Evolution of clinical and environmental health applications of exhaled breath research: Review of methods and instrumentation for gas-phase, condensate, and aerosols

Collection of Δ9-tetrahydrocannabinol from breath by liquid secondary adsorption analyzed with mass spectrometry: a technical note

We introduce a novel method for efficient collection of analytes of low volatility from human breath, liquid secondary adsorption (LSA), and the application of this method to drug detection with mass spectrometry. Cannabis legalization has occurred in many jurisdictions, creating a need for a simple method for detection of recency of use. Most existing breath sampling methods rely on a time consuming and complex process of adsorption of the analyte of interest, and still often result in low collection efficiencies. The pilot study shows the capability of a breath capture technique and mass spectrometry add on analysis device (Cannabix Breath Analysis System) to easily collect breath samples in the field and rapidly analyze them without complex sample preparation. The study also shows correlation between the breath data collected with this method and blood Δ9-tetrahydrocannabinol (THC) levels.

Supersulfide metabolome of exhaled breath condensate applied as diagnostic biomarkers for esophageal cancer

Early detection of esophageal cancer is essential for esophagogastroduodenoscopy and histopathological diagnosis. However, endoscopic examinations are sometimes invasive, which limits their clinical application and compliance, and traditional blood tumor markers are unsuitable for cancer screening. The current study aimed to evaluate the usefulness of sulfur metabolites as new biomarkers for esophageal cancer using blood samples and exhaled breath condensate (EBC), which can be readily obtained and is non-invasive. We collected EBC and plasma samples from 50 patients with esophageal cancer and 30 healthy controls. Sulfur metabolome analysis using tandem mass spectrometry was performed to compare the metabolic profile between the two groups. Supersulfide metabolic profiles were different between the two cohorts. Supersulfide metabolome analysis showed that cysteine hydropersulfide (CysSSH) and homocysteine hydropersulfide (HomoCysSSH) were increased in the plasma of patients with esophageal cancer. Elevated levels of HomoCysSSH could distinguish patients with esophageal cancer from healthy subjects (area under the curve [AUC]: 0.93, sensitivity: 89%, specificity: 96%). Interestingly, we also detected an elevation of supersulfides in the EBC analysis. CysSSH levels significantly increased in the EBC recovered from patients with esophageal cancer (AUC: 0.71, sensitivity: 60%, specificity: 96%). In addition, the observed level was correlated with that of HomoCysSSH in the plasma (r = 0.27). Supersulfides, such as CysSSH and HomoCysSSH, are potential biomarkers for detecting esophageal cancer. CysSSH from EBC may serve as a valuable non-invasive biomarker with similar detection ability but with superior precision and convenience compared with the currently available blood biomarkers.

Nanomaterial Innovations and Machine Learning in Gas Sensing Technologies for Real-Time Health Diagnostics

Breath sensors represent a frontier in noninvasive diagnostics, leveraging the detection of volatile organic compounds (VOCs) in exhaled breath for real-time health monitoring. This review highlights recent advancements in breath-sensing technologies, with a focus on the innovative materials driving their enhanced sensitivity and selectivity. Polymers, carbon-based materials like graphene and carbon nanotubes, and metal oxides such as ZnO and SnO2 have demonstrated significant potential in detecting biomarkers related to diseases including diabetes, liver/kidney dysfunction, asthma, and gut health. The structural and operational principles of these materials are examined, revealing how their unique properties contribute to the detection of key respiratory gases like acetone, ammonia (NH3), hydrogen sulfide, and nitric oxide. The complexity of breath samples is addressed through the integration of machine learning (ML) algorithms, including convolutional neural networks (CNNs) and support vector machines (SVMs), which optimize data interpretation and diagnostic accuracy. In addition to sensing VOCs, these devices are capable of monitoring parameters such as airflow, temperature, and humidity, essential for comprehensive breath analysis. This review also explores the expanding role of artificial intelligence (AI) in transforming wearable breath sensors into sophisticated tools for personalized health diagnostics, enabling real-time disease detection and monitoring. Together, advances in sensor materials and ML-based analytics present a promising platform for the future of individualized, noninvasive healthcare.

A handheld biofluorometric system for acetone detection in exhaled breath condensates

As a marker of human metabolism, acetone is important for lipid metabolism monitoring and early detection of diabetes. In this study, we developed a handheld biosensor for acetone based on fluorescence detection by utilizing the enzymatic reaction of secondary alcohol dehydrogenase (S-ADH) with β-nicotinamide adenine dinucleotide (NADH, λex = 340 nm, λem = 490 nm). In the reaction, NADH is oxidized when acetone is reduced to 2-propanol by S-ADH, and the acetone concentration can be measured by detecting the amount of NADH consumed in this reaction. First, we constructed a compact and light-weight fluorometric NADH detection system (209 g for the sensing system and 342 g for the PC), which worked using battery power. Then, sensor characteristics were evaluated after optimization of the working conditions. The developed system was able to quantify acetone in a range of 510 nM-1 mM within 1 minute. The developed battery-operated acetone biosensor demonstrated its ability to measure the acetone concentration in the exhaled breath condensate of 10 healthy subjects at rest (23.4 ± 15.1 μM) and after 16 h of fasting (37.7 ± 14.7 μM) and it distinguished the results with significant differences (p = 0.011). With the advantages of handheld portability, and high levels of sensitivity and selectivity, this sensor is expected to be widely used in clinical diagnosis and wearable biochemical sensors in the future.

Recent advances in biosensors detecting biomarkers from exhaled breath and saliva for respiratory disease diagnosis

The global demand for rapid and non-invasive diagnostic methods for respiratory diseases has significantly intensified due to the wide spread of respiratory infectious diseases. Recent advancements in respiratory disease diagnosis through the analysis of exhaled breath and saliva has attracted great attention all over the world. Among various analytical methods, biosensors can offer non-invasive, efficient, and cost-effective diagnostic capabilities, emerging as promising tools in this area. This review intends to provide a comprehensive overview of various biosensors for the detection of respiratory disease related biomarkers in exhaled breath and saliva. Firstly, the characteristics of exhaled breath and saliva, including their generation, composition, and relevant biomarkers are introduced. Subsequently, the design and application of various biosensors for detecting these biomarkers are presented, along with the innovative materials employed as sensitive components. Different types of biosensors are reviewed, including electrochemical, optical, piezoelectric, semiconductor, and other novel biosensors. At last, the challenges, limitations, and future trends of these biosensors are discussed. It is anticipated that biosensors will play a significant role in respiratory disease diagnosis in the future.

Unlocking the potential of miRNAs in detecting pulmonary tuberculosis: prospects and pitfalls

Tuberculosis (TB) is one of the deadliest infectious diseases globally, ranking as 13th leading cause of mortality and morbidity. According to the Global Tuberculosis Report 2022, TB claimed the lives of 1.6 million people worldwide in 2021. Among the casualties, 1 870 000 individuals with HIV co-infections contributed to 6.7% of the total fatalities, accounting TB as the second most lethal infectious disease following COVID-19. In the quest to identify biomarkers for disease progression and anti-TB therapy, microRNAs (miRNAs) have gained attention due to their precise regulatory role in gene expression in disease stages and their ability to distinguish latent and active TB, enabling the development of early TB prognostic signatures. miRNAs are stable in biological fluids and therefore will be useful for non-invasive and broad sample collection. However, their inherent lack of specificity and experimental variations may lead to false-positive outcomes. These limitations can be overcome by integrating standard protocols with machine learning, presenting a novel tool for TB diagnostics and therapeutics. This review summarizes, discusses and highlights the potential of miRNAs as a biomarker, particularly their differential expression at disease stages. The review assesses the advantages and obstacles associated with miRNA-based diagnostic biomarkers in pulmonary TB and facilitates rapid, point-of-care testing.

Analytical challenges and opportunities in the study of endocrine disrupting chemicals within an exposomics framework

Exposomics aims to measure human exposures throughout the lifespan and the changes they produce in the human body. Exposome-scale studies have significant potential to understand the interplay of environmental factors with complex multifactorial diseases widespread in our society and whose origin remain unclear. In this framework, the study of the chemical exposome aims to cover all chemical exposures and their effects in human health but, today, this goal still seems unfeasible or at least very challenging, which makes the exposome for now only a concept. Furthermore, the study of the chemical exposome faces several methodological challenges such as moving from specific targeted methodologies towards high-throughput multitargeted and non-targeted approaches, guaranteeing the availability and quality of biological samples to obtain quality analytical data, standardization of applied analytical methodologies, as well as the statistical assignment of increasingly complex datasets, or the identification of (un)known analytes. This review discusses the various steps involved in applying the exposome concept from an analytical perspective. It provides an overview of the wide variety of existing analytical methods and instruments, highlighting their complementarity to develop combined analytical strategies to advance towards the chemical exposome characterization. In addition, this review focuses on endocrine disrupting chemicals (EDCs) to show how studying even a minor part of the chemical exposome represents a great challenge. Analytical strategies applied in an exposomics context have shown great potential to elucidate the role of EDCs in health outcomes. However, translating innovative methods into etiological research and chemical risk assessment will require a multidisciplinary effort. Unlike other review articles focused on exposomics, this review offers a holistic view from the perspective of analytical chemistry and discuss the entire analytical workflow to finally obtain valuable results.

Identification potential biomarkers for diagnosis, and progress of breast cancer by using high-pressure photon ionization time-of-flight mass spectrometry

BACKGROUND

In this study, exhaled breath testing has been considered a promising method for the detection and monitoring of breast cancer (BC).

METHODS

A high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) platform was used to detect volatile organic compounds (VOCs) in breath samples. Then, machine learning (ML) models were constructed on VOCs for the diagnosis of BC and its progression monitoring. Ultimately, 1981 women with useable breath samples were included in the study, of whom 937 (47.3 %) had been diagnosed with BC. VOC panels were used for ML model construction for BC detection and progression monitoring.

RESULTS

On the blinded testing cohort, this VOC-based model successfully differentiated patients with and without BC with sensitivity, specificity, and area under receiver operator characteristic curve (AUC) values of 85.9 %, 90.4 %, and 0.946. The corresponding AUC values when differentiating between patients with and without lymph node metastasis (LNM) or between patients with tumor-node-metastasis (TNM) stage 0/I/II or III/IV disease were 0.840 and 0.708, respectively. While developed VOC-based models exhibited poor performance when attempting to differentiate between patients based on pathological patterns (Ductal carcinoma in situ (DCIS) vs Invasive BC (IBC)) or molecular subtypes (Luminal vs Human epidermal growth factor receptor 2 (HER2+) vs Triple-negative BC (TNBC)) of BC.

CONCLUSION

Collectively, the HPPI-TOFMS-based breathomics approaches may offer value for the detection and progression monitoring of BC. Additional research is necessary to explore the fundamental mechanisms of the identified VOCs.

Early Detection of Ventilator-associated Pneumonia From Exhaled Breath in Intensive Care Unit Patients

OBJECTIVE

Evaluate associations between volatile organic compounds (VOCs) in heat and moisture exchange (HME) filters and the presence of ventilator-associated pneumonia (VAP).

BACKGROUND

Clinical diagnostic criteria for VAP have poor interobserver reliability, and cultures are slow to result. Exhaled breath contains VOCs related to gram-negative bacterial proliferation, the most identified organisms in VAP. We hypothesized that exhaled VOCs on HME filters can predict nascent VAP in mechanically ventilated intensive care unit patients.

METHODS

Gas chromatography-mass spectrometry was used to analyze 111 HME filters from 12 intubated patients who developed VAP. Identities and relative amounts of VOCs were associated with dates of clinical suspicion and culture confirmation of VAP. Matched pairs t tests were performed to compare VOC abundances in HME filters collected within 3 days pre and postclinical suspicion of VAP (pneumonia days), versus outside of these days (non-pneumonia days). A receiver operating characteristic curve was generated to determine the diagnostic potential of VOCs.

RESULTS

Carbon disulfide, associated with the proliferation of certain gram-negative bacteria, was found in samples collected during pneumonia days for 11 of 12 patients. Carbon disulfide levels were significantly greater ( P = 0.0163) for filters on pneumonia days. The Area Under the Curve of the Reciever Operating Characteristic curve (AUC ROC) for carbon disulfide was 0.649 (95% CI: 0.419-0.88).

CONCLUSIONS

Carbon disulfide associated with gram-negative VAP can be identified on HME filters up to 3 days before the initial clinical suspicion, and approximately a week before culture confirmation. This suggests VOC sensors may have potential as an adjunctive method for early detection of VAP.

Development and validation of a method for analysis of 25 cannabinoids in oral fluid and exhaled breath condensate

Currently, there is a significant demand in forensic toxicology for biomarkers of cannabis exposure that, unlike ∆9-tetrahydrocannabinol, can reliably indicate time and frequency of use, be sampled with relative ease, and correlate with impairment. Oral fluid (OF) and exhaled breath condensate (EBC) are alternative, non-invasive sample matrices that hold promise for identifying cannabis exposure biomarkers. OF, produced by salivary glands, is increasingly utilized in drug screening due to its non-invasive collection and is being explored as an alternative matrix for cannabinoid analysis. EBC is an aqueous specimen consisting of condensed water vapor containing water-soluble volatile and non-volatile components present in exhaled breath. Despite potential advantages, there are no reports on the use of EBC for cannabinoid detection. This study developed a supported liquid extraction approach and LC-QqQ-MS dMRM analytical method for quantification of 25 major and minor cannabinoids and metabolites in OF and EBC. The method was validated according to the ANSI/ASB 036 standard and other published guidelines. LOQ ranged from 0.5 to 6.0 ng/mL for all cannabinoids in both matrices. Recoveries for most analytes were 60-90%, with generally higher values for EBC compared to OF. Matrix effects were observed with some cannabinoids, with effects mitigated by use of matrix-matched calibration. Bias and precision were within ± 25%. Method applicability was demonstrated by analyzing ten authentic OF and EBC samples, with positive detections of multiple analytes in both matrices. The method will facilitate comprehensive analysis of cannabinoids in non-invasive sample matrices for the development of reliable cannabis exposure biomarkers.

Colorimetric sensing for translational applications: from colorants to mechanisms

Colorimetric sensing offers instant reporting via visible signals. Versus labor-intensive and instrument-dependent detection methods, colorimetric sensors present advantages including short acquisition time, high throughput screening, low cost, portability, and a user-friendly approach. These advantages have driven substantial growth in colorimetric sensors, particularly in point-of-care (POC) diagnostics. Rapid progress in nanotechnology, materials science, microfluidics technology, biomarker discovery, digital technology, and signal pattern analysis has led to a variety of colorimetric reagents and detection mechanisms, which are fundamental to advance colorimetric sensing applications. This review first summarizes the basic components (e.g., color reagents, recognition interactions, and sampling procedures) in the design of a colorimetric sensing system. It then presents the rationale design and typical examples of POC devices, e.g., lateral flow devices, microfluidic paper-based analytical devices, and wearable sensing devices. Two highlighted colorimetric formats are discussed: combinational and activatable systems based on the sensor-array and lock-and-key mechanisms, respectively. Case discussions in colorimetric assays are organized by the analyte identities. Finally, the review presents challenges and perspectives for the design and development of colorimetric detection schemes as well as applications. The goal of this review is to provide a foundational resource for developing colorimetric systems and underscoring the colorants and mechanisms that facilitate the continuing evolution of POC sensors.

High and low pathogenicity avian influenza virus discrimination and prediction based on volatile organic compounds signature by SIFT-MS: a proof-of-concept study

High and low pathogenicity avian influenza viruses (HPAIV, LPAIV) are the primary causes of poultry diseases worldwide. HPAIV and LPAIV constitute a major threat to the global poultry industry. Therefore, early detection and well-adapted surveillance strategies are of the utmost importance to control the spread of these viruses. Volatile Organic Compounds (VOCs) released from living organisms have been investigated over the last decades as a diagnostic strategy. Mass spectrometry instruments can analyze VOCs emitted upon viral infection. Selected ion flow tube mass spectrometry (SIFT-MS) enables direct analysis of cell headspace in less than 20 min. As a proof-of-concept study, we investigated the ability of a SIFT-MS coupled sparse Partial Least Square-Discriminant Analysis analytical workflow to discriminate IAV-infected cells. Supernatants of HPAIV, LPAIV, and control cells were collected from 1 to 72 h post-infection and analyzed using our analytical workflow. At each collection point, VOCs' signatures were first identified based on four independent experiments and then used to discriminate the infectious status of external samples. Our results indicate that the identified VOCs signatures successfully discriminate, as early as 1-h post-infection, infected cells from the control cells and differentiated the HPAIV from the LPAIV infection. These results suggest a virus-dependent VOCs signature. Overall, the external samples' status was identified with 96.67% sensitivity, 100% specificity, and 97.78% general accuracy.

Rolosense: Mechanical Detection of SARS-CoV-2 Using a DNA-Based Motor

Assays that detect viral infections play a significant role in limiting the spread of diseases such as SARS-CoV-2. Here, we present Rolosense, a virus sensing platform that leverages the motion of 5 μm DNA-based motors on RNA fuel chips to transduce the presence of viruses. Motors and chips are modified with aptamers, which are designed for multivalent binding to viral targets and lead to stalling of motion. Therefore, the motors perform a "mechanical test" of the viral target and stall in the presence of whole virions, which represents a unique mechanism of transduction distinct from conventional assays. Rolosense can detect SARS-CoV-2 spiked in artificial saliva and exhaled breath condensate with a sensitivity of 103 copies/mL and discriminates among other respiratory viruses. The assay is modular and amenable to multiplexing, as demonstrated by our one-pot detection of influenza A and SARS-CoV-2. As a proof of concept, we show that readout can be achieved using a smartphone camera with a microscopic attachment in as little as 15 min without amplification reactions. Taken together, these results show that mechanical detection using Rolosense can be broadly applied to any viral target and has the potential to enable rapid, low-cost point-of-care screening of circulating viruses.

Exhaled breath analysis in patients with potentially curative lung cancer undergoing surgery: a longitudinal study

Exhaled breath analysis has emerged as a non-invasive and promising method for early detection of lung cancer, offering a novel approach for diagnosis through the identification of specific biomarkers present in a patient's breath. For this longitudinal study, 29 treatment-naive patients with lung cancer were evaluated before and after surgery. Secondary electrospray ionization high-resolution mass spectrometry was used for exhaled breath analysis. Volatile organic compounds with absolute log2fold change ⩾1 andq-values ⩾ 0.71 were selected as potentially relevant. Exhaled breath analysis resulted in a total of 3482 features. 515 features showed a substantial difference before and after surgery. The small sample size generated a false positive rate of 0.71, therefore, around 154 of these 515 features were expected to be true changes. Biological identification of the features with the highest consistency (m/z-242.18428 andm/z-117.0539) revealed to potentially be 3-Oxotetradecanoic acid and Indole, respectively. Principal component analysis revealed a primary cluster of patients with a recurrent lung cancer, which remained undetected in the initial diagnostic and surgical procedures. The change of exhaled breath patterns after surgery in lung cancer emphasizes the potential for lung cancer screening and detection.

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

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

Blazing the trail for innovative tuberculosis diagnostics

The COVID-19 pandemic brought diagnostics into the spotlight in an unprecedented way not only for case management but also for population health, surveillance, and monitoring. The industry saw notable levels of investment and accelerated research which sparked a wave of innovation. Simple non-invasive sampling methods such as nasal swabs have become widely used in settings ranging from tertiary hospitals to the community. Self-testing has also been adopted as standard practice using not only conventional lateral flow tests but novel and affordable point-of-care molecular diagnostics. The use of new technologies, including artificial intelligence-based diagnostics, have rapidly expanded in the clinical setting. The capacity for next-generation sequencing and acceptance of digital health has significantly increased. However, 4 years after the pandemic started, the market for SARS-CoV-2 tests is saturated, and developers may benefit from leveraging their innovations for other diseases; tuberculosis (TB) is a worthwhile portfolio expansion for diagnostics developers given the extremely high disease burden, supportive environment from not-for-profit initiatives and governments, and the urgent need to overcome the long-standing dearth of innovation in the TB diagnostics field. In exchange, the current challenges in TB detection may be resolved by adopting enhanced swab-based molecular methods, instrument-based, higher sensitivity antigen detection technologies, and/or artificial intelligence-based digital health technologies developed for COVID-19. The aim of this article is to review how such innovative approaches for COVID-19 diagnosis can be applied to TB to have a comparable impact.

Breathomics Detect the Cardiovascular Disease: Delusion or Dilution of the Metabolomic Signature

Volatile organic compounds (VOCs) can be subdivided into exogenous and endogenous categories based on their origin. Analyzing the endogenous VOCs can provide insights into maintaining the internal organs' homeostasis. Despite the ongoing development and the current understanding, studies have suggested a link between cardiovascular metabolic alterations in patients with ischemic heart disease and elevated levels of ethane and isoprene detectable through exhaled breath analysis. Conversely, patients with chronic heart failure exhibit elevated acetone and pentane in their exhaled air. These substances originate from disturbances in the heart tissue, including cellular and subcellular modulations. Hypothetically, ethane levels in the exhaled breath analysis can demonstrate the severity of ischemic heart disease and, consequently, the risk of death in the next 10 years due to cardiovascular disease (CVD). Real-time direct mass spectrometry is the preferred method for assessing VOCs in exhaled breath analysis. The accuracy of this analysis depends on several factors, including the selection of the relevant breath fraction, the type of breath collection container (if used), and the pre-concentration technique.

A novel, microvascular evaluation method and device for early diagnosis of peripheral artery disease and chronic limb-threatening ischemia in individuals with diabetes

Objective

A novel transdermal arterial gasotransmitter sensor (TAGS) has been tested as a diagnostic tool for lower limb microvascular disease in individuals with and without diabetes mellitus (DM).

Methods

The TAGS system noninvasively measures hydrogen sulfide (H2S) emitted from the skin. Measurements were made on the forearm and lower limbs of individuals from three cohorts, including subjects with DM and chronic limb-threatening ischemia, to evaluate skin microvascular integrity. These measurements were compared with diagnosis of peripheral artery disease (PAD) using the standard approach of the toe brachial index. Other measures of vascular health were made in some subjects including fasting blood glucose, hemoglobin A1c, plasma lipids, blood pressure, estimated glomerular filtration, and body mass index.

Results

The leg:arm ratio of H2S emissions correlated with risk factors for microvascular disease (ie, high-density lipoprotein levels, estimated glomerular filtration rate, systolic blood pressure, and hemoglobin A1c). The ratios were significantly lower in symptomatic DM subjects being treated for chronic limb-threatening ischemia (n = 8, 0.48 ± 0.21) compared with healthy controls (n = 5, 1.08 ± 0.30; P = .0001) and with asymptomatic DM subjects (n = 4, 0.79 ± 0.08; P = .0086). The asymptomatic DM group ratios were also significantly lower than the healthy controls (P = .0194). Using ratios of leg:arm transdermal H2S measurement (17 subjects, 34 ratios), the overall accuracy to identify limbs with severe PAD had an area under the curve of the receiver operating curve of 0.93.

Conclusions

Ratios of transdermal H2S measurements are lower in legs with impaired microvascular function, and the decrease in ratio precedes clinically apparent severe microvascular disease and diabetic ulcers. The TAGS instrument is a novel, sensitive tool that may aid in the early detection and monitoring of PAD complications and efforts for limb salvage.

Rolosense: Mechanical detection of SARS-CoV-2 using a DNA-based motor

Assays detecting viral infections play a significant role in limiting the spread of diseases such as SARS-CoV-2. Here we present Rolosense, a virus sensing platform that transduces the motion of synthetic DNA-based motors transporting 5-micron particles on RNA fuel chips. Motors and chips are modified with virus-binding aptamers that lead to stalling of motion. Therefore, motors perform a "mechanical test" of viral target and stall in the presence of whole virions which represents a unique mechanism of transduction distinct from conventional assays. Rolosense can detect SARS-CoV-2 spiked in artificial saliva and exhaled breath condensate with a sensitivity of 103 copies/mL and discriminates among other respiratory viruses. The assay is modular and amenable to multiplexing, as we demonstrated one-pot detection of influenza A and SARS-CoV-2. As a proof-of-concept, we show readout can be achieved using a smartphone camera in as little as 15 mins without any sample preparation steps. Taken together, mechanical detection using Rolosense can be broadly applied to any viral target and has the potential to enable rapid, low-cost, point-of-care screening of circulating viruses.

A novel set of volatile urinary biomarkers for late-life major depressive and anxiety disorders upon the progression of frailty: a pilot study

Mood and anxiety disorders are frequent in the elderly and increase the risk of frailty. This study aimed to identify novel biomarkers of major depressive disorder (MDD) and anxiety in the elderly. We examined 639 participants in the community-dwelling Otassha Study (518 individuals considered healthy control, 77 with depression, anxiety, etc.), mean age 75 years, 58.4% of female. After exclusion criteria, we analyzed VOCs from 18 individuals (9 healthy control, 9 of MDD/agoraphobia case). Urinary volatile and semi-volatile organic compounds (VOCs) were profiled using solid-phase microextraction and gas chromatography-mass spectrometry. Six urinary VOCs differed in the absolute area of the base peak between participants with MDD and/or agoraphobia and controls. High area under the receiver-operating characteristic curve (AUC) values were found for phenethyl isothiocyanate (AUC: 0.86, p = 0.009), hexanoic acid (AUC: 0.85, p = 0.012), texanol (AUC: 0.99, p = 0.0005), and texanol isomer (AUC: 0.89, p = 0.005). The combined indices of dimethyl sulfone, phenethyl isothiocyanate, and hexanoic acid, and texanol and texanol isomer showed AUCs of 0.91 (p = 0.003) and 0.99 (p = 0.0005) and correlated with the GRID-HAMD and the Kihon Checklist (CL score), respectively. These VOCs may be valuable biomarkers for evaluating MDD and/or agoraphobia in the elderly.

Use of Breath Analysis for Diagnosing COVID-19: Opportunities, Challenges, and Considerations for Future Pandemic Responses

Due to the coronavirus disease 2019 (COVID-19) pandemic, there is currently a need for accurate, rapid, and easy-to-administer diagnostic tools to help communities manage local outbreaks and assess the spread of disease. The use of artificial intelligence within the domain of breath analysis techniques has shown to have potential in diagnosing a variety of diseases, such as cancer and lung disease, by analyzing volatile organic compounds (VOCs) in exhaled breath. This combined with their rapid, easy-to-use, and noninvasive nature makes them a good candidate for use in diagnosing COVID-19 in large scale public health operations. However, there remains issues with their implementation when it comes to the infrastructure currently available to support their use on a broad scale. This includes issues of standardization, and whether or not a characteristic VOC pattern can be identified for COVID-19. Despite these difficulties, breathalyzers offer potential to assist in pandemic responses and their use should be investigated.

Comprehensive two-dimensional gas chromatographic platforms comparison for exhaled breath metabolites analysis

The high potential of exhaled breath for disease diagnosis has been highlighted in numerous studies. However, exhaled breath analysis is suffering from a lack of standardized sampling and analysis procedures, impacting the robustness of inter-laboratory results, and thus hampering proper external validation. The aim of this work was to verify compliance and validate the performance of two different comprehensive two-dimensional gas chromatography coupled to mass spectrometry platforms in different laboratories by monitoring probe metabolites in exhaled breath following the Peppermint Initiative guidelines. An initial assessment of the exhaled breath sampling conditions was performed, selecting the most suitable sampling bag material and volume. Then, a single sampling was performed using Tedlar bags, followed by the trapping of the volatile organic compounds into thermal desorption tubes for the subsequent analysis using two different analytical platforms. The thermal desorption tubes were first analyzed by a (cryogenically modulated) comprehensive two-dimensional gas chromatography system coupled to high-resolution time-of-flight mass spectrometry. The desorption was performed in split mode and the split part was recollected in the same tube and further analyzed by a different (flow modulated) comprehensive two-dimensional gas chromatography system with a parallel detection, specifically using a quadrupole mass spectrometer and a vacuum ultraviolet detector. Both the comprehensive two-dimensional gas chromatography platforms enabled the longitudinal tracking of the peppermint oil metabolites in exhaled breath. The increased sensitivity of comprehensive two-dimensional gas chromatography enabled to successfully monitor over a 6.5 h period a total of 10 target compounds, namely α-pinene, camphene, β-pinene, limonene, cymene, eucalyptol, menthofuran, menthone, isomenthone, and neomenthol.

Membrane inlet mass spectrometry method for food intake impact assessment on specific volatile organic compounds in exhaled breath

This research work describes the development of a novel bioanalytical method for the assessment of food impact on selected exhaled breath volatile organic compounds (VOCs) using a fast and portable screening VOC prototype sensor based on membrane inlet mass spectrometry (MIMS). Method and sensor prototype functionality was verified by obtaining good response times, linearity in the examined concentration ranges, and sensitivity and repeatability for several breath VOCs—acetone, ethanol, n-pentane, and isoprene. A new VOC sensor prototype was also proven to be sensitive enough for selected breath VOC quantification with limits of detection at low part per billion (ppb) levels—5 ppb for n-pentane, 10 ppb for acetone and ethanol, and 25 ppb for isoprene. Food impact assessment was accomplished by tracking the levels of acetone, ethanol, n-pentane, and isoprene in exhaled breath samples collected from 50 healthy participants before the meal and 60 min and 120 min after the meal. For acetone, isoprene, and n-pentane, a larger impact was noticed 120 min after the meal, while for ethanol, it was after 60 min. Obtained VOC levels were in the expected concentration ranges. Mean values at all time points were ~ 500–900 ppb for acetone and ~ 400–600 ppb for ethanol. Most of the results for n-pentane were below 5 ppb, but the mean value for those which were detected was ~ 30 ppb. Along with samples, data about participants’ lifestyle were collected via a short questionnaire, which were compared against obtained VOC levels in order to reveal some significant correlations between habits of participants and their breath VOC levels.

Volatile Organic Compounds Analysis as a Potential Novel Screening Tool for Breast Cancer: A Systematic Review

Introduction

An early diagnosis is crucial in reducing mortality among people who have breast cancer (BC). There is a shortfall of characteristic early clinical symptoms in BC patients, highlighting the importance of investigating new methods for its early detection. A promising novel approach is the analysis of volatile organic compounds (VOCs) produced and emitted through the metabolism of cancer cells.

Methods

The purpose of this systematic review is to outline the published research regarding BC-associated VOCs. For this, headspace analysis of VOCs was explored in patient-derived body fluids, animal model-derived fluids, and BC cell lines to identify BC-specific VOCs. A systematic search in PubMed and Web of Science databases was conducted according to the PRISMA guidelines.

Results

Thirty-two studies met the criteria for inclusion in this review. Results highlight that VOC analysis can be promising as a potential novel screening tool. However, results of in vivo, in vitro and case-control studies have delivered inconsistent results leading to a lack of inter-matrix consensus between different VOC sampling methods.

Discussion

Discrepant VOC results among BC studies have been obtained, highly due to methodological discrepancies. Therefore, methodological issues leading to disparities have been reviewed and recommendations have been made on the standardisation of VOC collection and analysis methods for BC screening, thereby improving future VOC clinical validation studies.

pyAIR-A New Software Tool for Breathomics Applications-Searching for Markers in TD-GC-HRMS Analysis

Volatile metabolites in exhaled air have promising potential as diagnostic biomarkers. However, the combination of low mass, similar chemical composition, and low concentrations introduces the challenge of sorting the data to identify markers of value. In this paper, we report the development of pyAIR, a software tool for searching for volatile organic compounds (VOCs) markers in multi-group datasets, tailored for Thermal-Desorption Gas-Chromatography High Resolution Mass-Spectrometry (TD-GC-HRMS) output. pyAIR aligns the compounds between samples by spectral similarity coupled with retention times (RT), and statistically compares the groups for compounds that differ by intensity. This workflow was successfully tested and evaluated on gaseous samples spiked with 27 model VOCs at six concentrations, divided into three groups, down to 0.3 nL/L. All analytes were correctly detected and aligned. More than 80% were found to be significant markers with a p-value < 0.05; several were classified as possibly significant markers (p-value < 0.1), while a few were removed due to background level. In all group comparisons, low rates of false markers were found. These results showed the potential of pyAIR in the field of trace-level breathomics, with the capability to differentially examine several groups, such as stages of illness.

Exhaled Breath Condensate (EBC) for SARS-CoV-2 diagnosis still an open debate

The real-time PCR (RT-PCR) on nasopharyngeal swabs (NPS) is the gold standard for the diagnosis of SARS-CoV-2. The exhaled breath condensate (EBC) is used to perform collection of biological fluid condensed in a refrigerated device from deep airways’ exhaled air. We aimed to verify the presence of SARS-CoV-2 virus in the EBC from patients with confirmed SARS-CoV-2 infection by RT-PCR, and to determine if the EBC may represent a valid alternative to the NPS. Methods: Seventeen consecutive patients admitted to the Emergency Department of the Policlinico were enrolled in the present study with RT-PCR, clinical and radiological evidence of SARS-CoV-2. Within 24 hours from the NPS collection the EBC collection was performed on SARS-CoV-2 positive patients. Informed written consent was gathered and the Ethic Committee approved the study. Results: The mean age of patients was 60 years (24-92) and 64.7% (11/17) were male. Patient n. 9 and n.17 died. All NPS resulted positive for SARS-CoV-2 at RT-PCR. RT-PCR on EBC resulted negative for all but one patients (patient n.12). Conclusion: In this study we did not find any correlation between positive NPS and the EBC in all but one patients enrolled. Based on these data which greatly differ from previous reports on the topic, this study opens several questions related to small differences in the complex process of EBC collection and how EBC could be really standardized for the diagnosis of SARS-CoV-2 infection. Further studies will be warranted to deepen this topic.

Clinical utility of diagnostic biomarkers in malignant pleural mesothelioma: a systematic review and meta-analysis

Malignant pleural mesothelioma (MPM) is characterised by late-stage diagnosis and poor prognosis. Currently, no screening tool is advocated and diagnosis is based on invasive techniques, which are not well tolerated. Non-invasive diagnostic biomarkers have shown potential and could have a huge clinical benefit. However, despite extensive research, there is no consensus yet on their clinical use, with many articles reporting contradicting results, limiting their clinical implementation. The aim of this systematic review is therefore to explore the different semi- and non-invasive diagnostic markers in several human matrices and identify those that might clinically be relevant. A total of 100 articles were selected through Web of Science and PubMed, with 56 articles included in the quantitative analysis. Although many studies have reported on the diagnostic accuracy of MPM biomarkers such as serum mesothelin and high-mobility group box protein 1 and plasma fibulin-3, none have resulted in a validated test for early detection. Future research should focus on external validation, combinations into biomarker panels, the inclusion of early stage MPM patients and a combination of different biomarker matrices, as well as new markers.

Diagnostic biomarkers for malignant pleural mesothelioma seem promising; however, further research is necessary to prove their clinical value. This review provides a thorough overview of the different markers and compares them in several matrices.https://bit.ly/35ni6UO

Cancer nanotechnology: current status and perspectives

Modern medicine has been waging a war on cancer for nearly a century with no tangible end in sight. Cancer treatments have significantly progressed, but the need to increase specificity and decrease systemic toxicities remains. Early diagnosis holds a key to improving prognostic outlook and patient quality of life, and diagnostic tools are on the cusp of a technological revolution. Nanotechnology has steadily expanded into the reaches of cancer chemotherapy, radiotherapy, diagnostics, and imaging, demonstrating the capacity to augment each and advance patient care. Nanomaterials provide an abundance of versatility, functionality, and applications to engineer specifically targeted cancer medicine, accurate early-detection devices, robust imaging modalities, and enhanced radiotherapy adjuvants. This review provides insights into the current clinical and pre-clinical nanotechnological applications for cancer drug therapy, diagnostics, imaging, and radiation therapy.

A multiple-method comparative study using GC-MS, AMDIS and in-house-built software for the detection and identification of "unknown" volatile organic compounds in breath

The human respiratory system is a highly complex matrix that exhales many volatile organic compounds (VOCs). Breath-exhaled VOCs are often "unknowns" and possess low concentrations, which make their analysis, peak digging and data processing challenging. We report a new methodology, applied in a proof-of-concept experiment, for the detection of VOCs in breath. For this purpose, we developed and compared four complementary analysis methods based on solid-phase microextraction and thermal desorption (TD) tubes with two GC-mass spectrometer (MS) methods. Using eight model compounds, we obtained an LOD range of 0.02-20 ng/ml. We found that in breath analysis, sampling the exhausted air from Tedlar bags is better when TD tubes are used, not only because of the preconcentration but also due to the stability of analytes in the TD tubes. Data processing (peak picking) was based on two data retrieval approaches with an in-house script written for comparison and differentiation between two populations: sick and healthy. We found it best to use "raw" AMDIS deconvolution data (.ELU) rather than its NIST (.FIN) identification data for comparison between samples. A successful demonstration of this method was conducted in a pilot study (n = 21) that took place in a closed hospital ward (Covid-19 ward) with the discovery of four potential markers. These preliminary findings, at the molecular level, demonstrate the capabilities of our method and can be applied in larger and more comprehensive experiments in the omics world.

Breath Biomarkers in Diagnostic Applications

The detection of chemical compounds in exhaled human breath presents an opportunity to determine physiological state, diagnose disease or assess environmental exposure. Recent advancements in metabolomics research have led to improved capabilities to explore human metabolic profiles in breath. Despite some notable challenges in sampling and analysis, exhaled breath represents a desirable medium for metabolomics applications, foremost due to its non-invasive, convenient and practically limitless availability. Several breath-based tests that target either endogenous or exogenous gas-phase compounds are currently established and are in practical and/or clinical use. This review outlines the concept of breath analysis in the context of these unique tests and their applications. The respective breath biomarkers targeted in each test are discussed in relation to their physiological production in the human body and the development and implementation of the associated tests. The paper concludes with a brief insight into prospective tests and an outlook of the future direction of breath research.

Mapping Aerosolized Saliva on Face Coverings for Biosensing Applications

Facemasks in congregate settings prevent the transmission of SARS-CoV-2 and help control the ongoing COVID-19 global pandemic because face coverings can arrest transmission of respiratory droplets. While many groups have studied face coverings as personal protective equipment, these respiratory droplets can also serve as a diagnostic fluid to report on health state; surprisingly, studies of face coverings from this perspective are quite limited. Here, we determined the concentration and distribution of aerosolized saliva (via α-amylase levels) captured on various face coverings. Our results showed that α-amylase accumulated on face coverings in a time-dependent way albeit at different levels, e.g., neck gaiters and surgical masks captured about 3-fold more α-amylase than cloth masks and N95 respirators. In addition, the saliva aerosols were primarily detected on the inner layer of multilayered face coverings. We also found that the distribution of salivary droplets on the mask correlated with the morphologies of face coverings as well as their coherence to the face curvature. These findings motivated us to extend this work and build multifunctional sensing strips capable of detecting biomarkers in situ to create "smart" masks. The work highlights that face coverings are promising platforms for biofluid collection and colorimetric biosensing, which bode well for developing surveillance tools for airborne diseases.

Exhaled breath biomarkers of influenza infection and influenza vaccination

Respiratory viral infections are considered a major public health threat, and breath metabolomics can provide new ways to detect and understand how specific viruses affect the human pulmonary system. In this pilot study, we characterized the metabolic composition of human breath for an early diagnosis and differentiation of influenza viral infection, as well as other types of upper respiratory viral infections. We first studied the non-specific effects of planned seasonal influenza vaccines on breath metabolites in healthy subjects after receiving the immunization. We then investigated changes in breath content from hospitalized patients with flu-like symptoms and confirmed upper respiratory viral infection. The exhaled breath was sampled using a custom-made breath condenser, and exhaled breath condensate (EBC) samples were analysed using liquid chromatography coupled to quadruplole-time-of-flight mass spectrometer (LC-qTOF). All metabolomic data was analysed using both targeted and untargeted approaches to detect specific known biomarkers from inflammatory and oxidative stress biomarkers, as well as new molecules associated with specific infections. We were able to find clear differences between breath samples collected before and after flu vaccine administration, together with potential biomarkers that are related to inflammatory processes and oxidative stress. Moreover, we were also able to discriminate samples from patients with flu-related symptoms that were diagnosed with confirmatory respiratory viral panels (RVP). RVP positive and negative differences were identified, as well as differences between specific viruses defined. These results provide very promising information for the further study of the effect of influenza A and other viruses in human systems by using a simple and non-invasive specimen like breath.

Comparative proteomic analysis of exhaled breath condensate between lung adenocarcinoma and CT-detected benign pulmonary nodule patients

BACKGROUND

Lung cancer is the leading cause of cancer mortality worldwide. The collection of exhaled breath condensate (EBC) is a non-invasive method that may have enormous potential as a biomarker for the early detection of lung cancer.

OBJECTIVE

To investigate the proteomic differences of EBC between lung cancer and CT-detected benign nodule patients, and determine whether these proteins could be potential biomarkers.

METHODS

Proteomic analysis was performed on individual samples from 10 lung cancer patients and 10 CT-detected benign nodule patients using data-independent acquisition (DIA) mass spectrometry.

RESULTS

A total of 1,254 proteins were identified, and 21 proteins were differentially expressed in the lung adenocarcinoma group compared to the benign nodule group (p< 0.05). The GO analysis showed that most of these proteins were involved in neutrophil-related biological processes, and the KEGG analysis showed these proteins were mostly annotated to pyruvate and propanoate metabolism. Through protein-protein interactions (PPIs) analysis, ME1 and LDHB contributed most to the interaction-network of these proteins.

CONCLUSION

Significantly differentially expressed proteins were detected between lung cancer and the CT-detected benign nodule group from EBC samples, and these proteins might serve as potential novel biomarkers of EBC for early lung cancer detection.

Determination of nitric oxide using light-emitting diode-based colorimeter with tubular porous polypropylene membrane cuvette

On the basis of the Griess-Saltzman (GS) reaction, an optical device for nitric oxide (NO) detection in exhaled breath and atmosphere was developed by employing the light-emitting diode (LED, 560 nm) as the light source, light-to-voltage converter (LVC) as the detector, and porous polypropylene membrane tube (PPMT) as the cuvette. The PPMT was filled with GS reagents and covered with a coaxial jacket tube for gas collection and color reaction; two ends of the PPMT were connected with the LED and LVC to detect the change of light transmissivity in the wavelength range of 530 to 590 nm mainly. A gas absorber filled with GS reagents was installed prior to another absorber filled with KMnO₄ solution to eliminate the interference of coexisting NO₂. Under the optimized experimental conditions, the device achieved a limit of detection (3σ/k) of 4.4 ppbv for NO detection. The linearity range of this device was divided into two segments, i.e., 25 to 100 ppbv and 50 to 1000 ppbv, with both coefficients of determination > 0.99. The relative standard deviation was 2.7% (n = 9, c = 100 ppbv), and the analytical time was 5.5 min per detection. The minimum detectable quantity was decreased to 1.18 ng, which was ~ 100 times lower than the original GS method (115 ng). The present device was applied for determination of NO in exhaled breath, vehicle exhaust, and air. In addition to satisfactory spiking recoveries (i.e., 103% and 107%), the analytical results of the present device were in agreement with the results obtained by the standard method. These results assured the practicality of the developed device for NO detection in real environmental samples.

Exhaled breath biomarker sensing

This goal of this minireview is to introduce the reader to the area of research concerned with exhaled breath analysis for the purpose of detecting abnormal levels of physiologically-relevant chemical markers reflective of respiratory diseases. Two main two groups of sensing methods are reviewed: mass spectrometry and (bio)sensors. The discussion focuses on biosensor applications for EB and EBC analyses, which are presented in detail. The review finishes with conclusions and future perspectives, including recommendations for future near-term and long-term development of EBC biomarker sensing.

Preliminary accuracy of COVID-19 odor detection by canines and HS-SPME-GC-MS using exhaled breath samples

The novel coronavirus SARS-CoV-2, since its initial outbreak in Wuhan, China has led to a worldwide pandemic and has shut down nations. As with any outbreak, there is a general strategy of detection, containment, treatment and/or cure. The authors would argue that rapid and efficient detection is critical and required to successful management of a disease. The current study explores and successfully demonstrates the use of canines to detect COVID-19 disease in exhaled breath. The intended use was to detect the odor of COVID-19 on contaminated surfaces inferring recent deposition of infectious material from a COVID-19 positive individual. Using masks obtained from hospitalized patients that tested positive for COVID-19 disease, four canines were trained and evaluated for their ability to detect the disease. All four canines obtained an accuracy >90% and positive predictive values ranging from ~73 to 93% after just one month of training.

Is a Mask That Covers the Mouth and Nose Free from Undesirable Side Effects in Everyday Use and Free of Potential Hazards?

Many countries introduced the requirement to wear masks in public spaces for containing SARS-CoV-2 making it commonplace in 2020. Up until now, there has been no comprehensive investigation as to the adverse health effects masks can cause. The aim was to find, test, evaluate and compile scientifically proven related side effects of wearing masks. For a quantitative evaluation, 44 mostly experimental studies were referenced, and for a substantive evaluation, 65 publications were found. The literature revealed relevant adverse effects of masks in numerous disciplines. In this paper, we refer to the psychological and physical deterioration as well as multiple symptoms described because of their consistent, recurrent and uniform presentation from different disciplines as a Mask-Induced Exhaustion Syndrome (MIES). We objectified evaluation evidenced changes in respiratory physiology of mask wearers with significant correlation of O2 drop and fatigue (p < 0.05), a clustered co-occurrence of respiratory impairment and O2 drop (67%), N95 mask and CO2 rise (82%), N95 mask and O2 drop (72%), N95 mask and headache (60%), respiratory impairment and temperature rise (88%), but also temperature rise and moisture (100%) under the masks. Extended mask-wearing by the general population could lead to relevant effects and consequences in many medical fields.

Isotopic gas analysis by means of mid-infrared photoacoustic spectroscopy targeting human exhaled air

There is a great need for cost-efficient non-invasive medical diagnostic tools for analyzing humanly exhaled air. Compared to present day methods, photoacoustic spectroscopy (PAS) can provide a compact and portable (bedside), sensitive and inexpensive solution. We demonstrate a novel portable photoacoustic spectroscopic platform for isotopic measurements of methane (CH₄). We identify and discriminate the ¹²CH₄- and ¹³CH₄ isotopologues and determine their mixing ratio. An Allan deviation analysis shows that the noise equivalent concentration for CH₄ is 200 ppt (pmol/mol) at 100 s of integration time, corresponding to a normalized noise equivalent absorption coefficient of 5.1×10^-9Wcm^-1Hz^-1/2, potentially making the PAS sensor a truly disruptive instrument for bedside monitoring using isotope tracers by providing real-time metabolism data to clinical personnel.

Using the US EPA CompTox Chemicals Dashboard to interpret targeted and non-targeted GC-MS analyses from human breath and other biological media

The U.S. EPA CompTox Chemicals Dashboard is a freely available web-based application providing access to chemistry, toxicity, and exposure data for ∼900 000 chemicals. Data, search functionality, and prediction models within the Dashboard can help identify chemicals found in environmental analyses and human biomonitoring. It was designed to deliver data generated to support computational toxicology to reduce chemical testing on animals and provide access to new approach methodologies including prediction models. The inclusion of mass and formula-based searches, together with relevant ranking approaches, allows for the identification and prioritization of exogenous (environmental) chemicals from high resolution mass spectrometry in need of further evaluation. The Dashboard includes chemicals that can be detected by liquid chromatography, gas chromatography-mass spectrometry (GC-MS) and direct-MS analyses, and chemical lists have been added that highlight breath-borne volatile and semi-volatile organic compounds. The Dashboard can be searched using various chemical identifiers (e.g. chemical synonyms, CASRN and InChIKeys), chemical formula, MS-ready formulae monoisotopic mass, consumer product categories and assays/genes associated with high-throughput screening data. An integrated search at a chemical level performs searches against PubMed to identify relevant published literature. This article describes specific procedures using the Dashboard as a first-stop tool for exploring both targeted and non-targeted results from GC-MS analyses of chemicals found in breath, exhaled breath condensate, and associated aerosols.

High-sensitivity biomedical sensor based on photoacoustic and cavity enhanced absorption spectroscopy with a new software platform for breath analysis

An easy-to-use highly sensitive sensor is reported for trace gas analysis. A near-infrared fiber-coupled external cavity diode laser in combination with a photoacoustic spectroscopy cell and a cavity enhanced absorption spectroscopy cell is used for analysis of trace gases. A software platform for direct absorption and wavelength modulation spectroscopy is developed in order to identify and quantify the molecules and to achieve a higher signal-to-noise ratio. Considering all of these features, the main advantage of our system is to analyze two different samples simultaneously and quickly. Trace gas measurement is assessed, and a detection limit of 1.5 ppb at 6528.76cm^-1 for ammonia is demonstrated. Furthermore, the sensor with our software platform can be easily used outside of the laboratory, for example, in hospitals.

Automating and Extending Comprehensive Two-Dimensional Gas Chromatography Data Processing by Interfacing Open-Source and Commercial Software

Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful analytical tool for both nontargeted and targeted analyses. However, there is a need for more integrated workflows for processing and managing the resultant high-complexity datasets. End-to-end workflows for processing GC×GC data are challenging and often require multiple tools or software to process a single dataset. We describe a new approach, which uses an existing underutilized interface within commercial software to integrate free and open-source/external scripts and tools, tailoring the workflow to the needs of the individual researcher within a single software environment. To demonstrate the concept, the interface was successfully used to complete a first-pass alignment on a large-scale GC×GC metabolomics dataset. The analysis was performed by interfacing bespoke and published external algorithms within a commercial software environment to automatically correct the variation in retention times captured by a routine reference standard. Variation in ¹t_R and ²t_R was reduced on average from 8 and 16% CV prealignment to less than 1 and 2% post alignment, respectively. The interface enables automation and creation of new functions and increases the interconnectivity between chemometric tools, providing a window for integrating data-processing software with larger informatics-based data management platforms.

Detection of Volatile Organic Compounds with Secondary Electrospray Ionization and Proton Transfer Reaction High-Resolution Mass Spectrometry: A Feature Comparison

The analysis of volatiles is of high relevance for a wide range of applications from environmental air sampling and security screening to potential medical applications. High-resolution mass spectrometry methods offer a particularly wide compound coverage, sensitivity, and selectivity. Online approaches allow direct analysis in real time without the need for sample preparation. For the first time, we systematically compared the analysis of volatile organic compounds with secondary electrospray ionization (SESI) and proton transfer reaction (PTR) high-resolution mass spectrometry. The selected instruments had comparable mass resolving powers with m/Δm ≥ 15000, which is particularly suitable for nontargeted analysis, for example, of exhaled breath. Exhalations from 14 healthy adults were analyzed simultaneously on both instruments. In addition, 97 reference standards from nine chemical classes were analyzed with a liquid evaporation system. Surprisingly, in breath, we found more complementary than overlapping features. A clear mass dependence was observed for each method with the highest number of detected m/z features for SESI in the high mass region (m/z = 150-250) and for PTR in the low mass region (m/z = 50-150). SESI yielded a significantly higher numbers of peaks (828) compared to PTR (491) among a total of 1304 unique breath m/z features. The number of signals observed by both methods was lower than expected (133 features) with 797 unique SESI features and 374 unique PTR features. Hypotheses to explain the observed mass-dependent differences are proposed.

Reference ranges of oxidative stress biomarkers selected for non-invasive biological surveillance of nanotechnology workers: Study protocol and meta-analysis results for 8-OHdG in exhaled breath condensate

In the field of engineered nanomaterials (ENMs) and other airborne particulate exposure biomonitoring, circulating oxidative stress biomarkers appear promising. These biomarkers could be monitored in different biological matrices. Exhaled breath condensate (EBC) enables their measurements in the respiratory tract, without affecting airway function or creating inflammation. The 8-hydroxy-2-deoxyguanosine (8-OHdG) was found increased in the EBC of ENM-exposed workers. Our objectives were to assess the reference range of 8-OHdG in the EBC and to identify determinants of its inter- and intra-individual variability. The meta-analysis was stratified by analytical method (chemical versus immunochemical analysis) and resulted in a between-study variability over 99 % of the total variability. The between-study variability completely dominated the within-studies variability. By using a mixed model with study ID as a random effect rather than a meta-regression, only smoking was evidenced as a potential determinant of 8-OHdG inter-individual variability, and only when immunochemical analysis was used. To our knowledge, this is the first meta-analysis aimed at estimating reference values for 8-OHdG in the EBC. The estimated values should be considered preliminary, as they are based on a limited number of studies, mostly of moderate to low quality of evidence. Further research is necessary to standardize EBC sampling, storage and analytical methods. Such a standardization would enable a more accurate estimation of the reference ranges of the 8-OHdG and potentially other biomarkers measurable in the EBC, which are essential for a meaningful interpretation of the biomonitoring results.