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There is a need for reliable biomarkers for the diagnosis of chronic lung allograft dysfunction (CLAD). In this light, we investigated the diagnostic value of exhaled breath analysis using an electronic nose (eNose) for CLAD, CLAD phenotype, and CLAD stage in lung transplant recipients (LTR). We performed eNose measurements in LTR with and without CLAD, visiting the outpatient clinic. Through supervised machine learning, the diagnostic value of eNose for CLAD was assessed in a random training and validation set. Next, we investigated the diagnostic value of the eNose measurements combined with known risk factors for CLAD. Model performance was evaluated using ROC-analysis. We included 152 LTR (median age 60 years, 49% females), of whom 38 with CLAD. eNose-based classification of patients with and without CLAD provided an AUC of 0.86 in the training set, and 0.82 in the validation set. After adding established risk factors for CLAD (age, gender, type of transplantation, time after transplantation and prior occurrence of acute cellular rejection) to a model with the eNose data, the discriminative ability of the model improved to an AUC of 0.94 (p = 0.02) in the training set and 0.94 (p = 0.04) in the validation set. Discrimination between BOS and RAS was good (AUC 0.95). Discriminative ability for other phenotypes (AUCs ranging 0.50-0.92) or CLAD stages (AUC 0.56) was limited. Exhaled breath analysis using eNose is a promising novel biomarker for enabling diagnosis and phenotyping CLAD. eNose technology could be a valuable addition to the diagnostic armamentarium for suspected graft failure in LTR. [ABSTRACT FROM AUTHOR]

• Exhaled breath analysis can predict immunotherapy response in NSCLC patients. • Analysis should be performed early during treatment. • Application could spare ineffective therapy in 76% of non-responding patients. • Secondary resistance can be assessed during course of treatment. • Undesirable events can be prevented and unnecessary costs can be reduced. Exhaled breath analysis by electronic nose (eNose) has shown to be a potential predictive biomarker before start of anti-PD-1 therapy in patients with non-small cell lung carcinoma (NSCLC). We hypothesized that the eNose could also be used as an early monitoring tool to identify responders more accurately at early stage of treatment when compared to baseline. In this proof-of-concept study we aimed to definitely discriminate responders from non-responders after six weeks of treatment. This was a prospective observational study in patients with advanced NSCLC eligible for anti-PD-1 treatment. The efficacy of treatment was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 at 3-month follow-up. We analyzed SpiroNose exhaled breath data of 94 patients (training cohort n = 62, validation cohort n = 32). Data analysis involved signal processing and statistics based on Independent Samples T-tests and Linear Discriminant Analysis (LDA) followed by Receiver Operating Characteristic (ROC) analysis. In the training cohort, a specificity of 73% was obtained at a 100% sensitivity level to identify objective responders. The Area Under the Curve (AUC) was 0.95 (CI: 0.89–1.00). In the validation cohort, these results were confirmed with an AUC of 0.97 (CI: 0.91–1.00). Exhaled breath analysis by eNose early during treatment allows for a highly accurate, non-invasive and low-cost identification of advanced NSCLC patients who benefit from anti-PD-1 therapy. [ABSTRACT FROM AUTHOR]

• Breath test is suited to large-scale screening, which might contribute to early detection of lung cancer without specific symptom. • Gradient boost decision trees algorithm not only exhibits high classification performance but also provides an explainable model. • We borrowed the idea of bootstrap to estimate the uncertainty of diagnosis, which is important to decide the necessity of further detection. Lung cancer is continuously the leading cause of cancer related death, resulting from the lack of specific symptoms at early stage. A large-scale screening method may be the key point to find asymptomatic patients, leading to the reduction of mortality. An alternative method combining breath test and a machine learning algorithm is proposed. 236 breath samples were analyzed by TD-GCMS. Breath profile of each sample is composed of 308 features extracted from chromatogram. Gradient boost decision trees algorithm was employed to recognize lung cancer patients. Bootstrap is performed to simulate real diagnostic practice, with which we evaluated the confidence of our methods. An accuracy of 85 % is shown in 6-fold cross validations. In statistical bootstrap, 72 % samples are marked as "confident", and the accuracy of confident samples is 93 % throughout the cross validations. We have proposed such a non-invasive, accurate and confident method that might contribute to large-scale screening of lung cancer. As a consequence, more asymptomatic patients with early lung cancer may be detected. [ABSTRACT FROM AUTHOR]

Background Immune checkpoint inhibitors have improved survival outcome of advanced non-small-cell lung cancer (NSCLC). However, most patients do not benefit. Therefore, biomarkers are needed that accurately predict response. We hypothesized that molecular profiling of exhaled air may capture the inflammatory milieu related to the individual responsiveness to anti-programmed death ligand 1 (PD-1) therapy. This study aimed to determine the accuracy of exhaled breath analysis at baseline for assessing nonresponders versus responders to anti-PD-1 therapy in NSCLC patients. Methods This was a prospective observational study in patients receiving checkpoint inhibitor therapy using both a training and validation set of NSCLC patients. At baseline, breath profiles were collected in duplicate by a metal oxide semiconductor electronic nose (eNose) positioned at the rear end of a pneumotachograph. Patients received nivolumab or pembrolizumab of which the efficacy was assessed by Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 at 3-month follow-up. Data analysis involved advanced signal-processing and statistics based on independent t -tests followed by linear discriminant and receiver operating characteristic (ROC) analysis. Results Exhaled breath data of 143 NSCLC patients (training: 92, validation: 51) were available at baseline. ENose sensors contributed significantly (P  < 0.05) at baseline in differentiating between patients with different responses at 3 months of anti-PD-1 treatment. The eNose sensors were combined into a single biomarker with an ROC-area under the curve (AUC) of 0.89 [confidence interval (CI) 0.82–0.96]. This AUC was confirmed in the validation set : 0.85 (CI 0.75–0.96). Conclusion ENose assessment was effective in the noninvasive prediction of individual patient responses to immunotherapy. The predictive accuracy and efficacy of the eNose for discrimination of immunotherapy responder types were replicated in an independent validation set op patients. This finding can potentially avoid application of ineffective treatment in identified probable nonresponders. [ABSTRACT FROM AUTHOR]

The development of non-selective sensors as an e-nose system for breath analysis application generates unique breath prints delineating the fundamental body metabolism aiding early diagnosis of lung cancer. In this work, size dependent ZnO quantum dots (QDs) were controlled by pH and finely dispersed on nitrogen and sulfur co-doped porous carbon nanosheet (CNS) for exploration of their chemi-resistive gas sensing property. Application of these nanocomposites to different lung cancer biomarkers (toluene, isoprene acetone and benzene) depicts unique responses to each at different operating temperatures. Particularly, the composite with lower ZnO QDs size on sulfur rich porous carbon nanosheet (SC-SQD) exhibited 2.5 times of the response to toluene compared to the composite with higher QDs size and sulfur poor porous carbon nanosheet (NC-LQD) in the concentration range between 1 ppm and 5 ppm. It was demonstrated that for 5 ppm of toluene, a response of 31.4 was observed with a response and recovery time of 18 s and 58 s, respectively. The sensor exhibited an estimated lower limit of detection of 800 ppb. Besides, the sensor exhibited a gradual decrease (∼9.55%) in response to 5 ppm toluene at 90% humidity. This superior performance can be explained by the density of defect states, reactive oxygen species (ROS) and oxidation states of functionalization. [Display omitted] • Size dependent ZnO quantum dots on N,S co-doped porous carbon are fabricated as gas sensor. • Sensor with rich sulfur doping and low QD size showed superior gas sensing performance. • The sensor exhibited lower detection limit of 800 ppb with R a /R g -1 of 0.92 towards toluene. • The sensor array exhibited cross selective response towards lung cancer biomarkers. [ABSTRACT FROM AUTHOR]

The development of advanced metal-oxide-semiconductor sensing technologies for the detection of Volatile Organic Compounds (VOCs) present in exhaled breath is of great importance for non-invasive, cheap and fast medical diagnostics. Our experimental studies investigate the effects of operating temperature selection and UV-light irradiation on improving the response of WO 3 nanowire sensors towards exhaled breath exposure. Herein, six WO 3 nanowire sensors (both pristine and doped with a range of metal nanoparticles such as Pt, Au, Au/Pt, Ni and Fe) were synthesised via Aerosol-Assisted Chemical Vapour Deposition (AACVD) and characterized by means of Atomic Force Microscopy (AFM), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM) and Energy Dispersive X-ray (EDX-ray). Breath measurements were performed in the dark and under UV-light irradiation at various sensor operating temperatures. The results demonstrate that UV-light irradiation combined with the optimisation of the sensors’ operating temperature can greatly enhance the sensors’ responses towards breath exposure. [ABSTRACT FROM AUTHOR]

Highlights • The Aeonose is a non-invasive diagnostic method that is able to detect lung cancer and its specific subtypes. • E-nose technology can be complementary in the early detection of lung cancer. • The Aeonose is able to discriminate between different types of lung cancer. Abstract Objectives Lung cancer is a leading cause of mortality. Exhaled-breath analysis of volatile organic compounds (VOC's) might detect lung cancer early in the course of the disease, which may improve outcomes. Subtyping lung cancers could be helpful in further clinical decisions. Materials and methods In a prospective, multi-centre study, using 10 electronic nose devices, 144 subjects diagnosed with NSCLC and 146 healthy subjects, including subjects considered negative for NSCLC after investigation, breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands). Also, analyses into subtypes of NSCLC, such as adenocarcinoma (AC) and squamous cell carcinoma (SCC), and analyses of patients with small cell lung cancer (SCLC) were performed. Results Choosing a cut-off point to predominantly rule out cancer resulted for NSCLC in a sensitivity of 94.4%, a specificity of 32.9%, a positive predictive value of 58.1%, a negative predictive value (NPV) of 85.7%, and an area under the curve (AUC) of 0.76. For AC sensitivity, PPV, NPV, and AUC were 81.5%, 56.4%, 79.5%, and 0.74, respectively, while for SCC these numbers were 80.8%, 45.7%, 93.0%, and 0.77, respectively. SCLC could be ruled out with a sensitivity of 88.9% and an NPV of 96.8% with an AUC of 0.86. Conclusion Electronic nose technology with the Aeonose™ can play an important role in rapidly excluding lung cancer due to the high negative predictive value for various, but not all types of lung cancer. Patients showing positive breath tests should still be subjected to further diagnostic testing. [ABSTRACT FROM AUTHOR]

Discrimination and quantitation of ammonia (NH 3) exhaled from human beings using oxide chemiresistors has shown great potential for non-invasive and direct diagnosis of helicobacter pylori gastropathy and end-stage renal disease. Herein, a newly humidity-activated sensor employing AlOOH that synthesized by a simple approach consisting of in-situ etching treatment and hydrothermal method is designed and demonstrated. The humidity-activated AlOOH sensor exhibits a high selectivity and response toward ppb-level of NH 3 under 98 % relative humidity at room temperature, neglecting the water poisoning effect on gas-sensing characteristics. The enhanced adsorption of NH 3 at abundant BrØnsted acid sites, and the effective ammonium ionic transmitting pathway induced by adsorbed moisture are proposed as the reasons behind the humidity-activated gas-sensing mechanism. Analysis using direct current analysis, complex impedance spectra and temperature programmed desorption (TPD) are performed to verify the above proposals. This study provides new pathway for rapid and stable detection of NH 3 in human breath. • The hollow AlOOH possesses mesoporous structure and BrØnsted acid sites. • The mesoporous structure helps to adsorb H 2 O to activate sensing properties. • The abundant BrØnsted acid sites are used to enhance the adsorption of NH 3. • The mesoporous AlOOH sensor exhibits high selectivity and relatively low detection limit to NH 3. [ABSTRACT FROM AUTHOR]

In this work, the development of a point-of-care (PoC) system to capture aerosol from litres of air directly onto a microfluidic lab-on-chip for subsequent analysis is addressed. The system involves an electrostatic precipitator that uses corona charging and electrophoretic transport to capture aerosol droplets onto a microfluidic air-to-liquid interface for downstream analysis. A theoretical study of the governing geometric and operational parameters for optimal electrostatic precipitation is presented. The fabrication of an electrostatic precipitator prototype and its experimental validation using a laboratory-generated aerosolized dye is described. Collection efficiencies were comparable to those of a state-of-the-art Biosampler impinger, with the significant advantage of providing samples that are at least 10 times more concentrated. Finally, we discuss the potential of such a system for breath-based diagnostics. [ABSTRACT FROM AUTHOR]

Abstract: The exhaled breath of humans contains droplets originating from the lung lining fluid. An analysis of these droplets for non-volatile proteinaceous biomarkers holds potential as a non-invasive diagnosis of lung diseases. To ease the interpretation of the diagnostic results, the source strength of the particles should be known und therefore an understanding of the particle generation process is required. It is assumed that during reopening of a collapsed terminal airway a liquid bridge of the lung lining fluid ruptures and droplets are generated. The objective of our experimental and theoretical study was to clarify the mechanisms of droplet generation for quiet breathing patterns by investigating in detail the number flux and the particle size distribution in the exhaled breath. The process of liquid film rupture is modelled by computational fluid dynamics analysis from which the droplet size distribution is calculated. In addition the number emission flux and the droplet size distribution are systematically measured in the exhaled breath of healthy volunteers. The strong increase of the particle emission flux with tidal volume and the good agreement between measured and calculated droplet number distribution both showing droplets primarily in the submicron range confirm the present hypothesis that reopening of collapsed airway structures associated with the rupture of a surfactant film is the physical mechanism of droplet generation. This was hypothesized previously in the literature. [Copyright &y& Elsevier]

Lung cancer (LC) is the leading cause of cancer mortality so, the analysis of exhaled human breath has great significance for early non-invasive diagnosis. Poor selectivity and strong humidity are two bottlenecks for the application of gas sensors to exhaled breath analysis. The development of novel extractive phases for the analysis of exhaled breath by chromatography is therefore a lucrative object. Polyhedral oligomeric silsesquioxanes (POSS) are among the 3D porous materials whose unique properties make them promising coatings for solid-phase microextraction (SPME). Selective enrichment of polar or nonpolar targets depends on the pore size and functional groups on the POSSs. Herein, we disclosed 3D network of POSS-naphthalene diimide (POSS-NDI) and perylene diimide (POSS-PDI) as extractive phases for analysis of lung cancer biomarkers with the capability of capturing different volatile organic compounds (VOCs). The synthesized diimides were fully characterized by FT-IR, multinuclear NMR, SEM, TEM, TGA, and BET analysis. After optimization of all the influential parameters on sorption/desorption, figures of merit for three aldehydes as biomarkers of LC were obtained. The exhaled breath of twelve participants including seven healthy candidates and five cases with LC were examined. The partial least squares-discriminant analysis (PLS-DA) was implemented which led to the proper classification of healthy and LC patients with R2 calibration and cross-validation of 0.9738 and o.7882, respectively. [Display omitted] • POSS-naphthalene dimide and perylene diimide networks were synthesized. • Extraction efficiency of two networks were compared. • POSS-NDI was utilized as extractive phase for lung cancer biomarkers. • POSS-NDI exhibited semi-selective behavior toward the aldehydes biomarkers. • Patients with lung cancer were discriminated from healthy volunteers by PLS-DA. [ABSTRACT FROM AUTHOR]

[Display omitted] • Commercial resistive gas sensors were applied to detect dengue disease. • We developed a portable set-up to analyse exhaled breath samples. • The data processing applied differential parameters to reduce drifts impact. • The random forest classification algorithm in Orange software was utilized. This paper presents a procedure and a set-up of an electronic nose system analyzing exhaled breath to detect the patients suffering from dengue – a mosquito-borne tropical disease. Low-power resistive gas sensors (MiCS-6814, TGS8100) were used to detect volatile organic compounds (VOCs) in the exhaled breath. The end-tidal phase of patients exhaled breath was collected with a BioVOCTM breath sampler. Two strategies were assessed for breath samples measurement: either direct transfer from the BioVOCTM into the sensors test chamber, or storage in Tenax TA sorbent tubes followed by VOCs release through thermal desorption and then transfer into sensors test chamber. DC sensor resistances were recorded and processed by multivariate classifier algorithms to detect infected patients. The experimental studies were run on a group of 26 individuals (16 dengue diagnosed patients and 10 control volunteers). The detection accuracy of dengue patients was over 90%. [ABSTRACT FROM AUTHOR]

The objective of this research was to evaluate the application of an electronic nose and chemometric analysis to discriminate volatile organic compounds between patients with COVID-19, post-COVID syndrome and controls in exhaled breath samples. A cross-sectional study was performed on 102 exhaled breath samples, 42 with COVID-19, 30 with the post-COVID syndrome and 30 control subjects. Breath-print analysis was performed by the Cyranose 320 electronic nose with 32 sensors. Group data were evaluated by Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA), and Support Vector Machine (SVM), and the test's diagnostic power was evaluated through a Receiver Operaring Characteristic curve(ROC curve). The results of the chemometric analysis indicate in the PCA a 97.6% (PC 1 = 95.9%, PC 2 = 1.0%, PC 3 = 0.7%) of explanation of the variability between the groups by means of 3 PCs, the CDA presents a 100% of correct classification of the study groups, SVM a 99.4% of correct classification, finally the PLS-DA indicates an observable separation between the groups and the 12 sensors that were related. The sensitivity, specificity of post-COVID vs. controls value reached 97.6% (87.4%–99.9%) and 100% (88.4%–100%) respectively, according to the ROC curve. As a perspective, we consider that this technology, due to its simplicity, low cost and portability, can support strategies for the identification and follow-up of post-COVID patients. The proposed classification model provides the basis for evaluating post-COVID patients; therefore, further studies are required to enable the implementation of this technology to support clinical management and mitigation of effects. [Display omitted] • Post-COVID syndrome screening is vital in the present pandemic. • Electronic nose is capable of distinguish between VOCs fingerprints in exhaled breath. • Discrimination between COVID-19, post-COVID and controls breath fingerprints. • PCA 96.39% of explanation of the variability. • CDA and SVM 100% and 99.4% of correct classification respectively. [ABSTRACT FROM AUTHOR]

Monomethylated alkanes have been proposed to be contained in exhaled breath, their concentration pattern serving for identification of lung carcinoma, breast carcinoma and rejection of foreign tissue after heart transplant rejection. Improving the accuracy of identification for monomethylated alkanes will enhance work on their biochemical background which presently is unknown. The programmed temperature linear retention indices of all 196 C4–C30 monomethylalkanes on OV-1 stationary phase were measured with an average repeatability of ±0.07 index units (i.u.). The mixture of C9–C30 monomethylalkanes was prepared by methylene insertion reaction to C8–C29 n-alkanes mixture. The preliminary identification of monomethylalkanes was performed on the basis of the dependence of homomorphy factors on the number of carbon atoms of individual homologous series of monomethylalkanes (retention indices extrapolated with s=0.15i.u.). The prediction of retention of isomers with new position of methyl group beginning at higher carbon atoms number, as well as for second, third, fourth, etc., member of homologous series allowed the dependence HP=f(Cn) for first, second, third, etc., members of beginning homologous of monomethylalkane series (retention indices extrapolated with s=0.17i.u.). The identification was confirmed by mass spectrometry. All gas chromatographic unseparated monomethylalkane isomers with methyl-group near the middle of molecule carbon chain were resoluted by mass spectrometric deconvolution. Obtained regular dependences HP=f(Cn) allow precise retention prediction of monomethylalkanes ...C30. [Copyright &y& Elsevier]

Summary: Background: Rhinitis is a major risk factor for asthma, so that evaluation of the lower airways is recommended in patients with rhinitis. Exhaled nitric oxide (FENO) is considered a marker of airway inflammation and it has been found to be useful for the screening of patients with suspected diagnosis of asthma. Our aim was to assess the validity and accuracy of FENO to identify patients with asthma in 48 non-smoking patients with persistent rhinitis and asthma-like symptoms. Methods: Asthma was diagnosed on the basis of 12% improvement in FEV1 after salbutamol or a methocholine PD20FEV1<800μg. Prior to lung function FENO was measured with the single exhalation method at 50ml/s. Results: The geometric mean (95% confidence interval) FENO was significantly higher in the 18/48 asthmatics than in the non-asthmatic patients (60ppb, CI 95%: 50–89, versus 30ppb, CI 95%: 28–45, ). Receiver operating characteristic (ROC) curve for the diagnosis of asthma indicated that FENO is an acceptable discriminator between patients with and without asthma (area under the ROC curve=0.78). None of the asthmatic patients had FENO values<25ppb and all the patients with FENO>100ppb () were asthmatics. The sensitivity and specificity of FENO for detecting asthma, using 36ppb as cut-off point, were 78% and 60% and the positive and negative predictive values were 54% and 82%, respectively. Conclusions: Measuring FENO may be useful for the screening of rhinitic patients with asthma-like symptoms. [Copyright &y& Elsevier]

Advanced stage detection of liver cirrhosis (LCi) would lead to high mortality rates in patients. Therefore, accurate and non-invasive tools for its early detection are highly needed using human emanations that may reflect this disease. Human breath, along with urine and blood, has long been one of the three main biological media for assessing human health and environmental exposure. The primary objective of this study was to explore the potential of using volatile organic compounds (VOCs) assay of exhaled breath and urine samples for the diagnosis of patients with LCi and healthy controls (HC). For this purpose, we used a hybrid electronic nose (E-nose) combining two sensor families, consisting of an array of five commercial chemical gas sensors and six interdigitated chemical gas sensors based on pristine or metal-doped WO 3 nanowires for sensing volatile gases in exhaled breath. A voltammetric electronic tongue (VE-tongue), composed of five working electrodes, was dedicated to the analysis of urinary VOCs using cyclic voltammetry as a measurement technique. 54 patients were recruited for this study, comprising 22 patients with LCi, and 32 HC. The two-sensing systems coupled with pattern recognition methods, namely Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA), were trained to classify data clusters associated with the health status of the two groups. The diagnostic performances of the E-nose and VE-tongue systems were studied by using the receiver operating characteristic (ROC) method. The use of the E-nose or the VE-tongue separately, trained with these appropriate classifiers, showed a slight overlap indicating no clear discrimination between LCi patients and HC. To improve the performance of both electronic sensing devices, an emerging strategy, namely a multi-sensor data fusion technique, was proposed as a second aim to overcome this shortcoming. The data fusion approach of the two systems, at a medium level of abstraction, has demonstrated the ability to assess human health and disease status using non-invasive screening tools based on exhaled breath and urinary VOC analysis. This suggests that exhaled breath as well as urinary VOCs are specific to a disease state and could potentially be used as diagnostic methods. [Display omitted] • LCi has high morbidity and mortality worldwide. • The E-nose and VE-tongue analyzed breath and urine from LCi and HC. • The multi-sensors used separately failed to discriminate between the study groups. • Using data fusion, chemometric tools help achieving good discrimination. • The E-nose and VE-tongue have proven applicable tools for biomedical analysis. [ABSTRACT FROM AUTHOR]

Due to interference by the high moisture content and complicated compositions of human exhaled breath, the trace-level detection of ammonia (NH 3) with desirable selectivity and stability is a large challenge for exhaled breath analysis. Carboxyl-sensitized hydrogels can be activated by moisture to exhibit a significant response and excellent selectivity to NH 3. However, the high activity of carboxyl groups in hydrogels is a double-edged sword, resulting in poor chemical stability during NH 3 detection. Herein, organic acids were embedded into a cross-linked poly(ethylene glycol) diacrylate (PEGDA) hydrogel via thiol−ene photochemistry to form stable hydrogels for NH 3 detection in a humid atmosphere. As a result, under high humidity conditions (80% RH), the optimal sensors exhibited superior selectivity to NH 3 among various interfering gas species, a remarkably high NH 3 response (Z a /Z g =6.20) towards 20 ppm NH 3 , and an extremely low actual detection limit (50 ppb) at room temperature. Moreover, the sensors exhibited excellent chemical stability due to the moderate equilibrium water content of the hydrogel composites and acid dissociation constant of the acid groups. The moisture-activated NH 3 sensing mechanism was thoroughly investigated by complex impedance spectroscopy (CIS), quartz crystal microbalance (QCM) measurements, Fourier transform infrared (FT-IR) spectroscopy and X-ray photoelectron spectroscopy (XPS). To explore the application prospects of cross-linked hydrogel sensors for detecting NH 3 in exhaled breath, a simulated exhaled breath test was also performed. • The strategy for improving the repeatability of hydrogel NH 3 sensors was approached. • The CA/PEGDA sensor exhibited a remarkable NH 3 response (Za/Zg=6.20, 20 ppm) at 80% RH. • The extremely low detection limit (50 ppb) to ammonia at 80% RH was achieved. • The novel sensing mechanism gives a potential way to realize the exhaled breath ammonia detection. [ABSTRACT FROM AUTHOR]

• Air pollution is a significant environmental trigger of asthma. Accurate methods to detect asymptomatic children with asthma are lacking. • This study showed the E-nose had high accuracy to detect air pollution-induced small airway dysfunction among asymptomatic schoolchildren. • Inhalation is a crucial exposure route of air pollutants, but there is still a lack of methods to assess the internal exposure dose of air pollutants. • The breathomics analysis could become a new personal exposure monitoring method to assess the acute respiratory effects of air pollutants. Accurate methods that can be used to detect asymptomatic children with asthma in schools are lacking. Small airway dysfunction is a precursor of asthma. The objective of this study was to use the electronic nose (E-nose) to detect small airway dysfunction in elementary schoolchildren at risk of asthma and evaluate the acute effects of air pollution on respiration. We conducted this study in an elementary school. The study recruited 40 asymptomatic students with a history of asthma and 40 age- and sex-matched children without a history of asthma to take the breath test. We used an E-nose to analyze volatile metabolites and gas chromatography-mass spectrometry to analyze common air pollutants in exhaled breath. After excluding eight subjects in pilot tests, we included 72 subjects in the final analysis of the breath test. The sensitivity of detecting small airway dysfunction with the E-nose was 0.92, the specificity was 0.95, the positive predictive value was 0.79, the negative predictive value was 0.98, the overall accuracy was 0.94, and the leave-one-out cross-validation accuracy was 0.74. The area under the curve was 0.98 (95 % confidence interval: 0.96–1.00). Methyl tert-butyl ether was the only ambient air pollutant that had a significant negative correlation with the maximum mid-expiratory flow (r = -0.33, P < 0.05). The E-nose is highly accurate at detecting small airway dysfunction in children at high risk for asthma. An analysis of exhaled breath can also be used as a personal monitoring method to assess the acute effects of air pollution on respiration. [ABSTRACT FROM AUTHOR]

[Display omitted] • The zine ferrite sensing materials with different Zn/Fe ratios were synthesized by a facile solvothermal route. • Gas sensors based on Zn 0.4 Fe 2.6 O 4 showed the highest response to acetone and great selectivity. • The simulated breath analysis proved that the sensors possessed the advantage and potential in diabetes diagnosis. Seeking for advanced gas sensing materials with high responses to trace acetone gases is of great importance for the diagnosis of diabetes. However, it is still challenging to exhibit a reliable response for gas sensing devices in high humidity environment. Herein, zinc ferrite (Zn x Fe 3-x O 4) sensing materials with different zinc contents of x varying from 0 to 0.6 were synthesized by a facile solvothermal route. The acetone sensing properties of Zn x Fe 3-x O 4 -sensors showed a highly sensitive dependence with Zn/Fe ratio variations. As a result, the Zn 0.4 Fe 2.6 O 4 spheres based sensors displayed the highest response (R a /R g = 157) towards 100 ppm acetone and excellent selectivity compared to the other zinc ferrite samples. The sensor also showed obvious signal to trace acetone in the simulated breath of diabetic patients. Controlling zinc content in ferrites provides an alternative new strategy to realize effective acetone detection and facilitate human exhaled breath analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • The PAA/GA sensor showed excellent selectivity and high response to NH 3 under 80 % RH at room temperature. • The investigation of PAA/GA sensor reveals a novel humidity-activated NH 3 sensing mechanism. • The humidity-activated sensing mechanism gives a potential way to realize the exhaled breath NH 3 detection. High ammonia (NH 3) concentration in the exhaled breath can be used as a crucial biomarker for end-stage of renal disease. Detecting exhaled ammonia via gas sensors remains a huge challenge, because of the complex components and high humidity environment of exhaled gases. Herein, a creative design for NH 3 sensing is realized using a humidity-activated mechanism at room temperature. The investigation reveals that the carboxylate formation reaction specific recognition of NH 3 can be activated under high humid environment. An environmentally friendly and non-toxic biomass hydrogel NH 3 sensor was developed, with poly- l -aspartic acid (PAA) and l -glutamic acid (GA) as the sensing material. The response to 50 ppm NH 3 can reach 9.2 under 80 % RH at room temperature. The humidity-activated sensing mechanism is proposed as a complex process involving the acid-base adsorption, the formation of ionic conduction and the reaction between carboxylic acid groups and NH 3. It is believed that the PAA/GA sensor presents a new pathway for highly selective detection of NH 3 under high humidity condition, which is a promising method for analyzing the exhaled ammonia. [ABSTRACT FROM AUTHOR]

Acetone is known as the breath biomarker of diabetes. In this paper we have reported on a highly sensitive, stable cobalt chromite (CoCr 2 O 4) thick film-based trace acetone sensor with quick response and recovery times promising for the detection of diabetes from exhaled breath. CoCr 2 O 4 nanoparticles were prepared by a cost-effective and easy sol-gel method. The as prepared nanoparticles were well characterized by sophisticated characterization techniques, such as, XRD, FESEM. TEM, HRTEM, XPS, and BET. Further these nanoparticles were exploited to fabricate a Taguchi type chemoresistive sensor using a customized drop coater. The developed sensor was characterized by I–V measurement. The developed sensor exhibited high p-type response towards 1 ppm of acetone vapor (~3.81 folds), and appreciable resolution between 1 ppm, 2 ppm (response = ~4.82 folds), and 5 ppm (response = ~6.64 folds) acetone vapor at 300 °C. Further, the sensor exhibited fast response and recovery times of ~1.65 s and ~62 s, respectively. Also, the response of the sensor to 1 ppm acetone vapor is appreciably higher than that of 0.2 ppm ethanol, 0.25 ppm ammonia vapor, and saturated moisture. Finally, the sensor is stable for at least 6 months and exhibits repeatable measurements. Image 1 • Facile sol-gel synthesis of cobalt chromite (CoCr 2 O 4) nanoparticles. • High p-type response to 1 ppm acetone vapor (~3.81 folds). • Appreciable resolution between 1 ppm, 2 ppm, and 5 ppm acetone vapor. • Minimal cross-sensitivities to 0.25 ppm ammonia, 0.2 ppm ethanol, and saturated moisture. • Fast response (~1.65s) and recovery (~62s) times and prolonged stability (≥6 months). [ABSTRACT FROM AUTHOR]

Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) has been applied in a clinical context as diagnostic tool for breath samples using target biomarkers. Exhaled breath sampling is non-invasive and therefore much more patient friendly compared to bronchoscopy, which is the golden standard for evaluating airway inflammation. In the actual pilot study, 55 exhaled breath samples of children with asthma, cystic-fibrosis and healthy individuals were included. Rather than focusing on the analysis of target biomarkers or on the identification of biomarkers, different data analysis strategies, including a variety of pretreatment, classification and discrimination techniques, are evaluated regarding their capacity to distinguish the three classes based on subtle differences in their full scan SIFT-MS spectra. Proper data-analysis strategies are required because these full scan spectra contain much external, i.e. unwanted, variation. Each SIFT-MS analysis generates three spectra resulting from ion-molecule reactions of analyte molecules with H 3 O+, NO+ and O 2 +. Models were built with Linear Discriminant Analysis, Quadratic Discriminant Analysis, Soft Independent Modelling by Class Analogy, Partial Least Squares - Discriminant Analysis, K-Nearest Neighbours, and Classification and Regression Trees. Perfect models, concerning overall sensitivity and specificity (100% for both) were found using Direct Orthogonal Signal Correction (DOSC) pretreatment. Given the uncertainty related to the classification models associated with DOSC pretreatments (i.e. good classification found also for random classes), other models are built applying other preprocessing approaches. A Partial Least Squares - Discriminant Analysis model with a combined pre-processing method considering single value imputation results in 100% sensitivity and specificity for calibration, but was less good predictive. Pareto scaling prior to Quadratic Discriminant Analysis resulted in 41/55 correctly classified samples for calibration and 34/55 for cross-validation. In future, the uncertainty with DOSC and the applicability of the promising preprocessing methods and models must be further studied applying a larger representative data set with a more extensive number of samples for each class. Nevertheless, this pilot study showed already some potential for the untargeted SIFT-MS application as a rapid pattern-recognition technique, useful in the diagnosis of clinical breath samples. Image 1 • Full scan SIFT-MS used as a non-invasive diagnostic tool for breath-sample analysis. • Spectra submitted to different pretreatment and classification approaches. • Promising PLS-DA models found. • Suitability of DOSC as data pretreatment technique needs further investigation. [ABSTRACT FROM AUTHOR]

• Sensitive, selective and rapid ammonia-sensing. • Suitable for monitoring end-stage chronic kidney disease. • Low limit of detection (212 ppb) and linear range (5–158 ppm) at 150 °C. • High intra-day repeatability and long-term stability over 56 days, once a week. • All by Au nanoparticle-sensitized V 2 O 5 /CuWO 4 designed for exhaled breath analysis. Marigold flower-like V 2 O 5 /CuWO 4 heterojunctions were synthesized and its volatile organic compound (VOC)-sensing properties were tested and significantly enhanced after sensitizing by Au nanoparticles. Detailed characterizations were carried out by SEM, XRD and XPS to determine the morphology, crystal structure, elemental and chemical composition of the sensing materials, respectively. The fabricated gold-sensitized sensor was found to be rapidly responsive (a few seconds), highly sensitive to ammonia with good selectivity as compared to various types of VOCs. The limit of detection and linear range of sensor at 150 °C were 212 ppb and 5–158 ppm, respectively, which is suitable for detection of exhaled breath ammonia of patients at their last stage of chronic kidney disease. Furthermore, it was found to be of high intra-day repeatability, which is properly explained by discussing the mechanism of NH 3 sensing. Very long-term stability of the sensor was investigated over 56 days, once a week. [ABSTRACT FROM AUTHOR]