Assessment, origin, and implementation of breath volatile cancer markers

Air Toxicity Surveillance across Thirteen Cities Using Rats

Current monitoring methods fail to assess air toxicity in real time, which is yet badly desired to better estimate the health impact. Here, we developed and deployed an automated, low-cost, and time-resolved system for noninvasive monitoring of air toxicity by detecting eight breath-borne biomarkers from rats, including VOCs, CO2, CO, NO, H2S, H2O2, O2, and NH3. Using this system, two large-scale monitoring campaigns were conducted across 13 cities in China during the 2023 winter and 2024 spring continuously on a 24-h-a-day basis. In characterizing overall air pollution health impact, a novel Air Toxic Index (ATI) was developed using the eight breath-borne biomarkers from the rats. Significant differences in diurnal patterns of ATI were observed across 13 cities. Among others, time, city, PM2.5, and O3 were identified as the primary influencing factors of ATI, exhibiting complex nonlinear relationships in real-world environments. The unique variation patterns of breath-borne NO and H2O2 from rats indicated the time-resolved impacts of ground human activities on weekends and PM components on air toxicity. Histopathologic changes in these deployed rats' lungs further validated the differential health effects of real-world air pollution from different cities as detected by the rat system. Here, we pioneer a new air pollution health effect monitoring system that realizes the in vivo detection of air toxicity in contrast to the traditional protocol of air sampling, exposure, and offline toxicity analysis steps. The system can be deployed easily to any location with minimal support for real-time monitoring of air pollution health impacts.

High-Sensitivity Room-Temperature Detection of H2S Using ZnO/Ti3C2TX Nanocomposite: Potential Applications in Exhaled Gas Monitoring

The detection of hydrogen sulfide (H2S) in exhaled breath at room temperature is essential for health monitoring and disease diagnosis. This study investigates a ZnO/Ti3C2TX nanocomposite synthesized by combining ZnO nanoparticles, prepared by a hydrothermal method, with Ti3C2TX MXene. Experimental results demonstrate that the ZnO/Ti3C2TX nanocomposite sensors exhibit excellent H2S detection performance at room temperature with high sensitivity, rapid response time, and robust recovery capability. Specifically, the ZnO/Ti3C2TX-1.0 wt % nanocomposite sensor shows a response of 85.116 at 5 ppm of H2S, which is 14-fold and 35-fold greater than that of pure ZnO and Ti3C2TX MXene, respectively. The sensor exhibits a rapid response (50 s) and recovery time (115 s) at 100 ppb of H2S. Additionally, it shows exceptional sensitivity to H2S at low concentrations, with a detection limit as low as 1 ppb. Based on the excellent sensing performance, oral exhaled gas detection demonstrates that the sensor effectively differentiates H2S levels in healthy and patient breath samples. These findings highlight the potential of the ZnO/Ti3C2TX nanocomposite in exhaled H2S detection at room temperature, offering new insights for developing ultrasensitive biogas sensors.

Breathomics: A Non-Invasive Approach for the Diagnosis of Breast Cancer

Breast cancer is the most commonly diagnosed cancer worldwide, underscoring the critical need for effective early detection methods to reduce mortality. Traditional detection techniques, such as mammography, present significant limitations, particularly in women with dense breast tissue, highlighting the need for alternative screening approaches. Breathomics, based on the analysis of Volatile Organic Compounds (VOCs) present in exhaled breath, offers a non-invasive, potentially transformative diagnostic tool. These VOCs are metabolic byproducts from various organs of the human body whose presence and varying concentrations in breath are reflective of different health conditions. This review explores the potential of breathomics, highlighting its promise as a rapid, cost-effective screening approach for breast cancer, facilitated through the integration of portable solutions like electronic noses (e-noses). Key considerations for clinical translation-including patient selection, environmental confounders, and different breath collection methods-will be examined in terms of how each of them affects the breath profile. However, there are also challenges such as patient variability in VOC signatures, and the need for standardization in breath sampling protocols. Future research should prioritize standardizing sampling and analytical procedures and validating their clinical utility through large-scale clinical trials.

Lung cancer detection by electronic nose analysis of exhaled breath: a multi-center prospective external validation study

BACKGROUND

Electronic nose (eNose) analysis of exhaled breath shows potential for accurate and timely lung cancer diagnosis, yet prospective external validation studies are lacking. Our study primarily aimed to prospectively and externally validate a published eNose model for lung cancer detection in COPD patients and assess its diagnostic performance alongside a new eNose model, specifically tailored to the target population, in a more general outpatient population.

PATIENTS AND METHODS

This multi-center prospective external validation study included adults with clinical and/or radiological suspicion of lung cancer who were recruited from thoracic oncology outpatient clinics of two sites in The Netherlands. Breath profiles were collected using a cloud-connected eNose (SpiroNose®). The diagnostic performance of the original and new eNose model was assessed in various population subsets based on ROC-AUC, specificity, positive predictive value (PPV), and negative predictive value (NPV), targeting 95% sensitivity. For the new eNose model, a training and validation cohort were used.

RESULTS

Between March 2019 and November 2023, 364 participants were included. The original eNose model detected lung cancer with a ROC-AUC of 0.92 (95% CI: 0.85-0.99) in COPD patients (n=98/116; 84%) and 0.80 (95% CI: 0.75-0.85) in all participants (n=216/364; 59%). At 95% sensitivity, the specificity, PPV, and NPV, were 72% and 51%, 95% and 74%, and 72% and 88%, respectively. In the validation cohort, the new eNose model identified lung cancer across all participants (n=72/121; 60%) with a ROC-AUC of 0.83 (95% CI: 0.75-0.91), 94% sensitivity, 63% specificity, PPV of 79%, and NPV of 89%. Notably, accurate detection was consistent across tumour characteristics, disease stage, diagnostic centers, and clinical characteristics.

CONCLUSION

This multi-center prospective external validation study confirms that eNose analysis of exhaled breath enables accurate lung cancer detection at thoracic oncology outpatient clinics, irrespective of tumour characteristics, disease stage, diagnostic center, and clinical characteristics.

Recent developments and applications of selected ion flow tube mass spectrometry (SIFT-MS)

Selected ion flow tube mass spectrometry (SIFT-MS) is now recognized as the most versatile analytical technique for the identification and quantification of trace gases down to the parts-per-trillion by volume, pptv, range. This statement is supported by the wide reach of its applications, from real-time analysis, obviating sample collection of very humid exhaled breath, to its adoption in industrial scenarios for air quality monitoring. This review touches on the recent extensions to the underpinning ion chemistry kinetics library and the alternative challenge of using nitrogen carrier gas instead of helium. The addition of reagent anions in the Voice200 series of SIFT-MS instruments has enhanced the analytical capability, thus allowing analyses of volatile trace compounds in humid air that cannot be analyzed using reagent cations alone, as clarified by outlining the anion chemistry involved. Case studies are reviewed of breath analysis and bacterial culture volatile organic compound (VOC), emissions, environmental applications such as air, water, and soil analysis, workplace safety such as transport container fumigants, airborne contamination in semiconductor fabrication, food flavor and spoilage, drugs contamination and VOC emissions from packaging to demonstrate the stated qualities and uniqueness of the new generation SIFT-MS instrumentation. Finally, some advancements that can be made to improve the analytical capability and reach of SIFT-MS are mentioned.

Development of Electronic Nose as a Complementary Screening Tool for Breath Testing in Colorectal Cancer

(1) Background: Colorectal cancer is one of the leading causes of cancer-related death, while early detection decreases incidence and mortality. Current screening programs involving fecal immunological testing and colonoscopy commonly bring about unnecessary colonoscopies, which adds burden to healthcare systems. The objective of this study was to provide an assessment of the diagnostic performance of an electronic nose serving as a complementary screening tool to improve current screening programs in clinical settings. (2) Methods: We conducted a case-control study that included patients from a medical center with colorectal cancer and non-colorectal cancer controls. We analyzed the composition of volatile organic compounds in their exhaled breath using the electronic nose. We then used machine learning algorithms to develop predictive models and provided the estimated accuracy and reliability of the breath testing. (3) Results: We enrolled 77 patients, with 40 cases and 37 controls. The area under the curve, Kappa coefficient, sensitivity, and specificity of the selected model were 0.87 (95% CI 0.76-0.95), 0.66 (95% CI 0.49-0.83), 0.81, and 0.85. For subjects at an early stage of disease, the sensitivity and specificity were 0.90 and 0.85. Excluding smokers, the sensitivity and specificity were 0.88 and 0.92. (4) Conclusions: This study highlights the promising potential of breath testing using an electronic nose for enabling early detection and reducing unnecessary treatments. However, more independent data for external validation are required to ensure applicability and generalizability.

Clinical applications of volatilomic assays

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

Microfluidic biosensors for biomarker detection in body fluids: a key approach for early cancer diagnosis

Early detection of cancer significantly improves patient outcomes, with biomarkers offering a promising avenue for earlier and more precise diagnoses. Microfluidic biosensors have emerged as a powerful tool for detecting these biomarkers in body fluids, providing enhanced sensitivity, specificity, and rapid analysis. This review focuses on recent advances in microfluidic biosensors from 2018 to 2024, detailing their operational principles, fabrication techniques, and integration with nanotechnology for cancer biomarker detection. Additionally, we have reviewed recent innovations in several aspects of microfluidic biosensors, such as novel detection technologies, nanomaterials and novel microfluidic chip structures, which significantly enhance detection capabilities. We highlight key biomarkers pertinent to early cancer detection and explore how these innovations in biosensor technology contribute to the evolving landscape of personalized medicine. We further explore how these technologies could be incorporated into clinical cancer diagnostic workflows to improve early detection and treatment outcomes. These innovations could help enable more precise and personalized cancer diagnostics. In addition, this review addresses several important issues such as enhancing the scalability and sensitivity of these biosensors in clinical settings and points out future possibilities of combining artificial intelligence diagnostics with microfluidic biosensors to optimize their practical applications. This overview aims to guide future research and clinical applications by addressing current challenges and identifying opportunities for further development in the field of biomarker research.

Graphene-Based Fiber Materials for Gas Sensing Applications: State of the Art Review

The importance of gas sensors is apparent as the detection of gases and pollutants is crucial for environmental monitoring and human safety. Gas sensing devices also hold the potential for medical applications as health monitoring and disease diagnostic tools. Gas sensors fabricated from graphene-based fibers present a promising advancement in the field of sensing technology due to their enhanced sensitivity and selectivity. The diverse chemical and mechanical properties of graphene-based fibers-such as high surface area, flexibility, and structural stability-establish them as ideal gas-sensing materials. Most significantly, graphene fibers can be readily tuned to detect a wide range of gases, making them highly versatile in gas-sensing technologies. This review focuses on graphene-based composite fibers for gas sensors, with an emphasis on the preparation processes used to achieve these fibers and the gas sensing mechanisms involved in their sensors. Graphene fiber gas sensors are presented based on the chemical composition of their target gases, with detailed discussions on their sensitivity and performance. This review reveals that graphene-based fibers can be prepared through various methods and can be effectively integrated into gas-sensing devices for a diverse range of applications. By presenting an overview of developments in this field over the past decade, this review highlights the potential of graphene-based fiber sensors and their prospective integration into future technologies.

Volatile organic compounds in exhaled breath, blood, and urine detected in patients with thyroid carcinoma using gas chromatography-ion mobility spectrometry-a pilot study

The differentiation between malignant and benign thyroid nodules represents a significant challenge for clinicians globally. The identification of volatile organic compounds (VOCs) has emerged as a novel approach in the field of cancer diagnosis. This prospective pilot study aims to identify VOCs in exhaled breath, blood, and urine that can differentiate benign from malignant thyroid nodules using gas chromatography-ion mobility spectrometry (GC-IMS). Patients with thyroid nodules scheduled for surgery were enrolled at the Maastricht University Medical Center (MUMC+). Breath samples were analyzed using a BreathSpec GC-IMS machine (G.A.S. Dortmund, Germany), specifically designed for breath analysis. All blood and urine samples were analyzed using a separate GC-IMS device, the FlavourSpec® (G.A.S., Dortmund, Germany). In this proof-of-concept study, 70 consecutive patients undergoing thyroid surgery at MUMC+ were included. Of these patients, 29 were confirmed to have thyroid cancer after surgical resection. The overall analysis did not reveal statistically significant differences in VOCs in breath, urine and blood, between patients with benign and malignant thyroid cancer. This proof-of-concept study demonstrated that GC-IMS could not effectively differentiate between the VOC profiles of malignant and benign thyroid nodules. However, due to the small sample size of this study, larger prospective studies are needed to investigate the potential of using VOCs to distinguish between benign and malignant thyroid nodules. Additionally, future research should focus on identifying potential confounding factors that may influence patient VOC profiles. (NCT04883294).

Inconsistencies in predictive models based on exhaled volatile organic compounds for distinguishing between benign pulmonary nodules and lung cancer: a systematic review

BACKGROUND

There is a general rise in incidentally found pulmonary nodules (PNs) requiring follow-up due to increased CT use. Biopsy and repeated CT scan are the most useful methods for distinguishing between benign PNs and lung cancer, while they are either invasive or involves radiation exposure. Therefore, there has been increasing interest in the analysis of exhaled volatile organic compounds (VOCs) to distinguish between benign PNs and lung cancer because it's cheap, noninvasive, efficient, and easy-to-use. However, the exact value of breath analysis in this regard remains unclear.

METHODS

A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-oriented systematic search was performed to include studies that established exhaled VOC-based predictive models to distinguish between benign PNs and lung cancer and reported the exact VOCs used. Data regarding study characteristics, performance of the models, which predictors were incorporated, and methodologies for breath collection and analysis were independently extracted by two researchers. The exhaled VOCs incorporated into the predictive models were narratively synthesized, and those compounds that were reported in > 2 studies and reportedly exhibited consistent associations with lung cancer were considered key breath biomarkers. A quality assessment was independently performed by two researchers using both the Newcastle-Ottawa Scale (NOS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST).

RESULTS

A total of 11 articles reporting on 46 VOC-based predictive models were included. The majority relied solely on exhaled VOCs (n = 44), while two incorporated VOCs, demographical factors, and radiological signs. The variation in the sensitivity, specificity, and AUC indicators of the models that incorporated multiple factors was lower compared with those of the models that relied solely on exhaled VOCs. A total of 84 VOCs were incorporated. Of these, 2-butanone, 3-hydroxy-2-butanone, and 2-hydroxyacetaldehyde were identified as key predictors that had significantly higher concentrations in the exhaled breath samples of patients with lung cancer. Substantial heterogeneity was observed in terms of the modeling and validation methods used, as well as the approaches to breath collection and analysis. Many of the reports were missing certain key pieces of clinical and methodological information.

CONCLUSIONS

Although exhaled VOC-based models for predicting cancer risk might be a conceivable role as monitoring tools for PNs risk, there has been little overall change in the accuracy of these tests over time, and their role in routine clinical practice has not yet been established.

CLINICAL TRIAL NUMBER

PROSPERO registration number CRD42023381458.

Plasmonic Particle Integration into Near-Infrared Photodetectors and Photoactivated Gas Sensors: Toward Sustainable Next-Generation Ubiquitous Sensing

Current challenges in environmental science, medicine, food chemistry as well as the emerging use of artificial intelligence for solving problems in these fields require distributed, local sensing. Such ubiquitous sensing requires components with 1) high sensitivity, 2) power efficiency, 3) miniaturizability, and 4) the ability to directly interface with electronic circuitry, i.e., electronic readout of sensing signals. Over the recent years, several nanoparticle-based approaches have found their way into this field and have demonstrated high performance. However, challenges remain, such as the toxicity of many of today's narrow bandgap semiconductors for NIR detection and the high energy consumption as well as low selectivity of state-of-the-art commercialized gas sensors. With their unique light-matter interaction and ink-based fabrication schemes, plasmonic nanostructures provide potential technological solutions to these challenges, leading also to better environmental performance. In this perspective recent approaches of using plasmonic nanoparticles are discussed for the fabrication of NIR photodetectors and light-activated, energy-efficient gas sensing devices. In addition, new strategies implying computational approaches are pointed out for miniaturizable spectrometers, exploiting the wide spectral tunability of plasmonic nanocomposites, and for selective gas sensors, utilizing dynamic light activation. The benefits of colloidal approaches for device fabrication are discussed with regard to technological advantages and environmental aspects, which are barely considered so far.

Fulfilling the Promise of Breathomics: Considerations for the Discovery and Validation of Exhaled Volatile Biomarkers

The exhaled breath represents an ideal matrix for noninvasive biomarker discovery, and exhaled metabolomics have the potential to be clinically useful in the era of precision medicine. In this concise translational review, we specifically address volatile organic compounds in the breath, with a view toward fulfilling the promise of these as actionable biomarkers, in particular, for lung diseases. We review the literature paying attention to seminal work linked to key milestones in breath research; discuss potential applications for breath biomarkers across disease areas and healthcare systems, including the perspectives of industry; and outline critical aspects of study design that will need to be considered for any pivotal research going forward if breath analysis is to provide robust validated biomarkers that meet the requirements for future clinical implementation.

Nanoplasmonic biosensors for environmental sustainability and human health

Monitoring the health conditions of the environment and humans is essential for ensuring human well-being, promoting global health, and achieving sustainability. Innovative biosensors are crucial in accurately monitoring health conditions, uncovering the hidden connections between the environment and human well-being, and understanding how environmental factors trigger autoimmune diseases, neurodegenerative diseases, and infectious diseases. This review evaluates the use of nanoplasmonic biosensors that can monitor environmental health and human diseases according to target analytes of different sizes and scales, providing valuable insights for preventive medicine. We begin by explaining the fundamental principles and mechanisms of nanoplasmonic biosensors. We investigate the potential of nanoplasmonic techniques for detecting various biological molecules, extracellular vesicles (EVs), pathogens, and cells. We also explore the possibility of wearable nanoplasmonic biosensors to monitor the physiological network and healthy connectivity of humans, animals, plants, and organisms. This review will guide the design of next-generation nanoplasmonic biosensors to advance sustainable global healthcare for humans, the environment, and the planet.

Advancing Breathomics through Accurate Discrimination of Endogenous from Exogenous Volatiles in Breath

Breathomics, a growing field in exposure monitoring and clinical diagnostics, has faced accuracy challenges due to unclear contributing factors. This study aims to enhance the potential of breathomics in various frontiers by categorizing exhaled volatile organic compounds (VOCs) as endogenous or exogenous. Analyzing ambient air and breath samples from 271 volunteers via TD-GC × GC-TOF MS/FID, we identify and quantify 50 common VOCs in exhaled breath. Advanced quantitative structure-property relationships and compartment models are employed to obtain VOCs kinetic parameters. This in-depth approach allows us to accurately determine the alveolar concentration of VOCs and further discern their origins, facilitating personalized application of breathomics in exposure assessment and disease diagnosis. Our findings demonstrate that prolonged external exposure turns humans into secondary pollutant sources. Analysis of endogenous VOCs reveals that internal exposure poses more significant health risks than external. Moreover, by correcting environmental backgrounds, we improve the accuracy of gastrointestinal disease diagnostic models by 15-25%. This advancement in identifying VOC origins via compartmental models promises to elevate the clinical relevance of breathomics, marking a leap forward in exposure assessment and precision medicine.

The human volatilome meets cancer diagnostics: past, present, and future of noninvasive applications

BACKGROUND

Cancer is a significant public health problem, causing dozens of millions of deaths annually. New cancer screening programs are urgently needed for early cancer detection, as this approach can improve treatment outcomes and increase patient survival. The search for affordable, noninvasive, and highly accurate cancer detection methods revealed a valuable source of tumor-derived metabolites in the human metabolome through the exploration of volatile organic compounds (VOCs) in noninvasive biofluids.

AIM OF REVIEW

This review discusses volatilomics-based approaches for cancer detection using noninvasive biomatrices (breath, saliva, skin secretions, urine, feces, and earwax). We presented the historical background, the latest approaches, and the required stages for clinical validation of volatilomics-based methods, which are still lacking in terms of making noninvasive methods available and widespread to the population. Furthermore, insights into the usefulness and challenges of volatilomics in clinical implementation steps for each biofluid are highlighted.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We outline the methodologies for using noninvasive biomatrices with up-and-coming clinical applications in cancer diagnostics. Several challenges and advantages associated with the use of each biomatrix are discussed, aiming at encouraging the scientific community to strengthen efforts toward the necessary steps to speed up the clinical translation of volatile-based cancer detection methods, as well as discussing in favor of (i) hybrid applications (i.e., using more than one biomatrix) to describe metabolite modulations that can be "cancer volatile fingerprints" and (ii) in multi-omics approaches integrating genomics, transcriptomics, and proteomics into the volatilomic data, which might be a breakthrough for diagnostic purposes, onco-pathway assessment, and biomarker validations.

Is an Electronic Nose Able to Predict Clinical Response following Neoadjuvant Treatment of Rectal Cancer? A Prospective Pilot Study

Introduction: A watch-and-wait strategy for patients with rectal cancer who achieve a clinical complete response after neoadjuvant (chemo) radiotherapy is a valuable alternative to rectal resection. In this pilot study, we explored the use of an electronic nose to predict response to neoadjuvant therapy by analyzing breath-derived volatile organic compounds. Materials and Methods: A pilot study was performed between 2020 and 2022 on patients diagnosed with intermediate- or high-risk rectal cancer who were scheduled for neoadjuvant therapy. Breath samples were collected before and after (chemo) radiotherapy. A machine-learning model was developed to predict clinical response using curatively treated rectal cancer patients as controls. Results: For developing the machine-learning model, a total of 99 patients were included: 45 patients with rectal cancer and 54 controls. In the training set, the model successfully discriminated between patients with and without rectal cancer, with a sensitivity and specificity of 0.80 and 0.65, respectively, and an accuracy of 0.72. In the test set, the model predicted partial or (near) complete response with a sensitivity and specificity of 0.64 and 0.47, respectively, and an accuracy of 0.58. The AUC of the ROC curve was 0.63. Conclusions: The prediction model developed in this pilot study lacks the ability to accurately differentiate between partial and (near) complete responders with an electronic nose. Machine-learning studies demand a substantial number of patients and operate in a rapidly evolving field. Therefore, the prevalence of disease and duration of a study are crucial considerations for future research.

Precision detection of select human lung cancer biomarkers and cell lines using honeybee olfactory neural circuitry as a novel gas sensor

Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers at different concentrations and their mixtures at concentration ranges relevant to biomarkers in human breath from parts-per-billion to parts-per-trillion. We also validated this brain-based sensing technology by detecting human non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cell lines using the 'smell' of the cell cultures. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we classified individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create 'synthetic lung cancer' vs. 'synthetic healthy' human breath, honeybee population neuronal responses were able to classify those complex breath mixtures reliably with exceedingly high accuracy (93-100% success rate with a leave-one-trial-out classification method). Finally, we employed this sensor to detect human NSCLC and SCLC cell lines and we demonstrated that honeybee brain olfactory neurons could distinguish between lung cancer vs. healthy cell lines and could differentiate between different NSCLC and SCLC cell lines successfully (82% classification success rate). These results indicate that the honeybee olfactory system can be used as a sensitive biological gas sensor to detect human lung cancer.

Lab-on-Device Synthesis of Hierarchical Macro/Mesoporous WO3 Semiconducting Films for High-Performance H2S Sensing

The performance consistency of the gas sensor is strongly dependent on the interface binding between the sensitive materials and the electrodes. Traditional powder coating methods can inevitably lead to differences in terms of substrate-film interface interaction and device performance, affecting the stability and lifetime. Thus, efficient growth of sensitive materials on device substrates is crucial and essential to enhance the sensing performance, especially for stability. Herein, hierarchically ordered macro/mesoporous WO3 films are in situ synthesized on the electrode via a facile soft/hard dual-template strategy. Orderly arrayed uniform polystyrene (PS) microspheres with tailored size (ca. 1.2 µm) are used as a hard template, and surfactant Pluronic F127 as a soft template can co-assemble with tungsten precursor into ordered mesostructure in the interstitials of PS colloidal crystal induced by solvent evaporation. Benefiting from its rich porosity and high stability, the macro/mesoporous WO3-based sensor shows high sensitivity (Rair/Rgas = 307), fast response/recovery speed (5/9 s), and excellent selectivity (SH2S/Smax > 7) toward 50 ppm H2S gas (a biomarker for halitosis). Significantly, the sensors exhibit an extended service life with a negligible change in sensing performance within 60 days. This lab-on-device synthesis provides a platform method for constructing stable nanodevices with good consistency and high stability, which are highly desired for developing high-performance sensors.

Application of electronic nose technology in the diagnosis of gastrointestinal diseases: a review

Electronic noses (eNoses) are electronic bionic olfactory systems that use sensor arrays to produce response patterns to different odors, thereby enabling the identification of various scents. Gastrointestinal diseases have a high incidence rate and occur in 9 out of 10 people in China. Gastrointestinal diseases are characterized by a long course of symptoms and are associated with treatment difficulties and recurrence. This review offers a comprehensive overview of volatile organic compounds, with a specific emphasis on those detected via the eNose system. Furthermore, this review describes the application of bionic eNose technology in the diagnosis and screening of gastrointestinal diseases based on recent local and international research progress and advancements. Moreover, the prospects of bionic eNose technology in the field of gastrointestinal disease diagnostics are discussed.

Prospect and Challenges of Volatile Organic Compound Breath Testing in Non-Cancer Gastrointestinal Disorders

Breath analysis, despite being an overlooked biomatrix, has a rich history in disease diagnosis. However, volatile organic compounds (VOCs) have yet to establish themselves as clinically validated biomarkers for specific diseases. As focusing solely on late-stage or malignant disease biomarkers may have limited relevance in clinical practice, the objective of this review is to explore the potential of VOC breath tests for the diagnosis of non-cancer diseases: (1) Precancerous conditions like gastro-esophageal reflux disease (GERD) and Barrett's esophagus (BE), where breath tests can complement endoscopic screening; (2) endoluminal diseases associated with autoinflammation and dysbiosis, such as inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), and coeliac disease, which currently rely on biopsy and symptom-based diagnosis; (3) chronic liver diseases like cirrhosis, hepatic encephalopathy, and non-alcoholic fatty liver disease, which lack non-invasive diagnostic tools for disease progression monitoring and prognostic assessment. A literature search was conducted through EMBASE, MEDLINE, and Cochrane databases, leading to an overview of 24 studies. The characteristics of these studies, including analytical platforms, disorder type and stage, group size, and performance evaluation parameters for diagnostic tests are discussed. Furthermore, how VOCs can be utilized as non-invasive diagnostic tools to complement existing gold standards is explored. By refining study designs, sampling procedures, and comparing VOCs in urine and blood, we can gain a deeper understanding of the metabolic pathways underlying VOCs. This will establish breath analysis as an effective non-invasive method for differential diagnosis and disease monitoring.

Volatile organic compounds in exhaled breath: a promising approach for accurate differentiation of lung adenocarcinoma and squamous cell carcinoma

Lung cancer subtyping, particularly differentiating adenocarcinoma (ADC) from squamous cell carcinoma (SCC), is paramount for clinicians to develop effective treatment strategies. In this study, we aimed: (i) to discover volatile organic compound (VOC) biomarkers for precise diagnosis of ADC and SCC, (ii) to investigated the impact of risk factors on ADC and SCC prediction, and (iii) to explore the metabolic pathways of VOC biomarkers. Exhaled breath samples from patients with ADC (n= 149) and SCC (n= 94) were analyzed by gas chromatography-mass spectrometry. Both multivariate and univariate statistical analysis method were employed to identify VOC biomarkers. Support vector machine (SVM) prediction models were developed and validated based on these VOC biomarkers. The impact of risk factors on ADC and SCC prediction was investigated. A panel of 13 VOCs was found to differ significantly between ADC and SCC. Utilizing the SVM algorithm, the VOC biomarkers achieved a specificity of 90.48%, a sensitivity of 83.50%, and an area under the curve (AUC) value of 0.958 on the training set. On the validation set, these VOC biomarkers attained a predictive power of 85.71% for sensitivity and 73.08% for specificity, along with an AUC value of 0.875. Clinical risk factors exhibit certain predictive power on ADC and SCC prediction. Integrating these risk factors into the prediction model based on VOC biomarkers can enhance its predictive accuracy. This work indicates that exhaled breath holds the potential to precisely detect ADCs and SCCs. Considering clinical risk factors is essential when differentiating between these two subtypes.

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.

Neonatal Exhaled Breath Sampling for Infrared Spectroscopy: Biomarker Analysis

Monitoring health conditions in neonates for early therapeutic intervention in case deviations from physiological conditions is crucial for their long-term development. Due to their immaturity preterm born neonates are dependent on particularly careful physical and neurological diagnostic methods. Ideally, these should be noninvasive, noncontact, and radiation free. Infrared spectroscopy was used to analyze exhaled breath from 71 neonates with a special emphasis on preterm infants, as a noninvasive, noncontact, and radiation-free diagnostic tool. Passive sample collection was performed by skilled clinicians. Depending on the mode of respiratory support of infants, four different sampling procedures were adapted to collect exhaled breath. With the aid of appropriate reference samples, infrared spectroscopy has successfully demonstrated its effectiveness in the analysis of breath samples of neonates. The discernible increase in concentrations of carbon dioxide, carbon monoxide, and methane in collected samples compared to reference samples served as compelling evidence of the presence of exhaled breath. With regard to technical hurdles and sample analysis, samples collected from neonates without respiratory support proved to be more advantageous compared to those obtained from intubated infants and those with CPAP (continuous positive airway pressure). The main obstacle lies in the significant dilution of exhaled breath in the case of neonates receiving respiratory support. Metabolic analysis of breath samples holds promise for the development of noninvasive biomarker-based diagnostics for both preterm and sick neonates provided an adequate amount of breath is collected.

Exhaled breath analysis for the discrimination of asthma and chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) and asthma are the most common chronic respiratory diseases. In middle-aged and elderly patients, it is difficult to distinguish between COPD and asthma based on clinical symptoms and pulmonary function examinations in clinical practice. Thus, an accurate and reliable inspection method is required. In this study, we aimed to identify breath biomarkers and evaluate the accuracy of breathomics-based methods for discriminating between COPD and asthma. In this multi-center cross-sectional study, exhaled breath samples were collected from 89 patients with COPD and 73 with asthma and detected on a high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) platform from 20 October 2022, to 20 May 2023, in four hospitals. Data analysis was performed from 15 June 2023 to 16 August 2023. The sensitivity, specificity, and accuracy were calculated to assess the overall performance of the volatile organic component (VOC)-based COPD and asthma discrimination models. Potential VOC markers related to COPD and asthma were also analyzed. The age of all participants ranged from to 18-86 years, and 54 (33.3%) were men. The age [median (minimum, maximum)] of COPD and asthma participants were 66.0 (46.0, 86.0), and 44.0 (17.0, 80.0). The male and female ratio of COPD and asthma participants were 14/75 and 40/33, respectively. Based on breathomics feature selection, ten VOCs were identified as COPD and asthma discrimination biomarkers via breath testing. The joint panel of these ten VOCs achieved an area under the curve of 0.843, sensitivity of 75.9%, specificity of 87.5%, and accuracy of 80.0% in COPD and asthma discrimination. Furthermore, the VOCs detected in the breath samples were closely related to the clinical characteristics of COPD and asthma. The VOC-based COPD and asthma discrimination model showed good accuracy, providing a new strategy for clinical diagnosis. Breathomics-based methods may play an important role in the diagnosis of COPD and asthma.

Recent Development on Sensing Strategies for Small Molecules Detections

Sensors play a critical role in the detection and monitoring of various substances present in our environment, providing us with valuable information about the world around us. Within the field of sensor development, one area that holds particular importance is the detection of small molecules. Small molecules encompass a wide range of organic or inorganic compounds with low molecular weight, typically below 900 Daltons including gases, volatile organic compounds, solvents, pesticides, drugs, biomarkers, toxins, and pollutants. The accurate and efficient detection of these small molecules has attracted significant interest from the scientific community due to its relevance in diverse fields such as environmental pollutants monitoring, medical diagnostics, industrial optimization, healthcare remedies, food safety, ecosystems, and aquatic and terrestrial life preservation. To meet the demand for precise and efficient monitoring of small molecules, this summary aims to provide an overview of recent advancements in sensing and quantification strategies for various organic small molecules including Hydrazine, Glucose, Morpholine, Ethanol amine, Nitrosamine, Oxygen, Nitro-aromatics, Phospholipids, Carbohydrates, Antibiotics, Pesticides, Drugs, Adenosine Triphosphate, Aromatic Amine, Glutathione, Hydrogen Peroxide, Acetone, Methyl Parathion, and Thiophenol. The focus is on understanding the receptor sensing mechanism, along with the electrical, optical, and electrochemical response. Additionally, the variations in UV-visible spectral properties of the ligands upon treatment with the receptor, fluorescence and absorption titration analysis for limit of detection (LOD) determination, and bioimaging analysis are discussed wherever applicable. It is anticipated that the information gathered from this literature survey will be helpful for the perusal of innovation regarding sensing strategies.

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.

Identifying potential breath biomarkers for early diagnosis of papillary thyroid cancer based on solid-phase microextraction gas chromatography-high resolution mass spectrometry with metabolomics

INTRODUCTION

Thyroid cancer incidence rate has increased substantially worldwide in recent years. Fine needle aspiration biopsy (FNAB) is currently the golden standard of thyroid cancer diagnosis, which however, is invasive and costly. In contrast, breath analysis is a non-invasive, safe and simple sampling method combined with a promising metabolomics approach, which is suitable for early cancer diagnosis in high volume population.

OBJECTIVES

This study aims to achieve a more comprehensive and definitive exhaled breath metabolism profile in papillary thyroid cancer patients (PTCs).

METHODS

We studied both end-tidal and mixed expiratory breath, solid-phase microextraction gas chromatography coupled with high resolution mass spectrometry (SPME-GC-HRMS) was used to analyze the breath samples. Multivariate combined univariate analysis was applied to identify potential breath biomarkers.

RESULTS

The biomarkers identified in end-tidal and mixed expiratory breath mainly included alkanes, olefins, enols, enones, esters, aromatic compounds, and fluorine and chlorine containing organic compounds. The area under the curve (AUC) values of combined biomarkers were 0.974 (sensitivity: 96.1%, specificity: 90.2%) and 0.909 (sensitivity: 98.0%, specificity: 74.5%), respectively, for the end-tidal and mixed expiratory breath, indicating of reliability of the sampling and analysis method CONCLUSION: This work not only successfully established a standard metabolomic approach for early diagnosis of PTC, but also revealed the necessity of using both the two breath types for comprehensive analysis of the biomarkers.

Specific molecular peak analysis by ion mobility spectrometry of volatile organic compounds in urine of COVID-19 patients: A novel diagnostic approach

INTRODUCTION

SARS-CoV-2 is usually diagnosed from naso-/oropharyngeal swabs which are uncomfortable and prone to false results. This study investigated a novel diagnostic approach to Covid-19 measuring volatile organic compounds (VOC) from patients' urine.

METHODS

Between June 2020 and February 2021, 84 patients with positive RT-PCR for SARS-CoV-2 were recruited as well as 54 symptomatic individuals with negative RT-PCR. Midstream urine samples were obtained for VOC analysis using ion mobility spectrometry (IMS) which detects individual molecular components of a gas sample based on their size, configuration, and charge after ionization.

RESULTS

Peak analysis of the 84 Covid and 54 control samples showed good group separation. In total, 37 individual specific peaks were identified, 5 of which (P134, 198, 135, 75, 136) accounted for significant differences between groups, resulting in sensitivities of 89-94% and specificities of 82-94%. A decision tree was generated from the relevant peaks, leading to a combined sensitivity and specificity of 98% each.

DISCUSSION

VOC-based diagnosis can establish a reliable separation between urine samples of Covid-19 patients and negative controls. Molecular peaks which apparently are disease-specific were identified. IMS is an additional non-invasive and cheap device for the diagnosis of this ongoing endemic infection. Further studies are needed to validate sensitivity and specificity.

Narrative review on artificially intelligent olfaction in halitosis

Halitosis, commonly known as oral malodor, is a multifactorial health concern that significantly impacts the psychological and social well-being of individuals. It is the third most frequent reason for individuals to seek dental treatment, after dental caries and periodontal diseases. For an in-depth exploration of the topic of halitosis, an extensive literature review was conducted. The review focused on articles published in peer-reviewed journals and only those written in the English language were considered. The search for relevant literature began by employing subject headings such as 'halitosis, oral malodor, volatile sulfur compounds, artificial intelligence, and olfaction' in databases such as PubMed/Medline, Scopus, Google Scholar, Web of Science, and EMBASE. Additionally, a thorough hand search of references was conducted to ensure the comprehensiveness of the review. After amalgamating the search outcomes, a comprehensive analysis revealed the existence of precisely 134 full-text articles that bore relevance to the study. Abstracts and editorial letters were excluded from this study, and almost 50% of the full-text articles were deemed immaterial to dental practice. Out of the remaining articles, precisely 54 full-text articles were employed in this review. As primary healthcare providers, dentists are responsible for diagnosing and treating oral issues that may contribute to the development of halitosis. To effectively manage this condition, dentists must educate their patients about the underlying causes of halitosis, as well as proper oral hygiene practices such as tongue cleaning, flossing, and selecting appropriate mouthwash and toothpaste. This narrative review summarises all possible AI olfaction in halitosis.

Volatilomic profiles of gastric juice in gastric cancer patients

Volatilomics is a powerful tool capable of providing novel biomarkers for the diagnosis of gastric cancer. The main objective of this study was to characterize the volatilomic signatures of gastric juice in order to identify potential alterations induced by gastric cancer. Gas chromatography with mass spectrometric detection, coupled with headspace solid phase microextraction as the pre-concentration technique, was used to identify volatile organic compounds (VOCs) released by gastric juice samples collected from 78 gastric cancer patients and two cohorts of controls (80 and 96 subjects) from four different locations (Latvia, Ukraine, Brazil, and Colombia). 1440 distinct compounds were identified in samples obtained from patients and 1422 in samples provided by controls. However, only 6% of the VOCs exhibited an incidence higher than 20%. Amongst the volatiles emitted, 18 showed differences in their headspace concentrations above gastric juice of cancer patients and controls. Ten of these (1-propanol, 2,3-butanedione, 2-pentanone, benzeneacetaldehyde, 3-methylbutanal, butylated hydroxytoluene, 2-pentyl-furan, 2-ethylhexanal, 2-methylpropanal and phenol) appeared at significantly higher levels in the headspace of the gastric juice samples obtained from patients; whereas, eight species showed lower abundance in patients than found in controls. Given that the difference in the volatilomic signatures can be explained by cancer-related changes in the activity of certain enzymes or pathways, the former set can be considered potential biomarkers for gastric cancer, which may assist in developing non-invasive breath tests for the diagnosis of this disease. Further studies are required to elucidate further the mechanisms that underlie the changes in the volatilomic profile as a result of gastric cancer.

Volatile organic compounds for diagnosis of early hepatocellular carcinoma in at-risk patients

BACKGROUND

Volatile organic compounds (VOCs) have been shown as promising biomarkers for hepatocellular carcinoma (HCC) diagnosis. We aimed to investigate the performance of VOCs for diagnosing early-stage HCC in patients at-risk for HCC.

METHODS

VOCs were identified in exhaled breath samples collected from 87 early-stage HCC patients, 90 cirrhotic patients, and 72 HBV-infected patients using thermal desorption-gas chromatography/field-asymmetric ion mobility spectrometry. The VOC levels were compared between the three groups. An association between VOCs and HCC was determined using logistic regression analysis. Diagnostic performance of VOCs was estimated using the AUROC and compared to serum alpha-fetoprotein (AFP).

RESULTS

The levels of acetone monomer, dimethyl sulfide, 1,4-pentadiene, isopropyl alcohol, and acetone dimer were significantly different between the three groups. After adjusting for liver function test and AFP, acetone dimer was significantly associated with HCC. Acetone dimer significantly outperformed AFP with 86.2 % vs. 61.2 % sensitivity, 87.6 % vs. 66.2 % specificity, 86.9 % vs. 63.5 % for accuracy, and AUROC of 0.908 vs. 0.665, p = 0.007, <0.001, <0.001, and 0.001, respectively, for differentiating between HCC and cirrhosis.

CONCLUSION

Acetone showed a better performance than AFP for diagnosing early HCC in at-risk patients. Further studies to validate the utility of VOCs as an HCC surveillance tool are needed.

Metal oxide-based gas sensor array for VOCs determination in complex mixtures using machine learning

Detection of volatile organic compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array of 3 component metal oxides that give maximal cross-sensitivity and can successfully use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array comprises NiO-Au (ohmic), CuO-Au (Schottky), and ZnO-Au (Schottky) sensors made by the DC reactive sputtering method and having a film thickness of 80-100 nm. The NiO and CuO films have ultrafine particle sizes of < 50 nm and rough surface texture, while ZnO films consist of nanoscale platelets. This array was subjected to various VOC concentrations, including ethanol, acetone, toluene, and chloroform, one by one and in a pair/mix of gases. Thus, the response values show severe interference and departure from commonly observed power law behavior. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with an R2 value of more than 0.99 and LOD of 0.012 ppm, 0.015 ppm, 0.014 ppm, and 0.025 ppm for predicting the concentrations of acetone, toluene, ethanol, and chloroform, respectively, in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring.

Overall perspective of electrospun semiconductor metal oxides as high-performance gas sensor materials for NOx detection

Gas sensors based on nanostructured semiconductor metal oxide (SMO) materials have been extensively investigated as key components due to their advantages over other materials, namely, high sensitivity, stability, affordability, rapid response and simplicity. However, the difficulty of working at high temperatures, response in lower concentration and their selectivity are huge challenges of SMO materials for detecting gases. Therefore, researchers have not stopped their quest to develop new gas sensors based on SMOs with higher performance. This paper begins by highlighting the importance of nitrogen monoxide (NO) and nitrogen dioxide (NO2) detection for human health and addresses the challenges associated with existing methods in effectively detecting them. Subsequently, the mechanism of SMO gas sensors, analysis of their structure and fabrication techniques focusing on electrospinning technique, as well as their advantages, difficulties, and structural design challenges are discussed. Research on enhancing the sensing performance through tuning the fabrication parameters are summarized as well. Finally, the problems and potential of SMO based gas sensors to detect NOx are revealed, and the future possibilities are stated.

Developing technologies and areas of interest in lung cancer screening adjuncts

Lung cancer remains the leading cause of cancer mortality. Screening guidelines have been implemented in the past decade to aid in earlier detection of at-risk groups. Nevertheless, computed tomography (CT) scans, the principal screening modality in use today, are still low yield, with 3.6% of lung cancer confirmed amongst 39.1% of lesions detected over a 3-year period. They also carry relatively high false positive rates, between 9% and 27%, which can bear unnecessary financial and emotional costs to patients. As such, research efforts have been dedicated to the development of lung cancer screening adjuncts to improve detection reliability. We herein review several emerging technologies in this specific arena and their efficacy. These include plasma markers (microDNA, DNA methylation, and tumor-associated antibodies), breath/sputum biomarkers [volatile organic compounds (VOCs) and exhaled breath condensate (EBC)], proteomics, metabolomics, and machine learning, such as radiomics technology. We find that, across the board, they offer promising results in terms of non-invasive diagnostics, genetic sequencing for higher-risk individuals, and accessibility for a diverse cohort of patients. While these screening adjuncts are unlikely to completely replace the current standard of care at the moment, continued research into these technologies is crucial to improve and personalize the identification, treatment, and outcome of lung cancer patients in the near future.

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.

Biodegradable, Biocompatible, and Implantable Multifunctional Sensing Platform for Cardiac Monitoring

Cardiac monitoring after heart surgeries is crucial for health maintenance and detecting postoperative complications early. However, current methods like rigid implants have limitations, as they require performing second complex surgeries for removal, increasing infection and inflammation risks, thus prompting research for improved sensing monitoring technologies. Herein, we introduce a nanosensor platform that is biodegradable, biocompatible, and integrated with multifunctions, suitable for use as implants for cardiac monitoring. The device has two electrochemical biosensors for sensing lactic acid and pH as well as a pressure sensor and a chemiresistor array for detecting volatile organic compounds. Its biocompatibility with myocytes has been tested in vitro, and its biodegradability and sensing function have been proven with ex vivo experiments using a three-dimensional (3D)-printed heart model and 3D-printed cardiac tissue patches. Moreover, an artificial intelligence-based predictive model was designed to fuse sensor data for more precise health assessment, making it a suitable candidate for clinical use. This sensing platform promises impactful applications in the realm of cardiac patient care, laying the foundation for advanced life-saving developments.

A structural color hydrogel for diagnosis of halitosis and screening of periodontitis

Oral pathogens can produce volatile sulfur compounds (VSCs), which is the main reason for halitosis and indicates the risk of periodontitis. High-sensitivity detection of exhaled VSCs is urgently desired for promoting the point-of-care testing (POCT) of halitosis and screening of periodontitis. However, current detection methods often require bulky and costly instruments, as well as professional training, making them impractical for widespread detection. Here, a structural color hydrogel for naked-eye detection of exhaled VSCs is presented. VSCs can reduce disulfide bonds within the network, leading to expansion of the hydrogel and thus change of the structural color. A linear detection range of 0-1 ppm with a detection limit of 61 ppb can be achieved, covering the typical VSC concentration in the breath of patients with periodontitis. Furthermore, visual and in situ monitoring of Porphyromonas gingivalis responsible for periodontitis can be realized. By integrating the hydrogels into a sensor array, the oral health conditions of patients with halitosis can be evaluated and distinguished, offering risk assessment of periodontitis. Combined with a smartphone capable of color analysis, POCT of VSCs can be achieved, providing an approach for the monitoring of halitosis and screening of periodontitis.

Cancer evaluation in dogs using cerumen as a source for volatile biomarker prospection

Cancer is one of the deadliest diseases in humans and dogs. Nevertheless, most tumor types spread faster in canines, and early cancer detection methods are necessary to enhance animal survival. Here, cerumen (earwax) was tested as a source of potential biomarkers for cancer evaluation in dogs. Earwax samples from dogs were collected from tumor-bearing and clinically healthy dogs, followed by Headspace/Gas Chromatography-Mass Spectrometry (HS/GC-MS) analyses and multivariate statistical workflow. An evolutionary-based multivariate algorithm selected 18 out of 128 volatile metabolites as a potential cancer biomarker panel in dogs. The candidate biomarkers showed a full discrimination pattern between tumor-bearing dogs and cancer-free canines with high accuracy in the test dataset: an accuracy of 95.0% (75.1-99.9), and sensitivity and specificity of 100.0% and 92.9%, respectively. In summary, this work raises a new perspective on cancer diagnosis in dogs, being carried out painlessly and non-invasive, facilitating sample collection and periodic application in a veterinary routine.

GC-MS-based metabolomics of volatile organic compounds in exhaled breath: applications in health and disease. A review

Exhaled breath analysis, with particular emphasis on volatile organic compounds, represents a growing area of clinical research due to its obvious advantages over other diagnostic tests. Numerous pathologies have been extensively investigated for the identification of specific biomarkers in exhalates through metabolomics. However, the transference of breath tests to clinics remains limited, mainly due to deficiency in methodological standardization. Critical steps include the selection of breath sample types, collection devices, and enrichment techniques. GC-MS is the reference analytical technique for the analysis of volatile organic compounds in exhalates, especially during the biomarker discovery phase in metabolomics. This review comprehensively examines and compares metabolomic studies focusing on cancer, lung diseases, and infectious diseases. In addition to delving into the experimental designs reported, it also provides a critical discussion of the methodological aspects, ranging from the experimental design and sample collection to the identification of potential pathology-specific biomarkers.

Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions

Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.

Detection technologies of volatile organic compounds in the breath for cancer diagnoses

Although there are new approaches in both cancer treatment and diagnosis, overall mortality is a major concern. New technologies have attempted to look at breath volatile organic compounds (VOCs) detection to diagnose cancer. Gas Chromatography and Mass Spectrometry (GC - MS) have remained the gold standard of VOC analysis for decades, but it has limitations in differentiating VOCs between cancer subtypes. To increase efficacy and accuracy, new methods to analyze these breath VOCs have been introduced, such as Solid Phase Microextraction/Gas Chromatography-Mass Spectrometry (SPME/GC-MS), Selected Ion Flow Tube - Mass Spectrometry (SIFT-MS), Proton Transfer Reaction - Mass Spectrometry (PRT-MS), Ion Mobility Spectrometry (IMS), and Colorimetric Sensors. This article highlights new technologies that have been studied and applied in the detection and quantification of breath VOCs for possible cancer diagnoses.

Wearable sensor platforms for real-time monitoring and early warning of metabolic disorders in humans

Nowadays, the prevalence of metabolic syndromes (MSs) has attracted increasing concerns as it is closely related to overweight and obesity, physical inactivity and overconsumption of energy, making the diagnosis and real-time monitoring of the physiological range essential and necessary for avoiding illness due to defects in the human body such as higher risk of cardiovascular disease, diabetes, stroke and diseases related to artery walls. However, the current sensing techniques are inconvenient and do not continuously monitor the health status of humans. Alternatively, the use of recent wearable device technology is a preferable method for the prevention of these diseases. This can enable the monitoring of the health status of humans in different health domains, including environment and structure. The use wearable devices with the purpose of facilitating rapid treatment and real-time monitoring can decrease the prevalence of MS and long-time monitor the health status of patients. This review highlights the recent advances in wearable sensors toward continuous monitoring of blood pressure and blood glucose, and further details the monitoring of abnormal obesity, triglycerides and HDL. We also discuss the challenges and future prospective of monitoring MS in humans.

Methanethiol: A Scent Mark of Dysregulated Sulfur Metabolism in Cancer

In order to cope with increased demands for energy and metabolites as well as to enhance stress resilience, tumor cells develop various metabolic adaptations, representing a hallmark of cancer. In this regard, the dysregulation of sulfur metabolism that may result in elevated levels of volatile sulfur compounds (VSCs) in body fluids, breath, and/or excretions of cancer patients has recently gained attention. Besides hydrogen sulfide (H2S), methanethiol is the predominant cancer-associated VSC and has been proposed as a promising biomarker for non-invasive cancer diagnosis. Gut bacteria are the major exogenous source of exposure to this foul-smelling toxic gas, with methanethiol-producing strains such as Fusobacterium nucleatum highly abundant in the gut microbiome of colorectal carcinoma (CRC) patients. Physiologically, methanethiol becomes rapidly degraded through the methanethiol oxidase (MTO) activity of selenium-binding protein 1 (SELENBP1). However, SELENBP1, which is considered a tumor suppressor, is often downregulated in tumor tissues, and this has been epidemiologically linked to poor clinical outcomes. In addition to impaired removal, an increase in methanethiol levels may derive from non-enzymatic reactions, such as a Maillard reaction between glucose and methionine, two metabolites enriched in cancer cells. High methionine concentrations in cancer cells may also result in enzymatic methanethiol production in mitochondria. Moreover, enzymatic endogenous methanethiol production may occur through methyltransferase-like protein 7B (METTL7B), which is present at elevated levels in some cancers, including CRC and hepatocellular carcinoma (HCC). In conclusion, methanethiol contributes to the scent of cancer as part of the cancer-associated signature combination of volatile organic compounds (VOCs) that are increasingly being exploited for non-invasive early cancer diagnosis.

Theoretical study on the interaction between acetone and BN monolayer doped with Ni for the clinical diagnosis of diabetes mellitus

Endogenous volatile organic compounds (VOCs) in human exhaled gases can reflect human health status and be used for clinical diagnosis and health monitoring. Acetone is the sign VOC gases of diabetes mellitus. In order to find a potential material for the detection of acetone in the application of the clinical diagnosis of diabetes mellitus. The adsorption properties, including adsorption energy, adsorption distance, charge transfer, density of states, electron localization function and electrons density difference, of acetone on BN monolayer doped with Ni were comprehensively investigated based on density functional theory. The results show that there could be chemisorption between acetone and Ni-BN monolayer and Ni-BN monolayer is probably suitable gas sensitive material for the detection of acetone in the application of diabetes mellitus monitoring and clinical diagnosis.

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

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

Exhaled volatile organic compounds associated with risk factors for obstructive pulmonary diseases: a systematic review

Background

Asthma and COPD are among the most common respiratory diseases. To improve the early detection of exacerbations and the clinical course of asthma and COPD new biomarkers are needed. The development of noninvasive metabolomics of exhaled air into a point-of-care tool is an appealing option. However, risk factors for obstructive pulmonary diseases can potentially introduce confounding markers due to altered volatile organic compound (VOC) patterns being linked to these risk factors instead of the disease. We conducted a systematic review and presented a comprehensive list of VOCs associated with these risk factors.

Methods

A PRISMA-oriented systematic search was conducted across PubMed, Embase and Cochrane Libraries between 2000 and 2022. Full-length studies evaluating VOCs in exhaled breath were included. A narrative synthesis of the data was conducted, and the Newcastle-Ottawa Scale was used to assess the quality of included studies.

Results

The search yielded 2209 records and, based on the inclusion/exclusion criteria, 24 articles were included in the qualitative synthesis. In total, 232 individual VOCs associated with risk factors for obstructive pulmonary diseases were found; 58 compounds were reported more than once and 12 were reported as potential markers of asthma and/or COPD in other studies. Critical appraisal found that the identified studies were methodologically heterogeneous and had a variable risk of bias.

Conclusion

We identified a series of exhaled VOCs associated with risk factors for asthma and/or COPD. Identification of these VOCs is necessary for the further development of exhaled metabolites-based point-of-care tests in these obstructive pulmonary diseases.

Assessment of an exhaled breath test using ultraviolet photoionization time-of-flight mass spectrometry for the monitoring of kidney transplant recipients

Continuous monitoring for immunosuppressive status, infection and complications are a must for kidney transplantation (KTx) recipients. Traditional monitoring including blood sampling and kidney biopsy, which caused tremendous medical cost and trauma. Therefore, a cheaper and less invasive approach was urgently needed. We thought that a breath test has the potential to become a feasible tool for KTx monitoring. A prospective-specimen collection, retrospective-blinded assessment strategy was used in this study. Exhaled breath samples from 175 KTx recipients were collected in West China Hospital and tested by online ultraviolet photoionization time-of-flight mass spectrometry (UVP-TOF-MS). The classification models based on breath test performed well in classifying normal and abnormal values of creatinine, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN) and tacrolimus, with AUC values of 0.889, 0.850, 0.849 and 0.889, respectively. Regression analysis also demonstrated the predictive ability of breath test for clinical creatinine, eGFR, BUN, tacrolimus level, as the predicted values obtained from the regression model correlated well with the clinical true values (p < 0.05). The findings of this investigation implied that a breath test by using UVP-TOF-MS for KTx recipient monitoring is possible and accurate, which might be useful for future clinical screenings.

Visible-Photoactive Perovskite Ferroelectric-Driven Self-Powered Gas Detection

Chemiresistive sensing has been regarded as the key monitoring technique, while classic oxide gas detection devices always need an external power supply. In contrast, the bulk photovoltage of photoferroelectric materials could provide a controllable power source, holding a bright future in self-powered gas sensing. Herein, we present a new photoferroelectric ([n-pentylaminium]₂[ethylammonium]₂Pb₃I₁₀, 1), which possesses large spontaneous polarization (∼4.8 μC/cm²) and prominent visible-photoactive behaviors. Emphatically, driven by the bulk photovoltaic effect, 1 enables excellent self-powered sensing responses for NO₂ at room temperature, including extremely fast response/recovery speeds (0.15/0.16 min) and high sensitivity (0.03 ppm^-1). Such figures of merit are superior to those of typical inorganic systems (e.g., ZnO) using an external power supply. Theoretical calculations and in situ diffuse reflectance infrared Fourier transform spectroscopy measurements confirm the great selectivity of 1 for NO₂. As far as we know, this is the first realization of ferroelectricity-driven self-powered gas detection. Our work sheds light on the self-powered sensing systems and provides a promising way to broaden the functionalities of photoferroelectrics.