Exhaled breath analysis in disease detection

Unveiling the systemic impact of airborne microplastics: Integrating breathomics and machine learning with dual-tissue transcriptomics

Airborne microplastics (MPs) pose significant respiratory and systemic health risks upon inhalation; however, current assessment methods remain inadequate. This study integrates breathomics and transcriptomics to establish a non-invasive approach for evaluating MP-induced damage to the lungs and heart. C57BL/6 mice were exposed to polystyrene MPs (0.1 μm, 2 μm, and 10 μm), and their exhaled volatile organic compounds (VOCs) were analyzed using photoinduced associative ionization time-of-flight mass spectrometry. Machine learning algorithms identified hydrogen sulfide, acetone, acrolein, propionitrile, and butyronitrile as key VOC biomarkers, linking MP exposure to oxidative stress and metabolic dysregulation. Transcriptomic analysis further revealed significant gene expression alterations in pulmonary and cardiac tissues, implicating immune dysregulation, metabolic disturbance, and cardiac dysfunction. Pathway enrichment analysis, supported by histological and immunohistochemical validation, confirmed pulmonary inflammation and cardiac injury. By integrating exhaled biomarker profiling with transcriptomic insights, this study advances non-invasive detection strategies for MP-related health effects, offering valuable prospects for public health monitoring and early diagnosis.

Volatilome and machine learning in ischemic heart disease: Current challenges and future perspectives

Integrating exhaled breath analysis into the diagnosis of cardiovascular diseases holds significant promise as a valuable tool for future clinical use, particularly for ischemic heart disease (IHD). However, current research on the volatilome (exhaled breath composition) in heart disease remains underexplored and lacks sufficient evidence to confirm its clinical validity. Key challenges hindering the application of breath analysis in diagnosing IHD include the scarcity of studies (only three published papers to date), substantial methodological bias in two of these studies, and the absence of standardized protocols for clinical implementation. Additionally, inconsistencies in methodologies—such as sample collection, analytical techniques, machine learning (ML) approaches, and result interpretation—vary widely across studies, further complicating their reproducibility and comparability. To address these gaps, there is an urgent need to establish unified guidelines that define best practices for breath sample collection, data analysis, ML integration, and biomarker annotation. Until these challenges are systematically resolved, the widespread adoption of exhaled breath analysis as a reliable diagnostic tool for IHD remains a distant goal rather than an imminent reality.

Breath biomarkers for esophageal cancer: identification, quantification, and diagnostic modeling

Esophageal cancer is a major global health issue with a high mortality rate. Early diagnosis is crucial for improving patient outcomes, but traditional diagnostic methods are often invasive and costly. This study explores the potential of exhaled volatile organic compounds (VOCs) as a non-invasive diagnostic tool for esophageal cancer. Using gas chromatography-mass spectrometry (GC-MS), we analyzed the breath samples of 80 esophageal cancer patients and 60 healthy controls, identifying and quantifying over 100 VOCs. The results revealed significant differences in the concentrations of VOCs such as acetone, ethanol, and isoprene between the two groups. A multi-parameter regression diagnostic model based on a neural network algorithm achieved an accuracy of 90.3% in distinguishing esophageal cancer patients from healthy individuals. Further optimization incorporating physiological factors, including smoking, drinking, and dietary habits, improved the model's accuracy to 92.4%, with a specificity of 93.1%, representing a significant improvement over previous studies. These results suggest that VOCs analysis in exhaled breath holds great promise as a non-invasive, cost-effective, and accurate method for early detection of esophageal cancer.

Stomach cancer identification based on exhaled breath analysis: a review

Early prediction of cancer is crucial for effective treatment decisions. Stomach cancer is one of the worst malignancies in the world because it does not reveal the growth in symptoms. In recent years, non-invasive diagnostic methods, particularly exhaled breath analysis, have attracted interest in detecting stomach cancer. This review discusses invasive and non-invasive diagnostic methods for stomach cancer, with a special emphasis on breath analysis and electronic nose (e-nose) technology. Various analytical methods have been used to analyze volatile organic compounds (VOCs) associated with stomach cancer. Gas chromatography-mass Spectrometry is one of the most widely used techniques. These techniques enable the detection and analysis of VOCs, offering a promising route for early stomach cancer diagnosis. The e-nose system has been introduced as a cost-effective and portable alternative for VOC detection in stomach cancer to overcome the challenges associated with conventional methods. This review discusses the advantages and disadvantages of the e-nose system. This review recommends that e-nose sensors, combined with advanced pattern recognition techniques, be utilized to enable rapid and reliable diagnosis of stomach cancer.

Portable collection of breath phenylpyruvic acid for noninvasive assessment of pediatric hyperphenylalaninemia by mass spectrometry

Despite the efforts of Hyperphenylalaninemia (HPA) prevention in prenatal testing, many infants with HPA are still found in new-born screening. Because there is no definitively effective cure for HPA, routine examination and monitoring is of paramount importance to effective management of HPA conditions through dietary restriction. Blood phenylalanine (Phe) has been one of the metabolic biomarkers of the HPA and thus is considered as indicators for HPA management but poses a challenge in invasive sampling. Phenylpyruvic acid (PPA) is also a biomarker of HPA with greatly increased levels in blood and urine of the patients. In the present study, a portable breath sampler was developed for in situ extraction and storage of PPA from exhaled breath metabolites of HPA patients and healthy volunteers, and breath PPA was then identified and quantified by mass spectrometry. The analytical parameters of PPA detection were optimized, showing the good sensitivity (LLOQ: 0.04 ng/mL, S/N = 10), reproducibility (intraday RSDs: 6.18 % for 0.2 ng/mL, 7.32 % for 1.0 ng/mL, n = 6; inter-day RSDs: 6.08 % for 0.2 ng/mL, 11.29 % for 1.0 ng/mL, n = 6), and recoveries (intraday: 83.34 % for 0.2 ng/mL, 81.73 % for 1.0 ng/mL, n = 6; inter-day: 88.44 % for 0.2 ng/mL, 85.11 % for 1.0 ng/mL, n = 6), good quantitation capacity (linear range: 0.05-1.0 ng/mL, R2 = 0.9968), and low matrix effect (1.0 ng/mL, 98.25 %). It was observed that there is a significant difference (p < 0.01) of breath PPA between HPA patients and healthy volunteers. Furthermore, the detection results of breath PPA and blood Phe were compared, showing a linear correlation (R2 = 0.6262) of breath PPA and blood Phe. Overall, this work provided valuable insights into breath PPA in HPA patients and showed a potential tool for noninvasive investigating of HPA.

Next-Generation Chemiresistive Wearable Breath Sensors for Non-Invasive Healthcare Monitoring: Advances in Composite and Hybrid Materials

Recently wearable breath sensors have received significant attention in personalized healthcare systems by offering new methods for remote, non-invasive, and continuous monitoring of various health indicators from breath samples without disrupting daily routines. The rising demand for rapid, personalized diagnostics has sparked concerns over electronic waste from short-lived silicon-based devices. To address this issue, the development of flexible and wearable sensors for breath sensing applications is a promising approach. Research highlights the development of different flexible, wearable sensors operating with different operating principles, such as chemiresistive sensors to detect specific target analytes due to their simple design, high sensitivity, selectivity, and reliability. Further, focusing on the non-invasive detection of biomarkers through exhaled breath, chemiresistive wearable sensors offer a comprehensive and environmentally friendly solution. This article presents a comprehensive discussion of the recent advancement in chemiresistive wearable breath sensors for the non-invasive detection of breath biomarkers. The article further emphasizes the intricate development and functioning of the sensor, including the selection criteria for both the flexible substrate and advanced functional materials, including their sensing mechanisms. The review then explores the potential applications of wearable gas sensing systems with specific disease detection, with modern challenges associated with non-invasive breath sensors.

Integrated breath volatolomics and metabolomics analyses reveals novel biomarker panels for the diagnosis of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) represents a major public health challenge, underscoring the need for reliable diagnostic biomarkers. Breath analysis has emerged as a rapid, convenient, and non-invasive diagnostic approach for various diseases. This study aimed to identify potential breath biomarkers associated with COPD using mass spectrometry and bioinformatic analysis. Breath volatile organic compounds (VOCs) and exhaled breath condensate (EBC) were collected from 75 participants, including COPD patients and healthy controls (HC). Untargeted volatolomics and metabolomics analyses identified 150 VOCs and 436 metabolites. Differentially expressed VOCs and metabolites between the COPD and HC groups were identified. LASSO logistic classification models were constructed and optimized based on differentially expressed VOCs, metabolites, and their combined data. The optimized diagnostic model, incorporating 4 VOCs and 3 metabolites, achieved superior performance with an area under the curve (AUC) of 0.97, sensitivity of 0.86, specificity of 0.89, and an accuracy of 0.88 in distinguishing COPD patients from healthy individuals. This study highlights the potential of breath analysis as a non-invasive approach for point-of-care COPD diagnosis and identifies a robust panel of VOCs and metabolites for this purpose. Further research is needed to investigate the underlying mechanisms of these biomarkers and to develop highly specific biosensors for non-invasive breath diagnosis of COPD.

Rapid, non-invasive breath analysis for enhancing detection of silicosis using mass spectrometry and interpretable machine learning

Occupational lung diseases, such as silicosis, are a significant global health concern, especially with increasing exposure to engineered stone dust. Early detection of silicosis is helpful for preventing disease progression, but existing diagnostic methods, including x-rays, computed tomography scans, and spirometry, often detect the disease only at late stages. This study investigates a rapid, non-invasive diagnostic approach using atmospheric pressure chemical ionization-mass spectrometry (APCI-MS) to analyze volatile organic compounds (VOCs) in exhaled breath from 31 silicosis patients and 60 healthy controls. Six different interpretable machine learning (ML) models with Shapley additive explanations (SHAP) were applied to classify these samples and determine VOC features that contribute the most significantly to model accuracy. The extreme gradient boosting classifier demonstrated the highest performance, achieving an area under the receiver-operator characteristic curve of 0.933 with the top ten SHAP features. Them/z442 feature, potentially corresponding to leukotriene-E3, emerged as a significant predictor for silicosis. The VOC sampling and measurement process takes less than five minutes per sample, highlighting its potential suitability for large-scale population screening. Moreover, the ML models are interpretable through SHAP, providing insights into the features contributing to the model's predictions. This study suggests that APCI-MS breath analysis could enable early and non-invasive diagnosis of silicosis, helping to improve disease outcomes.

Exploring exhaled volatile organic compounds as potential biomarkers in anti-MDA5 antibody-positive interstitial lung disease

Interstitial lung diseases (ILDs) are a group of pulmonary disorders characterized by fibrosis, inflammation, and lung tissue deterioration. Anti-melanoma differentiation-associated gene 5-positive dermatomyositis-associated interstitial lung disease (MDA5-ILD), a subtype, is associated with high mortality due to rapid progression and severe lung damage. Volatile organic compounds (VOCs) in exhaled breath, reflecting metabolic changes, have been identified as potential non-invasive biomarkers for various diseases, including respiratory conditions. However, their role in MDA5-ILD has not been extensively studied. This retrospective cohort study included 45 exhaled breath samples from 19 ILD patients, with 31 samples from 9 patients with MDA5-ILD and 10 samples from 7 patients with non-MDA5-ILD. VOCs were collected using thermal desorption tubes and analyzed via gas chromatography-mass spectrometry (GC-MS). Clinical data, including the APACHE II score, were integrated with VOC profiles. Two logistic regression models were developed: Model 1 based on 11 clinical indicators, and Model 2 integrating 11 clinical indicators with 5 VOC features. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, sensitivity, specificity, and accuracy metrics. Five VOCs-N-(2-Aziridinyl)ethanamine, Cyclohexanone, Nonanal, Dodecamethylcyclohexasiloxane, and 4-Methyltetradecane-were identified as significant biomarkers distinguishing MDA5-ILD from non-MDA5-ILD. Model 2, which integrated VOC data, outperformed Model 1, achieving an area under the curve (AUC) of 0.93 compared to 0.70. Model 2 also demonstrated enhanced accuracy (84.6% vs. 76.9%), specificity (66.7% vs. 33.3%), precision (90.0% vs. 81.8%), and F1-score (90.0% vs. 85.7%). Additionally, 1,3-Pentadiene and 3-Methylundecane were identified as potential markers of disease severity, with 1,3-Pentadiene negatively correlating and 3-Methylundecane positively correlating with both APACHE II scores and creatinine levels. VOCs in exhaled breath significantly enhance the diagnostic sensitivity and accuracy for detecting MDA5-ILD. In addition, VOCs show promise as disease severity markers, potentially aiding in the assessment of disease severity and progression. While the integration of VOCs holds great potential for improving diagnostic performance, further validation through larger, multicenter studies is necessary.

Design and Performance Analysis of Novel Mid-Infrared Enhanced Hollow Waveguide for Gas Isotope Ratio Measurements

Gas isotope ratio measurements based on laser spectroscopy are often used for ecosystem and medical analysis. This paper reports for the first time the design of a novel mid-infrared enhanced hollow waveguide (EHWG) gas sensor based on multiple-reflection cavity-enhanced absorption spectroscopy (CEAS). This method avoids the shortcomings of existing hollow waveguide (HWG) single path measurement, while the effective optical path length and measurement limit have also been greatly increased. Based on a 4.32 μm quantum cascade laser, three stable heavy isotope ratios in carbon dioxide were simultaneously measured, and the measurement precision (δ13C ∼ 0.36‰, δ18O ∼ 0.46‰, δ17O ∼ 0.88‰) was found to be better compared to previous studies. By comparing the 13C-urea breath "gold standard" test in hospital physical examinations, the enormous potential of this method in manufacturing miniaturized, broad-spectrum, multifunctional, and lightweight is demonstrated, and it is expected to become the first choice for trace volume gas measurements in the new generation.

Diagnostic accuracy of breath tests based on volatile organic compounds for cancer: A systematic review and meta-analysis

Exhaled volatile organic compounds (VOCs) are being extensively studied for the purposes of noninvasive cancer diagnoses. This systematic review and meta-analysis aims to evaluate the diagnostic accuracy of breath tests based on VOCs for cancer detection, and to propose potential cancer biomarkers. This study was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. Relevant studies up to February 2024 were retrieved from public databases, including PubMed, EMBASE, and Web of Science. A total of 114 articles were included, covering 125 non-duplicate studies involving 8768 cancer patients. Meta-analysis showed that the VOC breath test demonstrated a sensitivity of 87% and a specificity of 81% in cancer diagnosis, with an area under the receiver operating characteristic curve (AUC) of 0.93. Subgroup analyses based on cancer types and breath detection techniques also showed high sensitivity and specificity in diagnosing cancer patients. These suggest that breath analysis for VOCs has excellent diagnostic accuracy for cancer. The breath test based on VOCs, as a non-invasive detection method, shows great potential for cancer diagnosis.

Advancements in Breathomics: Special Focus on Electrochemical Sensing and AI for Chronic Disease Diagnosis and Monitoring

This Review examines the potential of breathomics in enhancing disease monitoring and diagnostic precision when integrated with artificial intelligence (AI) and electrochemical sensing techniques. It discusses breathomics' potential for early and noninvasive disease diagnosis with a focus on chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), and lung cancer, which have been well studied in the context of VOC association with diseases. The noninvasive nature of exhaled breath analysis can be advantageous compared to traditional diagnostic methods for CKD, which often rely on blood and urine testing. VOC analysis can enhance spirometry and imaging methods used in COPD diagnosis, providing a more comprehensive picture of the disease's progression. Breathomics could also provide a less intrusive and potentially earlier diagnostic approach for lung cancer, which is now dependent on imaging and biopsy. The combination of breathomics, electrochemical sensing, and AI could lead to more personalized and successful treatment plans for chronic illnesses using AI algorithms to decipher complicated VOC patterns. This Review assesses the viability and effectiveness of combining breathomics with electrochemical sensors and artificial intelligence by synthesizing recent research findings and technological developments.

Feasibility of detecting non-small cell lung cancer using exhaled breath condensate metabolomics

Lung cancer is one of the most common malignancy in the world, and early detection of lung cancer remains a challenge. The exhaled breath condensate (EBC) from lung and trachea can be collected totally noninvasively. In this study, our aim is to identify differential metabolites between non-small cell lung cancer (NSCLC) and control EBC samples and discriminate NSCLC group from control group by orthogonal projections to latent structures-discriminant analysis (OPLS-DA) models. The EBC differential metabolites between NSCLC patients (n= 29) and controls (n= 24) (20 healthy and 4 benign individuals) were identified using ultra-performance liquid chromatography-high resolution mass spectrometry based untargeted metabolomics method. The upregulated metabolites in EBC of NSCLC included amino acids and derivatives (phenylalanine, tryptophan, 1-carboxyethylisoleucine/1-carboxyethylleucine, and 2-octenoylglycine), dipeptides (leucyl-phenylalanine, leucyl-leucine, leucyl-histidine/isoleucyl-histidine, and prolyl-valine), and fatty acids (tridecenoic acid, hexadecadienoic acid, tetradecadienoic acid, 9,12,13-trihydroxyoctadec-10-enoic acid/9,10,13-trihydroxyoctadec-11-enoic acid (9,12,13-TriHOME/9,10,13-TriHOME), 3-hydroxysebacic acid/2-hydroxydecanedioic acid, 9-oxooctadeca-10,12-dienoic acid/9,10-Epoxy-12,15-octadecadienoate (9-oxoODE/9(10)-EpODE), and suberic acid). The downregulated metabolites in EBC of NSCLC were 3,4-methylenesebacic acid, 2-isopropylmalic acid/3-isopropylmalic acid/2,3-dimethyl-3-hydroxyglutaric acid, and trimethylamine-N-oxide. The OPLS-DA model based on 5 EBC metabolites achieved 86.2% sensitivity, 83.3% specificity and 84.9% accuracy, showing a potential to distinguish NSCLC patients from controls.

A comprehensive non-targeted approach for the analysis of biomarkers in exhaled breath across different nicotine product categories

In the context of the evolving landscape of nicotine consumption, the assessment of biomarkers plays a crucial role in understanding the health impact of different product categories. Exhaled breath (EB) emerges as a promising, non-invasive matrix for biomarker analysis, complementary to conventional urine and plasma data. This study explores distinctive EB biomarker profiles among users of combustible cigarettes (CC), heated tobacco products (HTP), electronic cigarettes (EC), smokeless/oral tobacco (OT), and oral/dermal nicotine products (NRT). We have successfully developed and validated a non-targeted GC-TOF-MS method for the analysis of EB samples across the aforementioned product categories. A total of 66 compounds were identified, with significantly elevated levels in at least one study group. The study found that CC users had higher levels of established VOCs associated with smoking, which supports the proof-of-concept of the method. Breathomic analysis identified increased levels of p-cymene and α-pinene in EC users, while HTP users showed potential biomarker candidates like γ-butyrolactone. This study underscores the utility of EB biomarkers for a comprehensive evaluation of diverse nicotine products. The unique advantages offered by EB analysis position it as a valuable tool for understanding the relationship between exposure and health outcomes.

Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine

Biopsy, including tissue and liquid biopsy, offers comprehensive and real-time physiological and pathological information for disease detection, diagnosis, and monitoring. Fluorescent probes are frequently selected to obtain adequate information on pathological processes in a rapid and minimally invasive manner based on their advantages for biopsy. However, conventional fluorescent probes have been found to show aggregation-caused quenching (ACQ) properties, impeding greater progresses in this area. Since the discovery of aggregation-induced emission luminogen (AIEgen) have promoted rapid advancements in molecular bionanomaterials owing to their unique properties, including high quantum yield (QY) and signal-to-noise ratio (SNR), etc. This review seeks to present the latest advances in AIEgen-based biofluorescent probes for biopsy in real or artificial samples, and also the key properties of these AIE probes. This review is divided into: (i) tissue biopsy based on smart AIEgens, (ii) blood sample biopsy based on smart AIEgens, (iii) urine sample biopsy based on smart AIEgens, (iv) saliva sample biopsy based on smart AIEgens, (v) biopsy of other liquid samples based on smart AIEgens, and (vi) perspectives and conclusion. This review could provide additional guidance to motivate interest and bolster more innovative ideas for further exploring the applications of various smart AIEgens in precision medicine.

A hybrid learning approach to better classify exhaled breath's infrared spectra: A noninvasive optical diagnosis for socially significant diseases

Early diagnosis is crucial for effective treatment of socially significant diseases, such as type 1 diabetes mellitus (T1DM), pneumonia, and asthma. This study employs a diagnostic method based on infrared laser spectroscopy of human exhaled breath. The experimental setup comprises a quantum cascade laser, which emits in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3-12.8 μm (780-1890 cm-1), and a Herriott multipass gas cell with a specific optical path length of 76 m. Using this setup, spectra of exhaled breath in the mid-infrared range were obtained from 165 volunteers, including healthy individuals, patients with T1DM, asthma, and pneumonia. The study proposes a hybrid approach for classifying these spectra, utilizing a variational autoencoder for dimensionality reduction and a support vector machine method for classification. The results demonstrate that the proposed hybrid approach outperforms other machine learning method combinations.

Functionalized Face Masks as Smart Wearable Sensors for Multiple Sensing

Wearable sensors provide continuous physiological information and measure deviations from healthy baselines, resulting in the potential to personalize health management and diagnosis of diseases. With the emergence of the COVID-19 pandemic, functionalized face masks as smart wearable sensors for multimodal and/or multiplexed measurement of physical parameters and biochemical markers have become the general population for physiological health management and environmental pollution monitoring. This Review examines recent advances in applications of smart face masks based on implantation of digital technologies and electronics and focuses on respiratory monitoring applications with the advantages of autonomous flow driving, enrichment enhancement, real-time monitoring, diversified sensing, and easily accessible. In particular, the detailed introduction of diverse respiratory signals including physical, inhalational, and exhalant signals and corresponding associations of health management and environmental pollution is presented. In the end, we also provide a personal perspective on future research directions and the remaining challenges in the commercialization of smart functionalized face masks for multiple sensing.

Nose-on-Chip Nanobiosensors for Early Detection of Lung Cancer Breath Biomarkers

Lung cancer remains a global health concern, demanding the development of noninvasive, prompt, selective, and point-of-care diagnostic tools. Correspondingly, breath analysis using nanobiosensors has emerged as a promising noninvasive nose-on-chip technique for the early detection of lung cancer through monitoring diversified biomarkers such as volatile organic compounds/gases in exhaled breath. This comprehensive review summarizes the state-of-the-art breath-based lung cancer diagnosis employing chemiresistive-module nanobiosensors supported by theoretical findings. It unveils the fundamental mechanisms and biological basis of breath biomarker generation associated with lung cancer, technological advancements, and clinical implementation of nanobiosensor-based breath analysis. It explores the merits, challenges, and potential alternate solutions in implementing these nanobiosensors in clinical settings, including standardization, biocompatibility/toxicity analysis, green and sustainable technologies, life-cycle assessment, and scheming regulatory modalities. It highlights nanobiosensors' role in facilitating precise, real-time, and on-site detection of lung cancer through breath analysis, leading to improved patient outcomes, enhanced clinical management, and remote personalized monitoring. Additionally, integrating these biosensors with artificial intelligence, machine learning, Internet-of-things, bioinformatics, and omics technologies is discussed, providing insights into the prospects of intelligent nose-on-chip lung cancer sniffing nanobiosensors. Overall, this review consolidates knowledge on breathomic biosensor-based lung cancer screening, shedding light on its significance and potential applications in advancing state-of-the-art medical diagnostics to reduce the burden on hospitals and save human lives.

Advancing Lung Cancer Diagnosis through NH2-MON-SPME-GC-MS/MS: Enhanced Sensitivity in Aldehyde Biomarker Detection from Exhaled Breath

The sensitive detection of trace biomarkers in exhaled breath for lung cancer diagnosis represents a critical area of research in life analytical chemistry, with profound implications for early disease detection, therapeutic intervention, and prognosis monitoring. Despite its potential, the analytical process faces significant challenges due to the ultratrace levels of disease biomarkers present and the complex, high-humidity composition of exhaled breath. This study introduces a highly sensitive method for detecting aldehyde biomarkers in exhaled breath by integrating the use of amino-functionalized microporous organic networks (NH2-MON) as a solid-phase microextraction (SPME) fiber coating with gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS) analysis. The method innovatively combines sample collection and extraction, achieving a dual-step enrichment process that significantly enhances both the enrichment efficiency and reproducibility of biomarker detection while effectively mitigating the interference caused by water vapor in exhaled breath. The NH2-MON, utilized as an SPME fiber coating, demonstrates exceptional enrichment capacity for five key aldehyde biomarkers, facilitating the development of a highly sensitive detection approach for these biomarkers in exhaled breath. Compared to previously reported methods, the proposed technique exhibits significantly lower limits of quantification, ranging from 0.77 to 11.89 pg mL-1, and achieves substantially higher enrichment factors, ranging from 9156- to 35723-fold. The practicality and feasibility of the method were validated through the analysis of exhaled breath samples from lung cancer patients, underscoring its potential application in the early diagnosis and monitoring of lung cancer.

Volatile organic compound analysis of malignant pleural mesothelioma chorioallantoic membrane xenografts

Malignant pleural mesothelioma (MPM) is an aggressive cancer associated with asbestos exposure. MPM is often diagnosed late, at a point where limited treatment options are available, but early intervention could improve the chances of successful treatment for MPM patients. Biomarkers to detect MPM in at-risk individuals are needed to implement early diagnosis technologies. Volatile organic compounds (VOCs) have previously shown diagnostic potential as biomarkers when analysed in MPM patient breath. In this study, chorioallantoic membrane (CAM) xenografts of MPM cell lines were used as models of MPM tumour development for VOC biomarker discovery with the aim of generating targets for investigation in breath, biopsies or other complex matrices. VOC headspace analysis of biphasic or epithelioid MPM CAM xenografts was performed using solid-phase microextraction and gas chromatography-mass spectrometry. We successfully demonstrated the capture, analysis and separation of VOC signatures from CAM xenografts and controls. A panel of VOCs was identified that showed discrimination between MPM xenografts generated from biphasic and epithelioid cells and CAM controls. This is the first application of the CAM xenograft model for the discovery of VOC biomarkers associated with MPM histological subtypes. These findings support the potential utility of non-invasive VOC profiling from breath or headspace analysis of tissues for detection and monitoring of MPM.

Multipressure Sampling for Improving the Performance of MOF-based Electronic Noses

Metal-organic frameworks (MOFs) are a promising class of porous materials for the design of gas sensing arrays, which are often called electronic noses. Due to their chemical and structural tunability, MOFs are a highly diverse class of materials that align well with the similarly diverse class of volatile organic compounds (VOCs) of interest in many gas detection applications. In principle, by choosing the right combination of cross-sensitive MOFs, layered on appropriate signal transducers, one can design an array that yields detailed information about the composition of a complex gas mixture. However, despite the vast number of MOFs from which one can choose, gas sensing arrays that rely too heavily on distinct chemistries can be impractical from the cost and complexity perspective. On the other hand, it is difficult for small arrays to have the desired selectivity and sensitivity for challenging sensing applications, such as detecting weakly adsorbing gases with weak signals, or conversely, strongly adsorbing gases that readily saturate MOF pores. In this work, we employed gas adsorption simulations to explore the use of a variable pressure sensing array as a means of improving both sensitivity and selectivity as well as increasing the information content provided by each array. We studied nine different MOFs (HKUST-1, IRMOF-1, MgMOF-74, MOF-177, MOF-801, NU-100, NU-125, UiO-66, and ZIF-8) and four different gas mixtures, each containing nitrogen, oxygen, carbon dioxide, and exactly one of the hydrogen, methane, hydrogen sulfide, or benzene. We found that by lowering the pressure, we can limit the saturation of MOFs, and by raising the pressure, we can concentrate weakly adsorbing gases, in both cases, improving gas detection with the resulting arrays. In many cases, changing the system pressure yielded a better improvement in performance (as measured by the Kullback-Liebler divergence of gas composition probability distributions) than including additional MOFs. We thus demonstrated and quantified how sensing at multiple pressures can increase information content and cross-sensitivity in MOF-based arrays while limiting the number of unique materials needed in the device.

A comprehensive meta-analysis and systematic review of breath analysis in detection of COVID-19 through Volatile organic compounds

BACKGROUND

The COVID-19 pandemic had profound global impacts on daily lives, economic stability, and healthcare systems. Diagnosis of COVID-19 infection via RT-PCR was crucial in reducing spread of disease and informing treatment management. While RT-PCR is a key diagnostic test, there is room for improvement in the development of diagnostic criteria. Identification of volatile organic compounds (VOCs) in exhaled breath provides a fast, reliable, and economically favorable alternative for disease detection.

METHODS

This meta-analysis analyzed the diagnostic performance of VOC-based breath analysis in detection of COVID-19 infection. A systematic review of twenty-nine papers using the grading criteria from Newcastle-Ottawa Scale (NOS) and PRISMA guidelines was conducted.

RESULTS

The cumulative results showed a sensitivity of 0.92 (95 % CI, 90 %-95 %) and a specificity of 0.90 (95 % CI 87 %-93 %). Subgroup analysis by variant demonstrated strong sensitivity to the original strain compared to the Omicron and Delta variant in detection of SARS-CoV-2 infection. An additional subgroup analysis of detection methods showed eNose technology had the highest sensitivity when compared to GC-MS, GC-IMS, and high sensitivity-MS.

CONCLUSION

Overall, these results support the use of breath analysis as a new detection method of COVID-19 infection.

Ultra-sensitive analysis of exhaled biomarkers in ozone-exposed mice via PAI-TOFMS assisted with machine learning algorithms

Ground-level ozone ranks sixth among common air pollutants. It worsens lung diseases like asthma, emphysema, and chronic bronchitis. Despite recent attention from researchers, the link between exhaled breath and ozone-induced injury remains poorly understood. This study aimed to identify novel exhaled biomarkers in ozone-exposed mice using ultra-sensitive photoinduced associative ionization time-of-flight mass spectrometry and machine learning. Distinct ion peaks for acetonitrile (m/z 42, 60, and 78), butyronitrile (m/z 70, 88, and 106), and hydrogen sulfide (m/z 35) were detected. Integration of tissue characteristics, oxidative stress-related mRNA expression, and exhaled breath condensate free-radical analysis enabled a comprehensive exploration of the relationship between ozone-induced biological responses and potential biomarkers. Under similar exposure levels, C57BL/6 mice exhibited pulmonary injury characterized by significant inflammation, oxidative stress, and cardiac damage. Notably, C57BL/6 mice showed free radical signals, indicating a distinct susceptibility profile. Immunodeficient non-obese diabetic Prkdc-/-/Il2rg-/- (NPI) mice exhibited minimal biological responses to pulmonary injury, with little impact on the heart. These findings suggest a divergence in ozone-induced damage pathways in the two mouse types, leading to alterations in exhaled biomarkers. Integrating biomarker discovery with comprehensive biopathological analysis forms a robust foundation for targeted interventions to manage health risks posed by ozone exposure.

Real-Time Monitoring of Air Pollution Health Impacts Using Breath-Borne Gaseous Biomarkers from Rats

Offline techniques are adopted for studying air pollution health impacts, thus failing to provide in situ observations. Here, we have demonstrated their real-time monitoring by online analyzing an array of gaseous biomarkers from rats' exhaled breath using an integrated exhaled breath array sensor (IEBAS) developed. The biomarkers include total volatile organic compounds (TVOC), CO2, CO, NO, H2S, H2O2, O2, and NH3. Specific breath-borne VOCs were also analyzed by a gas chromatography-ion mobility spectrometer (GC-IMS). After real-life ambient air pollution exposures (2 h), the pollution levels of PM2.5 and O3 were both found to significantly affect the relative levels of multiple gaseous biomarkers in rats' breath. Eleven biomarkers, especially NO, H2S, and 1-propanol, were detected as significantly correlated with PM2.5 concentration, while heptanal was shown to be significantly correlated with O3. Likewise, significant changes were also detected in multiple breath-borne biomarkers from rats under lab-controlled O3 exposures with levels of 150, 300, and 1000 μg/m3 (2 h), compared to synthetic air exposure. Importantly, heptanal was experimentally confirmed as a reliable biomarker for O3 exposure, with a notable dose-response relationship. In contrast, conventional biomarkers of inflammation and oxidative stress in rat sera exhibited insignificant differences after the 2 h exposures. The results imply that breath-borne gaseous biomarkers can serve as an early and sensitive indicator for ambient pollutant exposure. This work pioneered a new research paradigm for online monitoring of air pollution health impacts while obtaining important candidate biomarker information for PM2.5 and O3 exposures.

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

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

Research progress of electronic nose technology in exhaled breath disease analysis

Exhaled breath analysis has attracted considerable attention as a noninvasive and portable health diagnosis method due to numerous advantages, such as convenience, safety, simplicity, and avoidance of discomfort. Based on many studies, exhaled breath analysis is a promising medical detection technology capable of diagnosing different diseases by analyzing the concentration, type and other characteristics of specific gases. In the existing gas analysis technology, the electronic nose (eNose) analysis method has great advantages of high sensitivity, rapid response, real-time monitoring, ease of use and portability. Herein, this review is intended to provide an overview of the application of human exhaled breath components in disease diagnosis, existing breath testing technologies and the development and research status of electronic nose technology. In the electronic nose technology section, the three aspects of sensors, algorithms and existing systems are summarized in detail. Moreover, the related challenges and limitations involved in the abovementioned technologies are also discussed. Finally, the conclusion and perspective of eNose technology are presented.

Effect of ambient temperature and respiration rate on nasal dominance: preliminary findings from a nostril-specific wearable

The nasal dominance (ND) determination is crucial for nasal synchronized ventilator, optimum nasal drug delivery, identifying brain hemispheric dominance, nasal airway obstruction surgery, mindfulness breathing, and for possible markers of a conscious state. Given these wider applications of ND, it is interesting to understand the patterns of ND with varying temperature and respiration rates. In this paper, we propose a method which measures peak-to-peak temperature oscillations (difference between end-expiratory and end-inspiratory temperature) for the left and right nostrils during nasal breathing. These nostril-specific temperature oscillations are further used to calculate the nasal dominance index (NDI), nasal laterality ratio (NLR), inter-nostril correlation, and mean of peak-to-peak temperature oscillation for inspiratory and expiratory phase at (1) different ambient temperatures of 18 °C, 28 °C, and 38 °C and (2) at three different respiration rate of 6 bpm, 12 bpm, and 18 bpm. The peak-to-peak temperature (Tpp) oscillation range (averaged across participants;n= 8) for the left and right nostril were 3.80 ± 0.57 °C and 2.34 ± 0.61 °C, 2.03 ± 0.20 °C and 1.40 ± 0.26 °C, and 0.20 ± 0.02 °C and 0.29 ± 0.03 °C at the ambient temperature of 18 °C, 28 °C, and 38 °C respectively (averaged across participants and respiration rates). The NDI and NLR averaged across participants and three different respiration rates were 35.67 ± 5.53 and 2.03 ± 1.12; 8.36 ± 10.61 and 2.49 ± 3.69; and -25.04 ± 14.50 and 0.82 ± 0.54 at the ambient temperature of 18 °C, 28 °C, and 38 °C respectively. The Shapiro-Wilk test, and non-parametric Friedman test showed a significant effect of ambient temperature conditions on both NDI and NLR. No significant effect of respiration rate condition was observed on both NDI and NLR. The findings of the proposed study indicate the importance of ambient temperature while determining ND during the diagnosis of breathing disorders such as septum deviation, nasal polyps, nosebleeds, rhinitis, and nasal fractions, and in the intensive care unit for nasal synchronized ventilator.

Detection of abused drugs in human exhaled breath using mass spectrometry: A review

RATIONALE

Human breath analysis has been attracting increasing interest in the detection of abused drugs in forensic and clinical applications because of its noninvasive sampling and distinctive molecular information. Mass spectrometry (MS)-based approaches have been proven to be powerful tools for accurately analyzing exhaled abused drugs. The major advantages of MS-based approaches include high sensitivity, high specificity, and versatile couplings with various breath sampling methods.

METHODS

Recent advances in the methodological development of MS analysis of exhaled abused drugs are discussed. Breath collection and sample pretreatment methods for MS analysis are also introduced.

RESULTS

Recent advances in technical aspects of breath sampling methods are summarized, highlighting active and passive sampling. MS methods for detecting different exhaled abused drugs are reviewed, emphasizing their features, advantages, and limitations. The future trends and challenges in MS-based breath analysis of exhaled abused drugs are also discussed.

CONCLUSIONS

The coupling of breath sampling methods with MS approaches has been proven to be a powerful tool for the detection of exhaled abused drugs, offering highly attractive results in forensic investigations. MS-based detection of exhaled abused drugs in exhaled breath is a relatively new field and is still in the early stages of methodological development. New MS technologies promise a substantial benefit for future forensic analysis.

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

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

A nanostructured Al-doped ZnO as an ultra-sensitive room-temperature ammonia gas sensor

Novel chemi-resistive gas sensors with strong detection capabilities operating at room temperature are desirable owing to their extended cycle life, high stability, and low power consumption. The current study focuses on detecting NH3 at room temperature using lower gas concentrations. The co-precipitation technique was employed to produce pure and Al-doped ZnO nanoparticles, which were calcined at 300 °C for three hours. The effect of aluminium (Al) doping on the structural, morphological, optical, and gas-sensing abilities was investigated and reported. The presence of aluminium was confirmed by XRD, EDX, and FTIR spectroscopy. Additionally, to assess the various characteristics of Al-doped ZnO nanoparticles, scanning electron microscopy (SEM), ultraviolet-visible diffuse reflectance spectroscopy (UV-DRS), atomic force microscopy (AFM), and Brunauer-Emmett-Teller (BET) techniques were used. The crystallite size increased from 14.82 to 17.49 nm in the XRD analysis; the SEM pictures showed a flower-like morphology; and the energy gap decreased from 3.240 to 3.210 eV when Al doping was raised from 1 wt% to 4 wt%. AFM studies revealed topographical information with significant roughness in the range of 230-43 nm. BET analysis showed a mesoporous nature with surface areas varying from 25.274 to 14.755 m2/g and pore diameters ranging from 8.34 to 7.00 nm. The sensing capacities of pure and Al-doped ZnO nanoparticles towards methanol (CH3OH), toluene (C7H8), ethanol (C2H5OH), and ammonia (NH3) were investigated at room temperature. The one-wt% Al-doped ZnO sensor demonstrated an ultrafast response and recovery times at one ppm compared to other AZO-based sensors towards NH3.

Hollow-Out Fe2O3-Loaded NiO Heterojunction Nanorods Enable Real-Time Exhaled Ethanol Monitoring under High Humidity

The analysis of exhaled breath has opened up new exciting avenues in medical diagnostics, sleep monitoring, and drunk driving detection. Nevertheless, the detection accuracy is greatly affected due to high humidity in the exhaled breath. Here, we propose a regulation method to solve the problem of humidity adaptability in the ethanol-monitoring process by building a heterojunction and hollow-out nanostructure. Therefore, large specific surface area hollow-out Fe₂O₃-loaded NiO heterojunction nanorods assembled by porous ultrathin nanosheets were prepared by a well-tailored interface reaction. The excellent response (51.2 toward 10 ppm ethanol at 80% relative humidity) and selectivity to ethanol under high relative humidity with a lower operating temperature (150 °C) were obtained, and the detection limit was as low as 0.5 ppb with excellent long-term stability. The superior gas-sensing performance was attributed to the high surface activity of the heterojunction and hollow-out nanostructure. More importantly, GC-MS, diffuse reflectance Fourier transform infrared spectroscopy, and DFT were utilized to analyze the mechanisms of heterojunction sensitization, ethanol-sensing reaction, and high-humidity adaptability. Our integrated low-power MEMS Internet of Things (IoT) system based on Fe₂O₃@NiO successfully demonstrates the functional verification of ethanol detection in human exhalation, and the integrated voice alarm and IoT positioning functions are expected to solve the problem of real-time monitoring and rapid initial screening of drunk driving. Overall, this novel method plays a vital role in areas such as control of material morphology and composition, breath analysis, gas-sensing mechanism research, and artificial olfaction.

Two-Dimensional Nitrogen-Doped Carbon Nanosheets Derived from g-C3N4 /ZIF-8 for Solid-Phase Microextraction in Exhalation of Esophageal Cancer Patients

Here, two-dimensional (2D) nitrogen-doped carbon nanosheets (CNSs) were prepared through carbonizing MOFs (ZIF-8) in-situ grown using graphitic carbon nitride (g-C₃N₄) as a template. The developed ZIF-8 CNS was then used as solid-phase microextraction (SPME) fiber coating for beneficiation of five biomarkers in exhalation of patients with esophageal cancer and in gas chromatography-mass spectrometry (GC-MS) for determination. The ZIF-8 CNS fiber exhibits satisfactory enrichment factors (3490-5631), wide linearity (5-1000 μg L^-1), and low detection limits (0.26-0.96 μg L^-1). The relative standard deviations (RSDs) for six replicate extractions of the same ZIF-8 CNS fiber were between 2.0-3.9% (intra-day) and 2.8-5.2% (inter-day). The reproducibility of three fibers prepared by the same approach was in the range 6.8-12.3% (RSD). The developed ZIF-8 CNS fiber can persist in 120 SPME cycles with no prominent loss of extraction efficiency and precision. The high enrichment factors of the 2D ZIF-8 CNS coatings are attributed to the high specific surface area, ultrathin thickness, and nano-pore or interlayer channels; moreover, nitrogen doping also endows the π system with a strong electron absorption ability, which will enhance the π-π interaction between the ZIF-8 CNS and the aromatic ring. Ultimately, the self-made ZIF-8 CNS-coated SPME fiber was applied to the analysis of exhaled breath samples. The recoveries of spiked analytes are between 84 and 105%.

In situ laser-assisted synthesis and patterning of graphene foam composites as a flexible gas sensing platform

Gas-sensitive semiconducting nanomaterials (e.g., metal oxides, graphene oxides, and transition metal dichalcogenides) and their heterojunctions hold great promise in chemiresistive gas sensors. However, they often require a separate synthesis method (e.g., hydrothermal, so-gel, and co-precipitation) and their integration on interdigitated electrodes (IDE) via casting is also associated with weak interfacial properties. This work demonstrates in situ laser-assisted synthesis and patterning of various sensing nanomaterials and their heterojunctions on laser-induced graphene (LIG) foam to form LIG composites as a flexible and stretchable gas sensing platform. The porous LIG line or pattern with nanomaterial precursors dispensed on top is scribed by laser to allow for in situ growth of corresponding nanomaterials. The versatility of the proposed method is highlighted through the creation of different types of gas-sensitive materials, including transition metal dichalcogenide (e.g., MoS2), metal oxide (e.g., CuO), noble metal-doped metal oxide (e.g., Ag/ZnO) and composite metal oxides (e.g., In2O3/Cr2O3). By eliminating the IDE and separate heaters, the LIG gas sensing platform with self-heating also decreases the device complexity. The limit of detection (LOD) of the LIG gas sensor with in situ synthesized MoS2, CuO, and Ag/ZnO to NO2, H2S, and trimethylamine (TMA) is 2.7, 9.8, and 5.6 ppb, respectively. Taken together with the high sensitivity, good selectivity, rapid response/recovery, and tunable operating temperature, the integrated LIG gas sensor array can identify multiple gas species in the environment or exhaled breath.

Mask assistance to colorimetric sniffers for detection of Covid-19 disease using exhaled breath metabolites

According to World Health Organization reports, large numbers of people around the globe have been infected or died for Covid-19 due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Researchers are still trying to find a rapid and accurate diagnostic method for revealing infected people by low viral load with the overriding goal of effective diagnostic management. Monitoring the body metabolic changes is known as an effective and inexpensive approach for the evaluation of the infected people. Here, an optical sniffer is introduced to detect exhaled breath metabolites of patients with Covid-19 (60 samples), healthy humans (55 samples), and cured people (15 samples), providing a unique color pattern for differentiation between the studied samples. The sniffer device is installed on a thin face mask, and directly exposed to the exhaled breath stream. The interactions occurring between the volatile compounds and sensing components such as porphyrazines, modified organic dyes, porphyrins, inorganic complexes, and gold nanoparticles allowing for the change of the color, thus being tracked as the sensor responses. The assay accuracy for the differentiation between patient, healthy and cured samples is calculated to be in the range of 80%-84%. The changes in the color of the sensor have a linear correlation with the disease severity and viral load evaluated by rRT-PCR method. Interestingly, comorbidities such as kidney, lung, and diabetes diseases as well as being a smoker may be diagnosed by the proposed method. As a powerful detection device, the breath sniffer can replace the conventional rapid test kits for medical applications.

Mask assistance to colorimetric sniffers for detection of Covid-19 disease using exhaled breath metabolites

According to World Health Organization reports, large numbers of people around the globe have been infected or died for Covid-19 due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Researchers are still trying to find a rapid and accurate diagnostic method for revealing infected people by low viral load with the overriding goal of effective diagnostic management. Monitoring the body metabolic changes is known as an effective and inexpensive approach for the evaluation of the infected people. Here, an optical sniffer is introduced to detect exhaled breath metabolites of patients with Covid-19 (60 samples), healthy humans (55 samples), and cured people (15 samples), providing a unique color pattern for differentiation between the studied samples. The sniffer device is installed on a thin face mask, and directly exposed to the exhaled breath stream. The interactions occurring between the volatile compounds and sensing components such as porphyrazines, modified organic dyes, porphyrins, inorganic complexes, and gold nanoparticles allowing for the change of the color, thus being tracked as the sensor responses. The assay accuracy for the differentiation between patient, healthy and cured samples is calculated to be in the range of 80%-84%. The changes in the color of the sensor have a linear correlation with the disease severity and viral load evaluated by rRT-PCR method. Interestingly, comorbidities such as kidney, lung, and diabetes diseases as well as being a smoker may be diagnosed by the proposed method. As a powerful detection device, the breath sniffer can replace the conventional rapid test kits for medical applications.

Development and validation of a screening model for lung cancer using machine learning: A large-scale, multi-center study of biomarkers in breath

Objectives

Lung cancer (LC) is the largest single cause of death from cancer worldwide, and the lack of effective screening methods for early detection currently results in unsatisfactory curative treatments. We herein aimed to use breath analysis, a noninvasive and very simple method, to identify and validate biomarkers in breath for the screening of lung cancer.

Materials and methods

We enrolled a total of 2308 participants from two centers for online breath analyses using proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS). The derivation cohort included 1007 patients with primary LC and 1036 healthy controls, and the external validation cohort included 158 LC patients and 107 healthy controls. We used eXtreme Gradient Boosting (XGBoost) to create a panel of predictive features and derived a prediction model to identify LC. The optimal number of features was determined by the greatest area under the receiver‐operating characteristic (ROC) curve (AUC).

Results

Six features were defined as a breath-biomarkers panel for the detection of LC. In the training dataset, the model had an AUC of 0.963 (95% CI, 0.941–0.982), and a sensitivity of 87.1% and specificity of 93.5% at a positivity threshold of 0.5. Our model was tested on the independent validation dataset and achieved an AUC of 0.771 (0.718–0.823), and sensitivity of 67.7% and specificity of 73.0%.

Conclusion

Our results suggested that breath analysis may serve as a valid method in screening lung cancer in a borderline population prior to hospital visits. Although our breath-biomarker panel is noninvasive, quick, and simple to use, it will require further calibration and validation in a prospective study within a primary care setting.

Towards Multiplexed and Multimodal Biosensor Platforms in Real-Time Monitoring of Metabolic Disorders

Metabolic syndrome (MS) is a cluster of conditions that increases the probability of heart disease, stroke, and diabetes, and is very common worldwide. While the exact cause of MS has yet to be understood, there is evidence indicating the relationship between MS and the dysregulation of the immune system. The resultant biomarkers that are expressed in the process are gaining relevance in the early detection of related MS. However, sensing only a single analyte has its limitations because one analyte can be involved with various conditions. Thus, for MS, which generally results from the co-existence of multiple complications, a multi-analyte sensing platform is necessary for precise diagnosis. In this review, we summarize various types of biomarkers related to MS and the non-invasively accessible biofluids that are available for sensing. Then two types of widely used sensing platform, the electrochemical and optical, are discussed in terms of multimodal biosensing, figure-of-merit (FOM), sensitivity, and specificity for early diagnosis of MS. This provides a thorough insight into the current status of the available platforms and how the electrochemical and optical modalities can complement each other for a more reliable sensing platform for MS.

Utility of SIFT-MS to evaluate volatile organic compounds in nephropathic patients' breath

Several studies highlighted a correlation between exhaled air volatile organic compounds (VOCs) and some pathological conditions, such as chronic kidney disease (CKD), chronic liver disease, etc. In fact, in literature has been reported that CKD is characterized by an increased concentration of ammonia, trimethylamine (TMA) and isoprene compared to healthy subjects. Currently, there is not a validate and standardized method to detect VOCs. For this purpose, we examined the utility of selected ion flow tube-mass spectrometry (SIFT-MS) to measure VOCs in CKD patients and we evaluated the possible correlation between VOCs and the presence of CKD and its stage. We enrolled 68 CKD patients under conservative therapy and 54 healthy subjects. The analysis of the VOCs of the exhaled air of the enrolled subjects was performed by SIFT-MS. Among all the VOCs analyzed, the most relevant results by ROC curves were observed for TMA, acetone, ammonia and dimethyl sulfide. We found that a breath TMA concentration superior to 26 ppbv characterizes a 6.11 times greater risk of CKD, compared to subjects with lower levels. Moreover, we detected an increased concentration of acetone and ammonia in CKD patients compared to healthy subjects. We highlight the potential utility of SIFT-MS in CKD clinical management.

Clinical trial registry: R.S. 15.19 of 6 February 2019.

Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.

A novel temperature-controlled open source microcontroller based sampler for collection of exhaled breath condensate in point-of-care diagnostics

Exhaled breath condensate (EBC) is an attractive, non-invasive sample for clinical diagnostics. During EBC collection, its composition is influenced by the collection temperature, a factor that is often not thoroughly monitored and controlled. In this study, we assembled a novel, simple, portable, and inexpensive device for EBC collection, able to maintain a stable temperature at any value between -7 °C and +12 °C. The temperature was controlled using a microcontroller and a thermoelectric cooler that was employed to cool the aluminum block holding the glass tube or the polypropylene syringe. The performance of the novel sampler was compared with the passively cooled RTube™ and a simple EBC sampler, in which the temperature was steadily increasing during sampling. The developed sampler was able to maintain a stable temperature within ±1 °C. To investigate the influence of different sampling temperatures (i.e., +12, -7, -80 °C) on the analyte content in EBC, inorganic ions and organic acids were analyzed by capillary electrophoresis with a capacitively coupled contactless conductivity detector. It was shown that the concentration of metabolites decreased significantly with decreasing temperature. The portability and the ability to keep a stable temperature during EBC sampling makes the developed sampler suitable for point-of-care diagnostics.

Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review

Diabetes is a chronic and, according to the state of the art, an incurable disease. Therefore, to treat diabetes, regular blood glucose monitoring is crucial since it is mandatory to mitigate the risk and incidence of hyperglycemia and hypoglycemia. Nowadays, it is common to use blood glucose meters or continuous glucose monitoring via stinging the skin, which is classified as invasive monitoring. In recent decades, non-invasive monitoring has been regarded as a dominant research field. In this paper, electrochemical and electromagnetic non-invasive blood glucose monitoring approaches will be discussed. Thereby, scientific sensor systems are compared to commercial devices by validating the sensor principle and investigating their performance utilizing the Clarke error grid. Additionally, the opportunities to enhance the overall accuracy and stability of non-invasive glucose sensing and even predict blood glucose development to avoid hyperglycemia and hypoglycemia using post-processing and sensor fusion are presented. Overall, the scientific approaches show a comparable accuracy in the Clarke error grid to that of the commercial ones. However, they are in different stages of development and, therefore, need improvement regarding parameter optimization, temperature dependency, or testing with blood under real conditions. Moreover, the size of scientific sensing solutions must be further reduced for a wearable monitoring system.

A headspace collection chamber for whole body volatilomics

The human body secretes a complex blend of volatile organic compounds (VOCs) via the skin, breath and bodily fluids. In this study, we have developed a headspace collection chamber for whole body volatilome profiling.