Assessment of the exhalation kinetics of volatile cancer biomarkers based on their physicochemical properties

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

INTRODUCTION

Breath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning.

METHODS

Patients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning.

RESULTS

Sixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively.

CONCLUSION

Breath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer.

Chemoresistive Nanosensors Employed to Detect Blood Tumor Markers in Patients Affected by Colorectal Cancer in a One-Year Follow Up

Colorectal cancer (CRC) represents 10% of the annual tumor diagnosis and deaths occurring worldwide. Given the lack of specific symptoms, which could determine a late diagnosis, the research for specific CRC biomarkers and for innovative low-invasive methods to detect them is crucial. Therefore, on the basis of previously published results, some volatile organic compounds (VOCs), detectable through gas sensors, resulted in particularly promising CRC biomarkers, making these sensors suitable candidates to be employed in CRC screening devices. A new device was employed here to analyze the exhalations of blood samples collected from CRC-affected patients at different stages of their pre- and post-surgery therapeutic path, in order to assess the sensor’s capability for discriminating among these samples. The stages considered were: the same day of the surgical treatment (T1); before the hospital discharge (T2); after one month and after 10–12 months from surgery (T3 and T4, respectively). This device, equipped with four different sensors based on different metal–oxide mixtures, enabled a distinction between T1 and T4 with a sensitivity and specificity of 93% and 82%, respectively, making it suitable for clinical follow-up protocols, patient health status monitoring and to detect possible post-treatment relapses.

Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers

Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.

Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis

Machine learning (ML) algorithms are becoming increasingly pervasive in the domains of medical diagnostics and prognostication, afforded by complex deep learning architectures that overcome the limitations of manual feature extraction. In this systematic review and meta-analysis, we provide an update on current progress of ML algorithms in point-of-care (POC) automated diagnostic classification systems for lesions of the oral cavity. Studies reporting performance metrics on ML algorithms used in automatic classification of oral regions of interest were identified and screened by 2 independent reviewers from 4 databases. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. 35 studies were suitable for qualitative synthesis, and 31 for quantitative analysis. Outcomes were assessed using a bivariate random-effects model following an assessment of bias and heterogeneity. 4 distinct methodologies were identified for POC diagnosis: (1) clinical photography; (2) optical imaging; (3) thermal imaging; (4) analysis of volatile organic compounds. Estimated AUROC across all studies was 0.935, and no difference in performance was identified between methodologies. We discuss the various classical and modern approaches to ML employed within identified studies, and highlight issues that will need to be addressed for implementation of automated classification systems in screening and early detection.

Sensors for Volatile Organic Compounds

This paper provides an overview of recent developments in the field of volatile organic compound (VOC) sensors, which are finding uses in healthcare, safety, environmental monitoring, food and agriculture, oil industry, and other fields. It starts by briefly explaining the basics of VOC sensing and reviewing the currently available and quickly progressing VOC sensing approaches. It then discusses the main trends in materials' design with special attention to nanostructuring and nanohybridization. Emerging sensing materials and strategies are highlighted and their involvement in the different types of sensing technologies is discussed, including optical, electrical, and gravimetric sensors. The review also provides detailed discussions about the main limitations of the field and offers potential solutions. The status of the field and suggestions of promising directions for future development are summarized.

Fluorescent-based nanosensors for selective detection of a wide range of biological macromolecules: A comprehensive review

Thanks to their unique attributes, such as good sensitivity, selectivity, high surface-to-volume ratio, and versatile optical and electronic properties, fluorescent-based bioprobes have been used to create highly sensitive nanobiosensors to detect various biological and chemical agents. These sensors are superior to other analytical instrumentation techniques like gas chromatography, high-performance liquid chromatography, and capillary electrophoresis for being biodegradable, eco-friendly, and more economical, operational, and cost-effective. Moreover, several reports have also highlighted their application in the early detection of biomarkers associated with drug-induced organ damage such as liver, kidney, or lungs. In the present work, we comprehensively overviewed the electrochemical sensors that employ nanomaterials (nanoparticles/colloids or quantum dots, carbon dots, or nanoscaled metal-organic frameworks, etc.) to detect a variety of biological macromolecules based on fluorescent emission spectra. In addition, the most important mechanisms and methods to sense amino acids, protein, peptides, enzymes, carbohydrates, neurotransmitters, nucleic acids, vitamins, ions, metals, and electrolytes, blood gases, drugs (i.e., anti-inflammatory agents and antibiotics), toxins, alkaloids, antioxidants, cancer biomarkers, urinary metabolites (i.e., urea, uric acid, and creatinine), and pathogenic microorganisms were outlined and compared in terms of their selectivity and sensitivity. Altogether, the small dimensions and capability of these nanosensors for sensitive, label-free, real-time sensing of chemical, biological, and pharmaceutical agents could be used in array-based screening and in-vitro or in-vivo diagnostics. Although fluorescent nanoprobes are widely applied in determining biological macromolecules, unfortunately, they present many challenges and limitations. Efforts must be made to minimize such limitations in utilizing such nanobiosensors with an emphasis on their commercial developments. We believe that the current review can foster the wider incorporation of nanomedicine and will be of particular interest to researchers working on fluorescence technology, material chemistry, coordination polymers, and related research areas.

Use of Omics Technologies for the Detection of Colorectal Cancer Biomarkers

Colorectal cancer (CRC) is one of the most frequently diagnosed cancers with high mortality rates, especially when detected at later stages. Early detection of CRC can substantially raise the 5-year survival rate of patients, and different efforts are being put into developing enhanced CRC screening programs. Currently, the faecal immunochemical test with a follow-up colonoscopy is being implemented for CRC screening. However, there is still a medical need to describe biomarkers that help with CRC detection and monitor CRC patients. The use of omics techniques holds promise to detect new biomarkers for CRC. In this review, we discuss the use of omics in different types of samples, including breath, urine, stool, blood, bowel lavage fluid, or tumour tissue, and highlight some of the biomarkers that have been recently described with omics data. Finally, we also review the use of extracellular vesicles as an improved and promising instrument for biomarker detection.

Breath and plasma metabolomics to assess inflammation in acute stroke

Inflammation is strongly implicated in both injury and repair processes occurring after stroke. In this exploratory study we assessed the feasibility of repeated sampling of exhaled volatile organic compounds and performed an untargeted metabolomic analysis of plasma collected at multiple time periods after stroke. Metabolic profiles were compared with the time course of the inflammatory markers C-reactive protein (CRP) and interleukin-6 (IL-6). Serial breath sampling was well-tolerated by all patients and the measurement appears feasible in this group. We found that exhaled decanal tracks CRP and IL-6 levels post-stroke and correlates with several metabolic pathways associated with a post-stroke inflammatory response. This suggests that measurement of breath and blood metabolites could facilitate development of novel therapeutic and diagnostic strategies. Results are discussed in relation to the utility of breath analysis in stroke care, such as in monitoring recovery and complications including stroke associated infection.

Breath volatile organic compound analysis: an emerging method for gastric cancer detection

Gastric cancer is a common malignancy, being the fifth most frequently diagnosed cancer and the fourth leading cause of cancer-related deaths worldwide. Diagnosis of gastric cancer at the early stage is critical to effectively improve the survival rate. However, a substantial proportion of patients with gastric cancer in the early stages lack specific symptoms or are asymptomatic. Moreover, the imaging techniques currently used for gastric cancer screening, such as computed tomography and barium examination, are usually radioactive and have low sensitivity and specificity. Even though endoscopy has high accuracy for gastric cancer screening, its application is limited by the invasiveness of the technique. Breath analysis is an economic, effective, easy to perform, non-invasive detection method, and has no undesirable side effects on subjects. Extensive worldwide research has been conducted on breath volatile organic compounds (VOCs), which reveals its prospect as a potential method for gastric cancer detection. Many interesting results have been obtained and innovative methods have been introduced in this subject; hence, an extensive review would be beneficial. By providing a comprehensive list of breath VOCs identified by gastric cancer would promote further research in this field. This review summarizes the commonly used technologies for exhaled breath analysis, focusing on the application of analytical instruments in the detection of breath VOCs in gastric cancers, and the alterations in the profile of breath biomarkers in gastric cancer patients are discussed as well.

TiO2/Cu2O/CuO Multi-Nanolayers as Sensors for H2 and Volatile Organic Compounds: An Experimental and Theoretical Investigation

TiO₂/Cu₂O/CuO multi-nanolayers highly sensitive toward volatile organic compounds (VOCs) and H₂ have been grown in various thicknesses by a cost-effective and reproducible combined spray-sputtering-annealing approach. The ultrathin TiO₂ films were deposited by spray pyrolysis on top of sputtered-annealed Cu₂O/CuO nanolayers to enhance their gas sensing performance and improve their protection against corrosion at high operating temperatures. The prepared heterostructures were investigated using scanning electron microscopy (SEM), X-ray diffraction (XRD), and ultraviolet visible (UV-vis) and micro-Raman spectroscopy. The gas sensing properties were measured at several operating temperatures, where the nanolayered sensors with oxide thicknesses between 20 and 30 nm (Cu₂O/CuO nanolayers) exhibited a high response and an excellent selectivity to ethanol vapor after thermal annealing the samples at 420 °C. The results obtained at an operating temperature of 350 °C demonstrate that the CuO/Cu₂O nanolayers with thicknesses between 20 and 30 nm are sensitive mainly to ethanol vapor, with a response of ∼150. The response changes from ethanol vapors to hydrogen gas as the thickness of the CuO/Cu₂O nanolayers changes from 50 to 20 nm. Density functional theory-based calculations were carried out for the geometries of the CuO(1̅11)/Cu₂O(111) and TiO₂(111)/CuO(1̅11)/Cu₂O(111) heterostructures and their sensing mechanism toward alcohols of different chain lengths and molecular hydrogen. The reconstructed hexagonal Cu₂O(111) surface and the reconstructed monoclinic CuO(1̅11) and TiO₂(111) facets, all of which terminate in an O layer, lead to the lowest surface energies for each isolated material. We studied the formation of the binary and ternary heteroepitaxial interfaces for the surface planes with the best-matching lattices. Despite the impact of the Cu₂O(111) substrate in lowering the atomic charges of the CuO(1̅11) adlayer in the binary sensor, we found that it is the different surface structures of the CuO(1̅11)/Cu₂O(111) and TiO₂(111)/CuO(1̅11)/Cu₂O(111) devices that are fundamental in driving the change in the sensitivity response observed experimentally. The experimental data, supported by the computational results, are important in understanding the use of the multi-nanolayered films tested in this work as reliable, accurate, and selective sensor structures for the tracking of gases at low concentrations.

Statistical Evaluation of Total Expiratory Breath Samples Collected throughout a Year: Reproducibility and Applicability toward Olfactory Sensor-Based Breath Diagnostics

The endogenous volatile organic compounds (VOCs) in exhaled breath can be promising biomarkers for various diseases including cancers. An olfactory sensor has a possibility for extracting a specific feature from collective variations of the related VOCs with a certain health condition. For this approach, it is important to establish a feasible protocol for sampling exhaled breath in practical conditions to provide reproducible signal features. Here we report a robust protocol for the breath analysis, focusing on total expiratory breath measured by a Membrane-type Surface stress Sensor (MSS), which possesses practical characteristics for artificial olfactory systems. To assess its reproducibility, 83 exhaled breath samples were collected from one subject throughout more than a year. It has been confirmed that the reduction of humidity effects on the sensing signals either by controlling the humidity of purging room air or by normalizing the signal intensities leads to reasonable reproducibility verified by statistical analyses. We have also demonstrated the applicability of the protocol for detecting a target material by discriminating exhaled breaths collected from different subjects with pre- and post-alcohol ingestion on different occasions. This simple yet reproducible protocol based on the total expiratory breath measured by the MSS olfactory sensors will contribute to exploring the possibilities of clinical applications of breath diagnostics.

Detection of Single Cancer Cells in Blood with Artificially Intelligent Nanoarray

Detection and monitoring of single cancer cells (SCCs), such as circulating tumor cells (CTCs), would be of aid in an efficient early detection of cancer, a tailored (personalized) therapy, and in a fast bedside assessment of treatment efficacy. Nevertheless, currently available techniques, which mostly rely on the isolation of SCCs based on their physical or biological properties, suffer from low sensitivity, complicated technical procedures, low cost-effectiveness, and being unsuitable for continuous monitoring. We report here on the design and use of an artificially intelligent nanoarray based on a heterogeneous set of chemisensitive nanostructured films for the detection of SCCs using volatile organic compounds emanating in the air trapped above blood samples containing SCCs. For demonstration purposes, we have focused on samples containing A549 lung cancer cells (hereafter, SCC^A549). The nanoarray developed to detect SCC^A549 has >90% accuracy, >85% sensitivity, and >95% specificity. Detection works irrespective of the medium and/or the environment. These results were validated by complementary mass spectrometry. The ability to continuously record, store, and preprocess the signals increases the chances that this nanotechnology might also be useful in the early detection of cancer cells in the blood and continuous monitoring of their possible progression.

Application of chemoresistive gas sensors and chemometric analysis to differentiate the fingerprints of global volatile organic compounds from diseases. Preliminary results of COPD, lung cancer and breast cancer

BACKGROUND

Analysis of volatile organic compounds (VOCs) in exhaled breath has been proposed as a screening method that discriminates between disease and healthy subjects, few studies evaluate whether these chemical fingerprints are specific when compared between diseases. We evaluated global VOCs and their discrimination capacity in chronic obstructive pulmonary disease (COPD), lung cancer, breast cancer and healthy subjects by chemoresistive sensors and chemometric analysis.

METHODS

A cross-sectional study was conducted with the participation of 30 patients with lung cancer, 50 with breast cancer, 50 with COPD and 50 control subjects. Each participant's exhaled breath was analyzed with the electronic nose. A multivariate analysis was carried: principal component analysis (PCA) and, canonical analysis of principal coordinates (CAP). Twenty single-blind samples from the 4 study groups were evaluated by CAP.

RESULTS

A separation between the groups of patients to the controls was achieved through PCA with explanations of >90% of the data and with a correct classification of 100%. In the CAP of the 4 study groups, discrimination between the diseases was obtained with 2 canonical axes with a correct general classification of 91.35%. This model was used for the prediction of the single-blind samples resulting in correct classification of 100%.

CONCLUSIONS

The application of chemoresistive gas sensors and chemometric analysis can be used as a useful tool for a screening test for lung cancer, breast cancer and COPD since this equipment detects the set of VOCs present in the exhaled breath to generate a characteristic chemical fingerprint of each disease.

Non-invasive cancer detection using volatile biomarkers: Is urine superior to breath?

In recent years numerous reports have highlighted the options of chemical breath analysis with regard to non-invasive cancer detection. Certain volatile organic compounds (VOC) supposedly present in higher amounts or in characteristic patterns have been suggested as potential biomarkers. However, so far no clinical application based on a specific set of compounds appears to exist. Numerous reports on the capability of sniffer dogs and sensor arrays or electronic noses to distinguish breath of cancer patients and healthy controls supports the concept of genuine cancer-related volatile profiles. However, the actual compounds responsible for the scent are completely unknown and there is no correlation with the potential biomarkers suggested on basis of chemical trace analysis. It is outlined that specific features connected with the VOC analysis in breath - namely small concentrations of volatiles, interfering background concentrations, considerable sampling effort and sample instability, impracticability regarding routine application - stand in the way of substantial progress. The underlying chemical-analytical challenge can only be met considering the severe susceptibility of VOC determination to these adverse conditions. Therefore, the attention is drawn to the needs for appropriate quality assurance/quality control as the most important feature for the reliable quantification of volatiles present in trace concentration. Consequently, the advantages of urine as an alternative matrix for volatile biomarker search in the context of diagnosing lung and other cancers are outlined with specific focus on quality assurance and practicability in clinical chemistry. The headspace over urine samples as the VOC source allows adapting gas chromatographical procedures well-established in water analysis. Foremost, the selection of urine over breath as non-invasive matrix should provide considerably more resilience to adverse effects during sampling and analysis. The most important advantage of urine over breath is seen in the option to partition, dispense, mix, spike, store, and thus to dispatch taylor-made urine samples on demand for quality control measures. Although it is still open at this point if cancer diagnosis supported by non-invasively sampled VOC profiles will ultimately reach clinical application the advantages of urine over breath should significantly facilitate urgently required steps beyond the current proof-of-concept stage and towards standardisation.

Detecting head and neck squamous carcinoma using a portable handheld electronic nose

INTRODUCTION

Detecting volatile organic compounds in exhaled breath enables the diagnosis of cancer. We investigated whether a handheld version of an electronic nose is able to discriminate between patients with head and neck squamous cell cancer (HNSCC) and healthy controls.

METHODS

Ninety-one patients with HNSCC and 72 controls exhaled through an e-nose. An artificial neural network based model was built to separate between HNSCC patients and healthy controls. Additionally, three models were created for separating between the oral, oropharyngeal, and glottic subsites respectively, and healthy controls.

RESULTS

The results showed a diagnostic accuracy of 72% at a sensitivity of 79%, specificity of 63%, and area under the curve (AUC) of 0.75. Results for the subsites showed an AUC of 0.85, 0.82, and 0.83 respectively for oral, oropharyngeal, and glottic HNSCC.

CONCLUSION

This feasibility study showed that this portable noninvasive diagnostic tool can differentiate between HNSCC patients and healthy controls.

Breath Analysis for Early Detection of Malignant Pleural Mesothelioma: Volatile Organic Compounds (VOCs) Determination and Possible Biochemical Pathways

Malignant pleural mesothelioma (MPM) is a rare neoplasm, mainly caused by asbestos exposure, with a high mortality rate. The management of patients with MPM is controversial due to a long latency period between exposure and diagnosis and because of non-specific symptoms generally appearing at advanced stage of the disease. Breath analysis, aimed at the identification of diagnostic Volatile Organic Compounds (VOCs) pattern in exhaled breath, is believed to improve early detection of MPM. Therefore, in this study, breath samples from 14 MPM patients and 20 healthy controls (HC) were collected and analyzed by Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC/MS). Nonparametric test allowed to identify the most weighting variables to discriminate between MPM and HC breath samples and multivariate statistics were applied. Considering that MPM is an aggressive neoplasm leading to a late diagnosis and thus the recruitment of patients is very difficult, a promising data mining approach was developed and validated in order to discriminate between MPM patients and healthy controls, even if no large population data are available. Three different machine learning algorithms were applied to perform the classification task with a leave-one-out cross-validation approach, leading to remarkable results (Area Under Curve AUC = 93%). Ten VOCs, such as ketones, alkanes and methylate derivates, as well as hydrocarbons, were able to discriminate between MPM patients and healthy controls and for each compound which resulted diagnostic for MPM, the metabolic pathway was studied in order to identify the link between VOC and the neoplasm. Moreover, five breath samples from asymptomatic asbestos-exposed persons (AEx) were exploratively analyzed, processed and tested by the validated statistical method as blinded samples in order to evaluate the performance for the early recognition of patients affected by MPM among asbestos-exposed persons. Good agreement was found between the information obtained by gold-standard diagnostic methods such as computed tomography CT and model output.

Light-activated inorganic CsPbBr2I perovskite for room-temperature self-powered chemical sensing

Halide perovskite materials are excellent light harvesters that have generated enormous interest for photovoltaic technology and an increasing number of other optoelectronic applications. Very recently, their use for miniaturized chemical sensors has shown a promising room-temperature response. Here, we present some insights on the use of CsPbBr2I (CPBI) perovskites for self-powered room-temperature sensing of several environmentally and medically relevant compounds demonstrating rapid detection of down to concentrations of 1 ppm. Notably, the photocurrent of these self-powered CPBI-based devices increases under exposure to both reducing (e.g. acetone, propane) and oxidizing (e.g. NO2, O2) gas molecules and decreases rapidly upon reverting to an inert atmosphere. In situ photoluminescence (PL) analysis of the CPBI during exposure to oxidizing molecules reveals a strongly increased PL intensity and longer lifetime indicating a prevalent role of CPBI trap states in the sensing mechanism. These findings provide new insights for the engineering of perovskite-based materials for their future chemical sensing applications.

Exhaled volatile organic compounds in adult asthma: a systematic review

The search for biomarkers that can guide precision medicine in asthma, particularly those that can be translated to the clinic, has seen recent interest in exhaled volatile organic compounds (VOCs). Given the number of studies reporting "breathomics" findings and its growing integration in clinical trials, we performed a systematic review of the literature to summarise current evidence and understanding of breathomics technology in asthma.A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-oriented systematic search was performed (CRD42017084145) of MEDLINE, Embase and the Cochrane databases to search for any reports that assessed exhaled VOCs in adult asthma patients, using the following terms (asthma AND (volatile organic compounds AND exhaled) OR breathomics).Two authors independently determined the eligibility of 2957 unique records, of which 66 underwent full-text review. Data extraction and risk of bias assessment was performed on the 22 studies deemed to fulfil the search criteria. The studies are described in terms of methodology and the evidence narratively summarised under the following clinical headings: diagnostics, phenotyping, treatment stratification, treatment monitoring and exacerbation prediction/assessment.Our review found that most studies were designed to assess diagnostic potential rather than focus on underlying biology or treatable traits. Results are generally limited by a lack of methodological standardisation and external validation and by insufficiently powered studies, but there is consistency across the literature that exhaled VOCs are sensitive to underlying inflammation. Modern studies are applying robust breath analysis workflows to large multi-centre study designs, which should unlock the full potential of measurement of exhaled volatile organic compounds in airways diseases such as asthma.

Simultaneous Proton Transfer Reaction-Mass Spectrometry and electronic nose study of the volatile compounds released by Plasmodium falciparum infected red blood cells in vitro

The discovery that Volatile Organic Compounds (VOCs) can be biomarkers for several diseases has led to the conception of their possible application as diagnostic tools. In this study, we aimed at defining of diagnostic signatures for the presence of malaria transmissible stages in infected individuals. To do this, we compared VOCs released by asexual and sexual stage cultures of Plasmodium falciparum, the deadliest species of malaria, with those emitted by uninfected red blood cells (RBCs). VOC analysis was carried out with an innovative set-up, where each sample was simultaneously analysed by proton transfer reaction time of flight mass spectrometry (PTR-ToF-MS) and an electronic nose. PTR-Tof-MS results show that sexual stages are characterized by a larger emission of hexanal, compared with uninfected or asexual stage-infected RBCs, which makes them clearly identifiable. PTR-Tof-MS analysis also detected differences in VOC composition between asexual stages and uninfected RBCs. These results have been substantially replicated by the electronic nose analysis and may open the possibility to develop sensitive and easy-to-use devices able to detect sexual parasite stages in infected individuals. This study also demonstrates that the combination of mass spectrometry with electronic noses is a useful tool to identify markers of diseases and to support the development of optimized sensors.

Analysis of Nitrogen-containing Compounds in Mouth-exhaled Breath by Electrospray Ionization Quadrupole Time-of-Flight Mass Spectrometry

Nitrogen-containing compounds are important components in human breath. However, their origins have not yet been clearly understood. In this study, a modified electrospray ionization (ESI) source coupling with quadrupole time-of-flight mass spectrometry has been used for breath analysis. Fourteen nitrogen-containing compounds were identified in mouth-exhaled breath, and 10 of them were from the oral cavity and oropharynx. Moreover, 8 of these nitrogen-containing compounds were recognized as endogenous metabolites. This result provides important clues for exploring the biological origins of these nitrogen-containing compounds. Observation of the ion suppression phenomenon also indicates that breath analysis should be carried out after clearing of the oral cavity and oropharynx, or directly through nose-breathing to eliminate the influence of those nitrogen-containing compounds from the oral cavity.

Global Plasma Profiling for Colorectal Cancer-Associated Volatile Organic Compounds: a Proof-of-Principle Study

Volatile organic compounds (VOCs) could reflect changes resulting from ongoing pathophysiological processes and altered body metabolisms, and thus have been studied for various types of cancers. We aimed to test an advanced global metabolomic technique to characterize circulating VOCs in patients diagnosed with colorectal cancer (CRC). We employed solid-phase microextraction (SPME) and comprehensive two-dimensional gas chromatography mass-spectrometry (GC × GC-MS). We analyzed 30 random plasma samples from incident cases of CRC. The 30 samples were from population controls enrolled in a large population-based case-control study. The number of metabolite peaks detected in the cases was significantly lower than that detected in the controls (median 1530 vs. 1694, P = 0.02). Partial least squares-discriminant analysis showed clear VOC profile differences between the CRC and the controls. After adjustment for multiple comparisons at the 5% false discovery rate level, five VOCs were differentially expressed between the cases and the controls. Among these five VOCs, 2,3,4-trimethyl-hexane (decreased) and 2,4-dimethylhept-1-ene (increased) were both lipid peroxidation products but not previously reported for CRC. In summary, this study pointed to an intriguing observation that the richness of volatile metabolites may be reduced in CRC cases and demonstrated the utility of SPME GC × GC-MS in discovery of candidate markers for further validation.

Detecting recurrent head and neck cancer using electronic nose technology: A feasibility study

BACKGROUND

The aim of this feasibility study was to assess the diagnostic performance of an electronic nose (e-nose) as a noninvasive diagnostic tool in detecting locoregional recurrent and/or second (or third) primary head and neck squamous cell carcinoma (HNSCC) after curative treatment.

METHODS

Using an e-nose (Aeonose, The eNose Company, Zutphen, The Netherlands), breath samples were collected from patients after curative treatment of an HNSCC with a locoregional recurrence or second (or third) primary tumor (N = 20) and from patients without evidence of recurrent disease (N = 20). Analyses were performed utilizing artificial neural networking based on patterns of volatile organic compounds.

RESULTS

A diagnostic accuracy of 83% was observed in differentiating follow-up patients with locoregional recurrent or second (or third) primary HNSCC from those without evidence of disease.

CONCLUSION

This study has demonstrated the feasibility of using an e-nose to detect locoregional recurrent and/or second (or third) primary HNSCC.

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

Human breath, along with urine and blood, has long been one of the three major biological media for assessing human health and environmental exposure. In fact, the detection of odor on human breath, as described by Hippocrates in 400 BC, is considered the first analytical health assessment tool. Although less common in comparison to contemporary bio-fluids analyses, breath has become an attractive diagnostic medium as sampling is non-invasive, unlimited in timing and volume, and does not require clinical personnel. Exhaled breath, exhaled breath condensate (EBC), and exhaled breath aerosol (EBA) are different types of breath matrices used to assess human health and disease state. Over the past 20 years, breath research has made many advances in assessing health state, overcoming many of its initial challenges related to sampling and analysis. The wide variety of sampling techniques and collection devices that have been developed for these media are discussed herein. The different types of sensors and mass spectrometry instruments currently available for breath analysis are evaluated as well as emerging breath research topics, such as cytokines, security and airport surveillance, cellular respiration, and canine olfaction.

In Vitro Tracking of Intracellular Metabolism-Derived Cancer Volatiles via Isotope Labeling

Cancer detection relying on the release of volatile biomarkers has been extensively studied, but the individual biochemical processes of the cells from which biogenic volatiles originate have not been thoroughly elucidated to date. Inadequate determination of the metabolic origin of the volatile biomarkers has limited the progress of the scientific and practical applications of volatile biomarkers. To overcome the current limitations, we developed a metabolism tracking approach combining stable isotope labeling and flux analysis of volatiles to trace the intracellular metabolism-derived volatiles and to reveal their relation to cancer metabolic pathways. Specifically, after the ¹³C labeling of lung cancer cell, the isotopic ratio of whole cellular carbon was measured by nanoscale secondary ion mass spectrometry-based imaging. The kinetic modeling with the time-dependent isotopic ratio determined the period during which cancer cells reach the metabolic steady state, at which time all of the potential volatiles derived from intracellular metabolism were fully enriched isotopically. By measuring the isotopic enrichment of volatiles at the end-stage of isotopic flux, we found that 2-pentadecanone appeared to be derived from the metabolic cascade starting from glucose to fatty acid synthesis. Furthermore, this biosynthetic pathway was determined to be distinct in cancer, as it was upregulated in colon, breast, and pancreatic cancer cells but not in normal cells. The investigation of the metabolic footprint of 2-pentadecanone demonstrates that our novel approach could be applied to trace the metabolic origin of biogenic volatile organic compounds. This analytical strategy represents a potential cutting-edge tool in elucidating the biochemical authenticity of cancer volatiles and further expanding our understanding of the metabolic network of airborne metabolites in vitro.

Synergy between nanomaterials and volatile organic compounds for non-invasive medical evaluation

This article is an overview of the present and ongoing developments in the field of nanomaterial-based sensors for enabling fast, relatively inexpensive and minimally (or non-) invasive diagnostics of health conditions with follow-up by detecting volatile organic compounds (VOCs) excreted from one or combination of human body fluids and tissues (e.g., blood, urine, breath, skin). Part of the review provides a didactic examination of the concepts and approaches related to emerging sensing materials and transduction techniques linked with the VOC-based non-invasive medical evaluations. We also present and discuss diverse characteristics of these innovative sensors, such as their mode of operation, sensitivity, selectivity and response time, as well as the major approaches proposed for enhancing their ability as hybrid sensors to afford multidimensional sensing and information-based sensing. The other parts of the review give an updated compilation of the past and currently available VOC-based sensors for disease diagnostics. This compilation summarizes all VOCs identified in relation to sickness and sampling origin that links these data with advanced nanomaterial-based sensing technologies. Both strength and pitfalls are discussed and criticized, particularly from the perspective of the information and communication era. Further ideas regarding improvement of sensors, sensor arrays, sensing devices and the proposed workflow are also included.

Altered Volatile Organic Compound Profile in Transgenic Rats Bearing A53T Mutation of Human α-Synuclein: Comparison with Dopaminergic and Serotonergic Denervation

Early diagnosis of Parkinson's disease (PD) is of great importance due its progressive phenotype. Neuroprotective drugs could potentially slow down disease progression if used at early stages. Previously, we have reported an altered content of volatile organic compounds (VOCs) in the breath of rats following a 50% reduction in striatal dopamine (DA) content induced by 6-hydroxydopamine. We now report on the difference in the breath-print and content of VOCs between rats with mild and severe lesions of DA neurons, serotonergic neuronal lesions, and transgenic (Tg) rats carrying the PD-producing A53T mutation of the SNCA (α-synuclein) gene. The Tg rats had an increased content of 3-octen-1-ol and 4-chloro-3-methyl phenol in blood, while in brain tissue, hexanal, hexanol, and 2,3-octanedione were present in controls but absent in Tg rats. Levels of 1-heptyl-2-methyl cyclopropane were increased in brain tissue of Tg rats. The data confirm the potential of breath analysis for detection of human idiosyncratic as well as autosomal dominant PD.

Exhaled breath to screen for malignant pleural mesothelioma: a validation study

Malignant pleural mesothelioma (MPM) is predominantly caused by asbestos exposure and has a poor prognosis. Breath contains volatile organic compounds (VOCs) and can be explored as an early detection tool. Previously, we used multicapillary column/ion mobility spectrometry (MCC/IMS) to discriminate between patients with MPM and asymptomatic high-risk persons with a high rate of accuracy. Here, we aim to validate these findings in different control groups.Breath and background samples were obtained from 52 patients with MPM, 52 healthy controls without asbestos exposure (HC), 59 asymptomatic former asbestos workers (AEx), 41 patients with benign asbestos-related diseases (ARD), 70 patients with benign non-asbestos-related lung diseases (BLD) and 56 patients with lung cancer (LC).After background correction, logistic lasso regression and receiver operating characteristic (ROC) analysis, the MPM group was discriminated from the HC, AEx, ARD, BLD and LC groups with 65%, 88%, 82%, 80% and 72% accuracy, respectively. Combining AEx and ARD patients resulted in 94% sensitivity and 96% negative predictive value (NPV). The most important VOCs selected were P1, P3, P7, P9, P21 and P26.We discriminated MPM patients from at-risk subjects with great accuracy. The high sensitivity and NPV allow breath analysis to be used as a screening tool for ruling out MPM.

Detection of Lung Cancer and EGFR Mutation by Electronic Nose System

INTRODUCTION

Early detection of lung cancer (LC) has been well established as a significant key point in patient survival and prognosis. New highly sensitive nanoarray sensors for exhaled volatile organic compounds that have been developed and coupled with powerful statistical programs may be used when diseases such as LC are suspected. Detection of genetic aberration mutation by nanoarray sensors is the next target.

METHODS

Breath samples were taken from patients who were evaluated for suspicious pulmonary lesions. Patients were classified as those with benign nodules, as patients with LC with or without the EGFR mutation, and according to their smoking status. Breath prints were recognized by nanomaterial-based sensor array, and pattern recognition methods were used.

RESULTS

A total of 119 patients participated in this study, 30 patients with benign nodules and 89 patients with LC (16 with early disease and 73 with advanced disease). Patients with LC who harbored the EGFR mutation (n = 19) could be discriminated from those with wild-type EGFR (n = 34) with an accuracy of 83%, sensitivity of 79%, and specificity of 85%. Discrimination of early LC from benign nodules had 87% accuracy and positive and negative predictive values of 87.7 and 87.5% respectively. Moderate discrimination (accuracy of 76%) was found between LC of heavy smokers and that of never-smokers or distant past light smokers.

CONCLUSIONS

Breath analysis could discriminate patients with LC who harbor the EGFR mutation from those with wild-type EGFR and those with benign pulmonary nodules from those patients with early LC. A positive breath print for the EGFR mutation may be used in treatment decisions if tissue sampling does not provide adequate material for definitive mutation analysis.

Advanced Materials for Health Monitoring with Skin-Based Wearable Devices

Skin-based wearable devices have a great potential that could result in a revolutionary approach to health monitoring and diagnosing disease. With continued innovation and intensive attention to the materials and fabrication technologies, development of these healthcare devices is progressively encouraged. This article gives a concise, although admittedly non-exhaustive, didactic review of some of the main concepts and approaches related to recent advances and developments in the scope of skin-based wearable devices (e.g. temperature, strain, biomarker-analysis werable devices, etc.), with an emphasis on emerging materials and fabrication techniques in the relevant fields. To give a comprehensive statement, part of the review presents and discusses different aspects of these advanced materials, such as the sensitivity, biocompatibility and durability as well as the major approaches proposed for enhancing their chemical and physical properties. A complementary section of the review linking these advanced materials with wearable device technologies is particularly specified. Some of the strong and weak points in development of each wearable material/device are highlighted and criticized. Several ideas regarding further improvement of skin-based wearable devices are also discussed.

Fluorometric Biosniffer Camera "Sniff-Cam" for Direct Imaging of Gaseous Ethanol in Breath and Transdermal Vapor

Various volatile organic compounds can be found in human transpiration, breath and body odor. In this paper, a novel two-dimensional fluorometric imaging system, known as a "sniffer-cam" for ethanol vapor released from human breath and palm skin was constructed and validated. This imaging system measures ethanol vapor concentrations as intensities of fluorescence through an enzymatic reaction induced by alcohol dehydrogenase (ADH). The imaging system consisted of multiple ultraviolet light emitting diode (UV-LED) excitation sheet, an ADH enzyme immobilized mesh substrate and a high-sensitive CCD camera. This imaging system uses ADH for recognition of ethanol vapor. It measures ethanol vapor by measuring fluorescence of nicotinamide adenine dinucleotide (NADH), which is produced by an enzymatic reaction on the mesh. This NADH fluorometric imaging system achieved the two-dimensional real-time imaging of ethanol vapor distribution (0.5-200 ppm). The system showed rapid and accurate responses and a visible measurement, which could lead to an analysis of metabolism function at real time in the near future.

Possible photoacoustic gas detection for a smart endoscope

Endogenous gas analysis is a potentially fast and convenient noninvasive diagnostic method for a variety of diseases. However, sampling and sample preparation are error-prone steps and must be optimized to achieve reliable results. A miniature photoacoustic system was developed to allow gas sampling using a smart endoscope. The photoacoustic system was demonstrated to have a 1% detection limit for CO2, which is too high. Many improvements, including modifying the structure of PA cell, must be considered for further development.

A Compendium of Volatile Organic Compounds (VOCs) Released By Human Cell Lines

Volatile organic compounds (VOCs) offer unique insights into ongoing biochemical processes in healthy and diseased humans. Yet, their diagnostic use is hampered by the limited understanding of their biochemical or cellular origin and their frequently unclear link to the underlying diseases. Major advancements are expected from the analyses of human primary cells, cell lines and cultures of microorganisms. In this review, a database of 125 reliably identified VOCs previously reported for human healthy and diseased cells was assembled and their potential origin is discussed. The majority of them have also been observed in studies with other human matrices (breath, urine, saliva, feces, blood, skin emanations). Moreover, continuing improvements of qualitative and quantitative analyses, based on the recommendations of the ISO-11843 guidelines, are suggested for the necessary standardization of analytical procedures and better comparability of results. The data provided contribute to arriving at a more complete human volatilome and suggest potential volatile biomarkers for future validation.

Dedication: This review is dedicated to the memory of Prof. Dr. Anton Amann, who sadly passed away on January 6, 2015. He was motivator and motor for the field of breath research.

Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules

We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development.

Differentiation between genetic mutations of breast cancer by breath volatolomics

Mapping molecular sub-types in breast cancer (BC) tumours is a rapidly evolving area due to growing interest in, for example, targeted therapy and screening high-risk populations for early diagnosis. We report a new concept for profiling BC molecular sub-types based on volatile organic compounds (VOCs). For this purpose, breath samples were collected from 276 female volunteers, including healthy, benign conditions, ductal carcinoma in situ (DCIS) and malignant lesions. Breath samples were analysed by gas chromatography mass spectrometry (GC-MS) and artificially intelligent nanoarray technology. Applying the non-parametric Wilcoxon/Kruskal-Wallis test, GC-MS analysis found 23 compounds that were significantly different (p < 0.05) in breath samples of BC patients with different molecular sub-types. Discriminant function analysis (DFA) of the nanoarray identified unique volatolomic signatures between cancer and non-cancer cases (83% accuracy in blind testing), and for the different molecular sub-types with accuracies ranging from 82 to 87%, sensitivities of 81 to 88% and specificities of 76 to 96% in leave-one-out cross-validation. These results demonstrate the presence of detectable breath VOC patterns for accurately profiling molecular sub-types in BC, either through specific compound identification by GC-MS or by volatolomic signatures obtained through statistical analysis of the artificially intelligent nanoarray responses.

Detection of cancer through exhaled breath: a systematic review

Background

Timely diagnosis of cancer represents a challenging task; in particular, there is a need for reliable non-invasive screening tools that could achieve high levels of adherence at virtually no risk in population-based screening. In this review, we summarize the current evidence of exhaled breath analysis for cancer detection using standard analysis techniques and electronic nose.

Methods

Relevant studies were identified searching Pubmed and Web of Science databases until April 30, 2015. Information on breath test performance, such as sensitivity and specificity, was extracted together with volatile compounds that were used to discriminate cancer patients from controls. Performance of different breath analysis techniques is provided for various cancers together with information on methodological issues, such as breath sampling protocol and validation of the results.

Results

Overall, 73 studies were included, where two-thirds of the studies were conducted on lung cancer. Good discrimination usually required a combination of multiple biomarkers, and area under the receiver operating characteristic curve or accuracy reached levels of 0.9 or higher in multiple studies. In 25% of the reported studies, classification models were built and validated on the same datasets. Huge variability was seen in different aspects among the studies.

Conclusions

Analyses of exhaled breath yielded promising results, although standardization of breath collection, sample storage and data handling remain critical issues. In order to foster breath analysis implementation into practice, larger studies should be implemented in true screening settings, paying particular attention to standardization in breath collection, consideration of covariates, and validation in independent population samples.

Feasibility of electronic nose technology for discriminating between head and neck, bladder, and colon carcinomas

Electronic nose (e-nose) technology has the potential to detect cancer at an early stage and can differentiate between cancer origins. Our objective was to compare patients who had head and neck squamous cell carcinoma (HNSCC) with patients who had colon or bladder cancer to determine the distinctive diagnostic characteristics of the e-nose. Feasibility study An e-nose device was used to collect samples of exhaled breath from patients who had HNSCC and those who had bladder or colon cancer, after which the samples were analyzed and compared. One hundred patients with HNSCC, 40 patients with bladder cancer, and 28 patients with colon cancer exhaled through an e-nose for 5 min. An artificial neural network was used for the analysis, and double cross-validation to validate the model. In differentiating HNSCC from colon cancer, a diagnostic accuracy of 81 % was found. When comparing HNSCC with bladder cancer, the diagnostic accuracy was 84 %. A diagnostic accuracy of 84 % was found between bladder cancer and colon cancer. The e-nose technique using double cross-validation is able to discriminate between HNSCC and colon cancer and between HNSCC and bladder cancer. Furthermore, the e-nose technique can distinguish colon cancer from bladder cancer.

Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath

Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma, and chronic obstructive pulmonary disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples show that the optimized SiNW FETs can detect and discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct harmless way, which can reassure patients and prevent numerous unpleasant investigations.

Collecting Protein Biomarkers in Breath Using Electret Filters: A Preliminary Method on New Technical Model and Human Study

Biomarkers in exhaled breath are useful for respiratory disease diagnosis in human volunteers. Conventional methods that collect non-volatile biomarkers, however, necessitate an extensive dilution and sanitation processes that lowers collection efficiencies and convenience of use. Electret filter emerged in recent decade to collect virus biomarkers in exhaled breath given its simplicity and effectiveness. To investigate the capability of electret filters to collect protein biomarkers, a model that consists of an atomizer that produces protein aerosol and an electret filter that collects albumin and carcinoembryonic antigen-a typical biomarker in lung cancer development- from the atomizer is developed. A device using electret filter as the collecting medium is designed to collect human albumin from exhaled breath of 6 volunteers. Comparison of the collecting ability between the electret filter method and other 2 reported methods is finally performed based on the amounts of albumin collected from human exhaled breath. In conclusion, a decreasing collection efficiency ranging from 17.6% to 2.3% for atomized albumin aerosol and 42% to 12.5% for atomized carcinoembryonic antigen particles is found; moreover, an optimum volume of sampling human exhaled breath ranging from 100 L to 200 L is also observed; finally, the self-designed collecting device shows a significantly better performance in collecting albumin from human exhaled breath than the exhaled breath condensate method (p<0.05) but is not significantly more effective than reported 3-stage impactor method (p>0.05). In summary, electret filters are potential in collecting non-volatile biomarkers in human exhaled breath not only because it was simpler, cheaper and easier to use than traditional methods but also for its better collecting performance.

Detection of precancerous gastric lesions and gastric cancer through exhaled breath

OBJECTIVES

Timely detection of gastric cancer (GC) and the related precancerous lesions could provide a tool for decreasing both cancer mortality and incidence.

DESIGN

968 breath samples were collected from 484 patients (including 99 with GC) for two different analyses. The first sample was analysed by gas chromatography linked to mass spectrometry (GCMS) while applying t test with multiple corrections (p value<0.017); the second by cross-reactive nanoarrays combined with pattern recognition. For the latter, 70% of the samples were randomly selected and used in the training set while the remaining 30% constituted the validation set. The operative link on gastric intestinal metaplasia (OLGIM) assessment staging system was used to stratify the presence/absence and risk level of precancerous lesions. Patients with OLGIM stages III-IV were considered to be at high risk.

RESULTS

According to the GCMS results, patients with cancer as well as those at high risk had distinctive breath-print compositions. Eight significant volatile organic compounds (p value<0.017) were detected in exhaled breath in the different comparisons. The nanoarray analysis made it possible to discriminate between the patients with GC and the control group (OLGIM 0-IV) with 73% sensitivity, 98% specificity and 92% accuracy. The classification sensitivity, specificity, and accuracy between the subgroups was as follows: GC versus OLGIM 0-II-97%, 84% and 87%; GC versus OLGIM III-IV-93%, 80% and 90%; but OLGIM I-II versus OLGIM III-IV and dysplasia combined-83%, 60% and 61%, respectively.

CONCLUSIONS

Nanoarray analysis could provide the missing non-invasive screening tool for GC and related precancerous lesions as well as for surveillance of the latter.

TRIAL REGISTRATION NUMBER

Clinical Trials.gov number, NCT01420588 (3/11/2013).

Prediction of blood:air and fat:air partition coefficients of volatile organic compounds for the interpretation of data in breath gas analysis

In this article, a database of blood:air and fat:air partition coefficients (λ b:a and λ f:a) is reported for estimating 1678 volatile organic compounds recently reported to appear in the volatilome of the healthy human. For this purpose, a quantitative structure-property relationship (QSPR) approach was applied and a novel method for Henry's law constants prediction developed. A random forest model based on Molecular Operating Environment 2D (MOE2D) descriptors based on 2619 literature-reported Henry's constant values was built. The calculated Henry's law constants correlate very well (R(2) test  =  0.967) with the available experimental data. Blood:air and fat:air partition coefficients were calculated according to the method proposed by Poulin and Krishnan using the estimated Henry's constant values. The obtained values correlate reasonably well with the experimentally determined ones for a test set of 90 VOCs (R(2)  =  0.95). The provided data aim to fill in the literature data gap and further assist the interpretation of results in studies of the human volatilome.

Detection of volatile organic compounds (VOCs) from exhaled breath as noninvasive methods for cancer diagnosis

The detection of cancer at an early stage is often significant in the successful treatment of the disease. Tumor cells have been reported to generate unique cancer volatile organic compound (VOC) profiles which can reflect the disease conditions. The detection and analysis of VOC biomarkers from exhaled breath has been recognized as a new frontier in cancer diagnostics and health inspections owing to its potential in developing rapid, noninvasive, and inexpensive cancer screening tools. To detect specific VOCs of low concentrations from exhaled breath, and to enhance the accuracy of early diagnosis, many breath collection and analysis approaches have been developed. This paper will summarize and critically review the exhaled-breath VOC-related sampling, collection, detection, and analytical methods, especially the recent development in VOC sensors. VOC sensors are commonly inexpensive, portable, programmable, easy to use, and can obtain data in real time with high sensitivities. Therefore, many sensor-based VOC detection techniques have huge potential in clinical point-of-care use.

Nanoscale Sensor Technologies for Disease Detection via Volatolomics

The detection of many diseases is missed because of delayed diagnoses or the low efficacy of some treatments. This emphasizes the urgent need for inexpensive and minimally invasive technologies that would allow efficient early detection, stratifying the population for personalized therapy, and improving the efficacy of rapid bed-side assessment of treatment. An emerging approach that has a high potential to fulfill these needs is based on so-called "volatolomics", namely, chemical processes involving profiles of highly volatile organic compounds (VOCs) emitted from body fluids, including breath, skin, urine and blood. This article presents a didactic review of some of the main advances related to the use of nanomaterial-based solid-state and flexible sensors, and related artificially intelligent sensing arrays for the detection and monitoring of disease with volatolomics. The article attempts to review the technological gaps and confounding factors related to VOC testing. Different ways to choose nanomaterial-based sensors are discussed, while considering the profiles of targeted volatile markers and possible limitations of applying the sensing approach. Perspectives for taking volatolomics to a new level in the field of diagnostics are highlighted.

Dynamic Nanoparticle-Based Flexible Sensors: Diagnosis of Ovarian Carcinoma from Exhaled Breath

Flexible sensors based on molecularly modified gold nanoparticles (GNPs) were integrated into a dynamic cross-reactive diagnostic sensing array. Each bending state of the GNP-based flexible sensor gives unique nanoparticle spatial organization, altering the interaction between GNP ligands and volatile organic compounds (VOCs), which increases the amount of data obtainable from each sensor. Individual dynamic flexible sensor could selectively detect parts per billion (ppb) level VOCs that are linked with ovarian cancers in exhaled breath and discriminate them from environmental VOCs that exist in exhaled breath samples, but do not relate to ovarian cancer per se. Strain-related response successfully discriminated between exhaled breath collected from control subjects and those with ovarian cancer, with data from a single sensor being sufficient to obtain 82% accuracy, irrespective of important confounding factors, such as tobacco consumption and comorbidities. The approach raises the hope of achieving an extremely simple, inexpensive, portable, and noninvasive diagnostic procedure for cancer and other diseases.

Breath testing as potential colorectal cancer screening tool

Although colorectal cancer (CRC) screening is included in organized programs of many countries worldwide, there is still a place for better screening tools. In this study, 418 breath samples were collected from 65 patients with CRC, 22 with advanced or nonadvanced adenomas, and 122 control cases. All patients, including the controls, had undergone colonoscopy. The samples were analysed with two different techniques. The first technique relied on gas chromatography coupled with mass spectrometry (GC-MS) for identification and quantification of volatile organic compounds (VOCs). The T-test was used to identify significant VOCs (p values < 0.017). The second technique relied on sensor analysis with a pattern recognition method for building a breath pattern to identify different groups. Blind analysis or leave-one-out cross validation was conducted for validation. The GC-MS analysis revealed four significant VOCs that identified the tested groups; these were acetone and ethyl acetate (higher in CRC), ethanol and 4-methyl octane (lower in CRC). The sensor-analysis distinguished CRC from the control group with 85% sensitivity, 94% specificity and 91% accuracy. The performance of the sensors in identifying the advanced adenoma group from the non-advanced adenomas was 88% sensitivity, 100% specificity, and 94% accuracy. The performance of the sensors in identifying the advanced adenoma group was distinguished from the control group was 100% sensitivity, 88% specificity, and 94% accuracy. For summary, volatile marker testing by using sensor analysis is a promising noninvasive approach for CRC screening.

Hybrid volatolomics and disease detection

This Review presents a concise, but not exhaustive, didactic overview of some of the main concepts and approaches related to "volatolomics"-an emerging frontier for fast, risk-free, and potentially inexpensive diagnostics. It attempts to review the source and characteristics of volatolomics through the so-called volatile organic compounds (VOCs) emanating from cells and their microenvironment. It also reviews the existence of VOCs in several bodily fluids, including the cellular environment, blood, breath, skin, feces, urine, and saliva. Finally, the usefulness of volatolomics for diagnosis from a single bodily fluid, as well as ways to improve these diagnostic aspects by "hybrid" approaches that combine VOC profiles collected from two or more bodily fluids, will be discussed. The perspectives of this approach in developing the field of diagnostics to a new level are highlighted.

Monolayer-capped gold nanoparticles for disease detection from breath

The recognition of volatile organic compounds in breath samples is a promising approach for noninvasive safe diagnosis of disease. Spectrometry and spectroscopy methods used for breath analysis suffer from suboptimal accuracy, are expensive and are unsuitable for diagnostics. This article presents a concise review on arrays of monolayer-capped gold nanoparticle (GNP) sensors in conjugation with pattern recognition methods for cost-effective, fast and high-throughput point-of-care diagnostic results from exhaled breath samples. The article starts with a general introduction to the rationale and advantages of breath analysis as well as with a presentation of the utility of monolayer-capped GNP sensors in this field. The article continues with a presentation of the main fabrication and operation principles of these GNP sensors and concludes with selected examples regarding their utility in different fields of medicine, particularly in neurology, infectiology, respiratory medicine and oncology.