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

Prospects, advances and biological applications of MOF-based platform for the treatment of lung cancer

Nowadays in our society, lung cancer is exhibiting a high mortality rate and threat to human health. Conventional diagnostic techniques used in the field of lung cancer often necessitate the use of extensive instrumentation, exhibit a tendency for false positives, and are not suitable for widespread early screening purposes. Conventional approaches to treat lung cancer primarily involve surgery, chemotherapy, and radiotherapy. However, these broad-spectrum treatments suffer from drawbacks such as imprecise targeting and significant side effects, which restrict their widespread use. Metal-organic frameworks (MOFs) have attracted significant attention in the diagnosis and treatment of lung cancer owing to their tunable electronic properties and structures and potential applications. These porous nanomaterials are formed through the intricate assembly of metal centers and organic ligands, resulting in highly versatile frameworks. Compared to traditional diagnostic and therapeutic modalities, MOFs can improve the sensitivity of lung cancer biomarker detection in the diagnosis of lung cancer. In terms of treatment, they can significantly reduce side effects and improve therapeutic efficacy. Hence, this perspective provides an overview concerning the advancements made in the field of MOFs as potent biosensors for lung cancer biomarkers. It also delves into the latest research dealing with the use of MOFs as carriers for drug delivery. Additionally, it explores the applications of MOFs in various therapeutic approaches, including chemodynamic therapy, photodynamic therapy, photothermal therapy, and immunotherapy. Furthermore, this review comprehensively analyses potential applications of MOFs as biosensors in the field of lung cancer diagnosis and combines different therapeutic approaches aiming for enhanced therapeutic efficacy. It also presents a concise overview of the existing obstacles, aiming to pave the way for future advancements in lung cancer diagnosis and treatment.

Potentiality of phosphide-based nanotubes for breast cancer detection: A DFT investigation

Breast cancer is one of the most basilisk cancers for women due to its high mortality rate which can be prevented drastically with early-stage detection. In this work, the adsorption mechanism of two volatile organic compounds that are present in the breath of breast cancer patients, 2-Methyloctane and 3, 3-Dimethylpentane, has been investigated on aluminum phosphide nanotubes (AlPNT) and gallium phosphide nanotubes (GaPNT) in order to understand their feasibility as sensor materials to diagnosis breast cancer at early stage. We have used the quantum mechanical approach by employing density functional theory using B3LYP-D3 hybrid potential for noncovalent interaction along with the LanL2DZ basis in the Gaussian 09 software package. The adsorption properties analyses suggest that GaPNT exhibits better sensing behavior as well as proclaims 12.6% greater adsorption energy for 2-Methyloctane and 9.4% greater adsorption energy for 3, 3-Dimethylpentane than AlPNT. Other structural and electric properties analyses satisfy this conclusion and suggest that GaPNT exhibits higher stability than AlPNT and could possibly be a potential candidate for developing biosensors to detect breast cancer at the preliminary stages.

Research on the kinetics and degradation pathways of gaseous acetic acid ester organics

ABSTRACTDesigned to meet the specific needs of the printing industry exhaust gas emissions, this paper proposes a method for the degradation of gaseous acetic acid ester organics that is environmentally friendly, safe, and simple to use: micro-nano cavitation technology. In the process of using micro-nano cavitation technology to degrade acetic acid ester organics, the products in the degradation process were analyzed by gas chromatography-mass (GC-MS) spectrometry, and the degradation pathways of acetic acid ester organics were identified. Under high temperatures and high pressure caused by cavitation collapse, the C-C bond and C-O bond on the main chain of organic matter are cleaved to form low molecular products. Low-molecular intermediate products are continuously produced as the reaction advances, and these intermediate products are further oxidized and decomposed into carbon dioxide and water. Besides, the factors that influence the degradation rate of acetic acid ester organics were investigated. Based on the experimental data, acetic acid esters can degrade with the greatest efficiency when their initial concentration is 200 ± 50 mg/m3 and their treatment time is 20∼30 min. Moreover, the experiment was optimized using the response surface method. The results suggested that for an initial concentration of 155.544 mg/m3 and a reaction time of 21.961 min, the best degradation rate was 0.251 min-1. Micro-nano cavitation technology is a novel and promising technology for the degradation of volatile organic compounds, with a wide range of practical applications.

The Potential of Fecal Volatile Organic Compound Analysis for the Early Diagnosis of Late-Onset Sepsis in Preterm Infants: A Narrative Review

Early diagnosis and treatment of late-onset sepsis (LOS) is crucial for survival, but challenging. Intestinal microbiota and metabolome alterations precede the clinical onset of LOS, and the preterm gut is considered an important source of bacterial pathogens. Fecal volatile organic compounds (VOCs), formed by physiologic and pathophysiologic metabolic processes in the preterm gut, reflect a complex interplay between the human host, the environment, and microbiota. Disease-associated fecal VOCs can be detected with an array of devices with various potential for the development of a point-of-care test (POCT) for preclinical LOS detection. While characteristic VOCs for common LOS pathogens have been described, their VOC profiles often overlap with other pathogens due to similarities in metabolic pathways, hampering the construction of species-specific profiles. Clinical studies have, however, successfully discriminated LOS patients from healthy individuals using fecal VOC analysis with the highest predictive value for Gram-negative pathogens. This review discusses the current advancements in the development of a non-invasive fecal VOC-based POCT for early diagnosis of LOS, which may potentially provide opportunities for early intervention and targeted treatment and could improve clinical neonatal outcomes. Identification of confounding variables impacting VOC synthesis, selection of an optimal detection device, and development of standardized sampling protocols will allow for the development of a novel POCT in the near future.

The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection, which detects the patient's exhaled VOCs, combined with sensor arrays of convolutional neural network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, the low accuracy, high-complexity computation and large number of parameters make the CNN algorithms difficult to transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models, such as EfficientNet, LTNet has fewer parameters (32 K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than the scores of the six existing models, and it also offers the shortest training (844.38 s and 584.67 s) and inference times (23 s and 14 s) in the same validation sets. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.

Near-Room-Temperature Detection of Aromatic Compounds with Inkjet-Printed Plasticized Polymer Composites

Chemiresistive gas sensors composed of a thermoplastic polymer matrix and conductive fillers offer various advantages for detecting volatile organic compounds (VOCs), including low power consumption due to near-room-temperature operation, high sensitivity, and inherent selectivity toward VOCs. However, such sensors have a slow response time as the polymer matrix often has a glass transition temperature (Tg) higher than the sensor operating temperature slowing the analyte diffusion to/from the polymer. A plasticizer lowers polymer Tg to match the sensor operation temperature, reducing its response time. In this study, the effect of a plasticizer diethylene glycol dibenzoate (DEGDB) on the sensing properties of polystyrene (PS)-carbon black (CB) composite is investigated to obtain sensors with a fast response time and high sensitivity to VOCs. The sensors are fabricated via drop-on-demand inkjet printing, providing a high degree of control over the sensory film morphology and reproducibility. A design-of-experiment (DoE) approach is adopted to find the optimum ink and print parameters with a minimum number of experiments. As a result, sensors with 30 times faster response time and 25 times higher effective sensitivity are obtained while operating near room temperature (27 °C). Furthermore, the sensors show high sensitivity toward aromatic hydrocarbons (toluene, benzene, and ethylbenzene), with a sub-10 ppm limit of detection (LoD) and a negligible sensitivity toward humidity. Our results show the potential of PS-DEGDB-CB composite as a selective and cost-effective sensory material compatible with large-scale manufacturing techniques for selective near-room-temperature detection of toxic VOCs.

Identification of breath volatile organic compounds to distinguish pancreatic adenocarcinoma, pancreatic cystic neoplasm, and patients without pancreatic lesions

BACKGROUND

Volatile organic compounds (VOCs) are a promising potential biomarker that may be able to identify the presence of cancers.

AIM

To identify exhaled breath VOCs that distinguish pancreatic ductal adenocarcinoma (PDAC) from intraductal papillary mucinous neoplasm (IPMN) and healthy volunteers.

METHODS

We collected exhaled breath from histologically proven PDAC patients, radiological diagnosis IPMN, and healthy volunteers using the ReCIVA® device between 10/2021-11/2022. VOCs were identified by thermal desorption-gas chromatography/field-asymmetric ion mobility spectrometry and compared between groups.

RESULTS

A total of 156 participants (44% male, mean age 62.6 ± 10.6) were enrolled (54 PDAC, 42 IPMN, and 60 controls). Among the nine VOCs identified, two VOCs that showed differences between groups were dimethyl sulfide [0.73 vs 0.74 vs 0.94 arbitrary units (AU), respectively; P = 0.008] and acetone dimers (3.95 vs 4.49 vs 5.19 AU, respectively; P < 0.001). After adjusting for the imbalance parameters, PDAC showed higher dimethyl sulfide levels than the control and IPMN groups, with adjusted odds ratio (aOR) of 6.98 (95%CI: 1.15-42.17) and 4.56 (1.03-20.20), respectively (P < 0.05 both). Acetone dimer levels were also higher in PDAC compared to controls and IPMN (aOR: 5.12 (1.80-14.57) and aOR: 3.35 (1.47-7.63), respectively (P < 0.05 both). Acetone dimer, but not dimethyl sulfide, performed better than CA19-9 in PDAC diagnosis (AUROC 0.910 vs 0.796). The AUROC of acetone dimer increased to 0.936 when combined with CA19-9, which was better than CA19-9 alone (P < 0.05).

CONCLUSION

Dimethyl sulfide and acetone dimer are VOCs that potentially distinguish PDAC from IPMN and healthy participants. Additional prospective studies are required to validate these findings.

MXene-Based Elastomer Mimetic Stretchable Sensors: Design, Properties, and Applications

Flexible sensors based on MXene-polymer composites are highly prospective for next-generation wearable electronics used in human-machine interfaces. One of the motivating factors behind the progress of flexible sensors is the steady arrival of new conductive materials. MXenes, a new family of 2D nanomaterials, have been drawing attention since the last decade due to their high electronic conductivity, processability, mechanical robustness and chemical tunability. In this review, we encompass the fabrication of MXene-based polymeric nanocomposites, their structure-property relationship, and applications in the flexible sensor domain. Moreover, our discussion is not only limited to sensor design, their mechanism, and various modes of sensing platform, but also their future perspective and market throughout the world. With our article, we intend to fortify the bond between flexible matrices and MXenes thus promoting the swift advancement of flexible MXene-sensors for wearable technologies.

Exploiting volatile fingerprints for bladder cancer diagnosis: A scoping review of metabolomics and sensor-based approaches

Bladder cancer (BC) represents a significant global health concern, for which early detection is essential to improve patient outcomes. This review evaluates the potential of the urinary volatile organic compounds (VOCs) as biomarkers for detecting and staging BC. The methods used include gas chromatography-mass spectrometry (GC-MS)-based metabolomics and electronic-nose (e-nose) sensors. The GC-MS studies that have been published reveal diverse results in terms of diagnostic performance. The sensitivities range from 27 % to an impressive 97 %, while specificities vary between 43 % and 94 %. Furthermore, the accuracies reported in these studies range from 80 to 89 %. In the urine of BC patients, a total of 80 VOCs were discovered to be significantly altered when compared to controls. These VOCs encompassed a variety of chemical classes such as alcohols, aldehydes, alkanes, aromatic compounds, fatty acids, ketones, and terpenoids, among others. Conversely, e-nose-based studies displayed sensitivities from 60 to 100 %, specificities from 53 to 96 %, and accuracies from 65 to 97 %. Interestingly, conductive polymer-based sensors performed better, followed by metal oxide semiconductor and optical sensors. GC-MS studies have shown improved performance in detecting early stages and low-grade tumors, providing valuable insights into staging. Based on these findings, VOC-based diagnostic tools hold great promise for early BC detection and staging. Further studies are needed to validate biomarkers and their classification performance. In the future, advancements in VOC profiling technologies may significantly contribute to improving the overall survival and quality of life for BC patients.

Synergistic Integration of Machine Learning with Microstructure/Composition-Designed SnO2 and WO3 Breath Sensors

A high-performance semiconductor metal oxide gas sensing strategy is proposed for efficient sensor-based disease prediction by integrating a machine learning methodology with complementary sensor arrays composed of SnO2- and WO3-based sensors. The six sensors, including SnO2- and WO3-based sensors and neural network algorithms, were used to measure gas mixtures. The six constituent sensors were subjected to acetone and hydrogen environments to monitor the effect of diet and/or irritable bowel syndrome (IBS) under the interference of ethanol. The SnO2- and WO3-based sensors suffer from poor discrimination ability if sensors (a single sensor or multiple sensors) within the same group (SnO2- or WO3-based) are separately applied, even when deep learning is applied to enhance the sensing operation. However, hybrid integration is proven to be effective in discerning acetone from hydrogen even in a two-sensor configuration through the synergistic contribution of supervised learning, i.e., neural network approaches involving deep neural networks (DNNs) and convolutional neural networks (CNNs). DNN-based numeric data and CNN-based image data can be exploited for discriminating acetone and hydrogen, with the aim of predicting the status of an exercise-driven diet and IBS. The ramifications of the proposed hybrid sensor combinations and machine learning for the high-performance breath sensor domain are discussed.

Pattern Recognition with Temperature Regulation: A Single YSZ-Based Mixed Potential Sensor Classifies Multiple Mixtures of Isoprene, n-Propanol, and Acetone

Gas sensors integrated with machine learning algorithms have aroused keen interest in pattern recognition, which ameliorates the drawback of poor selectivity on a sensor. Among various kinds of gas sensors, the yttria-stabilized zirconia (YSZ)-based mixed potential-type sensor possesses advantages of low cost, simple structure, high sensitivity, and superior stability. However, as the number of sensors increases, the increased power consumption and more complicated integration technology may impede their extensive application. Herein, we focus on the development of a single YSZ-based mixed potential sensor from sensing material to machine learning for effective detection and discrimination of unary, binary, and ternary gas mixtures. The sensor that is sensitive to isoprene, n-propanol, and acetone is manufactured with the MgSb2O6 sensing electrode prepared by a simple sol-gel method. Unique response patterns for specific gas mixtures could be generated with temperature regulation. We chose seven algorithm models to be separately trained for discrimination. In order to realize more accurate discrimination, we further discuss the selection of suitable feature parameters and its reasons. With temperature regulation coefficients which are easily available as feature input to model, a single sensor is verified to achieve elevated accuracy rates of 95 and 99% for the discrimination of seven gases (three unary gases, three binary gas mixtures, and one ternary gas mixture) and redefined six gas mixtures. This article provides a potential new approach via a mixed potential sensor instead of a sensor array that could provide a wide application prospect in the field of electronic nose and artificial olfaction.

The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis

Colorectal cancer (CRC) is the third most common malignancy and the second most common cause of cancer-related deaths worldwide. While CRC screening is already part of organized programs in many countries, there remains a need for improved screening tools. In recent years, a potential approach for cancer diagnosis has emerged via the analysis of volatile organic compounds (VOCs) using sensor technologies. The main goal of this study was to demonstrate and evaluate the diagnostic potential of a table-top breath analyzer for detecting CRC. Breath sampling was conducted and CRC vs. non-cancer groups (105 patients with CRC, 186 non-cancer subjects) were included in analysis. The obtained data were analyzed using supervised machine learning methods (i.e., Random Forest, C4.5, Artificial Neural Network, and Naïve Bayes). Superior accuracy was achieved using Random Forest and Evolutionary Search for Features (79.3%, sensitivity 53.3%, specificity 93.0%, AUC ROC 0.734), and Artificial Neural Networks and Greedy Search for Features (78.2%, sensitivity 43.3%, specificity 96.5%, AUC ROC 0.735). Our results confirm the potential of the developed breath analyzer as a promising tool for identifying and categorizing CRC within a point-of-care clinical context. The combination of MOX sensors provided promising results in distinguishing healthy vs. diseased breath samples. Its capacity for rapid, non-invasive, and targeted CRC detection suggests encouraging prospects for future clinical screening applications.

Oxalic acid-modified activated carbons under hydrothermal condition for the adsorption of the 2-butanone

Growing volatile organic compound (VOC) emission will cause air pollution and further threaten human health. Activated carbon is widely applied to treatment of VOCs in virtue of lower cost and excellent adsorption ability. In this work, the adsorption capacity of polarity VOCs on activated carbon is improved by oxalic acid (H2C2O4) hydrothermal modification. After 2 M H2C2O4 modification, the adsorption capacity of 2-butanone rose from 312.60 to 345.98 mg/g, and the time reaching saturation adsorption became shorter. BET results showed that both the specific surface area and total pore volume of 2 M H2C2O4-modified activated carbon increased by 3.32% and 3.9%, respectively. Both FTIR and XPS characterization confirmed variation of the surface oxygen-containing functional groups (SOFGs), while quantitative analysis via Boehm titration showed the significant increase of total acidity (61.36%), particularly the carboxyl content increased by 96.28%. The results indicated modification process can not only change the pore structure but also the SOFGs of activated carbons. The dynamic adsorption curves conform to the Bangham kinetics model, indicating that the adsorption of 2-butanone on both activated carbon is controlled by the diffusion in the pore channel. The adsorption data was also modeled by the internal particle diffusion model, and the internal diffusion adsorption stage is the rate-controlling step. The stability before and after adsorption and the cycling performance were studied.

Alteration of the Exhaled Volatile Organic Compound Pattern in Colorectal Cancer Patients after Intentional Curative Surgery-A Prospective Pilot Study

As current follow-up modalities for colorectal carcinoma (CRC) have restricted sensitivity, novel diagnostic tools are needed. The presence of CRC changes the endogenous metabolism, resulting in the release of a specific volatile organic compounds (VOC) pattern that can be detected with an electronic nose or AeonoseTM. To evaluate the use of an electronic nose in the follow-up of CRC, we studied the effect of curative surgery on the VOC pattern recognition using AeonoseTM. A prospective cohort study was performed, in which 47 patients diagnosed with CRC were included, all of whom underwent curative surgical resection. Breath testing was performed before and after surgery using the AeonoseTM. A machine learning model was developed by discerning between the 94 pre-and postoperative breath samples. The training model differentiated between the pre-and postoperative CRC breath samples with a sensitivity and specificity of 0.78 (95%CI 0.61-0.90) and 0.73 (95%CI 0.56-0.86), respectively, with an accuracy of 0.76 (95%CI 0.66-0.85), and an area under the curve of 0.79 (95%CI 0.68-0.89). The internal validation of the test set resulted in an accuracy of 0.75 (95%CI 0.51-0.91) and AUC of 0.82 (95%CI 0.61-1). In conclusion, our results suggest that the VOC pattern of CRC patients is altered by curative surgery in a short period, indicating that the exhaled VOCs might be closely related to the presence of CRC. However, to use AeonoseTM as a potential diagnostic tool in the clinical follow-up of CRC patients, the performance of the models needs to be improved through further large-scale prospective research.

Does the last 20 years paradigm of clinical research using volatile organic compounds to non-invasively diagnose cancer need to change? Challenges and future direction

PURPOSE

Volatile organic compounds (VOCs) have shown great potential as novel biomarkers for cancer detection; however, comprehensive quantitative analysis is lacking. In this study, we performed a bibliometric analysis of non-invasive cancer diagnosis using VOCs to better characterise international trends and to predict future hotspots in this field, and then we focussed on human studies to analyse clinical characteristics for presenting the current controversies and future perspectives of further clinical work.

METHODS

Publications, from 2002 to 2022, were retrieved from the Web of Science Core Collection database. CiteSpace and VOSviewer were used to generate network maps and identify the annual publications, top countries, authors, institutions, journals, references, and keywords. Then, we further screened clinical trials, and the key information was extracted into Microsoft Excel for further systematical analysis.

RESULTS

Six hundred and forty-one articles were identified to evaluate research trends, of which 301 clinical trials were selected for further systematical analysis. Overall, the annual publications in this area increased, with an overall upward trend, while the quality of clinical research remains remarkably uneven.

CONCLUSION

The study of non-invasive cancer diagnosis using VOCs would continue to be an active field. However, without stringent clinical design criteria, most suitable acquisition and analysis devices and statistical approaches, a list of exclusive, specific, reliable and reproducible VOCs to identify a disease and these VOCs appearing in a breath at detectable levels at early stage disease, the clinical utility of VOC tests will be difficult to have any breakthroughs.

Breath Volatile Organic Compounds in Surveillance of Gastric Cancer Patients following Radical Surgical Management

As of today, there is a lack of a perfect non-invasive test for the surveillance of patients for potential relapse following curative treatment. Breath volatile organic compounds (VOCs) have been demonstrated to be an accurate diagnostic tool for gastric cancer (GC) detection; here, we aimed to prove the yield of the markers in surveillance, i.e., following curative surgical management. Patients were sampled in regular intervals before and within 3 years following curative surgery for GC; gas chromatography-mass spectrometry (GC-MS) and nanosensor technologies were used for the VOC assessment. GC-MS measurements revealed a single VOC (14b-Pregnane) that significantly decreased at 12 months, and three VOCs (Isochiapin B, Dotriacontane, Threitol, 2-O-octyl-) that decreased at 18 months following surgery. The nanomaterial-based sensors S9 and S14 revealed changes in the breath VOC content 9 months after surgery. Our study results confirm the cancer origin of the particular VOCs, as well as suggest the value of breath VOC testing for cancer patient surveillance, either during the treatment phase or thereafter, for potential relapse.

The accuracy of an electronic nose to diagnose tuberculosis in patients referred to an expert centre

INTRODUCTION

An electronic nose (eNose) device has shown a high specificity and sensitivity to diagnose or rule out tuberculosis (TB) in the past. The aim of this study was to evaluate its performance in patients referred to INERAM.

METHODS

Patients aged ≥15 years were included. A history, physical examination, chest radiography (CRX) and microbiological evaluation of a sputum sample were performed in all participants, as well as a 5-minute breath test with the eNose. TB diagnosis was preferably established by the gold standard and compared to the eNose predictions. Univariate and multivariate logistic regression analyses were performed to assess potential risk factors for erroneous classification results by the eNose.

RESULTS

107 participants with signs and symptoms of TB were enrolled of which 91 (85.0%) were diagnosed with TB. The blind eNose predictions resulted in an accuracy of 50%; a sensitivity of 52.3% (CI 95%: 39.6-64.7%) and a specificity of 36.4% (CI 95%: 12.4-68.4%). Risk factors for erroneous classifications by the eNose were older age (multivariate analysis: OR 1.55, 95% CI 1.10-2.18, p = 0.012) and antibiotic use (multivariate analysis: OR 3.19, 95% CI 1.06-9.66, p = 0.040).

CONCLUSION

In this study, the accuracy of the eNose to diagnose TB in a tertiary referral hospital was only 50%. The use of antibiotics and older age represent important factors negatively influencing the diagnostic accuracy of the eNose. Therefore, its use should probably be restricted to screening in high-risk communities in less complex healthcare settings.

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.

Non-invasive screening of breast cancer from fingertip smears-a proof of concept study

Breast cancer is a global health issue affecting 2.3 million women per year, causing death in over 600,000. Mammography (and biopsy) is the gold standard for screening and diagnosis. Whilst effective, this test exposes individuals to radiation, has limitations to its sensitivity and specificity and may cause moderate to severe discomfort. Some women may also find this test culturally unacceptable. This proof-of-concept study, combining bottom-up proteomics with Matrix Assisted Laser Desorption Ionisation Mass Spectrometry (MALDI MS) detection, explores the potential for a non-invasive technique for the early detection of breast cancer from fingertip smears. A cohort of 15 women with either benign breast disease (n = 5), early breast cancer (n = 5) or metastatic breast cancer (n = 5) were recruited from a single UK breast unit. Fingertips smears were taken from each patient and from each of the ten digits, either at the time of diagnosis or, for metastatic patients, during active treatment. A number of statistical analyses and machine learning approaches were investigated and applied to the resulting mass spectral dataset. The highest performing predictive method, a 3-class Multilayer Perceptron neural network, yielded an accuracy score of 97.8% when categorising unseen MALDI MS spectra as either the benign, early or metastatic cancer classes. These findings support the need for further research into the use of sweat deposits (in the form of fingertip smears or fingerprints) for non-invasive screening of breast cancer.

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.

Recent Progress on Flexible Room-Temperature Gas Sensors Based on Metal Oxide Semiconductor

With the rapid development of the Internet of Things, there is a great demand for portable gas sensors. Metal oxide semiconductors (MOS) are one of the most traditional and well-studied gas sensing materials and have been widely used to prepare various commercial gas sensors. However, it is limited by high operating temperature. The current research works are directed towards fabricating high-performance flexible room-temperature (FRT) gas sensors, which are effective in simplifying the structure of MOS-based sensors, reducing power consumption, and expanding the application of portable devices. This article presents the recent research progress of MOS-based FRT gas sensors in terms of sensing mechanism, performance, flexibility characteristics, and applications. This review comprehensively summarizes and discusses five types of MOS-based FRT gas sensors, including pristine MOS, noble metal nanoparticles modified MOS, organic polymers modified MOS, carbon-based materials (carbon nanotubes and graphene derivatives) modified MOS, and two-dimensional transition metal dichalcogenides materials modified MOS. The effect of light-illuminated to improve gas sensing performance is further discussed. Furthermore, the applications and future perspectives of FRT gas sensors are also discussed.

THz Gas Sensing Using Terahertz Time-Domain Spectroscopy with Ceramic Architecture

Ceramic architectures based on chemical vapor deposition (CVD) are used to create unique crystal structures, morphologies, and properties. This study proposed room-temperature THz gas sensing using terahertz time-domain spectroscopy (THz-TDS) with ceramic architectures. We synthesized ceramic films on porous glass. Zinc oxide films were created using atmospheric CVD and amorphous carbon nitride films using the dissociative excitation reaction of BrCN with metastable Ar atoms. The transmission method was used in THz-TDS. A stainless hand-made gas cell with a Si window was applied for THz gas sensing. We defined "phase delay" equals VOC sensing response amount of sensing materials at each duration. Ppm-order THz gas sensing was performed.

Analysis of volatile organic compounds from deep airway in the lung through intubation sampling

Exhaled volatile organic compounds (VOCs) have been widely applied for the study of disease biomarkers. Oral exhalation and nasal exhalation are two of the most common sampling methods. However, VOCs released from food residues and bacteria in the mouth or upper respiratory tract were also sampled and usually mistaken as that produced from body metabolism. In this study, exhalation from deep airway was first directly collected through intubation sampling and analyzed. The exhalation samples of 35 subjects were collected through a catheter, which was inserted into the trachea or bronchus through the mouth and upper respiratory tract. Then, the VOCs in these samples were detected by proton transfer reaction mass spectrometry (PTR-MS). In addition, fast gas chromatography proton transfer reaction mass spectrometry (FGC-PTR-MS) was used to further determine the VOCs with the same mass-to-charge ratios. The results showed that there was methanol, acetonitrile, ethanol, methyl mercaptan, acetone, isoprene, and phenol in the deep airway. Compared with that in oral exhalation, ethanol, methyl mercaptan, and phenol had lower concentrations. In detail, the median concentrations of ethanol, methyl mercaptan, and phenol were 7.3, 0.6, and 23.9 ppbv, while those in the oral exhalation were 80.0, 5.1, and 71.3 ppbv, respectively, which meant the three VOCs mainly originated from the food residues and bacteria in the mouth or upper respiratory tract, rather than body metabolism. The research results in our study can provide references for expiratory VOC research based on oral and nasal exhalation samplings, which are more feasible in clinical practice.

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

IMPORTANCE

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

OBJECTIVE

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

DATA SOURCES

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

STUDY SELECTION

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

DATA EXTRACTION AND SYNTHESIS

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

MAIN OUTCOMES AND MEASURES

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

RESULTS

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

CONCLUSIONS AND RELEVANCE

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

Single-Atom Tailoring of Two-Dimensional Atomic Crystals Enables Highly Efficient Detection and Pattern Recognition of Chemical Vapors

Low-dimensional semiconductor materials, such as single-walled carbon nanotubes, two-dimensional (2D) atomic crystals, and organic frameworks, have been widely adapted as ideal platforms to construct various chemo/biosensors with satisfying sensitivity. However, the general drawbacks in chemiresistive devices, including high operation temperatures, low response to low-polarity molecules, and poor selectivity, have limited their real-world applications. In this study, 2D materials (graphene, MoS₂, and WSe₂) were systematically functionalized with series of monodispersed single atomic sites (Pt, Co, and Ru) through a facile approach to construct single-atom sensors (SASs) for the detection of VOCs at room temperature. The structural and catalytic characteristics of SAs successfully translated into enhanced gas-sensing performance, with a 1-2 orders of magnitude increase in relative response to ethanol (@5 ppm) and acetone (@20 ppm) vapors (in all M-2D SASs as compared to pristine substrates), high selectivity to VOCs against relative humidity (M-WSe₂ SASs), and fast response/recovery time (11/58 s for Pt-Graphene and 22/48 s for Pt-MoS₂ to 50 ppm ethanol, 9/57 s for Pt-Graphene and 15/75 s for Pt-MoS₂ to 200 ppm acetone) that are several times faster than the pristine 2D materials. Density functional theory (DFT) calculations revealed the signaling mechanism in SASs, and the data were further trained to build machine learning (ML) models for predicting the adsorption energies and sensing performance using the features of adsorption heights, metal charge, and charge transfer between the adsorbed VOCs and SASs sites. Finally, the rich combination of the metal single atoms and 2D atomic crystal supports were converted to cross-sensitive SA sensor array that allows for detection and identification of different VOCs.

Proof of concept for the use of trained sniffer dogs to detect osteosarcoma

Sarcomas are mesenchymal cancers which often show an aggressive behavior and patient survival largely depends on an early detection. In last years, much attention has been given to the fact that cancer patients release specific odorous volatile organic compounds (VOCs) that can be efficiently detected by properly trained sniffer dogs. Here, we have evaluated for the first time the ability of sniffer dogs (n = 2) to detect osteosarcoma cell cultures and patient samples. One of the two dogs was successfully trained to discriminate osteosarcoma patient-derived primary cells from mesenchymal stem/stromal cells (MSCs) obtained from healthy individuals. After the training phase, the dog was able to detect osteosarcoma specific odor cues in a different panel of 6 osteosarcoma cell lines with sensitivity and specificity rates between 95 and 100%. Moreover, the same VOCs were also detected by the sniffer dog in saliva samples from osteosarcoma patients (n = 2) and discriminated from samples from healthy individuals with a similar efficacy. Altogether, these results indicate that there are common odor profiles shared by cultures of osteosarcoma cells and body fluid samples from patients and provide a first proof of concept about the potential of canine odor detection as a non-invasive screening method to detect osteosarcomas.

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.

Effect of household air pollutants on the composition of exhaled breath characterized by solid-phase microextraction and needle-trap devices

Exposure to household air pollutants is becoming a serious environmental health risk. Various methods can be applied to assess humans' exposure status to indoor pollutants, with breath monitoring being among the best options. Breath sampling is fast and non-invasive, and contains compounds that can be used as markers for evaluating exposure length and estimating internal concentrations of pollutants. However, the distribution of compounds between gas and droplets in breath samples represents one of the key challenges associated with this analytical method. In this work, a needle-trap device (NTD) was prepared by packing the needle with a porous filter, divinyl benzene, and Carboxen to enable the exhaustive capture of both droplet-bound and gaseous components. Furthermore, fiber-based solid-phase microextraction (SPME) was also applied to extract compounds from only the gas phase to distinguish this portion of analytes from the total concentration in the sample. Dynamic, real-time breath sampling was enabled via a new sampling tube equipped with 2 one-way valves, which was specially designed for this work. Both methods provided satisfactory reproducibility, repeatability, and sensitivity, with detection limits as low as 0.05 ng mL^-1. To investigate the real-world applicability of the proposed devices, breath samples were obtained from volunteers who had been exposed to candle and incense smoke and aerosol sprays, or had smoked cannabis. The results revealed the high concentration of organic air pollutants in inhaled air (maximum of 215 ng mL^-1) and exhaled breath (maximum of 14.4 ng mL^-1) and a correlation between the components in inhaled air and exhaled breath. Significantly, the findings further revealed that the developed NTD has enhanced breath-sample determinations, especially for polar compounds, which tend to remain trapped in breath droplets.

Breathing new life into clinical testing and diagnostics: perspectives on volatile biomarkers from breath

Human breath offers several benefits for diagnostic applications, including simple, noninvasive collection. Breath is a rich source of clinically-relevant biological information; this includes a volatile fraction, where greater than 1,000 volatile organic compounds (VOCs) have been described so far, and breath aerosols that carry nucleic acids, proteins, signaling molecules, and pathogens. Many of these factors, especially VOCs, are delivered to the lung by the systemic circulation, and diffusion of candidate biomarkers from blood into breath allows systematic profiling of organismal health. Biomarkers on breath offer the capability to advance early detection and precision medicine in areas of global clinical need. Breath tests are noninvasive and can be performed at home or in a primary care setting, which makes them well-suited for the kind of public screening program that could dramatically improve the early detection of conditions such as lung cancer. Since measurements of VOCs on breath largely report on metabolic changes, this too aids in the early detection of a broader range of illnesses and can be used to detect metabolic shifts that could be targeted through precision medicine. Furthermore, the ability to perform frequent sampling has envisioned applications in monitoring treatment responses. Breath has been investigated in respiratory, liver, gut, and neurological diseases and in contexts as diverse as infectious diseases and cancer. Preclinical research studies using breath have been ongoing for some time, yet only a few breath-based diagnostics tests are currently available and in widespread clinical use. Most recently, tests assessing the gut microbiome using hydrogen and methane on breath, in addition to tests using urea to detect Helicobacter pylori infections have been released, yet there are many more applications of breath tests still to be realized. Here, we discuss the strengths of breath as a clinical sampling matrix and the technical challenges to be addressed in developing it for clinical use. Historically, a lack of standardized methodologies has delayed the discovery and validation of biomarker candidates, resulting in a proliferation of early-stage pilot studies. We will explore how advancements in breath collection and analysis are in the process of driving renewed progress in the field, particularly in the context of gastrointestinal and chronic liver disease. Finally, we will provide a forward-looking outlook for developing the next generation of clinically relevant breath tests and how they may emerge into clinical practice.

Paving the Way for a Green Transition in the Design of Sensors and Biosensors for the Detection of Volatile Organic Compounds (VOCs)

The efficient and selective detection of volatile organic compounds (VOCs) provides key information for various purposes ranging from the toxicological analysis of indoor/outdoor environments to the diagnosis of diseases or to the investigation of biological processes. In the last decade, different sensors and biosensors providing reliable, rapid, and economic responses in the detection of VOCs have been successfully conceived and applied in numerous practical cases; however, the global necessity of a sustainable development, has driven the design of devices for the detection of VOCs to greener methods. In this review, the most recent and innovative VOC sensors and biosensors with sustainable features are presented. The sensors are grouped into three of the main industrial sectors of daily life, including environmental analysis, highly important for toxicity issues, food packaging tools, especially aimed at avoiding the spoilage of meat and fish, and the diagnosis of diseases, crucial for the early detection of relevant pathological conditions such as cancer and diabetes. The research outcomes presented in the review underly the necessity of preparing sensors with higher efficiency, lower detection limits, improved selectivity, and enhanced sustainable characteristics to fully address the sustainable manufacturing of VOC sensors and biosensors.

A headspace collection chamber for whole body volatilomics

The human body secretes a complex blend of volatile organic compounds (VOCs) via the skin, breath and bodily fluids. In this study, we have developed a headspace collection chamber for whole body volatilome profiling.

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

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

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

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

Prospects and Challenges of MXenes as Emerging Sensing Materials for Flexible and Wearable Breath-Based Biomarker Diagnosis

A fully integrated, flexible, and functional sensing device for exhaled breath analysis drastically transforms conventional medical diagnosis to non-invasive, low-cost, real-time, and personalized health care. 2D materials based on MXenes offer multiple advantages for accurately detecting various breath biomarkers compared to conventional semiconducting oxides. High surface sensitivity, large surface-to-weight ratio, room temperature detection, and easy-to-assemble structures are vital parameters for such sensing devices in which MXenes have demonstrated all these properties both experimentally and theoretically. So far, MXenes-based flexible sensor is successfully fabricated at a lab-scale and is predicted to be translated into clinical practice within the next few years. This review presents a potential application of MXenes as emerging materials for flexible and wearable sensor devices. The biomarkers from exhaled breath are described first, with emphasis on metabolic processes and diseases indicated by abnormal biomarkers. Then, biomarkers sensing performances provided by MXenes families and the enhancement strategies are discussed. The method of fabrications toward MXenes integration into various flexible substrates is summarized. Finally, the fundamental challenges and prospects, including portable integration with Internet-of-Thing (IoT) and Artificial Intelligence (AI), are addressed to realize marketization.

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

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

Early screening and diagnosis strategies of pancreatic cancer: a comprehensive review

Pancreatic cancer is a highly malignant digestive system tumor with a poor prognosis. Most pancreatic cancer patients are diagnosed at an advanced stage or even metastasis due to its highly aggressive characteristics and lack of typical early symptoms. Thus, an early diagnosis of pancreatic cancer is crucial for improving its prognosis. Currently, screening is often applied in high‐risk individuals to achieve the early diagnosis of pancreatic cancer. Fully understanding the risk factors of pancreatic cancer and pathogenesis could help us identify the high‐risk population and achieve early diagnosis and timely treatment of pancreatic cancer. Notably, accumulating studies have been undertaken to improve the detection rate of different imaging methods and the diagnostic accuracy of endoscopic ultrasound‐guided fine‐needle aspiration (EUS‐FNA) which is the golden standard for pancreatic cancer diagnosis. In addition, there are currently no biomarkers with sufficient sensitivity and specificity for the diagnosis of pancreatic cancer to be applied in the clinic. As the only serum biomarker approved by the United States Food and Drug Administration, carbohydrate antigen 19‐9 (CA19‐9) is not recommended to be used in the early screening of pancreatic cancer because of its limited specificity. Recently, increasing numbers of studies focused on the discovering of novel serum biomarkers and exploring their combination with CA19‐9 in the detection of pancreatic cancer. Besides, the application of liquid biopsy involving circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), microRNAs (miRNAs), and exosomes in blood and biomarkers in urine, and saliva in pancreatic cancer diagnosis are drawing more and more attention. Furthermore, many innovative technologies such as artificial intelligence, computer‐aided diagnosis system, metabolomics technology, ion mobility spectrometry (IMS) associated technologies, and novel nanomaterials have been tested for the early diagnosis of pancreatic cancer and have shown promising prospects. Hence, this review aims to summarize the recent progress in the development of early screening and diagnostic methods, including imaging, pathological examination, serological examination, liquid biopsy, as well as other potential diagnostic strategies for pancreatic cancer.

Biohybrid sensor for odor detection

Biohybrid odorant sensors that directly integrate a biological olfactory system have been increasingly studied and are suggested to be the next generation of ultrasensitive sensors by taking advantage of the sensitivity and selectivity of living organisms. In this review, we provide a detailed description of the recent developments of biohybrid odorant sensors, especially considering the requisites for their perspective of on-site applications. We introduce the methodologies to effectively capture the biological signals from olfactory systems by readout devices, and describe the essential properties regarding the gaseous detection, stability, quality control, and portability. Moreover, we address the recent progress on multiple odorant recognition using multiple sensors as well as the current screening approaches for pairs of orphan receptors and ligands necessary for the extension of the currently available range of biohybrid sensors. Finally, we discuss our perspectives for the future for the development of practical odorant sensors.

Point-of-Care Test Paper for Exhaled Breath Aldehyde Analysis via Mass Spectrometry

Volatile organic compounds (VOCs) from exhaled breath (EB) are considered to be promising biomarkers for lung diseases. A convenient and sensitive point-of-care (POC) testing method for EB VOCs is essential. Here, we developed a POC test paper for the analysis of EB aldehydes, which are potential biomarkers for lung cancer. A probe molecule, 4-aminothiophenol (4-ATP), was anchored on a paper substrate to specifically capture gas-phase aldehydes through the Schiff base reaction. Meanwhile, thin-film reaction acceleration was utilized to increase capture efficiency. By directly coupling the test paper to a mass spectrometer through paper spray, high sensitivity (0.1 ppt) and a wide quantification linear range (from 10 ppt to 1 ppm) were obtained. Analysis of EB from lung cancer patients with the test paper showed a significant increase in several reported aldehyde markers compared to EB from healthy volunteers, indicating the potential of this method for sensitive, low-cost, and convenient lung cancer screening and diagnosis.

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

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

Olfactory Detection of Toluene by Detection Rats for Potential Screening of Lung Cancer

Early detection is critical to successfully eradicating a variety of cancers, so the development of a new cancer primary screening system is essential. Herein, we report an animal nose sensor system for the potential primary screening of lung cancer. To establish this, we developed an odor discrimination training device based on operant conditioning paradigms for detection of toluene, an odor indicator component of lung cancer. The rats (N = 15) were trained to jump onto a floating ledge in response to toluene-spiked breath samples. Twelve rats among 15 trained rats reached performance criterion in 12 consecutive successful tests within a given set, or over 12 sets, with a success rate of over 90%. Through a total of 1934 tests, the trained rats (N = 3) showed excellent performance for toluene detection with 82% accuracy, 83% sensitivity, 81% specificity, 80% positive predictive value (PPV) and 83% negative predictive value (NPV). The animals also acquired considerable performance for odor discrimination even in rigorous tests, validating odor specificity. Since environmental and long-term stability are important factors that can influence the sensing results, the performance of the trained rats was studied under specified temperature (20, 25, and 30 °C) and humidity (30%, 45%, and 60% RH) conditions, and monitored over a period of 45 days. At given conditions of temperature and humidity, the animal sensors showed an average accuracy within a deviation range of ±10%, indicating the excellent environmental stability of the detection rats. Surprisingly, the trained rats did not differ in retention of last odor discrimination when tested 45 days after training, denoting that the rats’ memory for trained odor is still available over a long period of time. When taken together, these results indicate that our odor discrimination training system can be useful for non-invasive breath testing and potential primary screening of lung cancer.

Towards Novel Non-Invasive Colorectal Cancer Screening Methods: A Comprehensive Review

Colorectal cancer (CRC) is one of the leading cancer-related causes of death in the world. Since the 70s, many countries have adopted different CRC screening programs, which has resulted in a decrease in mortality. However, current screening test options still present downsides. The commercialized stool-based tests present high false-positive rates and low sensitivity, which negatively affects the detection of early stage carcinogenesis. The gold standard colonoscopy has low uptake due to its invasiveness and the perception of discomfort and embarrassment that the procedure may bring. In this review, we collected and described the latest data about alternative CRC screening techniques that can overcome these disadvantages. Web of Science and PubMed were employed as search engines for studies reporting on CRC screening tests and future perspectives. The searches generated 555 articles, of which 93 titles were selected. Finally, a total of 50 studies, describing 14 different CRC alternative tests, were included. Among the investigated techniques, the main feature that could have an impact on CRC screening perception and uptake was the ease of sample collection. Urine, exhaled breath, and blood-based tests promise to achieve good diagnostic performance (sensitivity of 63-100%, 90-95%, and 47-97%, respectively) while minimizing stress and discomfort for the patient.

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.

Induced volatolomics of pathologies

Volatolomics allows us to elucidate cell metabolic processes in real time. In particular, a volatile organic compound (VOC) excreted from our bodies may be specific for a certain disease, such that measuring this VOC may afford a simple, fast, accessible and safe diagnostic approach. Yet, finding the optimal endogenous volatile marker specific to a pathology is non-trivial because of interlaboratory disparities in sample preparation and analysis, as well as high interindividual variability. These limit the sensitivity and specificity of volatolomics and its applications in biological and clinical fields but have motivated the development of induced volatolomics. This approach aims to overcome issues by measuring VOCs that result not from an endogenous metabolite but, rather, from the pathogen-specific or metabolic-specific enzymatic metabolism of an exogenous biological or chemical probe. In this Review, we introduce volatile-compound-based probes and discuss how they can be exploited to detect and discriminate pathogenic infections, to assess organ function and to diagnose and monitor cancers in real time. We focus on cases in which labelled probes have informed us about metabolic processes and consider the potential and drawbacks of the probes for clinical trials. Beyond diagnostics, VOC-based probes may also be effective tools to explore biological processes more generally.

Utility of volatile organic compounds as a diagnostic tool in preterm infants

BACKGROUND

Volatile organic compounds (VOCs) are hydrocarbons that originate within different healthy and diseased tissues. VOCs can be secreted into the circulation and then excreted in the urine and faeces. In the lungs, VOCs are locally produced and can be detected in exhaled breath. VOCs can be identified using non-invasive techniques, which make their use in preterm infants safe and desirable.

METHODS

A systematic search of the literature in PubMed, Embase and Web of Science was conducted looking for VOCs techniques and diagnostic performance in preterm infants. A total of 50 articles identified with only seven papers were included in the final analysis in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

RESULTS

VOCs could diagnose necrotising enterocolitis up to 4 days before a clinical diagnosis; for late onset sepsis, up to 3 days before; and for bronchopulmonary dysplasia, up to 2 weeks before. In addition to these diagnostic uses, VOCs analysis could also distinguish breastfed from formula-fed preterm neonates in the first month of life.

CONCLUSION

VOCs analysis is a non-invasive tool that makes the use in preterm infants of preference. VOCs analytic techniques require more research and consensus between researchers to overcome their limitations.

IMPACT

Volatile organic compounds are hydrocarbons that can separate between healthy and diseased states in preterm infants. Biomarker panels developed from volatile organic compounds are potential diagnostic tools. The non-invasive nature of acquiring volatile organic compounds markers make it desirable in the paediatric patients. Research into exact chemical components of the volatile organic compounds can inform about the pathophysiology of disease in preterm infants. More robust longitudinal studies with repeated experiments are required before volatile organic compounds can be applied in clinical practice.

Early diagnosis of breast cancer from exhaled breath by gas chromatography-mass spectrometry (GC/MS) analysis: A prospective cohort study

BACKGROUND

It has proved that there is an association between cancer and volatile organic compounds (VOCs) of exhaled breath. This study targets on verifying the existence of specific VOCs in breathing in breast cancer patients, especially those with ductal carcinoma in situ (DCIS).

METHODS

There were a total of 203 participants included in the final analysis, which included 71 (35.0%) patients with histologically confirmed breast cancer (including 13 with DCIS, 31 with lymph node metastasis-negative status, and 27 with lymph node metastasis-positive status), 78 (38.4%) healthy volunteers, and 54 (26.6%) patients with histologically confirmed gastric cancer. Gas chromatography-mass spectrometry and solid-phase microextraction were used to analyze the breath samples for the presence of VOCs.

RESULTS

There were significant differences in the volatile organic metabolites between the DCIS, lymph node metastasis-negative breast cancer, and lymph node metastasis-positive breast cancer groups compared with the healthy controls as well as between the breast cancer and gastric cancer patients. An overlapping set of seven VOCs, including (S)-1,2-propanediol, cyclopentanone, ethylene carbonate, 3-methoxy-1,2-propanediol, 3-methylpyridine, phenol, and tetramethylsilane, was significantly different between the breast cancer patients and healthy individuals as well as between the breast cancer and gastric cancer patients. The combination of these seven compounds was considered as a biomarker for breast cancer. The sensitivity for predicting DCIS by this set of seven compounds was determined to be 80.77%, and the specificity was determined to be 100%.

CONCLUSIONS

This set of seven breast cancer-specific VOCs can be regarded as one particular expiratory marker for DCIS and will help to establish new screening methods for early breast cancer.

Human Breathomics Database

Breathomics is a special branch of metabolomics that quantifies volatile organic compounds (VOCs) from collected exhaled breath samples. Understanding how breath molecules are related to diseases, mechanisms and pathways identified from experimental analytical measurements is challenging due to the lack of an organized resource describing breath molecules, related references and biomedical information embedded in the literature. To provide breath VOCs, related references and biomedical information, we aim to organize a database composed of manually curated information and automatically extracted biomedical information. First, VOCs-related disease information was manually organized from 207 literature linked to 99 VOCs and known Medical Subject Headings (MeSH) terms. Then an automated text mining algorithm was used to extract biomedical information from this literature. In the end, the manually curated information and auto-extracted biomedical information was combined to form a breath molecule database—the Human Breathomics Database (HBDB). We first manually curated and organized disease information including MeSH term from 207 literatures associated with 99 VOCs. Then, an automatic pipeline of text mining approach was used to collect 2766 literatures and extract biomedical information from breath researches. We combined curated information with automatically extracted biomedical information to assemble a breath molecule database, the HBDB. The HBDB is a database that includes references, VOCs and diseases associated with human breathomics. Most of these VOCs were detected in human breath samples or exhaled breath condensate samples. So far, the database contains a total of 913 VOCs in relation to human exhaled breath researches reported in 2766 publications. The HBDB is the most comprehensive HBDB of VOCs in human exhaled breath to date. It is a useful and organized resource for researchers and clinicians to identify and further investigate potential biomarkers from the breath of patients.

Database URL: https://hbdb.cmdm.tw