Volatile Organic Compounds Biomarkers Research Articles

Rapid point-of-care breath test predicts breast cancer and abnormal mammograms in symptomatic women.

Previous studies have reported volatile organic compounds (VOCs) in the breath as biomarkers of breast cancer. These biomarkers may be derived from cancer-associated fibroblasts, in which oxidative stress degrades polyunsaturated fatty acids to volatile alkanes and methylated alkane derivatives that are excreted in the breath. We evaluated a rapid point-of-care test for breath VOC biomarkers as predictors of breast cancer and abnormal mammograms.&#xD;Methods: We studied 593 women aged ≥ 18 yr referred to three sites for mammography for a symptomatic breast-related concern (e.g. breast mass, nipple discharge). A rapid point-of-care breath testing system collected and concentrated alveolar breath VOCs on a sorbent trap and analyzed them with gas chromatography and surface acoustic wave detection in < 6 min. Breath VOC chromatograms were randomly assigned to a training set or to a validation set. Monte Carlo analysis identified significant breath VOC biomarkers of breast cancer and abnormal mammograms in the training set, and these biomarkers were incorporated into a multivariate algorithm to predict disease in the validation set. &#xD;Results: Prediction of breast cancer: 50 women had biopsy-proven breast cancer (invasive cancer 41, ductal non-invasive cancer 9)&#xD;Unsplit data set: Breath VOCs identified breast cancer with 83% accuracy (area under curve of receiver operating characteristic), 82% sensitivity and 77.1% specificity.&#xD;Split data sets: Training set breath VOCs identified breast cancer with 80.3% accuracy, 84% sensitivity and 74.3% specificity. Corresponding values in the validation set were 68%% accuracy, 72.4% sensitivity and 61.5% specificity.&#xD;Prediction of BIRADS 4 and 5 mammograms (versus BIRADS 1, 2 and 3): &#xD;Unsplit data set: Breath VOCs identified abnormal mammograms with 76.2% accuracy.&#xD;Split data sets: Breath VOCs identified abnormal mammograms with 74.2% accuracy, 73.3% sensitivity and 60% specificity. Corresponding values in the validation set were 60.5% accuracy, 64.2% sensitivity and 51% specificity.&#xD;Conclusions: A rapid point-of-care test for breath VOC biomarkers predicted risk of breast cancer and abnormal mammograms in women with breast-related symptoms.&#xD;&#xD.

Volatile organic compound analysis of malignant pleural mesothelioma chorioallantoic membrane xenografts

Malignant pleural mesothelioma (MPM) is an aggressive cancer associated with asbestos exposure. MPM is often diagnosed late, at a point where limited treatment options are available, but early intervention could improve the chances of successful treatment for MPM patients. Biomarkers to detect MPM in at-risk individuals are needed to implement early diagnosis technologies. Volatile organic compounds (VOCs) have previously shown diagnostic potential as biomarkers when analysed in MPM patient breath. In this study, chorioallantoic membrane (CAM) xenografts of MPM cell lines were used as models of MPM tumour development for VOC biomarker discovery with the aim of generating targets for investigation in breath, biopsies or other complex matrices. VOC headspace analysis of biphasic or epithelioid MPM CAM xenografts was performed using solid-phase microextraction and gas chromatography-mass spectrometry. We successfully demonstrated the capture, analysis and separation of VOC signatures from CAM xenografts and controls. A panel of VOCs was identified that showed discrimination between MPM xenografts generated from biphasic and epithelioid cells and CAM controls. This is the first application of the CAM xenograft model for the discovery of VOC biomarkers associated with MPM histological subtypes. These findings support the potential utility of non-invasive VOC profiling from breath or headspace analysis of tissues for detection and monitoring of MPM.

Innovative Wearable Sweat Sensor Array for Real-Time Volatile Organic Compound Detection in Noninvasive Diabetes Monitoring.

Wearable sweat sensors are reshaping healthcare monitoring, providing real-time data on hydration and electrolyte levels with user-friendly, noninvasive devices. This paper introduces a highly portable two-channel microfluidic device for simultaneous sweat sampling and the real-time detection of volatile organic compound (VOC) biomarkers. This innovative wearable microfluidic system is tailored for monitoring diabetes through the continuous and noninvasive tracking of acetone and ammonia VOCs, and it seamlessly integrates with smartphones for easy data management. The core of this system lies in the utilization of carbon polymer dots (CPDs) and carbon dots (CDs) derived from monomers such as catechol, resorcinol, o-phenylenediamine, urea, and citric acid. These dots are seamlessly integrated into hydrogels made from gelatin and poly(vinyl alcohol), resulting in an advanced solid-state fluorometric sensor coating on a cellulose paper substrate. These sensors exhibit exceptional performance, offering linear detection ranges of 0.05-0.15 ppm for acetone and 0.25-0.37 ppm for ammonia, with notably low detection limits of 0.01 and 0.08 ppm, respectively. Rigorous optimization of operational parameters, encompassing the temperature, sample volume, and assay time, has been undertaken to maximize device performance. Furthermore, these sensors demonstrate impressive selectivity, effectively discerning between biologically similar substances and other potential compounds commonly present in sweat. As this field matures, the prospect of cost-effective, continuous, personalized health monitoring through wearable VOC sensors holds significant potential for overcoming barriers to comprehensive medical care in underserved regions. This highlights the transformative capacity of wearable VOC sweat sensing in ensuring equitable access to advanced healthcare diagnostics, particularly in remote or geographically isolated areas.

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.

Metal-Organic Frameworks in Surface Enhanced Raman Spectroscopy-Based Analysis of Volatile Organic Compounds.

Volatile Organic Compounds (VOC) are a major class of environmental pollutants hazardous to human health, but also highly relevant in other fields including early disease diagnostics and organoleptic perception of aliments. Therefore, accurate analysis of VOC is essential, and a need for new analytical methods is witnessed for rapid on-site detection without complex sample preparation. Surface-Enhanced Raman Spectroscopy (SERS) offers a rapidly developing versatile analytical platform for the portable detection of chemical species. Nonetheless, the need for efficient docking of target analytes at the metallic surface significantly narrows the applicability of SERS. This limitation can be circumvented by interfacing the sensor surface with Metal-Organic Frameworks (MOF). These materials featuring chemical and structural versatility can efficiently pre-concentrate low molecular weight species such as VOC through their ordered porous structure. This review presents recent trends in the development of MOF-based SERS substrates with a focus on elucidating respective design rules for maximizing analytical performance. An overview of the status of the detection of harmful VOC is discussed in the context of industrial and environmental monitoring. In addition, a survey of the analysis of VOC biomarkers for medical diagnosis and emerging applications in aroma and flavor profiling is included.

Methanol gas sensing properties of transition metals (V, Cr, and Mn)-doped BC3 flake

Volatile organic compounds (VOCs) cause a considerable risk to human life, and it is vital to introduce highly efficient VOC biosensors. Methanol (CH3OH) was identified as a vital biomarker, showing significant elevation in both lung cancer and COVID-19 patients. Two-dimensional (2D) semiconductor gas sensors offer benefits such as excellent sensitivity, resistance to high temperatures and stability. In the present study, we explored methanol adsorption on the pristine and transition metal (TM)-doped (Sc, Ti, V, Cr, and Mn) C3B 2D flakes with the density functional theory (DFT) technique. Our results revealed that the V-, Cr-, and Mn-doped C3B show larger adsorption energy values as compared to the pristine C3B surface. The change of band gap energy of surfaces after methanol adsorption is obtained between 40 and 400 %. Besides, results show that methanol has a quick recovery at room temperature. The work function variation of studied flakes upon methanol adsorption has been also investigated and results show that V-, Cr-, and Mn-doped C3B systems are sensitive to methanol gas molecule. This work suggests that the C3B-based flakes can be used as a biosensor to identify VOC biomarkers such as methanol.

Biomarker detection using boron carbide layer: a first-principles study

A promising strategy for detecting lung cancer requires determining its biomarkers in a patient’s exhaled breath. Acetone has been identified as an important biomarker in the cases of lung cancer. Two-dimensional materials show characteristics such as excellent sensitivity, resistance to high temperatures, and stability which are essential for their effective use in gas-sensing applications. In this work, we investigated acetone adsorption on the pristine and transition metal (TM)-doped (Fe, Co, Ni, Cu, and Zn) C3B monolayers with the density functional theory calculations. We found that the Zn-doped C3B shows a larger adsorption energy value among designed monolayers. The change of band gap energy of surfaces after acetone adsorption is obtained between 29% and 200%. Besides, results show that acetone has a quick recovery at room temperature. The work function variation of studied monolayers upon acetone adsorption has also been investigated and results show that TM-doped C3B systems are sensitive to acetone gas molecules. This work suggests that the C3B-based layers can be used as a biosensor to identify volatile organic compound biomarkers such as acetone.

Volatile organic components as Covid-19 biomarkers: a systematic review

In March 2020, the WHO declared the novel Coronavirus pandemic with 118,000 cases, 4.2 thousand deaths, and 114 affected countries. Amidst the absence of effective treatments and vaccines, scientists focused on diagnosing and preventing transmission. Pathogenic agent infections induce cellular behavior changes, crucial for developing diagnostic techniques. A systematic review, following PRISMA guidelines, investigated COVID-19 and volatile organic compound (VOC) biomarkers. From 35 selected articles, two main VOC detection approaches emerged: trained dogs and chemical-analytical tools with artificial intelligence. VOC detection proved comparable to the gold standard PCR test for COVID-19 diagnosis. While not a replacement, VOC detection complements PCR testing, offering a unique health approach. Overall, the review highlights the efficacy of VOC detection, especially in areas with limited PCR access. However, these techniques should supplement other preventive measures like mask-wearing, social distancing, and hand hygiene.

Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform.

Background: The sensitivity and specificity of current breath biomarkers are often inadequate for effective cancer screening, particularly in colorectal cancer (CRC). While a few exhaled biomarkers in CRC exhibit high specificity, they lack the requisite sensitivity for early-stage detection, thereby limiting improvements in patient survival rates. Methods: In this study, we developed an advanced Mass Spectrometry-based volatilomics platform, complemented by an enhanced breath sampler. The platform integrates artificial intelligence (AI)-assisted algorithms to detect multiple volatile organic compounds (VOCs) biomarkers in human breath. Subsequently, we applied this platform to analyze 364 clinical CRC and normal exhaled samples. Results: The diagnostic signatures, including 2-methyl, octane, and butyric acid, generated by the platform effectively discriminated CRC patients from normal controls with high sensitivity (89.7%), specificity (86.8%), and accuracy (AUC = 0.91). Furthermore, the metastatic signature correctly identified over 50% of metastatic patients who tested negative for carcinoembryonic antigen (CEA). Fecal validation indicated that elevated breath biomarkers correlated with an inflammatory response guided by Bacteroides fragilis in CRC. Conclusion: This study introduces a sophisticated AI-aided Mass Spectrometry-based platform capable of identifying novel and feasible breath biomarkers for early-stage CRC detection. The promising results position the platform as an efficient noninvasive screening test for clinical applications, offering potential advancements in early detection and improved survival rates for CRC patients.

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

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

(Invited) Breath and Skin Gas Sensing for Rapid POC Medical Diagnostics

Antiquity’s first known physician, Hippocrates of Cos (460–370 BC), smelled his patients’ breath to diagnose disease and recommend the appropriate remedy. Some medical terms coined then survive today, such as “fetor hepaticus,” which describes the sweet, ketone- and ammonia- rich scent that indicates the late stages of liver failure. Yet, neither breath nor skin analysis, as a noninvasive means of disease detection, are common practice among physicians to-date nor are they available to the general population. Ceramic sensor nanotechnology and nanomedicine are, however, capable of making breath-based diagnostics. VOCs in breath and skin are products of core metabolic processes, while inorganic molecules are related to other health conditions and can be indicators of a potential disease, recent exposure to a drug or an environmental pollutant. Therefore, an abnormally high or low measured concentration of certain breath or skin gases, so-called biomarkers, could potentially provide clues for diagnosing corresponding diseases. This work focuses on electronic olfaction systems which we have developed and used in breath-based testing of Volatile Organic Compound (VOC) biomarkers with great success. It also addresses the concept of skin-gas “smelling” of disease. These gas sensing nanotechnologies allow for the rapid and reliable output signals which indicate the host’s response to infection or metabolic changes. Using the library of specific prints/unique patterns for each of the targeted diseases, the following types of diseases could be potentially diagnosed: Metabolic diseases: diabetes, cholesterol-induced heart disease; Neurological diseases: Alzheimers, Parkinson’s; Chronic diseases: obesity, sleep apnea; Pulmonary diseases: CF, asthma, COPD; GI tract diseases: IBS, colitis; Cancers: breast, lung, pancreatic, colon, and Infectious diseases: flu, COVID-19; as well as diseases commonly found in ICU patients: Urinary tract infections, pneumonia, infections of the blood stream. Focusing on the most common gaseous biomarkers in breath and skin, that is Nitric Oxide and Carbon Monoxide and VOCs (acetone, isoprene, ammonia, alcohols, sulfides) effective discrimination between the diseases mentioned above is possible, by capturing the relative sensor output signals from the detection of each of these biomarkers and analyzing them to reveal the distinct breath print for each disease. We focus here on an electronic olfaction device based on resistive gas sensing utilizing a small number, just four at a time, of metal-oxide sensors that are specifically sensitive to the biomarkers of interest.A single-scan skin testing device comprises an array of gas sensors, each of which satisfies the following stringent conditions: high sensitivity to the target gas, high selectivity, stable response over extended periods of time and rapid response. The concentration of a target gas over a selective sensor is quantified by measurement of the sensor resistance. We also present a novel wearable device interfacing with a smart phone for disease detection and monitoring. This device utilizes conducting polymer actuators, since conductive polymers exhibit a low operating potential (typically <2V), and a high mechanical strength. Quantitative medical diagnostics require simultaneous monitoring of multiple gases because markers are affected differently in different diseases and this device allows for continuous measurement of acetone for diabetes monitoring.

Exhaled VOC Biomarkers from Rats Injected with PMs from Thirty-One Major Cities in China.

Particulate matter (PMs) of different origins can cause diverse health effects. Here, a homemade box was used to facilitate real-time measurements of breath-borne volatile organic compounds (VOCs) by gas chromatography-ion mobility spectrometry. We have tracked exhaled VOC changes in 228 Wistar rats that were injected with water-soluble PM suspension filtrates (after 0.45 μm) from 31 China cities for 1 h to up to 1-6 days during the experiments. Rats exposed to the filtrates exhibited significant changes in breath-borne VOCs within hours, featuring dynamic fluctuations in the levels of acetone, butan-2-one, heptan-2-one-M, acetic acid-M, and ethanol. Subsequently, on the fifth to sixth day after the injection, there was a notable increase in the proportion of aldehydes (including hexanal-M, hexanal-D, pentanal, heptanal-M, and (E)-2-hexenal). The 10 dynamic VOC fingerprint patterns mentioned earlier showcased the capability to indirectly differentiate urban PM toxicity and categorize the 31 cities into four distinct groups based on their health effects. This study provides valuable insights into the mechanisms of exhaled VOCs and underscores their critical role as biomarkers for differentiating the toxicity of different PMs and detecting the early signs of potential diseases. The results from this work also provide a scientific basis for city-specific air pollution control and policy development.

Volatile profiling distinguishes Streptococcus pyogenes from other respiratory streptococcal species.

Sore throat is one of the most common complaints encountered in the ambulatory clinical setting. Rapid, culture-independent diagnostic techniques that do not rely on pharyngeal swabs would be highly valuable as a point-of-care strategy to guide outpatient antibiotic treatment. Despite the promise of this approach, efforts to detect volatiles during oropharyngeal infection have yet been limited. In our research study, we sought to evaluate for specific bacterial volatile organic compounds (VOC) biomarkers in isolated cultures in vitro, in order to establish proof-of-concept prior to initial clinical studies of breath biomarkers. A particular challenge for the diagnosis of pharyngitis due to Streptococcus pyogenes is the likelihood that many metabolites may be shared by S. pyogenes and other related oropharyngeal colonizing bacterial species. Therefore, we evaluated whether sufficient metabolic differences are present, which distinguish the volatile metabolome of Group A streptococci from other streptococcal species that also colonize the respiratory mucosa, such as Streptococcus pneumoniae and Streptococcus intermedius. In this work, we identified 27 discriminatory VOCs (q-values < 0.05), composed of aldehydes, alcohols, nitrogen-containing compounds, hydrocarbons, ketones, aromatic compounds, esters, ethers, and carboxylic acid. From this group of volatiles, we identify candidate biomarkers that distinguish S. pyogenes from other species and establish highly produced VOCs that indicate the presence of S. pyogenes in vitro, supporting future breath-based diagnostic testing for streptococcal pharyngitis. IMPORTANCE Acute pharyngitis accounts for approximately 15 million ambulatory care visits in the United States. The most common and important bacterial cause of pharyngitis is Streptococcus pyogenesis, accounting for 15%-30% of pediatric pharyngitis. Distinguishing between bacterial and viral pharyngitis is key to management in US practice. The culture of a specimen obtained by a throat swab is the standard laboratory procedure for the microbiologic confirmation of pharyngitis; however, this method is time-consuming, which delays appropriate treatment. If left untreated, S. pyogenes pharyngitis may lead to local and distant complications. In this study, we characterized the volatile metabolomes of S. pyogenes and other related oropharyngeal colonizing bacterial species. We identify candidate biomarkers that distinguish S. pyogenes from other species and provide evidence to support future breath-based diagnostic testing for streptococcal pharyngitis.

Highly sensitive and selective Sb2WO6 microspheres in detecting VOC biomarkers in cooked rice: Experimental and density functional theory study.

The palatability of cooked rice is susceptible to the flavor and effective detection of volatile organic compounds (VOCs) can avoid deterioration and improve the taste quality. Herein, hierarchical antimony tungstate (Sb2WO6) microspheres are synthesized through a solvothermal process and the effect of solvothermal temperature on the room temperature gas-sensing properties of gas sensors is investigated. Outstanding sensitivity towards VOC biomarkers (nonanal, 1-octanol, geranyl acetone and 2-pentylfuran) in cooked rice is achieved and the sensors exhibit remarkable stability and reproducibility, which are contributed to the formation of the hierarchical microsphere structure, larger specific surface area, narrower band gap and increased oxygen vacancy content. The kinetic parameters combined with principal component analysis (PCA) effectively distinguish the four VOCs while the enhanced sensing mechanism was substantiated through density functional theory (DFT) calculation. This work provides a strategy for fabricating high performance Sb2WO6 gas sensors which can be practically applied to food industry.

Low-Cost ZnO Spray-Coated Optical Fiber Sensor for Detecting VOC Biomarkers of Diabetes.

A non-invasive optical fiber sensor for detecting volatile organic compounds (VOCs) as biomarkers of diabetes is proposed and experimentally demonstrated. It offers a low-cost and straightforward fabrication approach by implementing a one-step spray coating of a ZnO colloidal solution on a glass optical fiber. The structure of the optical fiber sensor is based on a single-mode fiber-coreless silica fiber-single-mode fiber (SMF-CSF-SMF) structure, where the CSF is the sensor region spliced between two SMFs. The ZnO layer of a higher refractive index coated over the sensing region improves the light interaction with the surrounding medium, leading to sensitivity enhancement. The optical properties, morphology, and elemental composition of the ZnO layer were analyzed. The sensing mechanism of the developed sensor is based on a wavelength interrogation technique showing wavelength shifts when the sensor is exposed to various VOC vapor concentration levels. Various concentrations of the three VOCs (including acetone, isopropanol, and ethanol) ranging from 20% to 100% were tested and analyzed. The sensor noticeably shows a significant response towards acetone vapor, with a better sensitivity of 0.162 nm/% vapor than for isopropanol (0.082 nm/% vapor) and ethanol (0.075 nm/% vapor) vapors. The high sensitivity and selectivity towards acetone, a common biomarker for diabetes, offers the potential for further development of this sensor as a smart healthcare system for monitoring diabetic conditions.

Highly sensitive electrochemical volatile organic compound (Acetone) sensing based on TiO2 Nanocubes @Polyaniline nanostructure

The identification of volatile organic compounds (VOCs) as biomarkers has gained significant attention as a non-invasive point-of-care (POC) diagnostic route. VOC concentrations in the human body offer insights into pathophysiological states and aid in the detection of various diseases. This research article focuses on the synthesis of TiO2 Nanocubes (TNC) and TNC@Polyaniline nanocomposite (TNCP) via sol–gel and in-situ polymerization. Structural and morphological analyses were conducted using XRD, FESEM, and TEM techniques. Additionally, disposable screen-printed carbon electrodes (SPCE) were employed to investigate the electrochemical behaviour. The TNCP/SPCE-based VOC biomarkers exhibited exceptional performance, including a remarkably low detection limit of 0.002 µM and a superior sensitivity of 2869.76 µA mM−1cm−2 within the linear range of 1–1000 μM. These results demonstrate that TNCP holds significant potential as a superior VOC biomarker in biomedical applications, particularly for diabetic diagnosis.

Black phosphorus nanosheets-sensitized Zn-doped α-Fe2O3 nanoclusters for trace acetone detection

Acetone vapor not only poses a significant threat to the ecological environment and human health but serves as a specific volatile organic compounds (VOCs) biomarker within the exhaled breath to distinguish individual diabetes, thereby urgently necessitating a sensitive and selective acetone detection. To this end, conductometric gas sensors featuring black phosphorus nanosheets (BP)-decorated Zn-doped hematite (α-Fe2O3) nanoclusters as the sensitive material were prepared. By subtly modulating the componential ratio and operation temperature, the optimal Zn-doped α-Fe2O3/BP sensor delivered a prominent response of 5.32 and short response/recovery times of 8/68 s toward 0.5 ppm acetone at 160 °C as well as a large sensitivity of 5.3/ppm over the concentration range of 0.1–1.5 ppm. Moreover, favorable selectivity, repeatability, and long-term stability were demonstrated. The superior sensing performance after incorporating a tiny but moderate amount of BP over Zn-doped α-Fe2O3 sensors could be attributed to the consequent enlarged specific surface area, increased oxygen vacancies, and numerous p-n heterojunctions. Furthermore, additional polylactic acid (PLA) encapsulation membrane endowed the as-prepared sensors with a remarkably improved humidity tolerance, showcasing a huge application potential in non-invasive diabetes diagnosis from exhaled breath featuring high-humidity conditions in the future.

Electrospinning composites of polyvinylidene fluoride - carbon black on solid phase microextraction fibers for enhanced detection of volatile organic compounds by gas chromatography-mass spectrometry

Urine contains volatile organic compounds (VOCs) that can be a source of biomarkers for different medical conditions. Headspace solid phase microextraction (HS-SPME) gas chromatography-mass spectrometry (GC–MS) is the gold standard for VOC detection but is time-consuming and expensive. Gas sensors on the other hand are portable and provide rapid results, but qualification of sensing layers is tedious as VOCs need to be tested individually. Our previous study showed that dip coating SPME fibers, using them to extract VOCs from urine and analyzing them by GC–MS is a tool to rapidly characterize sensing elements. The current study aims to extend these findings to demonstrate that HS-SPME GC–MS can identify how electrospinning can enhance the detection of VOCs due to the increased surface area. Herein, previously optimized suspensions of polyvinylidene fluoride-carbon black (PVDF-CB) were deposited on SPME fiber substrates through dip coating and electrospinning, used to extract VOCs from urine and were subject to GC–MS. Electrospinning parameters (applied voltage, flow rate and duration) were optimized, which showed that an applied voltage of 17 kV and a flow rate of 0.25 mL/hr was optimal for electrospinning fibers with 1.5 µm diameter, high coverage and little to no beading. SPME fibers electrospun with PVDF-CB for 10 min were compared to dip coated fibers, and the results showed electrospun fibers had increased efficiency to extract an abundant number of VOCs in urine. These VOCs were enriched when sampled by the electrospun fiber by factors ranging from 1.5 – 5.5. Results also showed that the electrospun fibers had significantly higher reproducibility (p-value < 0.001) relative to dip coated fibers. In summary, electrospinning has the capability of fabricating more sensitive SPME fibers relative to dip coating, and therefore may be utilized in the future for an array of different applications including but not limited to VOC biomarker discovery.

Influence of bacterial and alveolar cell co-culture on microbial VOC production using HS-GC/MS.

Volatile organic compounds (VOCs) found in exhaled breath continue to garner interest as an alternative diagnostic tool in pulmonary infections yet, their clinical integration remains a challenge with difficulties in translating identified biomarkers. Alterations in bacterial metabolism secondary to host nutritional availability may explain this but is often inadequately modelled in vitro. The influence of more clinically relevant nutrients on VOC production for two common respiratory pathogens was investigated. VOCs from Staphylococcus aureus (S.aureus) and Pseudomonas aeruginosa (P.aeruginosa) cultured with and without human alveolar A549 epithelial cells were analyzed using headspace extraction coupled with gas chromatography-mass spectrometry. Untargeted and targeted analyses were performed, volatile molecules identified from published data, and the differences in VOC production evaluated. Principal component analysis (PCA) could differentiate alveolar cells from either S. aureus or P. aeruginosa when cultured in isolation based on PC1 (p = 0.0017 and 0.0498, respectively). However, this separation was lost for S. aureus (p = 0.31) but not for P. aeruginosa (p = 0.028) when they were cultured with alveolar cells. S. aureus cultured with alveolar cells led to higher concentrations of two candidate biomarkers, 3-methyl-1-butanol (p = 0.001) and 3-methylbutanal (p = 0.002) when compared to S. aureus, alone. P. aeruginosa metabolism resulted in less generation of pathogen-associated VOCs when co-cultured with alveolar cells compared to culturing in isolation. VOC biomarkers previously considered indicative of bacterial presence are influenced by the local nutritional environment and this should be considered when evaluating their biochemical origin.

Gas chromatography-mass spectrometry pilot study to identify volatile organic compound biomarkers of childhood obesity with dyslipidemia in exhaled breath.

Childhood obesity affects multiple organs in the body and is associated with both significant morbidity and ultimately premature mortality. Childhood obesity, especially dyslipidemia, can lead to early atherosclerosis and premature cardiovascular disease (CVD) in adulthood. The detection of exhaled volatile organic compounds (VOCs) in the breath offers the opportunity for the discovery of novel disease-specific biomarkers. This study aimed to identify VOCs that correlate with childhood obesity accompanied by dyslipidemia. A total of 82 overweight or obese children between the ages of 8 and 12 years were recruited from the exercise on obesity adolescents in Peking (EXCITING) study (NCT04984005). The breath VOCs of the participants were measured by gas chromatography-mass spectrometry (GC-MS). The classification was performed using principal component analysis (PCA) of the relative abundance of VOCs. The difference between the obese and overweight groups with or without dyslipidemia was analyzed. Among the 82 children, 25 were overweight, of whom 10 had dyslipidemia. The other 57 children were obese, and 17 of them had dyslipidemia. Obese children with dyslipidemia had higher triglycerides and elevated non-high-density lipoprotein-cholesterol compared to overweight children without dyslipidemia. We confirmed 13 compounds based on database well matches (average score > 80) for mass spectra and refractive index. These 13 VOCs were grouped into three chemical functional groups: saturated hydrocarbons, aromatic hydrocarbons and unsaturated aldehydes. For obese children with dyslipidemia, the PCA scatter plot of the three chemical groups was obviously separated from the other groups. Some of the candidates, including heptadecane, naphthalene, and cis-6-nonnenol, were significantly higher in obese children with dyslipidemia than in overweight groups with or without dyslipidemia. A suite of VOCs from three chemical function groups, saturated hydrocarbons, aromatic hydrocarbons, and unsaturated aldehydes, were separated in the obese children with dyslipidemia. Heptadecane, naphthalene, and cis-6-nonenol were significantly elevated in obese children with dyslipidemia. Our findings underscore the potential value of the candidate VOCs for future risk categorization.