Background: Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. Aims: To assess the changes in the exhaled volatile organic compounds (VOCs) in patients with vs. without ischemic heart disease (IHD) confirmed by stress computed tomography myocardial perfusion (CTP) imaging. Objectives: IHD early diagnosis and management remain underestimated due to the poor diagnostic and therapeutic strategies including the primary prevention methods. Materials and Methods: A single center observational study included 80 participants. The participants were aged ≥ 40 years and given an informed written consent to participate in the study and publish any associated figures. Both groups, G1 (n = 31) with and G2 (n = 49) without post stress-induced myocardial perfusion defect, passed cardiologist consultation, anthropometric measurements, blood pressure and pulse rate measurements, echocardiography, real time breathing at rest into PTR-TOF-MS-1000, cardio-ankle vascular index, bicycle ergometry, and immediately after performing bicycle ergometry repeating the breathing analysis into the PTR-TOF-MS-1000, and after three minutes from the end of the second breath, repeat the breath into the PTR-TOF-MS-1000, then performing CTP. LASSO regression with nested cross-validation was used to find the association between the exhaled VOCs and existence of myocardial perfusion defect. Statistical processing performed with R programming language v4.2 and Python v.3.10 [^R], STATISTICA program v.12, and IBM SPSS v.28. Results: The VOCs specificity 77.6% [95% confidence interval (CI); 0.666; 0.889], sensitivity 83.9% [95% CI; 0.692; 0.964], and diagnostic accuracy; area under the curve (AUC) 83.8% [95% CI; 0.73655857; 0.91493173]. Whereas the AUC of the bicycle ergometry 50.7% [95% CI; 0.388; 0.625], specificity 53.1% [95% CI; 0.392; 0.673], and sensitivity 48.4% [95% CI; 0.306; 0.657]. Conclusions: The VOCs analysis appear to discriminate individuals with vs. without IHD using machine learning models. Other: The exhaled breath analysis reflects the myocardiocytes metabolomic signature and related intercellular homeostasis changes and regulation perturbances. Exhaled breath analysis poses a promise result to improve the diagnostic accuracy of the physical stress tests using machine learning models.

Effect of Spirulina Nigrita® on Exercise-Induced Oxidative Stress in Humans: A Breath Analysis Study

In the current work, the non-invasive approach of breath analysis is implemented for the first time in an eccentric exercise protocol that investigated the effect of spirulina supplementation on exercise-induced oxidative stress. We assessed whether volatile alkanes in exhaled breath can serve as alternative biomarkers of oxidative stress. A randomized, double-blinded, placebo-controlled, crossover supplementation study was carried out enrolling 14 participants. The volunteers consumed 42 mg·kg−1 body weight of either Spirulina Nigrita® or maltodextrin, as a placebo, daily for 15 days. Afterward, they followed a damaging eccentric exercise protocol of the upper limbs. Expired breath samples were collected from them just before supplementation (baseline measurement), prior to exercise, and 1 h, 24 h, 48 h, and 72 h after exercise. The samples were analyzed by Gas Chromatography/Mass Spectrometry (GC-MS) coupled with a thermal desorption unit (TDU) to determine the alveolar gradient (AG) of several alkanes, C5–C14, that are known to be related to oxidative stress. Apart from breath analysis, TBARSs were also determined as a crude marker of lipid peroxidation. Two-way repeated measures ANOVA tests were applied to the alkanes’ AGs between the spirulina (SPI) and placebo (PL) groups across time. In the PL group, a trend of increasing almost all alkanes immediately after exercise, with a gradual return to pre-exercise levels up to 72 h later was revealed. A statistically significant time effect was observed for 2-methylhexane, 3-methylhexane, heptane, octane, and undecane. The administration of spirulina appeared to reduce the increases in alkanes after exercise, and a statistically significant attenuation was observed for 2-methylpentane and 2-methylhexane. An examination of TBARSs confirmed that the reduced increases observed in the SPI group were due to changes in lipid peroxidation, while a positive correlation between the iAUC of TBARSs and that of 2-methylhexane and 3-methylhexane was revealed. In conclusion, the analysis of volatile alkanes in exhaled breath may serve as an attractive alternative for assessing redox changes after eccentric exercise compared to traditional blood biomarkers.

A simple and effective non-invasive method for the analysis of volatile organic compounds in exhaled air

Breath analysis is a cutting-edge technique that collects significant medical information by analyzing the substances found in exhaled breath. Fluctuations in the levels of chemicals such as acetone can serve as indicators of medical ailments or contact with hazardous substances such as occupational exposure. In order to detect acetone in exhaled air, techniques such as needle trap coupled with gas chromatography have been devised. This procedure involves the use of a nanocomposite called Mil101(Fe)@Fe3O4@SiO2.The nanocomposite is synthesized using a chemical hydrothermal method. In order to examine the properties of the Mil101(Fe)@Fe3O4@SiO2 nanocomposite, researchers utilized a range of analytical techniques including scanning electron microscopy (SEM), thermal analysis (TGA), x-ray analysis (EDX), Brunauer-Emmett-Teller (BET) method, and Fourier Transform Infrared Spectroscopy (FT-IR). The efficiency of the NTD-GC-FID method depends on several parameters, such as temperature, desorption time, fracture passage volume, sampling time, sample temperature, and sampling flow rate. These parameters have been studied using the simplex method to optimize the process. The optimal conditions yielded a limit of detection (LODs) of 0.4 µg/L and a limit of quantification (LOQs) of 1.1 µg/L. The calibration curve for the NTD-GC-FID method ranged from 0.011 to 10 mgl−1 with an acceptable error range. Moreover, the method demonstrated good repeatability and reproducibility, with calculated values of 3.9 and 4.6 %, respectively. The proposed method was successfully employed in this study to identify and measure acetone in the exhaled air of individuals with diabetes, yielding satisfactory results. The needle trap tool was used over 100 times without any decline in its efficiency for adsorbing acetone. In summary, this research presents a new analytical approach, namely the NTD-GC-FID method, to detect and measure acetone in exhaled air. Compared to the other methods for analyzing acetone, the application of Mil101(Fe)@Fe3O4@SiO2 nanocomposite demonstrated promising results in detection of diabetes.

Human Breath Analysis; Clinical Application and Measurement: An Overview

Human Breath Analysis; Clinical Application and Measurement: An Overview

Osteopontin as a Novel Biomarker in Distinguishing Chronic Rhinosinusitis with Nasal Polyp Endotypes and Predicting Disease Severity

Introduction: The objective of this study was to ascertain the predictive value of osteopontin (OPN), a cytokine with pro-inflammatory properties implicated in inflammatory and allergic conditions, in nasal secretions for the identification of chronic rhinosinusitis with nasal polyp (CRSwNP) endotypes and the assessment of disease severity. Methods: A cohort comprising 81 individuals diagnosed with CRSwNP was enrolled, which included 37 subjects with the non-eosinophilic CRSwNP and 44 subjects with the eosinophilic CRSwNP (eCRSwNP), alongside 32 healthy controls (HCs). Nasal secretions and tissue samples were collected from all participants. The quantification of OPN in these samples was conducted using ELISA and immunohistochemistry. Nasal fractional exhaled nitric oxide levels were determined with the Nano Coulomb Breath Analyzer. The diagnostic efficacy of OPN levels in distinguishing eCRSwNP was assessed using the receiver operating characteristic (ROC) curve, while Pearson correlation analysis was employed to evaluate the correlation between OPN levels and disease severity indicators. Results: Concentrations of OPN in nasal secretions were found to be elevated in CRSwNP patients compared to the HC group and significantly higher in patients with eCRSwNP. A positive correlation was identified between OPN levels in nasal secretions and peripheral blood eosinophil counts and percentages, and tissue eosinophil counts, as well as the Visual Analog Scale (VAS) score, Lund-Mackay score, and Lund-Kennedy score. The ROC analysis demonstrated that the OPN level in nasal secretions possesses a robust discriminatory capacity for eCRSwNP, with a cutoff value of 121.05 ng/mL. Furthermore, the OPN concentration was determined to be a more precise predictor of the VAS score, Lund-Mackay score, and Lund-Kennedy score than the CRSwNP endotypes. Conclusion: The findings of this study indicate that OPN is differentially expressed in the nasal secretions of eCRSwNP patients and correlates with eosinophilic inflammation. The presence of OPN in nasal secretions emerges as a novel and potentially valuable biomarker for the differentiation of CRSwNP endotypes and the prognostication of disease severity.

Use of Respiratory Volatile Organic Compounds in the Diagnosis of Infections

Introduction: Infectious diseases continue to significantly affect global human health. Conducting infection diagnostic tests can be difficult due to limited resources, time limitations, or the inadequacy of current diagnostic techniques. There is a growing interest in developing diagnostic methods through the analysis of exhaled air, as breath sampling is non-invasive, safe, and convenient. Volatile organic compounds (VOCs) present in exhaled air provide insights into the metabolic and biophysical processes associated with various diseases. This review highlights recent advancements in breath-based diagnosis for respiratory infections, including those caused by the influenza virus, SARS-CoV-2, Mycobacterium tuberculosis, Pseudomonas aeruginosa, and Aspergillus fumigatus. Furthermore, this review also examines the contemporary viewpoint on diagnosing two significant infections: Helicobacter pylori and malaria. Conclusion: Certain volatile organic compounds (VOCs) linked to respiratory conditions are specifically and often associated with multiple infectious diseases, indicating that breath analysis could be a valuable approach for creating diagnostic tools. However, existing challenges encompass the absence of standardized protocols for collecting and analyzing breath samples, as well as a deficiency in validation studies. Additional research is essential to broaden the use of breath analysis in clinical practice.

Volatile organic compounds in exhaled breath, blood, and urine detected in patients with thyroid carcinoma using gas chromatography-ion mobility spectrometry—a pilot study

The differentiation between malignant and benign thyroid nodules represents a significant challenge for clinicians globally. The identification of volatile organic compounds (VOCs) has emerged as a novel approach in the field of 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). 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 using a separate GC-IMS device, the FlavourSpec® (G.A.S., Dortmund, Germany). In this proof-of-concept study, 70 consecutive patients undergoing thyroid surgery at MUMC+ were 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. This proof-of-concept study demonstrated that GC-IMS could not effectively differentiate between the VOC profiles of malignant and benign thyroid nodules. However, due to the small sample size of this study, larger prospective studies are needed to investigate the potential of using VOCs to distinguish between benign and malignant thyroid nodules. Additionally, future research should focus on identifying potential confounding factors that may influence patient VOC profiles. (NCT04883294).

MXene and Conducting Polymer Composites for Wearable Mask Sensors

Significant efforts have been dedicated to advancing wearable electronics, ranging from life-supporting gear for soldiers to trendy accessories like smartwatches. Miniature gas sensors integrated into wearable devices offer real-time atmospheric information, safeguarding individuals from potential health condition monitoring and early disease detection. For example, recent studies have focused on detecting COVID-19 through breath analysis, utilizing over 500 compounds in exhaled breath as biomarkers for disease and metabolic assessment. Of particular interest is CO2, a key greenhouse gas crucial for monitoring indoor air quality and respiratory health. Excessive indoor CO2 levels, primarily from human exhalation, elevate the risk of COVID-19 transmission via aerosols. Another example of a considerable gas species in exhaled breath can be ethanol, which could cause adverse effects such as nausea, vomiting, skin allergies, low blood pressure, low blood sugar, and Parkinson's disease, seriously threatening human health and safety driving. Various techniques, including optical spectroscopy and gas chromatography, have been employed for gas detection, but they often suffer from drawbacks like bulkiness and high cost. Therefore, there is a strong need for developing detection devices that are affordable, portable, and wearable. Conducting polymers (CPs) show promise in constructing flexible gas sensors, but their limited response to diverse gases necessitates exploring hybridization with other nanomaterials to enhance sensing capabilities.In this study, two kinds of conducting polymers, including polyaniline (PANI) and polypyrrole (Ppy) were synthesized in a composite form with Ti3C2Tx MXene and deposited into polypropylene fiber-based disposable masks. The PANI-MXene composite gas sensor displayed a wide detection range, reliable reproducibility, long-term stability, and excellent flexibility and selectivity, a remarkable response (15.2%) towards 500 ppm CO2 gas, which is 6.5 times higher than pristine Ti3C2Tx and 2.4 times higher than pristine PANI. When Ppy is hybridized with Ti3C2Tx, the as-fabricated composites exhibited a rapid response/recovery speed, a good sensing response of 76.3 % toward 400 ppm ethanol, and an admirable theoretical limit of detection of 2.21 ppm. The outstanding sensing characteristics of the composite sensor are attributed to the abundant functional groups (e.g., amino groups in conducting polymers, terminal groups in Ti3C2Tx) and the formation of the Schottky junction between Ti3C2Tx and conducting polymers. Furthermore, a wearable wireless Bluetooth sensing device was developed for human breath monitoring, showcasing the potential of these MXene-CP composite sensors for respiratory disease diagnosis [1, 2]. The proposed gas sensing mechanism of the CP-based composites is also discussed in detail.

Investigation of Fundamental Gas Sensing Mechanism of Conductive Metal-Organic Frameworks By in-Situ/Operando Spectroscopy Analysis

Detection of various gases is important for environmental monitoring and healthcare applications. For example, volatile organic compounds (VOCs) such as toluene and xylene are harmful to human health causing headache, dizziness, and nausea, which is known as the sick-building syndrome. In addition, hydrogen sulfide (H2S) is a biomarker gas for the diagnosis of halitosis by exhaled breath analysis. The development of gas-sensing materials with high surface area and large porosity is important for the accurate detection of target molecules. Among the various types of materials, conductive metal-organic frameworks (MOFs) are attractive for the development of chemiresistive-type gas sensors, transducing chemical interaction into an electrical signal.[1]Chemiresistive gas sensors have gained much attention considering their simple operation principle, potential for miniaturization, and capability of real-time detection. In our previous works, various nanostructures such as 1D nanofibers, 2D nanosheets, and 3D hierarchical composites were synthesized and highly sensitive gas sensing properties were demonstrated.[2-5] Conductive metal-organic frameworks exhibited characteristic sensing properties mainly resulting from the catalytic surface reaction of metal nodes. However, the fundamental sensing mechanism is largely unknown due to the difficulty of real-time surface chemical analysis during the gas sensing measurement.In this presentation, we propose a novel in-situ/operando spectroscopy analysis to investigate fundamental sensing mechanisms of conductive metal-organic frameworks toward H2S and VOCs. We synthesized conductive 2D copper-2,3,6,7,10,11-hexahydroxytriphenylene (Cu3(HHTP)2) as a gas sensing layer. In terms of the H2S sensing mechanism, we observed surface-adsorbed polysulfide generating a characteristic peak in the in-situ Raman spectroscopy analysis. In addition, we observed oxidation of toluene (i.e., benzaldehyde) after the surface reaction between Cu3(HHTP)2 and toluene, which is confirmed by in-situ GC-MS analysis. Our unique approaches utilizing the in-situ/operando spectroscopy analysis technique pave a new way for investigating fundamental gas sensing mechanisms of emerging gas sensing materials, which will rationalize high-performance gas sensors. Reference [1] Jeon, M.; Kim, M.; Lee, J. S.; Kim, H.; Choi, S. J.; Moon, H. R.; Kim, J., Computational Prediction of Stacking Mode in Conductive Two-Dimensional Metal-Organic Frameworks: An Exploration of Chemical and Electrical Property Changes. Acs Sensors 2023.[2] Kim, S. J.; Choi, S. J.; Jang, J. S.; Cho, H. J.; Kima, I. D., Innovative Nanosensor for Disease Diagnosis. Accounts Chem Res 2017, 50, (7), 1587-1596.[3] Choi, S. H.; Lee, J. S.; Choi, W. J.; Seo, J. W.; Choi, S. J., Nanomaterials for IoT Sensing Platforms and Point-of-Care Applications in South Korea. Sensors-Basel 2022, 22, (2).[4] Choi, S. J.; Kim, I. D., Recent Developments in 2D Nanomaterials for Chemiresistive-Type Gas Sensors. Electron Mater Lett 2018, 14, (3), 221-260.[5] Choi, S. J.; Persano, L.; Camposeo, A.; Jang, J. S.; Koo, W. T.; Kim, S. J.; Cho, H. J.; Kim, I. D.; Pisignano, D., Electrospun Nanostructures for High Performance Chemiresistive and Optical Sensors. Macromol Mater Eng 2017, 302, (8).

Pharmacometabolomics via real-time breath analysis captures metabotypes of asthmatic children associated with salbutamol responsiveness

Asthma is a widespread respiratory disease affecting millions of children. Salbutamol is a well-established bronchodilator available to treat asthma. However, response to bronchodilators is very heterogeneous, particularly in children. Pharmacometabolomics via exhaled breath analysis holds promise for patient stratification. Here, we integrate a real-time breath analysis platform in the workflow of an outpatient clinic to provide a detailed metabolic snapshot of patients with asthma undergoing standard clinical evaluations. We observed significant metabolic changes associated with salbutamol inhalation within ∼1 h. Our data supports the hypothesis that sphingolipid metabolism and arginine biosynthesis mediate the bronchodilator effect of salbutamol. Clustering analysis of 30 metabolites associated with these pathways revealed characteristic metabotypes related to clinical phenotypes of poor bronchodilator responsiveness. We propose that such a metabolic fingerprinting approach may be of utility in clinical practice to quantify response to inhaled medications or asthma outcomes.