Breath sensors for lung cancer diagnosis

Graphene and metal-organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation

Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high cost. As potential replacement, among several low-cost and portable methods, chemoresistive sensors for the detection of volatile organic compounds (VOCs) that represent biomarkers of lung cancer were explored as promising solutions, which unfortunately still face challenges. To address the key problems of these sensors, such as low sensitivity, high response time, and poor selectivity, this study presents the design of new chemoresistive sensors based on hybridised porous zeolitic imidazolate (ZIF-8) based metal-organic frameworks (MOFs) and laser-scribed graphene (LSG) structures, inspired by the architecture of the human lung. The sensing performance of the fabricated ZIF-8@LSG hybrid sensors was characterised using four dominant VOC biomarkers, including acetone, ethanol, methanol, and formaldehyde, which are identified as metabolomic signatures in lung cancer patients' exhaled breath. The results using simulated breath samples showed that the sensors exhibited excellent performance for a set of these biomarkers, including fast response (2-3 seconds), a wide detection range (0.8 ppm to 50 ppm), a low detection limit (0.8 ppm), and high selectivity, all obtained at room temperature. Intelligent machine learning (ML) recognition using the multilayer perceptron (MLP)-based classification algorithm was further employed to enhance the capability of these sensors, achieving an exceptional accuracy (approximately 96.5%) for the four targeted VOCs over the tested range (0.8-10 ppm). The developed hybridised nanomaterials, combined with the ML methodology, showcase robust identification of lung cancer biomarkers in simulated breath samples containing multiple biomarkers and a promising solution for their further improvements toward practical applications.

Breath Analysis for Lung Cancer Early Detection-A Clinical Study

This clinical study presents a comprehensive investigation into the utility of breath analysis as a non-invasive method for the early detection of lung cancer. The study enrolled 14 lung cancer patients, 14 non-lung cancer controls with diverse medical conditions, and 3 tuberculosis (TB) patients for biomarker discovery. Matching criteria including age, gender, smoking history, and comorbidities were strictly followed to ensure reliable comparisons. A systematic breath sampling protocol utilizing a BIO-VOC sampler was employed, followed by VOC analysis using Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC/MS). The resulting VOC profiles were subjected to stringent statistical analysis, including Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA), Kruskal-Wallis test, and Receiver Operating Characteristic (ROC) analysis. Notably, 13 VOCs exhibited statistically significant differences between lung cancer patients and controls. The combination of eight VOCs (hexanal, heptanal, octanal, benzaldehyde, undecane, phenylacetaldehyde, decanal, and benzoic acid) demonstrated substantial discriminatory power with an area under the curve (AUC) of 0.85, a sensitivity of 82%, and a specificity of 76% in the discovery set. Validation in an independent cohort yielded an AUC of 0.78, a sensitivity of 78%, and a specificity of 64%. Further analysis revealed that elevated aldehyde levels in lung cancer patients' breath could be attributed to overactivated Alcohol Dehydrogenase (ADH) pathways in cancerous tissues. Addressing methodological challenges, this study employed a matching of physiological and pathological confounders, controlled room air samples, and standardized breath sampling techniques. Despite the limitations, this study's findings emphasize the potential of breath analysis as a diagnostic tool for lung cancer and suggest its utility in differentiating tuberculosis from lung cancer. However, further research and validation are warranted for the translation of these findings into clinical practice.

Ultrahigh-acetone-sensitivity sensor based on Pt-loaded TiO2porous nanoparticles synthesized via a facile hydrothermal method

A simple hydrothermal method based on an orthogonal experimental design was used to synthesis Pt-loaded TiO2mesoporous nanoparticles in one step. The successful synthesis of Pt-loaded TiO2nanoparticles was demonstrated by various characterization methods. The effects of the modification of Pt and its explanation are described in detail by means of the test results. Through systematic gas-sensing tests, we found that the Pt-loaded TiO2nanoparticles outperform pure TiO2nanoparticles, with a high response value (S= 42.5) to 200 ppm acetone at 260 °C and with a film thickness of 0.45 mm, far superior to that of pure TiO2. The response time (8 s) and recovery time (11 s) of the material are also relatively good with excellent selectivity and long-term stability (30 days). The frequent use of acetone as an organic solution in factories and laboratories, as well as the possibility of making a preliminary diagnosis of diabetes by detecting acetone levels in exhaled gas, make this work promising for environmental monitoring and medical diagnosis.

Noninvasive Colorimetric Detection of Acetic Acid in Human Breath Based on an Alginate/Ni-Al-LDH/Dye Composite Film

Alginate/Ni-Al-layered double hydroxide/dye (Alg/Ni-Al-LDH/dye) composite films were fabricated using the solution casting method. The dyes used included methyl red, phenol red, thymol blue, bromothymol blue, m-cresol purple, methyl orange, bromocresol purple (BP), and bromocresol green (BG) in the overall pH range of 3.8 to 9.6. The chemical composition and morphology of the Alg/Ni-Al-LDH/dye composite film structure were investigated by Fourier transform infrared spectroscopy, FESEM, atomic force microscopy, and X-ray diffraction. The Alg/Ni-Al-LDH/dye composite films were semitransparent and mechanically flexible. Acetic acid was investigated as a respiratory biomarker related to gastrointestinal diseases. The parameters studied included color volume, response time, Ni-Al-LDH nanosheet volume, reusability, and drawing of the calibration curve along with statistical features including standard deviation, relative standard deviation, limit of detection, and limit of quantitation. Colorimetric indicators BP and BG in the presence of acetic acid produce color changes that are almost visible to the naked eye. However, other used indicators have shown almost no change. Therefore, it can be reported that the sensors made in the presence of BP and BG act selectively in relation to acetic acid.

Methods to Detect Volatile Organic Compounds for Breath Biopsy Using Solid-Phase Microextraction and Gas Chromatography-Mass Spectrometry

Volatile organic compounds (VOCs) are byproducts from metabolic pathways that can be detected in exhaled breath and have been reported as biomarkers for different diseases. The gold standard for analysis is gas chromatography-mass spectrometry (GC-MS), which can be coupled with various sampling methods. The current study aims to develop and compare different methods for sampling and preconcentrating VOCs using solid-phase microextraction (SPME). An in-house sampling method, direct-breath SPME (DB-SPME), was developed to directly extract VOCs from breath using a SPME fiber. The method was optimized by exploring different SPME types, the overall exhalation volume, and breath fractionation. DB-SPME was quantitatively compared to two alternative methods involving the collection of breath in a Tedlar bag. In one method, VOCs were directly extracted from the Tedlar bag (Tedlar-SPME) and in the other, the VOCs were cryothermally transferred from the Tedlar bag to a headspace vial (cryotransfer). The methods were verified and quantitatively compared using breath samples (n = 15 for each method respectively) analyzed by GC-MS quadrupole time-of-flight (QTOF) for compounds including but not limited to acetone, isoprene, toluene, limonene, and pinene. The cryotransfer method was the most sensitive, demonstrating the strongest signal for the majority of the VOCs detected in the exhaled breath samples. However, VOCs with low molecular weights, including acetone and isoprene, were detected with the highest sensitivity using the Tedlar-SPME. On the other hand, the DB-SPME was less sensitive, although it was rapid and had the lowest background GC-MS signal. Overall, the three breath-sampling methods can detect a wide variety of VOCs in breath. The cryotransfer method may be optimal when collecting a large number of samples using Tedlar bags, as it allows the long-term storage of VOCs at low temperatures (-80 °C), while Tedlar-SPME may be more effective when targeting relatively small VOCs. The DB-SPME method may be the most efficient when more immediate analyses and results are required.

Smelling the Disease: Diagnostic Potential of Breath Analysis

Breath analysis is a relatively recent field of research with much promise in scientific and clinical studies. Breath contains endogenously produced volatile organic components (VOCs) resulting from metabolites of ingested precursors, gut and air-passage bacteria, environmental contacts, etc. Numerous recent studies have suggested changes in breath composition during the course of many diseases, and breath analysis may lead to the diagnosis of such diseases. Therefore, it is important to identify the disease-specific variations in the concentration of breath to diagnose the diseases. In this review, we explore methods that are used to detect VOCs in laboratory settings, VOC constituents in exhaled air and other body fluids (e.g., sweat, saliva, skin, urine, blood, fecal matter, vaginal secretions, etc.), VOC identification in various diseases, and recently developed electronic (E)-nose-based sensors to detect VOCs. Identifying such VOCs and applying them as disease-specific biomarkers to obtain accurate, reproducible, and fast disease diagnosis could serve as an alternative to traditional invasive diagnosis methods. However, the success of VOC-based identification of diseases is limited to laboratory settings. Large-scale clinical data are warranted for establishing the robustness of disease diagnosis. Also, to identify specific VOCs associated with illness states, extensive clinical trials must be performed using both analytical instruments and electronic noses equipped with stable and precise sensors.

Online mass spectrometry of exhaled breath with a modified ambient ion source

Exhaled breath (EB) may contain metabolites that are closely related to human health conditions. Real time analysis of EB is important to study its true composition, however, it has been difficult. A robust ambient ionization mass spectrometry method using a modified direct analysis in real time (DART) ion source was developed for the online analysis of breath volatiles. The modified DART ion source can provide a confined region for direct sampling, rapid transmission and efficient ionization of exhaled breath. With different sampling methods, offline analysis and near real-time evaluation of exhaled breath were also achieved, and their unique molecular features were characterized. High resolution MS data aided the putative metabolite identification in breath samples, resulting in hundreds of volatile organic compounds being identified in the exhalome. The method was sensitive enough to be used for monitoring the breath feature changes after taking different food and over-the-counter medicine. Quantification was performed for pyridine and valeric acid with fasting and after ingesting different food. The developed method is fast, simple, versatile, and potentially useful for evaluating the true state of human exhaled breath.

The development progress of multi-array colourimetric sensors based on the M13 bacteriophage

Techniques for detecting chemicals dispersed at low concentrations in air continue to evolve. These techniques can be applied not only to manage the quality of agricultural products using a post-ripening process but also to establish a safety prevention system by detecting harmful gases and diagnosing diseases. Recently, techniques for rapid response to various chemicals and detection in complex and noisy environments have been developed using M13 bacteriophage-based sensors. In this review, M13 bacteriophage-based multi-array colourimetric sensors for the development of an electronic nose is discussed. The self-templating process was adapted to fabricate a colour band structure consisting of an M13 bacteriophage. To detect diverse target chemicals, the colour band was utilised with wild and genetically engineered M13 bacteriophages to enhance their sensing abilities. Multi-array colourimetric sensors were optimised for application in complex and noisy environments based on simulation and deep learning analysis. The development of a multi-array colourimetric sensor platform based on the M13 bacteriophage is likely to result in significant advances in the detection of various harmful gases and the diagnosis of various diseases based on exhaled gas in the future.

Cantilever-enhanced photoacoustic spectroscopy for gas sensing: A comparison of different displacement detection methods

Photoacoustic spectroscopy (PAS) combines the advantages of high sensitivity, high specificity and zero background, which is very suitable for trace gas detection. Cantilever-enhanced photoacoustic spectroscopy (CEPAS) utilizes highly sensitive mechanical cantilevers to further enhance the photoacoustic signal and shows a gas concentration detection limit of parts per trillion. This review is intended to summarize the recent advancements in CEPAS based on different displacement detection methods, such as Michelson interference, Fabry-Perot interference, light intensity detection, capacitive, piezoelectric and piezoresistive detection. Fundamental mechanisms and technical requirements of CEPAS are also provided in the literature. Finally, potential challenges and further opportunities are also discussed.

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

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

Design of an Artificial Opal/Photonic Crystal Interface for Alcohol Intoxication Assessment: Capillary Condensation in Pores and Photonic Materials Work Together

Alcohol intoxication has a dangerous effect on human health and is often associated with a risk of catastrophic injuries and alcohol-related crimes. A demand to address this problem adheres to the design of new sensor systems for the real-time monitoring of exhaled breath. We introduce a new sensor system based on a porous hydrophilic layer of submicron silica particles (SiO₂ SMPs) placed on a one-dimensional photonic crystal made of Ta₂O₅/SiO₂ dielectric layers whose operation relies on detecting changes in the position of surface wave resonance during capillary condensation in pores. To make the active layer of SiO₂ SMPs, we examine the influence of electrostatic interactions of media, particles, and the surface of the crystal influenced by buoyancy, gravity force, and Stokes drag force in the frame of the dip-coating preparation method. We evaluate the sensing performance toward biomarkers such as acetone, ammonia, ethanol, and isopropanol and test sensor system capabilities for alcohol intoxication assessment. We have found this sensor to respond to all tested analytes in a broad range of concentrations. By processing the sensor signals by principal component analysis, we selectively determined the analytes. We demonstrated the excellent performance of our device for alcohol intoxication assessment in real-time.

Beyond Pathogen Filtration: Possibility of Smart Masks as Wearable Devices for Personal and Group Health and Safety Management

Face masks are an important way to combat the COVID-19 pandemic. However, the prolonged pandemic has revealed confounding problems with the current face masks, including not only the spread of the disease but also concurrent psychological, social, and economic complications. As face masks have been worn for a long time, people have been interested in expanding the purpose of masks from protection to comfort and health, leading to the release of various “smart” mask products around the world. To envision how the smart masks will be extended, this paper reviewed 25 smart masks (12 from commercial products and 13 from academic prototypes) that emerged after the pandemic. While most smart masks presented in the market focus on resolving problems with user breathing discomfort, which arise from prolonged use, academic prototypes were designed for not only sensing COVID-19 but also general health monitoring aspects. Further, we investigated several specific sensors that can be incorporated into the mask for expanding biophysical features. On a larger scale, we discussed the architecture and possible applications with the help of connected smart masks. Namely, beyond a personal sensing application, a group or community sensing application may share an aggregate version of information with the broader population. In addition, this kind of collaborative sensing will also address the challenges of individual sensing, such as reliability and coverage. Lastly, we identified possible service application fields and further considerations for actual use. Along with daily-life health monitoring, smart masks may function as a general respiratory health tool for sports training, in an emergency room or ambulatory setting, as protection for industry workers and firefighters, and for soldier safety and survivability. For further considerations, we investigated design aspects in terms of sensor reliability and reproducibility, ergonomic design for user acceptance, and privacy-aware data-handling. Overall, we aim to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart masks as one of the promising wearable devices. By integrating biomarkers of respiration symptoms, a smart mask can be a truly cutting-edge device that expands further knowledge on health monitoring to reach the next level of wearables.

A compact gas chromatography platform for detection of multicomponent volatile organic compounds biomarkers

Some human exhaled volatile organic compounds (VOCs) can be employed to diagnose related human endogenous diseases as characteristic biomarkers, which is expected to be applied to rapid screening and grading because of their non-invasive and cost-effective advantages. In this study, we developed a compact gas chromatography (GC) platform mainly composed of an integrated silicon-based micro-column chip using micro-electromechanical system techniques and a miniaturized metal oxide semiconductor gas detector. In addition, the sampling/switching valve with related components and embedded microcontrollers was used for airflow control. The fabricated system selectively detected the five VOCs (pentane, acetone, toluene, octane, and decane) considered the typical endogenous disease biomarkers. In the experiments, the functional parameters of the system were investigated, and the optimum temperature conditions of the system for separation were determined. The results show that the system can successfully test the studied five VOCs as low as 1 ppm. In addition, the influence of interfering gas (carbon dioxide and ammonia) on the system for the VOC mixture is also investigated. Moreover, to prove the possibility of breath analysis of the fabricated system, the detection performance of isoprene and acetone at the ppb level is studied. Then, the concentration changes of the isoprene at the ppb concentration for human breath are successfully detected in the system. Therefore, we believe that the prepared compact GC system has potential applications in the human endogenous disease diagnosis for the VOC biomarkers.

Exhaled Breath Analysis for Diabetes Diagnosis and Monitoring: Relevance, Challenges and Possibilities

With the global population prevalence of diabetes surpassing 463 million cases in 2019 and diabetes leading to millions of deaths each year, there is a critical need for feasible, rapid, and non-invasive methodologies for continuous blood glucose monitoring in contrast to the current procedures that are either invasive, complicated, or expensive. Breath analysis is a viable methodology for non-invasive diabetes management owing to its potential for multiple disease diagnoses, the nominal requirement of sample processing, and immense sample accessibility; however, the development of functional commercial sensors is challenging due to the low concentration of volatile organic compounds (VOCs) present in exhaled breath and the confounding factors influencing the exhaled breath profile. Given the complexity of the topic and the skyrocketing spread of diabetes, a multifarious review of exhaled breath analysis for diabetes monitoring is essential to track the technological progress in the field and comprehend the obstacles in developing a breath analysis-based diabetes management system. In this review, we consolidate the relevance of exhaled breath analysis through a critical assessment of current technologies and recent advancements in sensing methods to address the shortcomings associated with blood glucose monitoring. We provide a detailed assessment of the intricacies involved in the development of non-invasive diabetes monitoring devices. In addition, we spotlight the need to consider breath biomarker clusters as opposed to standalone biomarkers for the clinical applicability of exhaled breath monitoring. We present potential VOC clusters suitable for diabetes management and highlight the recent buildout of breath sensing methodologies, focusing on novel sensing materials and transduction mechanisms. Finally, we portray a multifaceted comparison of exhaled breath analysis for diabetes monitoring and highlight remaining challenges on the path to realizing breath analysis as a non-invasive healthcare approach.

Neural mechanism mimetic selective electronic nose based on programmed M13 bacteriophage

The electronic nose is a reliable practical sensor device that mimics olfactory organs. Although numerous studies have demonstrated excellence in detecting various target substances with the help of ideal models, biomimetic approaches still suffer in practical realization because of the inability to mimic the signal processing performed by olfactory neural systems. Herein, we propose an electronic nose based on the programable surface chemistry of M13 bacteriophage, inspired by the neural mechanism of the mammalian olfactory system. The neural pattern separation (NPS) was devised to apply the pattern separation that operates in the memory and learning process of the brain to the electronic nose. We demonstrate an electronic nose in a portable device form, distinguishing polycyclic aromatic compounds (harmful in living environment) in an atomic-level resolution (97.5% selectivity rate) for the first time. Our results provide practical methodology and inspiration for the second-generation electronic nose development toward the performance of detection dogs (K9).

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.

Recognizing lung cancer and stages using a self-developed electronic nose system

Exhaled breath contains thousands of gaseous volatile organic compounds (VOCs) that could be used as non-invasive biomarkers of lung cancer. Breath-based lung cancer screening has attracted wide attention on account of its convenience, low cost and easy popularization. In this paper, the research of lung cancer detection and staging is conducted by the self-developed electronic nose (e-nose) system. In order to investigate the performance of the device in distinguishing lung cancer patients from healthy controls, two feature extraction methods and two different classification models were adopted. Among all the models, kernel principal component analysis (KPCA) combined with extreme gradient boosting (XGBoost) achieved the best results among 235 breath samples. The accuracy, sensitivity and specificity of e-nose system were 93.59%, 95.60% and 91.09%, respectively. Meanwhile, the device could innovatively classify stages of 90 lung cancer patients (i.e., 44 stage III and 46 stage IV). Experimental results indicated that the recognition accuracy of lung cancer stages was more than 80%. Further experiments of this research also showed that the combination of sensor array and pattern recognition algorithms could identify and distinguish the expiratory characteristics of lung cancer, smoking and other respiratory diseases.

Electrospun Nanofibers for Cancer Therapy

Lately, a remarkable progress has been recorded in the field of electrospinning for the preparation of numerous types of nanofiber scaffolds. These scaffolds present some remarkable features including high loading capacity and encapsulation efficiency, superficial area and porosity, potential for modification, structure for the co-delivery of various therapies, and cost-effectiveness. Their present and future applications for cancer diagnosis and treatment are promising and pioneering. In this chapter we provide a comprehensive overview of electrospun nanofibers (ESNFs) applications in cancer diagnosis and treatment, covering diverse types of drug-loaded electrospun nanofibers.

The promises and challenges of early non-small cell lung cancer detection: patient perceptions, low-dose CT screening, bronchoscopy and biomarkers

Lung cancer survival statistics are sobering with survival ranking among the poorest of all cancers despite the addition of targeted therapies and immunotherapies. However, improvements in tools for early detection hold promise. The Nederlands–Leuvens Longkanker Screenings Onderzoek (NELSON) trial recently corroborated the findings from the previous National Lung Screening Trial low‐dose Computerised Tomography (NLST) screening trial in reducing lung cancer mortality. Biomarker research and development is increasing at pace as the molecular life histories of lung cancers become further unravelled. Low‐dose CT screening (LDCT) is effective but targets only those at the highest risk and is burdensome on healthcare. An optimally designed CT screening programme at best will only detect a low proportion of overall lung cancers as only those at very high‐risk meet screening criteria. Biomarkers that help risk stratify suitable patients for LDCT screening, and those that assist in determining which LDCT detected nodules are likely to represent malignant disease are needed. Some biomarkers have been proposed as standalone lung cancer diagnosis tools. Bronchoscopy technology is improving, with better capacity to identify and obtain samples from early lung cancers. Clinicians need to be aware of each early lung cancer detection method’s inherent limitations. We anticipate that the future of early lung cancer diagnosis will involve a synergistic, multimodal approach, combining several early detection methods.

2D/2D heterojunction of g-C3N4/SnS2: room-temperature sensing material for ultrasensitive and rapid-recoverable NO2 detection

Heterojunction engineering plays an indispensable role in improving gas-sensing performance. However, rational heterojunction engineering to achieve room-temperature NO2 sensing with both high response and rapid recovery is still a challenge. Herein, a 2D/2D heterojunction of g-C3N4/SnS2 is designed to improve the sensing performance of SnS2 and used for ultrasensitive and rapid-recoverable NO2 detection at room temperature. The pristine SnS2 fails to work at room temperature because of its high resistivity and weak adsorption to NO2. After combination with g-C3N4 nanosheets, the g-C3N4/SnS2-based sensor exhibits an extremely high response (503%) and short recovery time (166 s) towards 1 ppm NO2 at room temperature. The improved sensing performance is primarily attributed to the increased adsorption sites and enhanced charge transfer induced by the 2D/2D heterojunctions with large interface contact area. This achievement of g-C3N4/SnS2 2D/2D heterostructures demonstrates a promising pathway for the design of sensitive gas-sensing material based on a 2D/2D heterojunction strategy.

Amine-Functionalized ZIF-8 as a Fluorescent Probe for Breath Volatile Organic Compound Biomarker Detection of Lung Cancer Patients

The highly thermally and chemically stable imidazole framework ZIF-8 samples were separately postmodified with amine groups by using N,N'-dimethylethylenediamine (MMEN) and N,N-dimethylaminoethylamine (MAEA), which had the same molecular formula but different structures. The modified ZIF-8 samples (ZIF-8@amine) were thoroughly characterized, including powder X-ray diffractometry, Fourier-transformed infrared spectroscopy, and physical adsorption at 77 K by nitrogen, thermogravimetric analysis, and photophysical characterization. Results showed that after modification, the Brunauer-Emmett-Teller surface area and total pore volume both increased, almost one time higher than those of the original ZIF-8 sample, and followed the order: ZIF-8-MMEN > ZIF-8-MAEA > ZIF-8. Furthermore, the N-H group was successfully grafted into the modified ZIF-8 samples. To examine the sensing properties of the modified ZIF-8@amine samples toward the breath biomarkers of lung cancer, five potential volatile organic compound biomarkers were used as analytes. ZIF-8-MMEN and ZIF-8-MAEA revealed a unique capacity for sensing hexanal, ethylbenzene, and 1-propanol with high efficiency and sensitivity. The three samples all did not show sensing ability toward styrene and isoprene. In addition, ZIF-8, ZIF-8-MMEN, and ZIF-8-MAEA all can sense hexanal with a detection limit as low as 1 ppb.

Genetic Algorithm Design of MOF-based Gas Sensor Arrays for CO2-in-Air Sensing

Gas sensor arrays, also known as electronic noses, leverage a diverse set of materials to identify the components of complex gas mixtures. Metal-organic frameworks (MOFs) have emerged as promising materials for electronic noses due to their high-surface areas and chemical as well as structural tunability. Using our recently reported genetic algorithm design approach, we examined a set of 50 MOFs and searched through over 1.125 × 1015 unique array combinations to identify optimal arrays for the detection of CO2 in air. We found that despite individual MOFs having lower selectivity for O2 or N2 relative to CO2, intelligently selecting the right combinations of MOFs enables accurate prediction of the concentrations of all components in the mixture (i.e., CO2, O2, N2). We also analyzed the physical properties of the elements in the arrays to develop an intuition for improving array design. Notably, we found that an array whose MOFs have diversity in their volumetric surface areas has improved sensing. Consistent with this observation, we found that the best arrays consistently had greater structural diversity (e.g., pore sizes, void fractions, and surface areas) than the worst arrays.

Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer

The analysis of exhaled breath is drawing a high degree of interest in the diagnostics of various diseases, including lung cancer. Electronic nose (E-nose) technology is one of the perspective approaches in the field due to its relative simplicity and cost efficiency. The use of an E-nose together with pattern recognition algorithms allow 'breath-prints' to be discriminated. The aim of this study was to develop an efficient online E-nose-based lung cancer diagnostic method via exhaled breath analysis with the use of some statistical classification methods. A developed multisensory system consisting of six metal oxide chemoresistance gas sensors was employed in three temperature regimes. This study involved 118 individuals: 65 in the lung cancer group (cytologically verified) and 53 in the healthy control group. The exhaled breath samples of the volunteers were analysed using the developed E-nose system. The dataset obtained, consisting of the sensor responses, was pre-processed and split into training (70%) and test (30%) subsets. The training data was used to fit the classification models; the test data was used for the estimation of prediction possibility. Logistic regression was found to be an adequate data-processing approach. The performance of the developed method was promising for the screening purposes (sensitivity-95.0%, specificity-100.0%, accuracy-97.2%). This shows the applicability of the gas-sensitive sensor array for the exhaled breath diagnostics. Metal oxide sensors are highly sensitive, low-cost and stable, and their poor sensitivity can be enhanced by integrating them with machine learning algorithms, as can be seen in this study. All experiments were carried out with the permission of the N.N. Petrov Research Institute of Oncology ethics committee no. 15/83 dated March 15, 2017.

Detection and Discrimination of Volatile Organic Compounds using a Single Film Bulk Acoustic Wave Resonator with Temperature Modulation as a Multiparameter Virtual Sensor Array

This paper describes the detection and discrimination of volatile organic compounds (VOCs) using an e-nose system based on a multiparameter virtual sensor array (VSA), which consists of a single-chip temperature-compensated film bulk acoustic wave resonator (TC-FBAR) coated with 20-bilayer self-assembled poly(sodium 4-styrenesulfonate)/poly(diallyldimethylammonium chloride) thin films. The high-frequency and microscale FBAR multiparameter VSA was realized by temperature modulation, which can greatly reduce the cost and complexity compared to those of a traditional e-nose system and can allow it to operate at different temperatures. The discrimination effect depends on the synergy of temperature modulation and the sensing material. For proof-of-concept validation purposes, the TC-FBAR was exposed to six different VOC vapors at six different gas partial pressures by real-time VOC static detection and dynamic detection. The resulting frequency shifts and impedance responses were measured at different temperatures and evaluated using principal component analysis and linear discriminant analysis, which revealed that all analytes can be distinguished and classified with more than 97% accuracy. To the best of our knowledge, this report is the first on an FBAR multiparameter VSA based on temperature modulation, and the proposed novel VSA shows great potential as a compact and promising e-nose system integrated in commercial electronic products.

Intelligent Selection of Metal-Organic Framework Arrays for Methane Sensing via Genetic Algorithms

Gas sensor arrays, also called electronic noses, use many chemically diverse materials to adsorb and subsequently identify gas species in complex mixture environments. Ideally these materials should have maximally complementary adsorption profiles to achieve the best sensing performance, but in practice they are selected by trial-and-error. Thus current electronic noses do not achieve optimal detection. In this work, we employ metal-organic frameworks (MOFs) as sensing materials and leverage a genetic algorithm to identify optimal combinations of them for detecting methane leaks in air. We build on our previously reported computational design methodology, which ranked MOF arrays by their Kullback-Liebler divergence (KLD) values for probabilistically describing the concentrations of each gas species in an unknown mixture. We ran the genetic algorithm to find optimal MOF arrays of various sizes when selecting from a library of 50 different MOF materials. The genetic algorithm was able to accurately predict the best arrays of any desired size when compared to brute-force screening. Thus, this search optimization can be integrated into the efficient design of MOF-based electronic noses.

A volatile biomarker in breath predicts lung cancer and pulmonary nodules

BACKGROUND

previous studies have reported volatile organic compounds (VOCs) in the breath as apparent biomarkers of lung cancer. We tested the hypothesis that a robust breath VOC biomarker of lung cancer should also predict pulmonary nodules in chest CT images.

METHODS

Biomarker discovery study (unblinded): 301 subjects were screened for lung cancer with low dose chest CT (LDCT), and donated duplicate samples of alveolar breath for analysis with gas chromatography mass spectrometry (GC MS). Monte Carlo analysis of breath chromatograms revealed a mass ion as a biomarker that identified biopsy-proven lung cancer as well as suspicious pulmonary nodules on LDCT. The biomarker was termed Mass Abnormalities in Gaseous Ions with Imaging Correlates (MAGIIC). The chemical structure of MAGIIC was tentatively identified from the NIST library of mass spectra; the best-fit compounds included C4 and C5 alkane derivatives that were consistent with metabolic products of oxidative stress. Blinded validation of MAGIIC: the abundance of the MAGIIC biomarker was determined in a different group of 161 subjects undergoing screening with LDCT. They donated duplicate alveolar breath VOC samples that were analyzed at two independent laboratories. The study was blinded and monitored with Good Clinical Practice. The abundance of MAGIIC in breath predicted biopsy-proven lung cancer with 84% accuracy, sensitivity = 75.4% and specificity = 85.0%. MAGIIC also predicted pulmonary nodules in LDCT with 80.5% accuracy, sensitivity = 80.1% and specificity = 75.0%. Breath MAGIIC abundance was not significantly affected by tobacco smoking history.

CONCLUSIONS

in a blinded study, breath VOC MAGIIC accurately predicted lung cancer confirmed on a tissue biopsy, as well as suspicious pulmonary nodules observed on LDCT. MAGIIC may have been a product of oxidative stress and it could potentially be employed as an ancillary to LDCT to predict the likelihood that a pulmonary nodule is malignant.

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.

Multi-layer graphene as a selective detector for future lung cancer biosensing platforms

Highly selective, fast detection of specific lung-cancer biomarkers (CMs) in exhaled human breath is vital to the development of enhanced sensing devices. Today, e-nose is a promising approach for the diagnosis of lung cancer. Nevertheless, considerable challenges to early-stage disease diagnostics still remain: e.g. decrease in sensor sensitivities in the presence of water vapor, sensor drift leading to the inability to calibrate exactly, relatively short sensor lifetimes, and difficulty discriminating between multiple diseases. However, there is a wide scope for breath diagnostics techniques, and all advanced electrodes applicable to e-nose devices will benefit them. Here, we present the promising sensing capabilities of bare multi-layer graphene (MLG) as a proof of concept for advanced e-nose devices and demonstrate its utility for biomolecule discrimination of the most common lung CMs (ethanol, isopropanol, and acetone). We report on a comparative study involving exposure of the three CM solutions on flat MLG (f-MLG) and patterned MLG (p-MLG) electrodes, where the electrical conductivity of p-MLG is significantly increased while applying acetone. Based on sensitivity tests, we demonstrate the ability to monitor the electrical response of graphene electrodes employing graphene of various wettabilities. Specifically, the f-MLG electrode displays almost 2 times higher sheet resistance (30 Ω sq-1) compared to the hydrophilic p-MLG (12 Ω sq-1). We show significant sensitivity to selected specific molecules of pristine f-MLG and p-MLG while applying CM solutions with a 1.4 × 105 ppm concentration. Finally, we show the selectivity of f-MLG and p-MLG-based sensors when exposed to 2.0 × 105 ppm solutions containing different CM combinations. Both sensors were selective in particular to acetone, since the presence of acetone leads to a sheet resistance increase. We demonstrate that an advanced e-nose approach integrated with MLG electrodes has significant potential as a design concept for utilization of molecular detection at variable concentrations such as in early-stage disease diagnosis. This early-stage approach will provide convenient and reusable complex monitoring of CMs compared to typical contact sensors which require target analysis and are limited by disposable measuring. Moreover, further integration of the Internet of Things will introduce advanced e-nose devices as a biotechnological innovation for disease resilience with the potential for commercialization.

Assessing Lung Cancer Screening Programs under Uncertainty in a Heterogeneous Population

BACKGROUND

Lung cancer screening can reduce cancer mortality. Most implementation studies focus only on low-dose computed tomography (LDCT) and clinical attributes of screening and do not include preferences of potential participants. In this study we evaluated the perceived value of screening programs based on LDCT, breath analysis (BA), or blood biomarkers (BB) according to the perspective of the target population.

METHODS

A multi-criteria decision analysis framework was adopted. The weights of seven attributes of screening (sensitivity, specificity, radiation burden, duration of screening process, waiting time until results are communicated, location of screening, and mode of screening) were obtained from an earlier study that included a broad sample from the Netherlands. Performance data for the screening modalities was obtained from clinical trials and expert opinion. Parameter uncertainty about clinical performances was incorporated probabilistically, while heterogeneity in preferences was analyzed through subgroup analyses.

RESULTS

The mean overall values were 0.58 (CI: 0.57 to 0.59), 0.57 (CI: 0.56 to 0.59), and 0.44 (CI: 0.43 to 0.45) for BB, BA, and LDCT, respectively. Seventy-seven per cent of respondents preferred BB or BA. For most subgroups, the overall values were similar to those of the entire sample. BA had the highest value for respondents who would have been eligible for earlier screening trials.

DISCUSSION

BB and BA seem valuable to participants because they can be applied in a primary care setting. Although LDCT still seems preferable given its strong and positive evidence base, it is important to take non-clinical attributes into account to maximize attendance.

Early detection of lung cancer biomarkers through biosensor technology: A review

Lung cancer is undoubtedly one of the most serious health issues of the 21 st century. It is the second leading cause of cancer-related deaths in both men and women　worldwide, accounting for about 1.5 million deaths annually. Despite advances in the treatment of lung cancer with new pharmaceutical products and technological improvements, morbidity and mortality rates remains a significant challenge for the cancer biologists and oncologists. The vast majority of lung cancer patients present with advanced-stage of pathological process that ultimately leads to poor prognosis and a five-year survival rate less than 20%. Early and accurate screening and analysis using cost-effective means are urgently needed to effectively diagnose the disease, improve the survival rate or to reduce mortality and morbidity associated with lung cancer patients. Thus, the only hope for early recognition of risk factors and timely diagnosis and treatment of lung cancer is biosensors technology. Novel biosensing based diagnostics approaches for predicting metastatic risks are likely to have significant therapeutic and clinical impact in the near future. This article systematically provides a brief overview of various biosensing platforms for identification of lung cancer disease biomarkers, with a specific focus on recent advancements in electrochemical and optical biosensors, analytical performances of different biosensors, challenges and further research opportunities for routine clinical analysis.

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.

Flexible, Disposable Cellulose-Paper-Based MoS2/Cu2S Hybrid for Wireless Environmental Monitoring and Multifunctional Sensing of Chemical Stimuli

Multifunctional sensors responding to different chemical stimuli fabricated using functional nanomaterials still remain a challenge because of the usage of the same sensor multiple times for different sensing applications and unreliable front-end processing of the sensing data. This challenge is intensified by the lack of suitable techniques for fabricating disposable sensors, which can be integrated into smartphones with a dedicated application developed for each sensing application. A novel MoS₂/Cu₂S hybrid grown on disposable cellulose paper by the hydrothermal method is reported for its utilization in sensing humidity, temperature, breath, and ethanol adulteration, wherein the data can be wirelessly transmitted to a smartphone with the dedicated application module for each sensing application. The sensor can be utilized for a particular sensing application and then can be disposed, avoiding the need for utilizing the same sensor for different sensing applications, thereby increasing the accuracy of the sensing data. The sensing mechanism of the fabricated sensor is explained for each stimulus in terms of change in the transport properties of the MoS₂/Cu₂S hybrid. The development of such unique hybrid materials for wireless disposable multifunctional sensors is a great step ahead in flexible and wearable electronics having potential applications in medical, security, Internet of things, etc.

Breathprinting and Early Diagnosis of Lung Cancer

The electronic nose (e-nose) is a promising technology as a useful addition to the currently available modalities to achieve lung cancer diagnosis. The e-nose can assess the volatile organic compounds detected in the breath and derived from the cellular metabolism. Volatile organic compounds can be analyzed to identify the individual chemical elements as well as their pattern of expression to reproduce a sensorial combination similar to a fingerprint (breathprint). The e-nose can be used alone, mimicking the combinatorial selectivity of the human olfactory system, or as part of a multisensorial platform. This review analyzes the progress made by investigators interested in this technology as well as the perspectives for its future utilization.

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods.

Cancer breath testing: a patent review

INTRODUCTION

Human breath can contain thousands of volatile organic compounds (VOCs) and semi-volatile compounds that are related to metabolism and other biochemical processes. The presence of cancer cells can affect the identity and abundances of chemicals in breath when compared to those in healthy control subjects, which can be used to indicate the likelihood of a patient having cancer. Recently, the chemical analysis of exhaled breath from patients has been shown to be promising for diagnosing many different types of cancers, including lung, breast, colon, head, neck, and prostate, along with pre-cancerous conditions (dysplasia).

AREAS COVERED

Here, we reviewed the sampling, analytical and data analysis methods reported in the recent patent literature related to cancer breath testing (2014-2017). In addition, the different types of cancer biomarkers that were disclosed are discussed.

EXPERT OPINION

The major advantages of breath testing compared to conventional X-ray and imaging based methods includes simplicity of use, non-invasiveness, and the potential to detect cancer at a relatively early stage. Such methods are also suitable to perform population screening because of their non-invasiveness. However, the establishment of standard sampling, detection and quantification methods for breath testing is required before the methods can be employed for clinical diagnosis.

On the importance of accurate quantification of individual volatile metabolites in exhaled breath

It is argued that shortcomings of certain approaches to breath analysis research based on superficial interpretation of non-quantitative data are inadvertently inhibiting the progression of non-invasive breath analysis into clinical practice. The objective of this perspective is to suggest more clinically profitable approaches to breath research. Thus, following a discourse on the challenges and expectations in breath research, a brief indication is given of the analytical techniques currently used for the analysis of very humid exhaled breath. The seminal work that has been carried out using GC-MS revealed that exhaled breath comprises large numbers of trace volatile organic compounds, VOCs. Unfortunately, analysis of these valuable GC-MS data is mostly performed using chemometrics to distinguish the VOC content of breath samples collected from patients and healthy controls, and reliable quantification of the VOCs is rarely deemed necessary. This limited approach ignores the requirements of clinically acceptable biomarkers and misses the opportunity to identify relationships between the concentrations of individual VOCs and certain related physiological or metabolic parameters. Therefore, a plea is made for more effort to be directed towards the positive identification and accurate quantification of individual VOCs in exhaled breath, which are more physiologically meaningful as best exemplified by the quantification of breath nitric oxide, NO. Support for the value of individual VOC quantification is illustrated by the SIFT-MS studies of breath hydrogen cyanide, HCN, a biomarker of Pseudomonas aeruginosa infection, breath acetic acid as an indicator of airways acidification in cystic fibrosis patients, and n-pentane as a breath biomarker of inflammation in idiopathic bowel disease patients. These single VOCs could be used as non-invasive monitors of the efficacy of therapeutic intervention. The increase of breath methanol following the ingestion of a known amount of the sweetener aspartame impressively shows that accurate breath analysis is a reliable indicator of blood concentrations. However, using individual VOCs for specific disease diagnosis does have its problems and it is, perhaps, more appropriate to see their concentrations as proxy markers of general underlying physiological change. We dedicate this perspective to Lars Gustafsson for his seminal work on breath research and especially for his pioneering work on nitric oxide measurements in exhaled breath in asthma, which best shows the utility and value of the quantification of individual breath biomarkers on which this perspective focuses.

Array of Chemosensitive Resistors with Composites of Gas Chromatography (GC) Materials and Carbon Black for Detection and Recognition of VOCs: A Basic Study

Mimicking the biological olfaction, large odor-sensor arrays can be used to acquire a broad range of chemical information, with a potentially high degree of redundancy, to allow for enhanced control over the sensitivity and selectivity of artificial olfaction systems. The arrays should consist of the largest possible number of individual sensing elements while being miniaturized. Chemosensitive resistors are one of the sensing platforms that have a potential to satisfy these two conditions. In this work we test viability of fabricating a 16-element chemosensitive resistor array for detection and recognition of volatile organic compounds (VOCs). The sensors were fabricated using blends of carbon black and gas chromatography (GC) stationary-phase materials preselected based on their sorption properties. Blends of the selected GC materials with carbon black particles were subsequently coated over chemosensitive resistor devices and the resulting sensors/arrays evaluated in exposure experiments against vapors of pyrrole, benzenal, nonanal, and 2-phenethylamine at 150, 300, 450, and 900 ppb. Responses of the fabricated 16-element array were stable and differed for each individual odorant sample, proving the blends of GC materials with carbon black particles can be effectively used for fabrication of large odor-sensing arrays based on chemosensitive resistors. The obtained results suggest that the proposed sensing devices could be effective in discriminating odor/vapor samples at the sub-ppm level.