The lung cancer breath signature: a comparative analysis of exhaled breath and air sampled from inside the lungs

Enhanced Breath Aldehyde Analysis by Dual-Membrane-Assisted Charge Tagging, Enrichment, and Onsite Elution NanoESI-MS

Aldehydes, crucial volatile organic compounds present in exhaled breath, have been established as promising biomarkers for cancer diagnosis. However, their rapid and sensitive detection through widely employed spray-based ionization mass spectrometry is still challenging. To address this, we introduce a charged "iridium isotopic signature" probe tailored for efficient capture and unambiguous identification of ubiquitous aldehydes in the gas phase. This 191/193Ir-tagged mass spectrometric probe, equipped with a reactive amine moiety capable of interacting with aldehydes, is immobilized on the porous Nylon-6 membrane that facilitates efficient gas transport and enriches aldehydes from the complex breath matrix. Following a rapid solvent extraction, the Ir-tagging aldehyde derivatives were successfully eluted with efficient removal of excess probes by the oxidized cellulose membrane, yielding a purified sample ideally suited for direct, rapid, and ultrasensitive (with a detection limit below 0.1 ppt) nanoelectrospray ionization mass spectrometry (nanoESI-MS) analysis. By utilizing an analogous iridium complex as an internal standard, our method precisely identified and quantified 12 aldehydes in exhaled breath (EB), with several exhibiting significant elevations in esophageal cancer patients compared with healthy controls. This highlights its efficacy as a rapid and accurate tool for detecting breath aldehyde biomarkers, offering promising avenues for cancer diagnosis.

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

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

Clinical applications of volatilomic assays

The study of metabolomics is revealing immense potential for diagnosis, therapy monitoring, and understanding of pathogenesis processes. Volatilomics is a subcategory of metabolomics interested in the detection of molecules that are small enough to be released in the gas phase. Volatile compounds produced by cellular processes are released into the blood and lymph, and can reach the external environment through different pathways, such as the blood-air interface in the lung that are detected in breath, or the blood-water interface in the kidney that leads to volatile compounds detected in urine. Besides breath and urine, additional sources of volatile compounds such as saliva, blood, feces, and skin are available. Volatilomics traces its roots back over fifty years to the pioneering investigations in the 1970s. Despite extensive research, the field remains in its infancy, hindered by a lack of standardization despite ample experimental evidence. The proliferation of analytical instrumentations, sample preparations and methods of volatilome sampling still make it difficult to compare results from different studies and to establish a common standard approach to volatilomics. This review aims to provide an overview of volatilomics' diagnostic potential, focusing on two key technical aspects: sampling and analysis. Sampling poses a challenge due to the susceptibility of human samples to contamination and confounding factors from various sources like the environment and lifestyle. The discussion then delves into targeted and untargeted approaches in volatilomics. Some case studies are presented to exemplify the results obtained so far. Finally, the review concludes with a discussion on the necessary steps to fully integrate volatilomics into clinical practice.

LC-MS/MS Based Volatile Organic Compound Biomarkers Analysis for Early Detection of Lung Cancer

(1) Background: lung cancer is the world's deadliest cancer, but early diagnosis helps to improve the cure rate and thus reduce the mortality rate. Annual low-dose computed tomography (LD-CT) screening is an efficient lung cancer-screening program for a high-risk population. However, LD-CT has often been characterized by a higher degree of false-positive results. To meet these challenges, a volatolomic approach, in particular, the breath volatile organic compounds (VOCs) fingerprint analysis, has recently received increased attention for its application in early lung cancer screening thanks to its convenience, non-invasiveness, and being well tolerated by patients. (2) Methods: a LC-MS/MS-based volatolomics analysis was carried out according to P/N 5046800 standard based breath analysis of VOC as novel cancer biomarkers for distinguishing early-stage lung cancer from the healthy control group. The discriminatory accuracy of identified VOCs was assessed using subject work characterization and a random forest risk prediction model. (3) Results: the proposed technique has good performance compared with existing approaches, the differences between the exhaled VOCs of the early lung cancer patients before operation, three to seven days after the operation, as well as four to six weeks after operation under fasting and 1 h after the meal were compared with the healthy controls. The results showed that only 1 h after a meal, the concentration of seven VOCs, including 3-hydroxy-2-butanone (TG-4), glycolaldehyde (TG-7), 2-pentanone (TG-8), acrolein (TG-11), nonaldehyde (TG-19), decanal (TG-20), and crotonaldehyde (TG-22), differ significantly between lung cancer patients and control, with the invasive adenocarcinoma of the lung (IAC) having the most significant difference. (4) Conclusions: this novel, non-invasive approach can improve the detection rate of early lung cancer, and LC-MS/MS-based breath analysis could be a promising method for clinical application.

The Use of Breath Analysis in the Management of Lung Cancer: Is It Ready for Primetime?

Breath analysis is a promising non-invasive method for the detection and management of lung cancer. Exhaled breath contains a complex mixture of volatile and non-volatile organic compounds that are produced as end-products of metabolism. Several studies have explored the patterns of these compounds and have postulated that a unique breath signature is emitted in the setting of lung cancer. Most studies have evaluated the use of gas chromatography and mass spectrometry to identify these unique breath signatures. With recent advances in the field of analytical chemistry and machine learning gaseous chemical sensing and identification devices have also been created to detect patterns of odorant molecules such as volatile organic compounds. These devices offer hope for a point-of-care test in the future. Several prospective studies have also explored the presence of specific genomic aberrations in the exhaled breath of patients with lung cancer as an alternative method for molecular analysis. Despite its potential, the use of breath analysis has largely been limited to translational research due to methodological issues, the lack of standardization or validation and the paucity of large multi-center studies. It is clear however that it offers a potentially non-invasive alternative to investigations such as tumor biopsy and blood sampling.

Diagnostic Performance of Electronic Noses in Cancer Diagnoses Using Exhaled Breath: A Systematic Review and Meta-analysis

IMPORTANCE

There has been a growing interest in the use of electronic noses (e-noses) in detecting volatile organic compounds in exhaled breath for the diagnosis of cancer. However, no systematic evaluation has been performed of the overall diagnostic accuracy and methodologic challenges of using e-noses for cancer detection in exhaled breath.

OBJECTIVE

To provide an overview of the diagnostic accuracy and methodologic challenges of using e-noses for the detection of cancer.

DATA SOURCES

An electronic search was performed in the PubMed and Embase databases (January 1, 2000, to July 1, 2021).

STUDY SELECTION

Inclusion criteria were the following: (1) use of e-nose technology, (2) detection of cancer, and (3) analysis of exhaled breath. Exclusion criteria were (1) studies published before 2000; (2) studies not performed in humans; (3) studies not performed in adults; (4) studies that only analyzed biofluids; and (5) studies that exclusively used gas chromatography-mass spectrometry to analyze exhaled breath samples.

DATA EXTRACTION AND SYNTHESIS

PRISMA guidelines were used for the identification, screening, eligibility, and selection process. Quality assessment was performed using Quality Assessment of Diagnostic Accuracy Studies 2. Generalized mixed-effects bivariate meta-analysis was performed.

MAIN OUTCOMES AND MEASURES

Main outcomes were sensitivity, specificity, and mean area under the receiver operating characteristic curve.

RESULTS

This review identified 52 articles with a total of 3677 patients with cancer. All studies were feasibility studies. The sensitivity of e-noses ranged from 48.3% to 95.8% and the specificity from 10.0% to 100.0%. Pooled analysis resulted in a mean (SE) area under the receiver operating characteristic curve of 94% (95% CI, 92%-96%), a sensitivity of 90% (95% CI, 88%-92%), and a specificity of 87% (95% CI, 81%-92%). Considerable heterogeneity existed among the studies because of differences in the selection of patients, endogenous and exogenous factors, and collection of exhaled breath.

CONCLUSIONS AND RELEVANCE

Results of this review indicate that e-noses have a high diagnostic accuracy for the detection of cancer in exhaled breath. However, most studies were feasibility studies with small sample sizes, a lack of standardization, and a high risk of bias. The lack of standardization and reproducibility of e-nose research should be addressed in future research.

Exhaled Breath Volatile Organic Compound Analysis for the Detection of Lung Cancer- A Systematic Review

A rapid and effective diagnostic method is essential for lung cancer since it shows symptoms only at its advanced stage. Research is being carried out in the area of exhaled breath analysis for the diagnosis of various pulmonary diseases including lung cancer. In this method exhaled breath volatile organic compounds (VOC) are analyzed with various techniques such as gas chromatography-mass spectrometry, ion mobility spectrometry, and electronic noses. The VOC analysis is suitable for lung cancer detection since it is non-invasive, fast, and also a low-cost method. In addition, this technique can detect primary stage nodules. This paper presents a systematic review of the various method employed by researchers in the breath analysis field. The articles were selected through various search engines like EMBASE, Google Scholar, Pubmed, and Google. In the initial screening process, 214 research papers were selected using various inclusion and exclusion criteria and finally, 55 articles were selected for the review. The results of the reviewed studies show that detection of lung cancer can be effectively done using the VOC analysis of exhaled breath. The results also show that this method can be used for detecting the different stages and histology of lung cancer. The exhaled breath VOC analysis technique will be popular in the future, bypassing the existing imaging techniques. This systematic review conveys the recent research opportunities, obstacles, difficulties, motivations, and suggestions associated with the breath analysis method for lung cancer detection.

Facile Synthesis of Polyaniline/Carbon-Coated Hollow Indium Oxide Nanofiber Composite with Highly Sensitive Ammonia Gas Sensor at the Room Temperature

Hollow carbon-coated In₂O₃ (C#In₂O₃) nanofibers were prepared using an efficiently combined approach of electrospinning, high-temperature calcination, and hydrothermal process. The polyaniline (PANI)/hollow C#In₂O₃ nanofiber composites were synthesized used hollow C#In₂O₃ nanofibers worked as a core through the in situ chemical oxidative polymerization. The morphology and crystalline structure of the PANI/hollow C#In₂O₃ nanofiber composite were identified using wide-angle X-ray diffraction and transmission electron microscopy. The gas-sensing performances of the fabricated PANI/hollow C#In₂O₃ nanofiber composite sensor were estimated at room temperature, and the response value of the composite sensor with an exposure of 1 ppm NH₃ was 18.2, which was about 5.74 times larger than that of the pure PANI sensor. The PANI/hollow C#In₂O₃ nanofiber composite sensor was demonstrated to be highly sensitive to the detection of NH₃ in the concentration range of 0.6~2.0 ppm, which is critical for kidney or hepatic disease detection from the human breath. This composite sensor also displayed superior repeatability and selectivity at room temperature with exposures of 1.0 and 2.0 ppm NH₃. Because of the outstanding repeatability and selectivity to the detection of NH₃ at 1.0 and 2.0 ppm confirmed in this investigation, the PANI/hollow C#In₂O₃ nanofiber composite sensor will be considered as a favorable gas-sensing material for kidney or hepatic disease detection from human breath.

OUP accepted manuscript

Background

COVID-19 is a highly contagious respiratory disease that can be transmitted through human exhaled breath. It has caused immense loss and has challenged the healthcare sector. It has affected the economy of countries and thereby affected numerous sectors. Analysis of human breath samples is an attractive strategy for rapid diagnosis of COVID-19 by monitoring breath biomarkers.

Content

Breath collection is a noninvasive process. Various technologies are employed for detection of breath biomarkers like mass spectrometry, biosensors, artificial learning, and machine learning. These tools have low turnaround time, robustness, and provide onsite results. Also, MS-based approaches are promising tools with high speed, specificity, sensitivity, reproducibility, and broader coverage, as well as its coupling with various chromatographic separation techniques providing better clinical and biochemical understanding of COVID-19 using breath samples.

Summary

Herein, we have tried to review the MS-based approaches as well as other techniques used for the analysis of breath samples for COVID-19 diagnosis. We have also highlighted the different breath analyzers being developed for COVID-19 detection.

Odors and cancer: Current status and future directions

Cancer is the second leading cause of death in the world. Because tumors detected at early stages are easier to treat, the search for biomarkers-especially non-invasive ones-that allow early detection of malignancies remains a central goal to reduce cancer mortality. Cancer, like other pathologies, often alters body odors, and much has been done by scientists over the last few decades to assess the value of volatile organic compounds (VOCs) as signatures of cancers. We present here a quantitative review of 208 studies carried out between 1984 and 2020 that explore VOCs as potential biomarkers of cancers. We analyzed the main findings of these studies, listing and classifying VOCs related to different cancer types while considering both sampling methods and analysis techniques. Considering this synthesis, we discuss several of the challenges and the most promising prospects of this research direction in the war against cancer.

Improved Non-Invasive Diagnosis of Bladder Cancer with an Electronic Nose: A Large Pilot Study

BACKGROUND

Bladder cancer (BCa) emits specific volatile organic compounds (VOCs) in the urine headspace that can be detected by an electronic nose. The diagnostic performance of an electronic nose in detecting BCa was investigated in a pilot study.

METHODS

A prospective, single-center, controlled, non-randomized, phase 2 study was carried out on 198 consecutive subjects (102 with proven BCa, 96 controls). Urine samples were evaluated with an electronic nose provided with 32 volatile gas analyzer sensors. The tests were repeated at least two times per sample. Accuracy, sensitivity, specificity, and variability were evaluated using mainly the non-parametric combination method, permutation tests, and discriminant analysis classification.

RESULTS

Statistically significant differences between BCa patients and controls were reported by 28 (87.5%) of the 32 sensors. The overall discriminatory power, sensitivity, and specificity were 78.8%, 74.1%, and 76%, respectively; 13/96 (13.5%) controls and 29/102 (28.4%) BCa patients were misclassified as false positive and false negative, respectively. Where the most efficient sensors were selected, the sensitivity and specificity increased up to 91.1% (72.5-100) and 89.1% (81-95.8), respectively. None of the tumor characteristics represented independent predictors of device responsiveness.

CONCLUSIONS

The electronic nose might represent a potentially reliable, quick, accurate, and cost-effective tool for non-invasive BCa diagnosis.

The Room Temperature Highly Sensitive Ammonia Gas Sensor Based on Polyaniline and Nitrogen-Doped Graphene Quantum Dot-Coated Hollow Indium Oxide Nanofiber Composite

Hollow indium trioxide (In₂O₃) nanofibers fabricated via an effectively combined method of electrospinning and high-temperature calcination were coated with nitrogen-doped graphene quantum dots (N-GQDs) prepared by a hydrothermal process through electrostatic interaction. The N-GQD-coated hollow In₂O₃ nanofibers served as a core for the synthesis of polyaniline (PANI)/N-GQD/hollow In₂O₃ nanofiber ternary composites using in situ chemical oxidative polymerization. The chemical structure and morphology of the fabricated ternary composites were characterized using Fourier transform infrared, field-emission scanning electron microscopy, and transmission electron microscopy. The gas-sensing performances of the ternary composites were estimated by a homemade dynamic test system which was supplied with a real-time resistance acquisition platform at room temperature. The response value of the PANI/N-GQD/hollow In₂O₃ nanofiber sensor with a loading of 20 wt% N-GQD-coated hollow In₂O₃ nanofiber and an exposure of 1 ppm NH₃ was 15.2, which was approximately more than 4.4 times higher than that of the PANI sensor. This ternary composite sensor was proved to be very sensitive in the detection of NH₃ at a range of concentration between 0.6 ppm and 2.0 ppm at room temperature, which is crucial in the detection of hepatic or kidney disease in human breath. The PANI/N-GQD/hollow In₂O₃ nanofiber sensor also revealed higher selectivity and repeatability when exposed to 1.0 and 2.0 ppm NH₃ at room temperature. Because of the excellent selectivity and repeatability in the detection of 1.0 and 2.0 ppm NH₃ at room temperature achieved in this study, it is considered that the PANI/N-GQD/hollow In₂O₃ nanofiber composite sensor will be a favored gas-sensing material applied on human breath for the detection of hepatic or kidney disease.

Exhaled breath analysis using GC-MS and an electronic nose for lung cancer diagnostics

Exhaled breath analysis is an interesting and promising approach for the diagnostics of various diseases. Being non-invasive, convenient and simple, this approach has tremendous potential utility for further translation into clinical practice. In this study, gas chromatography-mass spectrometry (GC-MS) and quartz microbalance sensor-based "electronic nose" were applied for analysis of the exhaled breath of 40 lung cancer patients and 40 healthy individuals. It was found that the electronic nose was unable to distinguish the samples of different groups. However, the application of GC-MS allowed identifying statistically significant differences in compound peak areas and their ratios for investigated groups. Diagnostic models were created using random forest classifier based on peak areas and their ratios with the sensitivity and specificity of peak areas (ratios) of 85.7-96.5% (75.0-93.1%) and 73.3-85.1% (90.0-92.5%) on training data and 63.6-75.0% (72.7-100.0%) and 50.0-69.2% (76.9-84.6%) on test data, respectively. The exhaled breath samples of lung cancer patients and healthy volunteers could be distinguished by GC-MS with the use of individual compounds, but application of their ratios could help to determine specific differences between investigated groups and the level the influence of individual metabolism features alternating from one person to another as well as daily instrument reproducibility deviations. The electronic nose has to be significantly improved to apply it to lung cancer diagnostics of exhaled breath analysis and the influence of water vapour has to be lowered to increase the sensitivity of the sensors to detect lung cancer biomarkers.

Study of confounding factors influence on lung cancer diagnostics effectiveness using gas chromatography-mass spectrometry analysis of exhaled breath

Aim: The purpose of this study was to estimate volatile organic compounds (VOCs) ability to distinguish exhaled breath samples of lung cancer patients and healthy volunteers and to assess the effect of smoking status and gender on parameters. Patients & methods: Exhaled breath samples of 40 lung cancer patients and 40 healthy individuals were analyzed using gas chromatography-mass spectrometry. Influence of other factors on the exhaled breath VOCs profile was investigated. Results: Some parameters correlating with the disease status were affected by other factors. Excluding these parameters allows creating a logistic regression diagnostic model with 83% sensitivity and 81% specificity. Conclusion: Influence of other factors on the exhaled breath VOCs profile has to be taken into account to avoid misleading results.

Breath profile as composite biomarkers for lung cancer diagnosis

OBJECTIVES

Lung cancer is continuously the leading cause of cancer related death, resulting from the lack of specific symptoms at early stage. A large-scale screening method may be the key point to find asymptomatic patients, leading to the reduction of mortality.

METHODS

An alternative method combining breath test and a machine learning algorithm is proposed. 236 breath samples were analyzed by TD-GCMS. Breath profile of each sample is composed of 308 features extracted from chromatogram. Gradient boost decision trees algorithm was employed to recognize lung cancer patients. Bootstrap is performed to simulate real diagnostic practice, with which we evaluated the confidence of our methods.

RESULTS

An accuracy of 85 % is shown in 6-fold cross validations. In statistical bootstrap, 72 % samples are marked as "confident", and the accuracy of confident samples is 93 % throughout the cross validations.

CONCLUSION

We have proposed such a non-invasive, accurate and confident method that might contribute to large-scale screening of lung cancer. As a consequence, more asymptomatic patients with early lung cancer may be detected.

BIONOTE as an Innovative Biosensor for Measuring Endocannabinoid Levels

In this study, a novel approach was developed to quantify endocannabinoids (eCBs), and was based on the liquid biosensor BIONOTE. This device is composed of a probe that can be immersed in a solution, and an electronic interface that can record a current related to the oxy-reductive reactions occurring in the sample. The two most representative members of eCBs have been analysed in vitro by BIONOTE: anandamide (N-arachidonoylethanolamine, AEA) and 2-arachidonoylglycerol (2-AG). Bovine serum albumin was used to functionalize the probe and improve the sensibility of the whole analytical system. We show that BIONOTE is able to detect both AEA and 2-AG at concentrations in the low nanomolar range, and to discriminate between these eCBs and their moieties arachidonic acid, ethanolamine and glycerol. Notably, BIONOTE distinguished these five different molecules, and it was also able to quantify AEA in human plasma. Although this is just a proof-of-concept study, we suggest BIONOTE as a cheap and user-friendly prototype sensor for high throughput quantitation of eCB content in biological matrices, with an apparent diagnostic potential for tomorrow’s medicine.

Mass Spectrometry-based Metabolomics in Translational Research

Metabolomics is the systematic study of metabolite profiles of complex biological systems, and involves the systematic identification and quantification of metabolites. Metabolism is integrated with all biochemical reactions in biological systems; thus metabolite profiles provide collective information on biochemical processes induced by genetic or environmental perturbations. Transcriptomes or proteomes may not be functionally active and not always reflect phenotypic variations. The metabolome, however, consists of the biomolecules closest to the phenotype of living organisms, and is often called the molecular phenotype of biological systems. Thus, metabolome alterations can easily result in disease states, providing important clues to understand pathophysiological mechanisms contributing to various biomedical symptoms. The metabolome and metabolomics have been emphasized in translational research related to biomarker discovery, drug target discovery, drug responses, and disease mechanisms. This review describes the basic concepts, workflows, and applications of mass spectrometry-based metabolomics in translational research.

Target Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Discrimination from Other Pulmonary Diseases and Healthy Persons

The aim of the present study was to investigate the ability of breath analysis to distinguish lung cancer (LC) patients from patients with other respiratory diseases and healthy people. The population sample consisted of 51 patients with confirmed LC, 38 patients with pathological computed tomography (CT) findings not diagnosed with LC, and 53 healthy controls. The concentrations of 19 volatile organic compounds (VOCs) were quantified in the exhaled breath of study participants by solid phase microextraction (SPME) of the VOCs and subsequent gas chromatography-mass spectrometry (GC-MS) analysis. Kruskal-Wallis and Mann-Whitney tests were used to identify significant differences between subgroups. Machine learning methods were used to determine the discriminant power of the method. Several compounds were found to differ significantly between LC patients and healthy controls. Strong associations were identified for 2-propanol, 1-propanol, toluene, ethylbenzene, and styrene (p-values < 0.001-0.006). These associations remained significant when ambient air concentrations were subtracted from breath concentrations. VOC levels were found to be affected by ambient air concentrations and a few by smoking status. The random forest machine learning algorithm achieved a correct classification of patients of 88.5% (area under the curve-AUC 0.94). However, none of the methods used achieved adequate discrimination between LC patients and patients with abnormal computed tomography (CT) findings. Biomarker sets, consisting mainly of the exogenous monoaromatic compounds and 1- and 2- propanol, adequately discriminated LC patients from healthy controls. The breath concentrations of these compounds may reflect the alterations in patient's physiological and biochemical status and perhaps can be used as probes for the investigation of these statuses or normalization of patient-related factors in breath analysis.

Electronic Nose as a Novel Method for Diagnosing Cancer: A Systematic Review

Cancer is fast becoming the most important cause of death worldwide, its mortality being mostly caused by late or wrong diagnosis. Novel strategies have been developed to identify early signs of cancer in a minimally obtrusive way, including the Electronic Nose (E-Nose) technology, user-friendly, cost- and time-saving alternative to classical approaches. This systematic review, conducted under the PRISMA guidelines, identified 60 articles directly dealing with the E-Nose application in cancer research published up to 31 January 2020. Among these works, the vast majority reported successful E-Nose use for diagnosing Lung Cancer, showing promising results especially when employing the Aeonose tool, discriminating subjects with Lung Cancer from controls in more than 80% of individuals, in most studies. In order to tailor the main limitations of the proposed approach, including the application of the protocol to advanced stage of cancer, sample heterogeneity and massive confounders, future studies should be conducted on early stage patients, and on larger cohorts, as to better characterize the specific breathprint associated with the various subtypes of cancer. This would ultimately lead to a better and faster diagnosis and to earlier treatment, possibly reducing the burden associated to such conditions.

Investigation of different approaches for exhaled breath and tumor tissue analyses to identify lung cancer biomarkers

Development of early noninvasive methods for lung cancer diagnosis is among the most promising technologies, especially using exhaled breath as an object of analysis. Simple sample collection combined with easy and quick sample preparation, as well as the long-term stability of the samples, make it an ideal choice for routine analysis. The conditions of exhaled breath analysis by preconcentrating volatile organic compounds (VOCs) in sorbent tubes, two-stage thermal desorption and gas-chromatographic determination with flame-ionization detection have been optimized. These conditions were applied to estimate differences in exhaled breath VOC profiles of lung cancer patients and healthy volunteers. The combination of statistical methods was used to evaluate the ability of VOCs and their ratios to classify lung cancer patients and healthy volunteers. The performance of diagnostic models on the test data set was greater than 90 % for both VOC peak areas and their ratios. Some of the exhaled breath samples were analyzed using gas chromatography coupled with mass spectrometry (GC-MS) to identify VOCs present in exhaled breath at lower concentration levels. To confirm the endogenous origin of VOCs found in exhaled breath, GC-MS analysis of tumor tissues was conducted. Some of the VOCs identified in exhaled breath were found in tumor tissues, but their frequency of occurrence was significantly lower than in the case of exhaled breath.

Recent advances in breath analysis to track human health by new enrichment technologies

Detection of biomarkers in exhaled breath has been gaining increasing attention as a tool for diagnosis of specific diseases. However, rapid and accurate quantification of biomarkers associated with specific diseases requires the use of analytical methods capable of fast sampling and preconcentration from breath matrix. In this regard, solid phase microextraction and needle trap technology are becoming increasingly popular in the field of breath analysis due to the unique benefits imparted by such methods, such as the integration of sampling, extraction, and preconcentration in a single step. This review discusses recent advances in breath analysis using these sample preparation techniques, providing a summary of recent developments of analytical methods based on breath volatile organic compounds analysis, including the successful identification of various biomarkers related to human diseases.

Volatile Compounds Are Involved in Cellular Crosstalk and Upregulation

Cell-cell cross talk is of great importance in cancer research due to its major role in proliferation, differentiation, migration, and influence on the apoptotic pathway. Different cell-cell communication mechanisms have come mainly from proteomic and genomic approaches. In this paper, a new route is reported for cross talk between cancer cells that occurs, even when they are far away from each other. Single-cell and culture analysis shows that upregulation of cancer cells emits hundreds of volatile organic compounds (VOCs) into their headspace. Part of the VOCs remains without any change, disregarding the biological environment around it. The other part of the VOCs is exchanged between monocultures of the cells as well as between co-cultures of the cells with no physical contact between them, leading to different changes in growth than when left on their own. The chemical nature and composition of these VOCs have been determined and are discussed herein. Cell-to-cell cross talk has the advantage of being suitable for transfer/diffusion over relatively long distances. It would thus be expected to serve as a shuttling pad toward the development of advanced approaches that could enable very early detection of cancer and/or monitoring of metastasis and related cancer therapy.

High sensitivity NH3 gas sensor with electrical readout made on paper with perovskite halide as sensor material

In this paper we report a cheap, paper electronics based solid state gas sensor to detect NH₃ gas selectively with a detection capability of better than 1 ppm. The sensor uses perovskite halide CH₃NH₃PbI₃ (MAPI) as the active sensor material grown on a paper. This paper based sensor works at room temperature. The current through the paper sensor increases by one order on exposure to only 10 ppm NH₃ gas. The calibrated sensitivity is ~55% for 1 ppm of NH₃ gas in Nitrogen or Air. The current noise limited resolution estimated to be ~10 ppb. This work establishes perovskite halide as a new solid state gas sensing material that can reach sub ppm sensitivity using simple paper electronics. Use of paper and also solution method used to grow the active material makes the sensor cost effective and easy to manufacture. This type of disposable high sensitive paper sensor can be used for detection of NH₃ as a marker in exhaled breathes for non-invasive diagnosis. The sensor formed on the paper, since it supports unheated operation, needs less than few nanowatt power for its operation.

Critical Review of Volatile Organic Compound Analysis in Breath and In Vitro Cell Culture for Detection of Lung Cancer

Breath analysis is a promising technique for lung cancer screening. Despite the rapid development of breathomics in the last four decades, no consistent, robust, and validated volatile organic compound (VOC) signature for lung cancer has been identified. This review summarizes the identified VOC biomarkers from both exhaled breath analysis and in vitro cultured lung cell lines. Both clinical and in vitro studies have produced inconsistent, and even contradictory, results. Methodological issues that lead to these inconsistencies are reviewed and discussed in detail. Recommendations on addressing specific issues for more accurate biomarker studies have also been made.

Sensors for Lung Cancer Diagnosis

The positive outcome of lung cancer treatment is strongly related to the earliness of the diagnosis. Thus, there is a strong requirement for technologies that could provide an early detection of cancer. The concept of early diagnosis is immediately extended to large population screening, and then, it is strongly related to non-invasiveness and low cost. Sensor technology takes advantage of the microelectronics revolution, and then, it promises to develop devices sufficiently sensitive to detect lung cancer biomarkers. A number of biosensors for the detection of cancer-related proteins have been demonstrated in recent years. At the same time, the interest is growing towards the analysis of volatile metabolites that could be measured directly from the breath. In this paper, a review of the state-of-the-art of biosensors and volatile compound sensors is presented.

Breath analysis in respiratory diseases: state-of-the-art and future perspectives

INTRODUCTION

The vast majority of respiratory diseases are associated with the production of volatile organic compounds (VOCs), the analysis of which might improve our knowledge about these disorders and their clinical management. The aim of this narrative review is to provide a comprehensive summary of current evidence supporting the application of breath analysis in the field of respiratory diseases, as well as suggesting potential applications available in the near future. Areas covered: A computerized literature search was performed to identify relevant articles reporting original data on the clinical use of breath analysis in respiratory diseases. Papers focusing on diseases other than respiratory, technical issues of VOC sampling and analysis, in vitro experiments or exogenous compounds were excluded. Expert commentary: Currently available evidence on the application of breath analysis in respiratory diseases is encouraging; however, it is mostly based on single-center studies without external validation. The standardization of the technique, together with multicenter clinical trials with external validation, will ensure it is ready for clinical use. Current and new applications in respiratory diseases may represent a major breakthrough in the field, so much so as to deserve further efforts in outlining the most effective way to apply VOC analysis for clinical purposes.

Indium Nitrite (InN)-Based Ultrasensitive and Selective Ammonia Sensor Using an External Silicone Oil Filter for Medical Application

Ammonia is an essential biomarker for noninvasive diagnosis of liver malfunction. Therefore, selective detection of ammonia is essential for medical application. Here, we demonstrate a portable device to selectively detect sub-ppm ammonia gas. The presented gas sensor is composed of a Pt coating on top of an ultrathin Indium nitrite (InN) epilayer with a lower detection limit of 0.2 ppm, at operating temperature of 200 °C, and detection time of 1 min. The sensor connected with the external filter of nonpolar 500 CS silicone oil to diagnose liver malfunction. The absorption of 0.7 ppm acetone and 0.4 ppm ammonia gas in 10 cc silicone oil is 80% (0.56 ppm) and 21.11% (0.084 ppm), respectively, with a flow rate of 10 cc/min at 25 °C. The absorption of acetone gas is 6.66-fold higher as compared to ammonia gas. The percentage variation in response for 0.7 ppm ammonia and 0.7 ppm acetone with and without silicone oil on InN sensor is 17.5% and 4%, and 22.5%, and 14% respectively. Furthermore, the percentage variation in response for 0.7 ppm ammonia gas with silicone oil on InN sensor is 4.3-fold higher than that of 0.7 ppm acetone. The results show that the InN sensor is suitable for diagnosis of liver malfunction.

A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer

Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath test for the detection of lung cancer using a chemical sensor array and a machine learning technique. We conducted a prospective study to enroll lung cancer cases and non-tumour controls between 2016 and 2018 and analysed alveolar air samples using carbon nanotube sensor arrays. A total of 117 cases and 199 controls were enrolled in the study of which 72 subjects were excluded due to having cancer at another site, benign lung tumours, metastatic lung cancer, carcinoma in situ, minimally invasive adenocarcinoma, received chemotherapy or other diseases. Subjects enrolled in 2016 and 2017 were used for the model derivation and internal validation. The model was externally validated in subjects recruited in 2018. The diagnostic accuracy was assessed using the pathological reports as the reference standard. In the external validation, the areas under the receiver operating characteristic curve (AUCs) were 0.91 (95% CI = 0.79–1.00) by linear discriminant analysis and 0.90 (95% CI = 0.80–0.99) by the supportive vector machine technique. The combination of the sensor array technique and machine learning can detect lung cancer with high accuracy.

Breath based volatile organic compounds in the detection of breast, lung, and colorectal cancers: A systematic review

BACKGROUND

Detecting volatile organic compounds (VOCs) could provide a rapid, noninvasive, and inexpensive screening tool for detecting cancer.

OBJECTIVE

In this systematic review, we identified specific exhaled breath VOCs correlated with lung, colorectal, and breast cancer.

METHODS

We identified relevant studies published in 2015 and 2016 by searching Pubmed and Web of Science. The protocol for this systematic review was registered in PROSPERO and the PRISMA guidelines were used in reporting. VOCs and performance data were extracted.

RESULTS

Three hundred and thirty three records were identified and 43 papers were included in the review, of which 20 were review articles themselves. We identified 17 studies that listed the VOCs with at least a subset of statistics on detection cutoff levels, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and gradient.

CONCLUSIONS

Breath analysis for cancer screening and early detection shows promise, because samples can be collected easily, safely, and frequently. While gas chromatography-mass spectrometry is considered the gold standard for identifying specific VOCs, breath analysis has moved into analyzing patterns of VOCs using a variety of different multiple sensor techniques, such as eNoses and nanomaterials. Further development of VOCs for early cancer detection requires clinical trials with standardized breath sampling methods.

Power-efficient self-cleaning hydrophilic condenser surface for portable exhaled breath condensate (EBC) metabolomic sampling

In this work, we present a hydrophilic self-cleaning condenser surface for the collection of biological and environmental aerosol samples. The condenser is installed in a battery-operated hand-held breath sampling device. The device performance is characterized by the collection and analysis of exhaled breath samples from a group of volunteers. The exhaled breath condensate is collected on a subcooled condenser surface, transferred into a storage vial, and its chemical content is analyzed using mass spectrometric methods. The engineered surface supports upon it a continuous condensation cycle, and this allows the collection of liquid samples exceeding the saturation mass/area limit of a plain hydrophilic surface. The condenser surface employs two constituent parameters: a low surface energy barrier to enhance nucleation and condensation efficiency, and a network of surface microstructures to create a self-cleaning mechanism for fluid aggregation into a reservoir. Removal of the liquid condensate from the condenser surface prevents the formation of a thick liquid layer, and thus maintains a continuous condensation cycle with a minimum decrease in heat transfer efficiency as condensation occurs on the surface. The self-cleaning condenser surfaces may have a number of applications in the collection of biological, chemical, or environmental aerosol samples. Sample phase conversion to liquid can facilitate sample manipulation and chemical analysis of matrices with low concentrations. Here, we demonstrate the use of a self-cleaning microcondenser for the collection of exhaled breath condensate with a hand-held portable device. All breath collections with the two devices were performed with the same group of volunteers under UC Davis IRB protocol 63701-3.

Nonhuman primate breath volatile organic compounds associate with developmental programming and cardio-metabolic status

Rodent and nonhuman primate studies indicate that developmental programming by reduced perinatal nutrition negatively impacts life course cardio-metabolic health. We have developed a baboon model in which we feed control mothers (CON) ad libitum while nutrient restricted mothers are fed 70% of ad libitum global feed in pregnancy and lactation. Offspring of nutrient restricted mothers are intrauterine growth restricted (IUGR) at term. By 3.5 years IUGR baboons showed signs of insulin resistance, indicating a pre-diabetic phenotype, in contrast to healthy CON offspring. We hypothesized that a novel breath analysis approach would provide markers of the altered cardio-metabolic state in a non-invasive manner. Here we assess whether exhaled breath volatile organic compounds (VOCs) collected from this unique cohort of juvenile baboons with documented cardio-metabolic dysfunction resulting from in utero programming can be detected from their breath signatures. Breath was collected from male and female CON and IUGR baboons at 4.8 ± 0.2 years (human equivalent ~13 years). Breath VOCs were quantified using a two-dimensional gas chromatography mass spectrometer. Two-way ANOVA, on 76 biologically relevant VOCs identified 27 VOCs (p < 0.05) with altered abundances between groups (sex, birthweight, and sex x birthweight). The 27 VOCs included 2-pentanone, 2-octanone, 2,2,7,7-tetramethyloctane and 3-methyl-1-heptene, which have not previously been associated with cardio-metabolic disease. Unsupervised principal component analysis of these VOCs could discriminate the four clusters defining males, females, CON and IUGR. This study, which is the first to assess quantifiable breath signatures associated with cardio-metabolic programing for any model of IUGR, demonstrates the translational value of this unique model to identify metabolites of programmed cardio-metabolic dysfunction in breath signatures. Future studies are required to validate the translatability of these findings to humans.

Circulating and tumor-associated caspase-4: a novel diagnostic and prognostic biomarker for non-small cell lung cancer

Late diagnosis limits therapeutic options and survival rate of non-small cell lung cancer (NSCLC) patients. Therefore the identification of biomarkers represents an emerging medical need.

A highly sensitive and specific test was developed to identify/quantify a novel/selective diagnostic biomarker for NSCLC patients, caspase-4. This test was validated by using i) plasma from 125 NSCLC patients and 79 healthy (non-pathological) subjects, ii) plasma from 139 smokers and iii) from 70 chronic-obstructive pulmonary disease (COPD) patients. Caspase-4 quantification was also assessed in the lung tumor mass of 98 paired NSCLC patients compared to 10 non-tumor lung tissues (i.e. tuberculosis).

Circulating caspase-4 was detected in both healthy and NSCLC patients; however at different range values: 2.603–3.372 ng/ml for NSCLC patients (95% CI) compared to 0.3994-0.6219 ng/ml for healthy subjects (95% CI). The sensitivity of the test ranged from 97.07% to 100%; the specificity was 88.1% with a positive predictive value of 92.54%, accuracy of 95.19% and AUC of 0.971. Smokers (95% CI, 0.3947–0.6197 ng/ml) and COPD patients (95% CI, 1.703–2.995 ng/ml) showed intermediate values of circulating caspase-4. Tissue levels of caspase-4 in the tumor mass showed that 72 (72.7%) out of 99 patients were positive. More importantly, higher levels (cut-off value = 0.307 ng/ml) of caspase-4 in the tumor mass were associated to reduced overall survival (median 0.92 years) compared to NSCLC patients with lower levels (median 3.02 years).

We report for the first time caspase-4 as a novel diagnostic and prognostic biomarker, opening new therapeutic perspectives for NSCLC patients.

Hollow Pt-Functionalized SnO2 Hemipill Network Formation Using a Bacterial Skeleton for the Noninvasive Diagnosis of Diabetes

Hollow-structured nanomaterials are presented as an outstanding sensing platform because of their unique combination of high porosity in both the micro- and nanoscale, their biocompatibility, and flexible template applicability. Herein, we introduce a bacterial skeleton method allowing for cost-effective fabrication with nanoscale precision. As a proof-of-concept, we fabricated a hollow SnO₂ hemipill network (HSHN) and a hollow Pt-functionalized SnO₂ hemipill network (HPN). A superior detecting capability of HPN toward acetone, a diabetes biomarker, was demonstrated at low concentration (200 ppb) under high humidity (RH 80%). The detection limit reaches 3.6 ppb, a level satisfying the minimum requirement for diabetes breath diagnosis. High selectivity of the HPN sensor against C₆H₆, C₇H₈, CO, and NO vapors is demonstrated using principal component analysis (PCA), suggesting new applications of HPN for human-activity monitoring and a personal healthcare tool for diagnosing diabetes. The skeleton method can be further employed to mimic nanostructures of biomaterials with unique functionality for broad applications.

Fiber-Optic Bio-sniffer (Biochemical Gas Sensor) Using Reverse Reaction of Alcohol Dehydrogenase for Exhaled Acetaldehyde

Volatile organic compounds (VOCs) exhaled in breath have huge potential as indicators of diseases and metabolisms. Application of breath analysis for disease screening and metabolism assessment is expected since breath samples can be noninvasively collected and measured. In this research, a highly sensitive and selective biochemical gas sensor (bio-sniffer) for gaseous acetaldehyde (AcH) was developed. In the AcH bio-sniffer, a reverse reaction of alcohol dehydrogenase (ADH) was employed for reducing AcH to ethanol and simultaneously consuming a coenzyme, reduced form of nicotinamide adenine dinucleotide (NADH). The concentration of AcH can be quantified by fluorescence detection of NADH that was consumed by reverse reaction of ADH. The AcH bio-sniffer was composed of an ultraviolet light-emitting diode (UV-LED) as an excitation light source, a photomultiplier tube (PMT) as a fluorescence detector, and an optical fiber probe, and these three components were connected with a bifurcated optical fiber. A gas-sensing region of the fiber probe was developed with a flow-cell and an ADH-immobilized membrane. In the experiment, after optimization of the enzyme reaction conditions, the selectivity and dynamic range of the AcH bio-sniffer were investigated. The AcH bio-sniffer showed a short measurement time (within 2 min) and a broad dynamic range for determination of gaseous AcH, 0.02-10 ppm, which encompassed a typical AcH concentration in exhaled breath (1.2-6.0 ppm). Also, the AcH bio-sniffer exhibited a high selectivity to gaseous AcH based on the specificity of ADH. The sensor outputs were observed only from AcH-contained standard gaseous samples. Finally, the AcH bio-sniffer was applied to measure the concentration of AcH in exhaled breath from healthy subjects after ingestion of alcohol. As a result, a significant difference of AcH concentration between subjects with different aldehyde dehydrogenase type 2 (ALDH2) phenotypes was observed. The AcH bio-sniffer can be used for breath measurement, and further, an application of breath analysis-based disease screening or metabolism assessment can be expected due to the versatility of its detection principle, which allows it to measure other VOCs by using NADH-dependent dehydrogenases.

Towards the use of breath for detecting mycobacterial infection: a case study in a murine model

In the present research, the potential of breath analysis by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) was investigated for the discrimination between healthy and infected mice. A pilot study employing a total of 16 animals was used to develop a method for breath analysis in a murine model for studying Mycobacterium tuberculosis complex (MTBC) using the M. bovis bacillus Calmette-Guérin. Breath was collected in Tedlar bags and concentrated onto thermal desorption tubes for subsequent analysis by GC×GC-MS. Immunological test and bacterial cell count in bronchoalveolar lavage fluid and mice lung homogenate confirmed the presence of bacteria in the infected group. From the GC×GC-MS analysis, 23 molecules were found to mainly drive the separation between control and infected mice and their tentative identification is provided.This study shows that the overall used methodology is able to differentiate breath between healthy and infected animals, and the information herein can be used to further develop the mouse breath model to study MTBC pathogenesis, evaluate pre-clinical drug regimen efficacy, and to further develop the concept of breath-based diagnostics.

Porphyrins for olfaction mimic: The Rome Tor Vergata approach

The impressive chemistry shown by porphyrins in natural systems is particularly attractive for exploitation in chemical sensors. In these devices the sensing mechanisms can mimic most of the porphyrin biological reactivity, such as reversible binding, activation of small molecules, redox activity, and photoactivated processes. The simultaneous presence of multiple binding mechanisms allows porphyrins to interact with a large variety of analytes. This feature reduces the selectivity, but prompts the development of sensor arrays, where the cross-selectivity of more sensors is used to classify and identify samples characterized by a complex composition. Since 1995 the Sensors Group of the University of Rome Tor Vergata has exploited these features to prepare sensor arrays based on different transducers and aimed at several applications. These kinds of devices have been reported as electronic noses (gaseous phase analytes) and electronic tongues (liquid phase analytes) to underline that their working mechanisms are tentatively similar to that of biological senses. We report here some of the results obtained.

Solid-phase microextraction coupled to gas chromatography-mass spectrometry followed by multivariate data analysis for the identification of volatile organic compounds as possible biomarkers in lung cancer tissues

Solid-phase microextraction and gas chromatography-mass spectrometry followed by multivariate data analysis were used to analyze lung tissues from both healthy and carcinogenic patients. A total of 78 volatile compounds belonging to different chemical classes were identified, seven of which were able to discriminate between the two groups. Discriminant analysis allowed to correctly classify 98.3% of the cases. By using the leave-one-method, 100% of the cross-validated samples belonging to the "tumor" group was correctly classified, whereas 2 cross-validated healthy samples out of 48 were erroneously allocated in the "tumor" group. Achieved results suggest the need of further investigation to assess the role of the seven identified compounds as lung cancer biomarkers in breath analysis, thus allowing the development of low-cost diagnostic devices.

Screening of Obstructive Sleep Apnea Syndrome by Electronic-Nose Analysis of Volatile Organic Compounds

Obstructive Sleep Apnea Syndrome (OSAS) carries important social and economic implications. Once the suspicion of OSAS has arisen, Polysomnography (PSG) represents the diagnostic gold standard. However, about 45% of people who have undergone PSG are free from OSAS. Thus, efforts should be made to improve the selection of subjects. We verified whether the pattern of Volatile Organic Compounds (VOCs) helps to select patients amenable to PSG. We studied 136 subjects (20 obese non-OSAS, 20 hypoxic OSAS, 20 non-hypoxic OSAS, and 20 non-hypoxic Chronic Obstructive Pulmonary Disease (COPD) vs 56 healthy controls) without any criteria of exclusion for comorbidity to deal with a real-life population. VOCs patterns were analyzed using electronic-nose (e-nose) technology. A Discriminant Analysis (Partial Least Square-Discriminant Analysis) was performed to predict respiratory functions and PSG parameters. E-nose distinguished controls (100% correct classification) from others and identified 60% of hypoxic, and 35% of non-hypoxic OSAS patients. Similarly, it identified 60% of COPD patients. One-by-one group comparison yielded optimal discrimination of OSAS vs controls and of COPD vs controls (100% correct classification). In conclusion, e-nose technology applied to breath-analysis can discriminate non-respiratory from respiratory diseased populations in real-life multimorbid populations and exclude OSAS. If confirmed, this evidence may become pivotal for screening purposes.

Detection of gas traces using semiconductor sensors, ion mobility spectrometry, and mass spectrometry

This article deals with the state-of-the-art instrumentation and application in the field of solid state gas sensorics, ion mobility spectrometry and mass-spectrometry-related research for the detection and measurements of low gas and vapor concentrations. The advantages and disadvantages of gas-analytical devices and systems are discussed, as well as the possibilities of its complex and/or complementary applications. Ion mobility spectrometry-mass spectrometry and subsequent techniques based on solid-state gas sensors are proposed for planned medical study.

Smelling, Seeing, Tasting-Old Senses for New Sensing

The senses are the physiological mechanisms of perception that enable an organism to interact with the surrounding media. For centuries, humans have utilized these senses in science; vision and olfaction have been used the most extensively in laboratories followed by gustation and somatosensation, whereas audition has only rarely been employed. Most of these applications of senses were developed spontaneously based on the natural behavior of the chemistry of the reactants producing changes in scent, taste, or color. In recent years, by exploiting the outstanding properties of nanoparticles, many groups have demonstrated alternative sensing scenarios where the detection limits are remarkably improved, enabling the recognition of hazardous substances by mere sight, smell, or taste. Such alternative sensing approaches can be divided into two main groups: (i) methods that identify a single analyte by engineering a reaction that promotes a change in color or the generation of a characteristic scent, and (ii) methods that emulate or even improve mammalian senses, especially those related to taste and smell. In this Perspective, we discuss the context of each technology, present prominent examples, and evaluate the complexities, potential pitfalls, and opportunities presented by different re-engineering strategies.

Exhaled breath analysis for the early detection of lung cancer: recent developments and future prospects

In lung cancer, the prognosis and treatment options depend directly on tumor size and its spread at the time of diagnosis. There is therefore a constant search for methods that will allow early detection of cancerous lung nodules. With advancing imaging technology and implantation of screening routines in high-risk populations by low-dose computerized tomography, a significant increase in the number of diagnosed small peripheral lesions can be expected. While early detection of small cancerous lesions carries the benefit of wider treatment options and better prognosis, the process of obtaining a biopsy to confirm a cancerous tissue is not free of complications and bears inconveniences and stress to the patient. This review discusses the potential use of exhaled breath analysis as a simple, noninvasive tool for early detection of lung cancer and characterization of suspicious lung nodules.

A preliminary analysis of volatile metabolites of human induced pluripotent stem cells along the in vitro differentiation

Cellular metabolism of stem cell biology is still an unexplored field. However, considering the amount of information carried by metabolomes, this is a promising field for a fast identification of stem cells itself and during the differentiation process. One of the goals of such application is the identification of residual pluripotent cells before cell transplantation to avoid the occurrence of teratomas. In this paper, we investigated in vitro the volatile compounds (VOCs) released during human induced pluripotent stem cells (hiPSCs) reprogramming. In particular, we studied hiPSCs differentiation to floating and adherent embryoid bodies until early neural progenitor cells. A preliminary Gas Chromatographic/Mass Spectrometer (GC/MS) analysis, based on a single extraction method and chromatographic separation, indicated 17 volatile compounds whose relative abundance is altered in each step of the differentiation process. The pattern of VOCs shown by hiPSCs is well distinct and makes these cells sharply separated from the other steps of differentiations. Similar behaviour has also been observed with an array of metalloporphyrins based gas sensors. The use of electronic sensors to control the process of differentiation of pluripotent stem cells might suggest a novel perspective for a fast and on-line control of differentiation processes.