Analysis of volatile organic compounds in the exhaled breath for the diagnosis of lung cancer

Unveiling Colorectal Cancer Biomarkers: Harnessing Biosensor Technology for Volatile Organic Compound Detection

Conventional screening options for colorectal cancer (CRC) detection are mainly direct visualization and invasive methods including colonoscopy and flexible sigmoidoscopy, which must be performed in a clinical setting and may be linked to adverse effects for some patients. Non-invasive CRC diagnostic tests such as computed tomography colonography and stool tests are either too costly or less reliable than invasive ones. On the other hand, volatile organic compounds (VOCs) are potentially ideal non-invasive biomarkers for CRC detection and monitoring. The present review is a comprehensive presentation of the current state-of-the-art VOC-based CRC diagnostics, with a specific focus on recent advancements in biosensor design and application. Among them, breath-based chromatography pattern analysis and sampling techniques are overviewed, along with nanoparticle-based optical and electrochemical biosensor approaches. Limitations of the currently available technologies are also discussed with an outlook for improvement in combination with big data analytics and advanced instrumentation, as well as expanding the scope and specificity of CRC-related volatile biomarkers.

Isopropanol sensor based on sprayed In2S3 thin film using linear discriminant analysis for real-time selectivity

Metal sulfides have been studied for their high performance as new sensitive materials for gas detection. These material innovations contribute significantly to the development of more sensitive, stable and specific conductivity sensors, opening the way to new applications in detecting gases at low concentrations. Hence, this work reports on the sensing performance of In2S3 for isopropanol detection. Numerous VOCs, such as ketones, alcohols, and aldehydes, serve as cancer biomarkers. Notably, isopropanol, as a biomarker, shows a substantial increase (20-1007 ppb) in lung cancer patients, suggesting its potential as an early diagnostic criterion. To explore this, we fabricated a thin film of In2S3 onto a platinum interdigitated silicon dioxide substrate by simple and low-cost spray pyrolysis technology. Structural and morphological analyses via XRD, MEB, AFM, and TEM were conducted on the prepared samples. Isopropanol vapor response was assessed within a controlled temperature range of 250 °C-450 °C. The In2S3-based sensor demonstrated notable sensitivity (0.034 ppm-1), maintained stability over three weeks, and reliably detected isopropanol activation. In2S3 emerges as a promising candidate for detecting isopropanol, with a limit of detection (LOD) of 162 ppb. In this work, seven VOCs, including isopropanol, ethanol, methanol, butan-1-ol, formaldehyde, toluene, and acetone, were evaluated. Cross-responses among these VOCs were observed, indicating a lack of assured sensor selectivity. However, isopropanol recognition was achieved by employing linear discriminant analysis (LDA) on pertinent features derived from transient current change measurements. As a result, the sensor's sensitivity enables the deduction of the isopropanol concentration.

Exhaled VOC detection in lung cancer screening: a comprehensive meta-analysis

BACKGROUND

Lung cancer (LC), characterized by high incidence and mortality rates, presents a significant challenge in oncology. Despite advancements in treatments, early detection remains crucial for improving patient outcomes. The accuracy of screening for LC by detecting volatile organic compounds (VOCs) in exhaled breath remains to be determined.

METHODS

Our systematic review, following PRISMA guidelines and analyzing data from 25 studies up to October 1, 2023, evaluates the effectiveness of different techniques in detecting VOCs. We registered the review protocol with PROSPERO and performed a systematic search in PubMed, EMBASE and Web of Science. Reviewers screened the studies' titles/abstracts and full texts, and used QUADAS-2 tool for quality assessment. Then performed meta-analysis by adopting a bivariate model for sensitivity and specificity.

RESULTS

This study explores the potential of VOCs in exhaled breath as biomarkers for LC screening, offering a non-invasive alternative to traditional methods. In all studies, exhaled VOCs discriminated LC from controls. The meta-analysis indicates an integrated sensitivity and specificity of 85% and 86%, respectively, with an AUC of 0.93 for VOC detection. We also conducted a systematic analysis of the source of the substance with the highest frequency of occurrence in the tested compounds. Despite the promising results, variability in study quality and methodological challenges highlight the need for further research.

CONCLUSION

This review emphasizes the potential of VOC analysis as a cost-effective, non-invasive screening tool for early LC detection, which could significantly improve patient management and survival rates.

Periodic open and closed resonators as a biosensor using two computational methods

The volatile particles and molecules in our dry exhaled breath can reveal enormous information about the health of any person, such as the person's respiratory and metabolic functioning. Beyond the carbon dioxide level is an indicator of life, it provides important health-related data like people's metabolic rate. This study considers periodic open and closed resonators for measuring carbon dioxide concentration in dry exhaled breath. Transfer matrix and green methods are used to simulate the interaction between acoustic waves and the proposed sensor. The band gaps using the green method coincide with the transmittance spectra by the transfer matrix. The suggested sensor recorded a sensitivity of 5.3 H z . m - 1 . s , a figure of merit of 10,254m - 1 . s , a detection limit of 5 × 10 - 6 m . s - 1 , and a quality factor of 3 × 10 6 . Furthermore, the efficiency shows that the proposed design is appropriate as a diagnostic sensor for different diseases such as chronic obstructive pulmonary. Besides, cylindrical-adapted sensors are urgently needed in medicine, industry, and biology because they can simultaneously be used for fluid transport and detection.

Investigating the enhancement of lung cancer sensing: the effect of edge halogenation in armchair stanene nanoribbons

In this research, we explore the impact of edge passivation using halogen atoms on armchair stanene nanoribbon (ASNR) for the early detection of lung cancer biomarkers. We employ non-equilibrium green function (NEGF) and density functional theory (DFT) methods to evaluate sensing characteristics. The edges of ASNR are passivated with fluorine, chlorine, bromine, and iodine atoms. Our findings indicate a significant enhancement in sensing performance upon halogenation of ASNR. Notable changes in adsorption energy and current for edge-halogenated ASNR configurations demonstrate improved sensing behavior. Moreover, current curves exhibit greater distinctiveness of halogenated ASNR in comparison to hydrogenated ASNR. The calculations indicate a change in adsorption energy (Eads) of -7.59 eV, -7.6 eV, -8.3 eV, and -8.6 eV for the adsorption by styrene on I-ASnNR, Br-ASnNR, toluene on Cl-ASnNR, and styrene on F-ASnNR, respectively. The corresponding sensitivity improves up to 37.33%, 38.09%, 38.35%, and 45.5%, respectively. These findings highlight that the most significant change occurs with the edge fluorination of ASnNR. Our findings underscore the effectiveness of halogen atom edge passivation in ASNR for heightened sensing performance, making it a promising choice for the development of early-detection lung cancer sensors.

Unlocking the secrets: Volatile Organic Compounds (VOCs) and their devastating effects on lung cancer

Lung cancer (LCs) is still a serious health problem globally, with many incidences attributed to environmental triggers such as Volatile Organic Compounds (VOCs). VOCs are a broad class of compounds that can be released via various sources, including industrial operations, automobile emissions, and indoor air pollution. VOC exposure has been linked to an elevated risk of lung cancer via multiple routes. These chemicals can be chemically converted into hazardous intermediate molecules, resulting in DNA damage and genetic alterations. VOCs can also cause oxidative stress, inflammation, and a breakdown in the cellular protective antioxidant framework, all of which contribute to the growth of lung cancer. Moreover, VOCs have been reported to alter critical biological reactions such as cell growth, apoptosis, and angiogenesis, leading to tumor development and metastasis. Epidemiological investigations have found a link between certain VOCs and a higher probability of LCs. Benzene, formaldehyde, and polycyclic aromatic hydrocarbons (PAHs) are some of the most well-researched VOCs, with comprehensive data confirming their cancer-causing potential. Nevertheless, the possible health concerns linked with many more VOCs and their combined use remain unknown, necessitating further research. Identifying the toxicological consequences of VOCs in LCs is critical for establishing focused preventative tactics and therapeutic strategies. Better legislation and monitoring mechanisms can limit VOC contamination in occupational and environmental contexts, possibly reducing the prevalence of LCs. Developing VOC exposure indicators and analyzing their associations with genetic susceptibility characteristics may also aid in early identification and targeted therapies.

Diabetes noninvasive diagnostics and monitoring through volatile biomarkers analysis in the exhaled breath using optical absorption spectroscopy

The review is aimed on the analysis the abilities of noninvasive diagnostics and monitoring of diabetes mellitus (DM) and DM-associated complications through volatile molecular biomarkers detection in the exhaled breath. The specific biochemical reactions in the body of DM patients and their associations with volatile molecular biomarkers in the breath are considered. The applications of optical spectroscopy methods, including UV, IR, and terahertz spectroscopy for DM-associated volatile molecular biomarkers measurements, are described. The applications of similar technique combined with machine learning methods in DM diagnostics using the profile of DM-associated volatile molecular biomarkers in exhaled air or "pattern-recognition" approach are discussed.

microRNAs in exhaled breath condensate for diagnosis of lung cancer in a resource-limited setting: a concise review

Lung cancer is one of the common cancers globally with high mortality and poor prognosis. Most cases of lung cancer are diagnosed at an advanced stage due to limited diagnostic resources. Screening modalities, such as sputum cytology and annual chest radiographs, have not proved sensitive enough to impact mortality. In recent years, annual low-dose computed tomography has emerged as a potential screening tool for early lung cancer detection, but it may not be a feasible option for developing countries. In this context, exhaled breath condensate (EBC) analysis has been evaluated recently as a noninvasive tool for lung cancer diagnosis. The breath biomarkers also have the advantage of differentiating various types and stages of lung cancer. Recent studies have focused more on microRNAs (miRNAs) as they play a key role in tumourigenesis by regulating the cell cycle, metastasis and angiogenesis. In this review, we have consolidated the current published literature suggesting the utility of miRNAs in EBC for the detection of lung cancer.

Research progress of electronic nose technology in exhaled breath disease analysis

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

Recent Advances in Gas Detection Methodologies with a Special Focus on Environmental Sensing and Health Monitoring Applications─A Critical Review

With the expansion of the Internet-of-Things (IoT), the use of gas sensors in the field of wearable technology, smart devices, and smart homes has increased manifold. These gas sensors have two key applications─one is the detection of gases present in the environment and the other is the detection of Volatile Organic Compounds (VOCs) that are found in the breath. In this review, we focus systematically on the advancements in the field of various spectroscopic methods such as mass spectrometry-based analysis and point-of-care approach to detect VOCs and gases for environmental monitoring and disease diagnosis. Additionally, we highlight the development of smart sensors that work on the principle of electrochemical detection and provide examples of the same through an extensive literature review. At the end of this review, we highlight various challenges and future perspectives.

Molecular modeling and simulation of transition metal-doped molybdenum disulfide biomarkers in exhaled gases for early detection of lung cancer

BACKGROUND

The presence of volatile organic compounds (VOCs) in the exhaled breath of lung cancer patients is the only available source for detecting the disease at its initial stage. Exhaled breath analysis depends purely on the performance of the biosensors. The interaction between VOCs and pristine MoS₂ is repulsive in nature. Therefore, modifying MoS₂ via surficial adsorption of the transition metal nickel is of prime importance. The surficial interaction of six VOCs with Ni-doped MoS₂ led to substantial variations in the structural and optoelectronic properties compared to those of the pristine monolayer. The remarkable improvement in the conductivity, thermostability, good sensing response, and recovery time of the sensor exposed to six VOCs revealed that a Ni-doped MoS₂ exhibits impressive properties for the detection of exhaled gases. Different temperatures have a significant impact on the recovery time. Humidity has no effect on the detection of exhaled gases upon exposure to VOCs. The obtained results may encourage the use of exhaled breath sensors by experimentalists and oncologists to enable potential advancements in lung cancer detection.

METHODS

The surface adsorption of transition metal and its interaction with volatile organic compounds on a MoS₂ surface was studied by using Spanish Initiative for Electronic Simulations with Thousands of Atoms (SIESTA). The pseudopotentials used in the SIESTA calculations are norm-conserving in their fully nonlocal forms. The atomic orbitals with finite support were used as a basis set, allowing unlimited multiple-zeta and angular momenta, polarization, and off-site orbitals. These basis sets are the key for calculating the Hamiltonian and overlap matrices in O(N) operations. The present hybrid density functional theory (DFT) is a combination of PW92 and RPBE methods. Additionally, the DFT+U approach was employed to accurately ascertain the coulombic repulsion in the transition elements.

The Role of Nano-Sensors in Breath Analysis for Early and Non-Invasive Disease Diagnosis

Early-stage, precise disease diagnosis and treatment has been a crucial topic of scientific discussion since time immemorial. When these factors are combined with experience and scientific knowledge, they can benefit not only the patient, but also, by extension, the entire health system. The development of rapidly growing novel technologies allows for accurate diagnosis and treatment of disease. Nanomedicine can contribute to exhaled breath analysis (EBA) for disease diagnosis, providing nanomaterials and improving sensing performance and detection sensitivity. Through EBA, gas-based nano-sensors might be applied for the detection of various essential diseases, since some of their metabolic products are detectable and measurable in the exhaled breath. The design and development of innovative nanomaterial-based sensor devices for the detection of specific biomarkers in breath samples has emerged as a promising research field for the non-invasive accurate diagnosis of several diseases. EBA would be an inexpensive and widely available commercial tool that could also be used as a disease self-test kit. Thus, it could guide patients to the proper specialty, bypassing those expensive tests, resulting, hence, in earlier diagnosis, treatment, and thus a better quality of life. In this review, some of the most prevalent types of sensors used in breath-sample analysis are presented in parallel with the common diseases that might be diagnosed through EBA, highlighting the impact of incorporating new technological achievements in the clinical routine.

Breath VOC analysis and machine learning approaches for disease screening: a review

Early disease detection is often correlated with a reduction in mortality rate and improved prognosis. Currently, techniques like biopsy and imaging that are used to screen chronic diseases are invasive, costly or inaccessible to a large population. Thus, a non-invasive disease screening technology is the need of the hour. Existing non-invasive methods like gas chromatography-mass spectrometry, selected-ion flow-tube mass spectrometry, and proton transfer reaction-mass-spectrometry are expensive. These techniques necessitate experienced operators, making them unsuitable for a large population. Various non-invasive sources are available for disease detection, of which exhaled breath is preferred as it contains different volatile organic compounds (VOCs) that reflect the biochemical reactions in the human body. Disease screening by exhaled breath VOC analysis can revolutionize the healthcare industry. This review focuses on exhaled breath VOC biomarkers for screening various diseases with a particular emphasis on liver diseases and head and neck cancer as examples of diseases related to metabolic disorders and diseases unrelated to metabolic disorders, respectively. Single sensor and sensor array-based (Electronic Nose) approaches for exhaled breath VOC detection are briefly described, along with the machine learning techniques used for pattern recognition.

Early detection of lung cancer biomarkers in exhaled breath by modified armchair stanene nanoribbons

In this study, we analyze armchair stanene nanoribbons as excellent sensing substances for the early diagnosis of lung cancer using density functional theory and the non-equilibrium Green function. Four modified configurations of surface- and edge-defected armchair stanene nanoribbons were studied to improve the sensing performance. Our probes indicated that the adsorption energy of armchair stanene nanoribbons is at least five times greater than that of other previously reported substances, such as single-wall carbon nanotubes, phosphorene, and silicene. A noticeable reduction in the current was observed, implying the high sensitivity of our sensing configurations. The adsorption energy and current results suggest that configurations with a single vacancy and edge defects improve the sensitivity and selectivity of the system because of their free dangling bonds. The calculated results demonstrate that the both-side edge defected armchair stanene nanoribbons reduce the adsorption energy to -8.35 eV and increase the sensitivity up to 45% for toluene detection. This reduction in adsorption energy and the surge of sensitivity shows ultra-high sensing performance, yielding a more efficient structure for the future design of early-diagnosis lung cancer sensing applications, thus improving lung cancer patients' survival and life expectancy.

Exhaled Air Metabolome Analysis for Pulmonary Arterial Hypertension Fingerprints Identification-The Preliminary Study

Pulmonary arterial hypertension (PAH) is a rare disease with a serious prognosis. The aim of this study was to identify biomarkers for PAH in the breath phase and to prepare an automatic classification method to determine the changing metabolome trends and molecular mapping. A group of 37 patients (F/M: 8/29 women, mean age 60.4 ± 10.9 years, BMI 27.6 ± 6.0 kg/m²) with diagnosed PAH were enrolled in the study. The breath phase of all the patients was collected on a highly porous septic material using a special patented holder PL230578, OHIM 002890789-0001. The collected air was then examined with gas chromatography coupled with mass spectrometry (GC/MS). The algorithms of Spectral Clustering, KMeans, DBSCAN, and hierarchical clustering methods were used to perform the cluster analysis. The identification of the changes in the ratio of the whole spectra of biomarkers allowed us to obtain a multidimensional pathway for PAH characteristics and showed the metabolome differences in the four subgroups divided by the cluster analysis. The use of GC/MS, supported with novel porous polymeric materials, for the breath phase analysis seems to be a useful tool in selecting bio-fingerprints in patients with PAH. The four metabolome classes which were obtained constitute novel data in the PAH population.

Diagnosis by Volatile Organic Compounds in Exhaled Breath in Exhaled Breath from Patients with Gastric and Colorectal Cancers

One in three cancer deaths worldwide are caused by gastric and colorectal cancer malignancies. Although the incidence and fatality rates differ significantly from country to country, the rates of these cancers in East Asian nations such as South Korea and Japan have been increasing each year. Above all, the biggest danger of this disease is how challenging it is to recognize in its early stages. Moreover, most patients with these cancers do not present with any disease symptoms before receiving a definitive diagnosis. Currently, volatile organic compounds (VOCs) are being used for the early prediction of several other diseases, and research has been carried out on these applications. Exhaled VOCs from patients possess remarkable potential as novel biomarkers, and their analysis could be transformative in the prevention and early diagnosis of colon and stomach cancers. VOCs have been spotlighted in recent studies due to their ease of use. Diagnosis on the basis of patient VOC analysis takes less time than methods using gas chromatography, and results in the literature demonstrate that it is possible to determine whether a patient has certain diseases by using organic compounds in their breath as indicators. This study describes how VOCs can be used to precisely detect cancers; as more data are accumulated, the accuracy of this method will increase, and it can be applied in more fields.

Helicene-Hydrazide Encapsulated Ethyl Cellulose as a Potential Fluorescence Sensor for Highly Specific Detection of Nonanal in Aqueous Solutions and a Proof-of-Concept Clinical Study in Lung Fluid

Over the past years, lung cancer has been one of the vital cancer-related mortalities worldwide and has inevitably exhibited the highest death rate with the subsequent need for facile and convenient diagnosis approaches to identify the severity of cancer. Previous research has reported long-chain aldehyde compounds such as hexanal, heptanal, octanal, and nonanal as potential biomarkers of lung cancer. Herein, the helicene dye-encapsulated ethyl cellulose (EC@dye-NH) nanosensors have been applied for the potentially sensitive and specific detection of long-chain aldehydes in aqueous media. The sensors contain the intrinsic hydrazide group of dye-NH, which is capable of reacting an aldehyde group via imine formation and the EC backbone. This offers the synergistic forces of hydrophobic interactions with alkyl long-chain aldehydes, which could induce self-assembly encapsulation of EC@dye-NH nanosensors and strong fluorescence responses. The addition of long-chain aldehyde would induce the complete micellar-like nanoparticle formation within 15 min in acetate buffer pH 5.0. The limit of detection (LOD) values of EC@dye-NH nanosensors toward heptanal, octanal, and nonanal were 40, 100, and 10 μM, respectively, without interference from the lung fluid matrices and short-chain aldehydes. For practical applicability, this sensing platform was developed for quantification of the long-chain aldehydes in lung fluid samples with 98-101% recoveries. This EC@dye-NH nanosensor was applied to quantify nonanal contents in lung fluid samples. The results of this method based on EC@dye-NH nanosensors were then validated using standard gas chromatography-mass spectrometry (GC-MS), which gave results consistent with the proposed method. With intracellular imaging application, the EC@dye-NH nanosensors demonstrated excellent intracellular uptake and strong green fluorescence emission upon introducing the nonanal into the lung cancer cells (A549). Thus, the developed nanosensing approach served as the potential fluorescent probes in medical and biological fields, especially for lung cancer disease diagnosis based on highly selective and sensitive detection of long-chain aldehydes.

Development of a Non-invasive Diagnostic Method for Esophageal Squamous Cell Carcinoma by Gas Chromatographic Analysis of Exhaled Breath

Objectives

Since esophageal carcinoma progresses asymptomatically, for many patients the disease is already advanced at the time of diagnosis. The main methods that are currently used to diagnose esophageal carcinoma are upper gastrointestinal radiographic contrast examinations and upper gastrointestinal endoscopy, but early discovery of this disease remains difficult. There is a need to develop a diagnostic method using biomarkers that is non-invasive while both highly sensitive and specific.

Materials and Methods

Exhaled breath was collected from 17 patients with esophageal squamous cell carcinoma (ESCC), as well as 9 control subjects without history of any cancer. For each fasting subject, 1L of exhaled breath was collected in a gas sampling bag. Volatile organic compounds (VOCs) were then extracted from each sample using Solid phase micro-extraction (SPME) fibers and analyzed by gas chromatography-mass spectrometry (GC-MS).

Results

Levels of acetonitrile, acetic acid, acetone, and 2-butanone in exhaled breath were significantly higher in the patient group than in the control group (p = 0.0037, 0.0024, 0.0024 and 0.0037, respectively). ROC curves were drawn for these 4 VOCs, and the results for the area-under-the-curve (AUC) indicated that ESCC patients can be identified with a high probability of 0.93.

Conclusion

We found distinctive VOCs in exhaled breath of ESCC patients. These VOCs have a potential as new clinical biomarkers for ESCC. The measurement of VOCs in exhaled breath may be a useful, non-invasive method for diagnosis of ESCC.

Quantitative Analysis of Exhaled Breath Collected on Filter Substrates via Low-Temperature Plasma Desorption/Ionization Mass Spectrometry

Breath analysis has attracted increasing attention in recent years due to its great potential for disease diagnostics at early stages and for clinical drug monitoring. There are several recent examples of successful development of real-time, in vivo quantitative analysis of exhaled breath metabolites via mass spectrometry. On the other hand, current mass spectrometer accessibility limitations restrict point-of-care applications. Here now, an offline method is developed for quantitative analysis of exhaled breath collected on inexpensive filter substrates for direct desorption and ionization by using low-temperature plasma-mass spectrometry (LTP-MS). In particular, different operating conditions of the ionization source were systematically studied to optimize desorption/ionization by using glycerol, a low volatility compound. Applications with respect to propofol, γ-valprolactone, and nicotine analysis in exhaled breath are demonstrated in this study. The effects of several filter substrate properties, including filter material and pore size, on the analyte signal were characterized. Cellulose filter papers performed best with the present analytes. In addition, filters with smaller pores enabled a more efficient sample collection. Furthermore, sample-collection flow rate was determined to have a very significant effect, with slower flow rates yielding the best results. It was also found that filters loaded with sample can be successfully stored in glass vials with no observable sample loss even after 3 days. Limits of detection under optimized conditions are shown to be competitive or significantly better compared with relevant techniques and with additional benefits of cost-efficiency and sample storage capabilities.

Green Phosphorene as a Promising Biosensor for Detection of Furan and p-Xylene as Biomarkers of Disease: A DFT Study

In this work, Green Phosphorene (GP) monolayers are studied as an electronic sensing element for detecting prostate cancer biomarkers from human urine. The adsorption of furan, C₈H₁₀ (p-xylene), and H₂O on pristine GP and S- and Si-doped GP are investigated using the density functional theory (DFT) calculation. Furan and C₈H₁₀ molecules have been considered as important biomarkers of prostate cancer patients. First-principles DFT calculations are applied, and the results divulged that pristine GP could be a promising candidate for furan and C₈H₁₀ detection. It is manifested that furan and C₈H₁₀ are physisorbed on the S-, and Si-doped GP with small adsorption energy and negligible charge transfer. However, the calculations disclose that furan and C₈H₁₀ are chemically adsorbed on the pristine GP with adsorption energy of −0.73, and −1.46 eV, respectively. Moreover, we observe that a large charge is transferred from furan to the pristine GP with amount of −0.106 e. Additionally, pristine GP shows short recovery time of 1.81 s at room temperature under the visible light, which make it a reusable sensor device. Overall, our findings propose that the pristine GP sensor is a remarkable candidate for sensing of furan and other biomarkers of prostate cancer in the urine of patients.

Volatile Organic Compounds as Potential Biomarkers for Noninvasive Disease Detection by Nanosensors: A Comprehensive Review

Biomarkers are biological molecules associated with physiological changes of the body and aids in the detecting the onset of disease in patients. There is an urgent need for self-monitoring and early detection of cardiovascular and other health complications. Several blood-based biomarkers have been well established in diagnosis and monitoring the onset of diseases. However, the detection level of biomarkers in bed-side analysis is difficult and complications arise due to the endothelial dysfunction. Currently single volatile organic compounds (VOCs) based sensors are available for the detection of human diseases and no dedicated nanosensor is available for the elderly. Moreover, accuracy of the sensors based on a single analyte is limited. Hence, breath analysis has received enormous attention in healthcare due to its relatively inexpensive, rapid, and noninvasive methods for detecting diseases. This review gives a detailed analysis of how biomarker imprinted nanosensor can be used as a noninvasive method for detecting VOC to health issues early using exhaled breath analysis.

Volatile compounds in human breath: critical review and meta-analysis

Volatile compounds contained in human breath reflect the inner workings of the body. A large number of studies have been published that link individual components of breath to disease, but diagnostic applications remain limited, in part due to inconsistent and conflicting identification of breath biomarkers. New approaches are therefore required to identify effective biomarker targets. Here, volatile organic compounds have been identified in the literature from four metabolically and physiologically distinct diseases and grouped into chemical functional groups (e.g. - methylated hydrocarbons or aldehydes; based on known metabolic and enzymatic pathways) to support biomarker discovery and provide new insight on existing data. Using this functional grouping approach, principal component analysis doubled explanatory capacity from 19.1% to 38% relative to single individual compound approaches. Random forest and linear discriminant analysis reveal 93% classification accuracy for cancer. This review and meta-analysis provides insight for future research design by identifying volatile functional groups associated with disease. By incorporating our understanding of the complexities of the human body, along with accounting for variability in methodological and analytical approaches, this work demonstrates that a suite of targeted, functional volatile biomarkers, rather than individual biomarker compounds, will improve accuracy and success in diagnostic research and application.

The Role of Oxidative Stress in Sarcoidosis

Sarcoidosis is a rare, systemic inflammatory disease whose diagnosis and management can pose a challenge for clinicians and specialists. Scientific knowledge on the molecular pathways that drive its development is still lacking, with no standardized therapies available and insufficient strategies to predict patient outcome. In recent years, oxidative stress has been highlighted as an important factor in the pathogenesis of sarcoidosis, involving several enzymes and molecules in the mechanism of the disease. This review presents current data on the role of oxidative stress in sarcoidosis and its interaction with inflammation, as well as the application of antioxidative therapy in the disease.

Quantification of Volatile Acetone Oligomers Using Ion-Mobility Spectrometry

BACKGROUND

Volatile acetone is a potential biomarker that is elevated in various disease states. Measuring acetone in exhaled breath is complicated by the fact that the molecule might be present as both monomers and dimers, but in inconsistent ratios. Ignoring the molecular form leads to incorrect measured concentrations. Our first goal was to evaluate the monomer-dimer ratio in ambient air, critically ill patients, and rats. Our second goal was to confirm the accuracy of the combined (monomer and dimer) analysis by comparison to a reference calibration system.

METHODS

Volatile acetone intensities from exhaled air of ten intubated, critically ill patients, and ten ventilated Sprague-Dawley rats were recorded using ion-mobility spectrometry. Acetone concentrations in ambient air in an intensive care unit and in a laboratory were determined over 24 hours. The calibration reference was pure acetone vaporized by a gas generator at concentrations from 5 to 45 ppb_v (parts per billion by volume).

RESULTS

Acetone concentrations in ambient laboratory air were only slightly greater (5.6 ppb_v; 95% CI 5.1-6.2) than in ambient air in an intensive care unit (5.1 ppb_v; 95% CI 4.4-5.5; p < 0.001). Exhaled acetone concentrations were only slightly greater in rats (10.3 ppb_v; 95% CI 9.7-10.9) than in critically ill patients (9.5 ppb_v; 95% CI 7.9-11.1; p < 0.001). Vaporization yielded acetone monomers (1.3-5.3 mV) and dimers (1.4-621 mV). Acetone concentrations (ppb_v) and corresponding acetone monomer and dimer intensities (mV) revealed a high coefficient of determination (R ² = 0.96). The calibration curve for acetone concentration (ppb_v) and total acetone (monomers added to twice the dimers; mV) was described by the exponential growth 3-parameter model, with an R ² = 0.98.

CONCLUSION

The ratio of acetone monomer and dimer is inconsistent and varies in ambient air from place-to-place and across individual humans and rats. Monomers and dimers must therefore be considered when quantifying acetone. Combining the two accurately assesses total volatile acetone.

Medical diagnosis at the point-of-care by portable high-field asymmetric waveform ion mobility spectrometry: a systematic review and meta-analysis

Non-invasive medical diagnosis by analysing volatile organic compounds (VOCs) at the point-of-care is becoming feasible owing to recent advances in portable instrumentation. A number of studies have assessed the performance of a state-of-the-art VOC analyser (micro-chip high-field asymmetric waveform ion mobility spectrometry, FAIMS) for medical diagnosis. However, a comprehensive meta-analysis is needed to investigate the overall diagnostic performance of these novel methods across different medical conditions. An electronic search was performed using the CAplus and MEDLINE database through the SciFinder platform. The review identified a total of 23 studies and 2312 individuals. Eighteen studies were used for meta-analysis. A pooled analysis found an overall sensitivity of 80% (95% CI, 74%-85%,I2= 62%), and specificity of 78% (95% CI, 70%-84%,I2= 80%), which corresponds to the overall diagnostic performance of micro-chip FAIMS across many different medical conditions. The diagnostic accuracy was particularly high for coeliac and inflammatory bowel disease (sensitivity and specificity from 74% to 97%). The overall diagnostic performance was similar across breath, urine, and faecal matrices with sparse logistic regression and random forests algorithms resulting in higher diagnostic accuracy. Sources of variability likely arise from differences in sample storage, sampling protocol, method of data analysis, type of disease, sample matrix, and potentially to clinical and disease factors. The results of this meta-analysis indicate that micro-chip FAIMS is a promising candidate for disease screening at the point-of-care, particularly for gastroenterology diseases. This review provides recommendations that should improve the techniques relevant to diagnostic accuracy of future VOC and point-of-care studies.

Deciphering Exhaled Aerosol Fingerprints for Early Diagnosis and Personalized Therapeutics of Obstructive Respiratory Diseases in Small Airways

Respiratory diseases often show no apparent symptoms at their early stages and are usually diagnosed when permanent damages have been made to the lungs. A major site of lung pathogenesis is the small airways, which make it highly challenging to detect using current techniques due to the diseases’ location (inaccessibility to biopsy) and size (below normal CT/MRI resolution). In this review, we present a new method for lung disease detection and treatment in small airways based on exhaled aerosols, whose patterns are uniquely related to the health of the lungs. Proof-of-concept studies are first presented in idealized lung geometries. We subsequently describe the recent developments in feature extraction and classification of the exhaled aerosol images to establish the relationship between the images and the underlying airway remodeling. Different feature extraction algorithms (aerosol density, fractal dimension, principal mode analysis, and dynamic mode decomposition) and machine learning approaches (support vector machine, random forest, and convolutional neural network) are elaborated upon. Finally, future studies and frequent questions related to clinical applications of the proposed aerosol breath testing are discussed from the authors’ perspective. The proposed breath testing has clinical advantages over conventional approaches, such as easy-to-perform, non-invasive, providing real-time feedback, and is promising in detecting symptomless lung diseases at early stages.

Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.

Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles

Volatile organic compounds (VOCs) have been assessed in breath samples as possible indicators of diseases. The present study aimed to quantify 29 VOCs (previously reported as potential biomarkers of lung diseases) in breath samples collected from controls and individuals with lung cancer, chronic obstructive pulmonary disease and asthma. Besides that, global VOC profiles were investigated. A needle trap device (NTD) was used as pre-concentration technique, associated to gas chromatography-mass spectrometry (GC-MS) analysis. Univariate and multivariate approaches were applied to assess VOC distributions according to the studied diseases. Limits of quantitation ranged from 0.003 to 6.21 ppbv and calculated relative standard deviations did not exceed 10%. At least 15 of the quantified targets presented themselves as discriminating features. A random forest (RF) method was performed in order to classify enrolled conditions according to VOCs' latent patterns, considering VOCs responses in global profiles. The developed model was based on 12 discriminating features and provided overall balanced accuracy of 85.7%. Ultimately, multinomial logistic regression (MLR) analysis was conducted using the concentration of the nine most discriminative targets (2-propanol, 3-methylpentane, (E)-ocimene, limonene, m-cymene, benzonitrile, undecane, terpineol, phenol) as input and provided an average overall accuracy of 95.5% for multiclass prediction.

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.

Exhaled breath analysis in disease detection

Investigating the use of exhaled breath analysis to diagnose and monitor different diseases has attracted much interest in recent years. This review introduces conventionally used methods and some emerging technologies aimed at breath analysis and their relevance to lung disease, airway inflammation, gastrointestinal disorders, metabolic disorders and kidney diseases. One section correlates breath components and specific diseases, whereas the other discusses some unique ideas, strategies, and devices to analyze exhaled breath for the diagnosis of some common diseases. This review aims to briefly introduce the potential application of exhaled breath analysis for the diagnosis and screening of various diseases, thereby providing a new avenue for the detection of non-invasive diseases.

An eNose-based method performing drift correction for online VOC detection under dry and humid conditions

Our recently demonstrated innovative concept of electronic nose (eNose) based on a combination of gas sensors is further tested and benchmarked in the present study. The system is a test bed for gas sensors of different principal technologies distributed within three compartments, which share a compact, very low volume, temperature-controlled sensing chamber. Here, the eNose-based analyser contains three sensing arrays of commercially available semiconducting metal oxide (MOX) gas sensors: one compartment contains 8 analog MOX sensors, while the other two compartments comprise 10 digital MOX sensors. The presented instrument is explicitly tested for the discrimination between mid-range (3-18 ppm) concentrations of different volatile organic compounds (VOCs) including acetaldehyde, acetone, ethanol, ethyl acetate, isoprene and n-pentane under dry and humid conditions, which are all considered relevant gases in future breath diagnostic applications. Since the experiments were performed in periods of time separated by around 20 days, they are affected by drift. For this reason, we explore the opportunity of drift mitigation using methods based on component removal computed by linear discriminant analysis, partial least squares discriminant analysis and direct orthogonalization, which lend themselves to future in-field applications of the developed device and sensing methodology.

Recognizing lung cancer using a homemade e-nose: A comprehensive study

In recent years, breath analysis has been used as a tool for lung cancer detection and many gas sensors were developed for this purpose. Although they are fabricated with advanced materials, for now, gas sensors are still limited in their medical application due to their unfavorable performance. Here, we hypothesized that a combination of diverse types of sensors could aid in improving the detection performance. We fabricated an e-nose based on 10 gas sensors of 4 types and directly tested it using samples from 153 healthy participants and 115 lung cancer patients, without gas pre-concentration. Additionally, we studied and compared five feature extraction algorithms. The extracted features were then used in 2 optimized clustering algorithms and 3 supervised classification strategies, and their performance was investigated. As a result, "breath-prints" for all subjects were successfully obtained. The combined features extracted by LDA and Fast ICA formed the best feature space. Within this feature space, both clustering algorithms grouped all "breath-prints" into exactly 2 clusters with an Adjusted Rand Index greater than 0.95. Among the 3 supervised classification strategies, random forest with 3-fold cross validation showed the best performance with 86.42% of mean classification accuracy and 0.87 of AUC, which was somewhat better than many recently reported sensor arrays. It can be concluded that, the diversity of sensors may play a role in improving the performance of the e-nose though to what extent still requires evaluation.

Can oral health and oral-derived biospecimens predict progression of dementia?

Growing evidence indicates that oral health and brain health are interconnected. Declining cognition and dementia coincide with lack of self‐preservation, including oral hygiene. The oral microbiota plays an important role in maintaining oral health. Emerging evidence suggests a link between oral dysbiosis and cognitive decline in patients with Alzheimer's disease. This review showcases the recent advances connecting oral health and cognitive function during aging and the potential utility of oral‐derived biospecimens to inform on brain health. Collectively, experimental findings indicate that the connection between oral health and cognition cannot be underestimated; moreover, oral biospecimens are abundant and readily obtainable without invasive procedures, which may help inform on cognitive health.

Digging deeper into volatile organic compounds associated with cancer

Volatile organic compounds (VOCs), produced and emitted through the metabolism of cancer cells or the body's immune system, are considered novel cancer biomarkers for diagnostic purposes. Of late, a large number of work has been done to find a relationship between VOCs' signature of body and cancer. Cancer-related VOCs can be used to detect several types of cancers at the earlier stages which in turn provide a significantly higher chance of survival. Here we aim to provide an updated picture of cancer-related VOCs based on recent findings in this field focusing on cancer odor database.

An Innovative Modular eNose System Based on a Unique Combination of Analog and Digital Metal Oxide Sensors

An innovative concept for an electronic nose (eNose) system based on a unique combination of analog and digital sensors for online monitoring is presented. The developed system consists of small sensing arrays of commercially available semiconducting metal oxide (MOX) gas sensors in a compact, modular, low sample volume, temperature-controlled sensing chamber. The sensing chamber comprises three compartments, each of which may contain several analog and/or digital MOX sensors. Additional sensors within the digital compartment allow for pressure, humidity, and temperature measurements. The presented prototype eNose system comprises one compartment with 8 analog MOX sensors and two compartments with 10 digital sensors each and was explicitly tested here for the discrimination between midrange (3-18 ppm) concentrations of ethanol and acetone at dry and moderately humid conditions.

Exhaled breath condensate biomarkers for lung cancer

Lung cancer is the main cause of cancer incidence and mortality worldwide and the identification of clinically useful biomarkers for lung cancer detection at both early and metastatic stage is a pressing medical need. Although many improvements have been made in the treatment and in the early screening of this cancer, most diagnosis are made at a late stage, when a lot of genetic and epigenetic changes have occurred. A promising source of biomarkers reflective of the pathogenesis of lung cancer is exhaled breath condensate (EBC), a biological fluid and a natural matrix of the respiratory tract. Molecules such as DNAs, RNAs, proteins, metabolites and volatile compounds are present in EBC, and their presence/absence or their variation in concentrations can be used as biomarkers. The aims of this review are to briefly describe exhaled breath composition, firstly, and then to document some of the EBC candidate biomarkers for lung cancer by dividing them according to their origin (genome, transcriptome, epigenome, metabolome, proteome and microbiota) in order to demonstrate the potential use of EBC as a helpful tool in cancer diagnostics, molecular profiling, therapy monitoring and screening of high risk individuals.

Breath analysis of cancer in the present and the future

Most of the currently used diagnostics for cancerous diseases have yet to meet the standards of screening, as they are insufficiently accurate and/or invasive and risky. In this review, we describe the rationale, the progress made to date, and the potential of analysing the exhaled volatile organic compounds as a pathway for enabling early diagnosis of cancer and, therefore, for achieving better clinical prognosis and survival rates. The review highlights the major advancements made in this field, from fundamentals, up to translational phases and clinical trials, with a special emphasis on sensing platforms based on nanomaterials. The prospects for breath analysis in early cancerous disease are presented and discussed.

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.

Health and temperaments of cloned working dogs

Dogs serve human society in various ways by working at tasks that are based on their superior olfactory sensitivity. However, it has been reported that only about half of all trained dogs may qualify as working dogs through conventional breeding management because proper temperament and health are needed in addition to their innate scent detection ability. To overcome this low efficiency of breeding qualified working dogs, and to reduce the enormous costs of maintaining unqualified dogs, somatic cell nuclear transfer has been applied in the propagation of working dogs. Herein, we review the history of cloning working dogs and evaluate the health development, temperaments, and behavioral similarities among the cloned dogs. We also discuss concerns about dog cloning including those related to birth defects, lifespan, and cloning efficiency.

Multi-centre prospective study on diagnosing subtypes of lung cancer by exhaled-breath analysis

OBJECTIVES

Lung cancer is a leading cause of mortality. Exhaled-breath analysis of volatile organic compounds (VOC's) might detect lung cancer early in the course of the disease, which may improve outcomes. Subtyping lung cancers could be helpful in further clinical decisions.

MATERIALS AND METHODS

In a prospective, multi-centre study, using 10 electronic nose devices, 144 subjects diagnosed with NSCLC and 146 healthy subjects, including subjects considered negative for NSCLC after investigation, breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands). Also, analyses into subtypes of NSCLC, such as adenocarcinoma (AC) and squamous cell carcinoma (SCC), and analyses of patients with small cell lung cancer (SCLC) were performed.

RESULTS

Choosing a cut-off point to predominantly rule out cancer resulted for NSCLC in a sensitivity of 94.4%, a specificity of 32.9%, a positive predictive value of 58.1%, a negative predictive value (NPV) of 85.7%, and an area under the curve (AUC) of 0.76. For AC sensitivity, PPV, NPV, and AUC were 81.5%, 56.4%, 79.5%, and 0.74, respectively, while for SCC these numbers were 80.8%, 45.7%, 93.0%, and 0.77, respectively. SCLC could be ruled out with a sensitivity of 88.9% and an NPV of 96.8% with an AUC of 0.86.

CONCLUSION

Electronic nose technology with the Aeonose™ can play an important role in rapidly excluding lung cancer due to the high negative predictive value for various, but not all types of lung cancer. Patients showing positive breath tests should still be subjected to further diagnostic testing.

Next-Generation Novel Noninvasive Cancer Molecular Diagnostics Platforms Beyond Tissues

In recent years, there has been a revolutionary expansion in technologic advances and therapeutic innovations in cancer medicine. Cancer diagnostics has begun to move away from a sole dependence on direct tumor tissue biopsy for cancer detection, diagnosis, and treatment monitoring. The need for improvement in molecular cancer diagnostics has never been more important, with not only the advent of cancer genomics and genomics-guided precision medicine but also the recent arrival of cancer immunotherapies. Owing to the practical limitations and risks associated with tissue-based biopsy diagnostics, novel noninvasive cancer diagnostics platforms have continued to evolve and expand in recent years. Examples of these platforms include the liquid biopsy, which is used to interrogate ctDNA or circulating tumor cells, proteomics, metabolomics, and exosomes; the urine biopsy, which is used to assay ctDNAs; saliva and stool biopsies, which are used for molecular genomics assays; and the breath biopsy, which measures volatile organic compounds. These next-generation noninvasive molecular diagnostics assays beyond tissues fundamentally transform the potential utilities of cancer diagnostics to enable repeat, prospective, and serial longitudinal "biopsies" to monitor disease response resistance and progression on therapies. Moreover, they allow continual interrogation and molecular in-depth analysis of the evolving tumor's pan-canceromics under therapeutic stress. These technological and diagnostic advances have already brought about paradigm-changing next-generation cancer therapeutic strategies to enhance overall treatment efficacies. This article reviews the key noninvasive next-generation molecular diagnostics platforms beyond tissues, with emphasis on clinical utilities and applications.

A study on volatile organic compounds emitted by in-vitro lung cancer cultured cells using gas sensor array and SPME-GCMS

Background

Volatile organic compounds (VOCs) emitted from exhaled breath from human bodies have been proven to be a useful source of information for early lung cancer diagnosis. To date, there are still arguable information on the production and origin of significant VOCs of cancer cells. Thus, this study aims to conduct in-vitro experiments involving related cell lines to verify the capability of VOCs in providing information of the cells.

Method

The performances of e-nose technology with different statistical methods to determine the best classifier were conducted and discussed. The gas sensor study has been complemented using solid phase micro-extraction-gas chromatography mass spectrometry. For this purpose, the lung cancer cells (A549 and Calu-3) and control cell lines, breast cancer cell (MCF7) and non-cancerous lung cell (WI38VA13) were cultured in growth medium.

Results

This study successfully provided a list of possible volatile organic compounds that can be specific biomarkers for lung cancer, even at the 24th hour of cell growth. Also, the Linear Discriminant Analysis-based One versus All-Support Vector Machine classifier, is able to produce high performance in distinguishing lung cancer from breast cancer cells and normal lung cells.

Conclusion

The findings in this work conclude that the specific VOC released from the cancer cells can act as the odour signature and potentially to be used as non-invasive screening of lung cancer using gas array sensor devices.

Sensing Technologies for Detection of Acetone in Human Breath for Diabetes Diagnosis and Monitoring

The review describes the technologies used in the field of breath analysis to diagnose and monitor diabetes mellitus. Currently the diagnosis and monitoring of blood glucose and ketone bodies that are used in clinical studies involve the use of blood tests. This method entails pricking fingers for a drop of blood and placing a drop on a sensitive area of a strip which is pre-inserted into an electronic reading instrument. Furthermore, it is painful, invasive and expensive, and can be unsafe if proper handling is not undertaken. Human breath analysis offers a non-invasive and rapid method for detecting various volatile organic compounds thatare indicators for different diseases. In patients with diabetes mellitus, the body produces excess amounts of ketones such as acetoacetate, beta-hydroxybutyrate and acetone. Acetone is exhaled during respiration. The production of acetone is a result of the body metabolising fats instead of glucose to produce energy. There are various techniques that are used to analyse exhaled breath including Gas Chromatography Mass Spectrometry (GC-MS), Proton Transfer Reaction Mass Spectrometry (PTR-MS), Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS), laser photoacoustic spectrometry and so on. All these techniques are not portable, therefore this review places emphasis on how nanotechnology, through semiconductor sensing nanomaterials, has the potential to help individuals living with diabetes mellitus monitor their disease with cheap and portable devices.