Exhaled Breath Research Articles

Wireless Flexible System for Highly Sensitive Ammonia Detection Based on Polyaniline/Carbon Nanotubes

Ammonia (NH3) is a harmful atmospheric pollutant and an important indicator of environment, health, and food safety conditions. Wearable devices with flexible gas sensors offer convenient real-time NH3 monitoring capabilities. A flexible ammonia gas sensing system to support the internet of things (IoT) is proposed. The flexible gas sensor in this system utilizes polyaniline (PANI) with multiwall carbon nanotubes (MWCNTs) decoration as a sensitive material, coated on a silver interdigital electrode on a polyethylene terephthalate (PET) substrate. Gas sensors are combined with other electronic components to form a flexible electronic system. The IoT functionality of the system comes from a microcontroller with Wi-Fi capability. The flexible gas sensor demonstrates commendable sensitivity, selectivity, humidity resistance, and long lifespan. The experimental data procured from the sensor reveal a remarkably low detection threshold of 0.3 ppm, aligning well with the required specifications for monitoring ammonia concentrations in exhaled breath gas, which typically range from 0.425 to 1.8 ppm. Furthermore, the sensor demonstrates a negligible reaction to the presence of interfering gases, such as ethanol, acetone, and methanol, thereby ensuring high selectivity for ammonia detection. In addition to these attributes, the sensor maintains consistent stability across a range of environmental conditions, including varying humidity levels, repeated bending cycles, and diverse angles of orientation. A portable, stable, and effective flexible IoT system solution for real-time ammonia sensing is demonstrated by collecting data at the edge end, processing the data in the cloud, and displaying the data at the user end.

Potential application of C-MoS2 monolayer for identifying lung cancer biomarkers in exhaled breath: a DFT study

Potential application of C-MoS2 monolayer for identifying lung cancer biomarkers in exhaled breath: a DFT study

Proteomic Analysis of Primary Graft Dysfunction in Porcine Lung Transplantation Reveals Alveolar-Capillary Barrier Changes Underlying the High Particle Flow Rate in Exhaled Breath

Primary graft dysfunction (PGD) remains a challenge for lung transplantation (LTx) recipients as a leading cause of poor early outcomes. New methods are needed for more detailed monitoring and understanding of the pathophysiology of PGD. The measurement of particle flow rate (PFR) in exhaled breath is a novel tool to monitor and understand the disease at the proteomic level. In total, 22 recipient pigs underwent orthotopic left LTx and were evaluated for PGD on postoperative day 3. Exhaled breath particles (EBPs) were evaluated by mass spectrometry and the proteome was compared to tissue biopsies and bronchoalveolar lavage fluid (BALF). Findings were confirmed in EBPs from 11 human transplant recipients. Recipients with PGD had significantly higher PFR [686.4 (449.7–8,824.0) particles per minute (ppm)] compared to recipients without PGD [116.6 (79.7–307.4) ppm, p = 0.0005]. Porcine and human EBP proteins recapitulated proteins found in the BAL, demonstrating its utility instead of more invasive techniques. Furthermore, adherens and tight junction proteins were underexpressed in PGD tissue. Histological and proteomic analysis found significant changes to the alveolar-capillary barrier explaining the high PFR in PGD. Exhaled breath measurement is proposed as a rapid and non-invasive bedside measurement of PGD.

Colorectal Cancer Diagnosis through Breath Test Using a Portable Breath Analyzer-Preliminary Data.

Screening methods available for colorectal cancer (CRC) to date are burdened by poor reliability and low patient adherence and compliance. An altered pattern of volatile organic compounds (VOCs) in exhaled breath has been proposed as a non-invasive potential diagnostic tool for distinguishing CRC patients from healthy controls (HC). The aim of this study was to evaluate the reliability of an innovative portable device containing a micro-gas chromatograph in enabling rapid, on-site CRC diagnosis through analysis of patients' exhaled breath. In this prospective trial, breath samples were collected in a tertiary referral center of colorectal surgery, and analysis of the chromatograms was performed by the Biomedical Engineering Department. The breath of patients with CRC and HC was collected into Tedlar bags through a Nafion filter and mouthpiece with a one-way valve. The breath samples were analyzed by an automated portable gas chromatography device. Relevant volatile biomarkers and discriminant chromatographic peaks were identified through machine learning, linear discriminant analysis and principal component analysis. A total of 68 subjects, 36 patients affected by histologically proven CRC with no evidence of metastases and 32 HC with negative colonoscopies, were enrolled. After testing a training set (18 CRC and 18 HC) and a testing set (18 CRC and 14 HC), an overall specificity of 87.5%, sensitivity of 94.4% and accuracy of 91.2% in identifying CRC patients was found based on three VOCs. Breath biopsy may represent a promising non-invasive method of discriminating CRC patients from HC.

Discriminative potential of exhaled breath condensate biomarkers with respect to chronic obstructive pulmonary disease

BackgroundChronic obstructive pulmonary disease (COPD) affecting 334 million people in the world remains a major cause of morbidity and mortality. Proper diagnosis of COPD is still a challenge and largely solely based on spirometric criteria. We aimed to investigate the potential of nitrosative/oxidative stress and related metabolic biomarkers in exhaled breath condensate (EBC) to discriminate COPD patients.MethodsThree hundred three participants were randomly selected from a 15,000-transit worker cohort within the Respiratory disease Occupational Biomonitoring Collaborative Project (ROBoCoP). COPD was defined using the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria as post-bronchodilator ratio of Forced Expiratory Volume in 1st second to Forced Vital Capacity < 0.7 in spirometry validated by an experienced pulmonologist. Discriminative power of biomarker profiles in EBC was analyzed using linear discriminant analyses.ResultsAmongst 300 participants with validated spirometry, 50.3% were female, 52.3 years old in average, 36.0% were current smokers, 12.7% ex-smokers with mean tobacco exposure of 15.4 pack-years. Twenty-one participants (7.0%) were diagnosed as COPD, including 19 new diagnoses, 12 of which with a mild COPD stage (GOLD 1). Amongst 8 biomarkers measured in EBC, combination of 2 biomarkers, Lactate and Malondialdehyde (MDA) significantly discriminated COPD subjects from non-COPD, with a 71%-accuracy, area under the receiver curve of 0.78 (p-value < 0.001), and a negative predictive value of 96%.ConclusionsThese findings support the potential of biomarkers in EBC, in particular lactate and MDA, to discriminate COPD patients even at a mild or moderate stage. These EBC biomarkers present a non-invasive and drugless technique, which can improve COPD diagnosis in the future.

Analyzing the performance of a bio-sensor integrated improved blended learning model for accurate pneumonia prediction

Pneumonia has been considered a life-threatening disease for elderly human beings and those with weakened immune systems in the present medical era. The contemporary scenario highlights the significance of intelligent and automatic handheld devices to detect pneumonia and other pulmonary diseases. Hence, this research has designed an improved blended learning paradigm (IBLP) for real-time pneumonia detection from chest X-rays, and early detection of lung diseases from alveolar gas using biosensors with a graphical processing unit (GPU) developed to overcome and resolve such challenges. It emphasizes the medical applications of blended learning techniques, particularly for identifying pneumonia from chest X-ray images and other pulmonary diseases from exhaled breath using biosensors and support vector machine (SVM). The experimental findings indicate that the blended learning based VGG16 (91.99%) consistently outperforms the VGG19 (88.91%) and ResNet50 (87.02%) model in diagnostic accuracy. IBLP VGG16 provided 95.5% precision, 97.69% F1 score, and a 100% recall rate with no false-negative results. The future of X-ray classification in pneumonia diagnosis will likely involve using artificial intelligence-based systems that can provide accurate and timely analysis of X-ray images, thereby improving patient outcomes and reducing healthcare costs.

An efficient convolutional neural network-extreme gradient boosting hybrid deep learning model for disease detection applications

In this paper, we present an efficient deep-learning hybrid model comprising an extreme gradient boosting (XGBoost) supervised learning algorithm and convolutional neural networks (CNN) for the automated detection of diseases. The proposed model is implemented and tested to detect type-2 diabetes by measuring the acetone concentration in the exhaled breath. Acetone will be present in much higher concentrations in type-2 diabetic patients compared to non-diabetic people. A novel sensing module is designed and implemented in our study to measure the acetone concentration in exhaled breath. The proposed approach delivered good results, with a classification accuracy of 97.14%. The findings of this study show how effectively the proposed detection module functions in disease diagnosis applications. As the detection process is simple and non-invasive, people can undergo routine checks for diabetes with the proposed detection module.

CNN-CatBoost ensemble deep learning model for enhanced disease detection and classification of kidney disease

An efficient deep-learning prediction model for identifying chronic kidney disease (CKD) from exhaled breath is presented in this paper. The concentration of urea will be higher in CKD patients. Salivary urease breaks down the stored urea into ammonia, which is then excreted through breath. Thus, by monitoring the breath ammonia content, it is possible to identify the presence of high urea levels in the body. In this work, a novel sensing module is developed and applied to measure and assess the amount of ammonia in exhaled breath. Moreover, an effective deep learning prediction model that combines the CatBoost algorithm and convolutional neural network (CNN) is used to automate the prediction of disease. The proposed model, which combines the benefits of gradient-boosting and CNN, attained an exceptional accuracy of 98.37%. Experiments are conducted to evaluate the proposed model using real-time data and to assess how well it performs in comparison with existing deep learning methods. Our study's findings demonstrate that kidney disease can be accurately and noninvasively diagnosed using the proposed approach.

347 Aerodynamic Size Distribution of SARS-CoV-2 Aerosol Shedding

OBJECTIVES/GOALS: We designed the Biocascade Exhaled Breath Sampler (BEBS) to characterize viral aerosol shedding among individuals with influenza and other respiratory virus infections. We first aimed to test the BEBS on volunteer COVID-19 cases and report the aerodynamic size distribution of exhaled breath aerosol particles carrying SARS-CoV-2 RNA. METHODS/STUDY POPULATION: From June 15 through December 15, 2022, we recruited 27 PCR-confirmed COVID-19 cases from a college campus and the surrounding community to provide 30-minute breath samples into a well-validated Gesundheit-II (G-II) exhaled breath aerosol sampler. Among these individuals, 17 provided an additional exhaled breath sample into the newly designed BEBS. We quantified samples for viral RNA using reverse transcription digital polymerase chain reaction (RT-dPCR) and determined the viral RNA copies collected within two aerosol size fractions (≤5 µm and &gt;5 µm in diameter) from the G-II, and four aerosol size fractions (&lt;1.15 µm, 1.15–3.2 µm, 3.3–8.2 µm, and &gt;8.2 µm) from the BEBS. RESULTS/ANTICIPATED RESULTS: Individuals with a SARS-CoV-2 Omicron BA.4 or BA.5 infection shed virus in aerosols at an average rate of 7.5x103 RNA copies per 30-minute G-II sample, with 78% of the total RNA in aerosols ≤5 µm in diameter. Among the BEBS samples, 10% of the total viral RNA was detected in aerosols &lt;1.15 µm, 43% in 1.15–3.2 µm, 37% in 3.3–8.2 µm, and 10% in the &gt;8.2 µm size fraction. Based on viral RNA loads, our results indicate that exhaled aerosols ≤3.2 µm contribute the majority of SARS-CoV-2 inhalation exposure. DISCUSSION/SIGNIFICANCE: Our data provide additional evidence that respirable aerosols contribute to the spread of SARS-CoV-2. Thus, our data suggest that mitigation measures designed to reduce infectious aerosol inhalation, such as ventilation and the use of air cleaners and respirators, are needed to control the spread.

Wearable sensor for real-time monitoring of oxidative stress in simulated exhaled breath

High concentrations of H2O2, indicative of increased oxidative stress in the lung, are observed in the exhaled breath of individuals affected by different respiratory diseases. Therefore, measuring H2O2 in breath exhale represents a promising and non-invasive approach for monitoring the onset and progression of these diseases. Herein, we have developed an innovative, inexpensive, and easy-to-use device for the measurement of H2O2 in exhaled breath. The device is based on a silver layer covered with an electrodeposited thin film of chitosan, that ensures the wettability of the sensor in a humid atmosphere. Sensors were calibrated in the aerosol phase using both phosphate buffer solution and cell culture medium. In the buffer, a sensitivity of 0.110 ± 0.0042 μA μM−1 cm−2 (RSD: 4%) and a limit of detection of 30 μM was calculated, while in the cell culture medium, a sensitivity of 0.098 ± 0.0022 μA μM−1 cm−2 (RSD 2%) and a limit of detection of 40 μM were obtained. High selectivity to different interfering species was also verified. Sensors were further tested versus an aerosol phase obtained by nebulizing the culture medium derived from human bronchial epithelial cells that had been exposed to pro-oxidant and antioxidant treatments. The results were comparable with those obtained using the conventional cytofluorimetric method. Finally, sensors were tested in real breath exhalate samples and even after undergoing physical deformations. Data presented herein support that in future applications, these sensors can be integrated into face masks allowing for easy breath monitoring.

Identification of chronic obstructive pulmonary disease using graph convolutional network in electronic nose

Chronic obstructive pulmonary disease (COPD) is a progressive lung dysfunction that can be triggered by exposure to chemicals. This disease can be identified with spirometry, but the patient feels uncomfortable, affecting the diagnosis results. Other disease markers are being investigated, including exhaled breath. This method can be applied easily, is non-invasive, has minimal side effects, and provides accurate results. This study applies the electronic nose method to distinguish healthy people and COPD suspects using exhaled breath samples. Twenty semiconductor gas sensors combined with machine learning algorithms were employed as an electronic nose system. Experimental results show that the frequency feature of the sensor responses used by the principal component analysis (PCA) method combined with graph convolutional network (GCN) can provide the highest accuracy value of 97.5% in distinguishing between healthy and COPD subjects. This method can improve the detection performance of electronic nose systems, which can help diagnose COPD.

Unobtrusive Sensors for Synchronous Monitoring of Different Breathing Parameters in Care Environments.

Respiratory problems are common amongst older people. The rapid increase in the ageing population has led to a need for developing technologies that can monitor such conditions unobtrusively. This paper presents a novel study that investigates Wi-Fi and ultra-wideband (UWB) antenna sensors to simultaneously monitor two different breathing parameters: respiratory rate, and exhaled breath. Experiments were carried out with two subjects undergoing three breathing cases in breaths per minute (BPM): (1) slow breathing (12 BPM), (2) moderate breathing (20 BPM), and (3) fast breathing (28 BPM). Respiratory rates were captured by Wi-Fi sensors, and the data were processed to extract the respiration rates and compared with a metronome that controlled the subjects' breathing. On the other hand, exhaled breath data were captured by a UWB antenna using a vector network analyser (VNA). Corresponding reflection coefficient data (S11) were obtained from the subjects at the time of exhalation and compared with S11 in free space. The exhaled breath data from the UWB antenna were compared with relative humidity, which was measured with a digital psychrometer during the breathing exercises to determine whether a correlation existed between the exhaled breath's water vapour content and recorded S11 data. Finally, captured respiratory rate and exhaled breath data from the antenna sensors were compared to determine whether a correlation existed between the two parameters. The results showed that the antenna sensors were capable of capturing both parameters simultaneously. However, it was found that the two parameters were uncorrelated and independent of one another.

Measurement uncertainty of ethyl alcohol concentration in the exhaled breath of drivers and determination of sobriety at the time of incident

The aim of the study was to determine the components of measurement uncertainty in the concentration of alcohol in exhaled breath and to determine the state of sobriety at the time of incident. Based on the literature review and the authors’ experience in providing opinions for law enforcement and the judiciary, the influence of various factors on the final interpretation of sobriety state is described on the basis of measurement uncertainty of breath analyzers, uncertainty of retrospective and prospective calculations, and uncertainty related to the conversion of alcohol concentrations detected during breath and blood tests. The paper pays particular attention to interpreting the concentrations of ethanol in exhaled breath close to the legal limits of the state of sobriety and the state after alcohol use, or the state after alcohol use and the state of insobriety. Analyzing the results of an exhaled breath test concerning concentrations close to the values of 0.1 mg/dm3 and 0.25 mg/dm3, it is necessary to take into account the factors affecting the measurements obtained, including the measurement uncertainty of the determination of alcohol in exhaled breath, the processes of absorption, distribution and metabolism of ethyl alcohol, and the possibility of the presence of alcohol lingering in the oral cavity. The incorrect execution of measurements of the tested person’s alcohol concentration is also a problematic issue. When determining sobriety state by means of retrospective and prospective calculations, it is important to remember that the uncertainty of the result is affected by a number of factors and depends, among other things, on the information provided by the suspect. Hence, the expert should draw conclusions particularly cautiously and any overestimation or underestimation of the components of uncertainty can lead to erroneous conclusions. Awareness of the uncertainties inherent in the results of a sobriety test or alcohol calculation allows for meaningful interpretation of test results and determination of the sobriety state of the person tested.

Detection of Clostridioides difficile infection by assessment of exhaled breath volatile organic compounds

Clostridioides difficile infection (CDI) is the leading cause of hospital-acquired infective diarrhea. Current methods for diagnosing CDI have limitations; enzyme immunoassays for toxin have low sensitivity and Clostridioides difficile polymerase chain reaction cannot differentiate infection from colonization. An ideal diagnostic test that incorporates microbial factors, host factors, and host-microbe interaction might characterize true infection. Assessing volatile organic compounds (VOCs) in exhaled breath may be a useful test for identifying CDI. To identify a wide selection of VOCs in exhaled breath, we used thermal desorption-gas chromatography-mass spectrometry to study breath samples from 17 patients with CDI. Age- and sex-matched patients with diarrhea and negative C.difficile testing (no CDI) were used as controls. Of the 65 VOCs tested, 9 were used to build a quadratic discriminant model that showed a final cross-validated accuracy of 74%, a sensitivity of 71%, a specificity of 76%, and a receiver operating characteristic area under the curve of 0.72. If these findings are proven by larger studies, breath VOC analysis may be a helpful adjunctive diagnostic test for CDI.

Optimization of volatile organic compounds sampling from dairy cow exhaled breath using polymer-based solid-phase extraction cartridges for gas chromatographic analysis.

We explored appropriate technical setups for the detection of volatile organic compounds (VOCs) from exhaled cow breath by comparing six different polymer-based solid-phase extraction (SPE) cartridges currently on the market for GC-MS screening. Exhaled breath was sampled at a single timepoint from five lactating dairy cows using six different SPE cartridges (Bond Elut ENV; Chromabond HRX; Chromabond HRP; Chromabond HLB; Chromabond HR-XCW and Chromabond HR-XAW). The trapped VOCs were analyzed by Dynamic Headspace Vacuum In-Tube Extraction gas chromatography/mass spectrometry (DHS-V-ITEX-GC-MS). Depending on the SPE cartridge, we detected 1174 to 1312 VOCs per cartridge. Most VOCs were alkenes, alkanes, esters, ketones, alcohols, aldehydes, amines, nitriles, ethers, amides, carboxylic acids, alkynes, azoles, terpenes, pyridines, or sulfur-containing compounds. The six SPE cartridges differed in their specificity for the chemical compounds, with the XAW cartridge showing the best specificity for ketones. The greatest differences between the tested SPE cartridges appeared in the detection of specific VOCs. In total, 176 different VOCs were detected with a match factor >80%. The greatest number of specific VOCs was captured by XAW (149), followed by ENV (118), HLB (117), HRP (115), HRX (114), and XCW (114)). We conclude that the tested SPE cartridges are suitable for VOC sampling from exhaled cow breath, but the SPE cartridge choice enormously affects the detected chemical groups and the number of detected VOCs. Therefore, an appropriate SPE adsorbent cartridge should be selected according to our proposed inclusion criteria. For targeted metabolomics approaches, the SPE cartridge choice depends on the VOCs or chemical compound groups of interest based on our provided VOC list. For untargeted approaches without information on the animals' metabolic condition, we suggest using multi-sorbent SPE cartridges or multiple cartridges per animal.

Wearable sampling of proteins from human exhaled aerosols for nano-liquid chromatography-tandem mass spectrometry analysis: A pilot study.

Human exhaled breath usually contains unique proteins that may provide clues to characterize individual physiological activities and many diseases. However, the concentration of exhaled proteins in exhaled breath is extremely low and usually does not reach the detection limits of all online breath mass spectrometry instruments. Therefore, developing a new breath sampler for collecting and characterizing exhaled proteins is important. In this study, a new mask-based wearable sampler was developed by fixing metal materials into the inner surface of the KN95 mask. Human exhaled proteins could be directly adsorbed onto the metal material while wearing the mask. After sampling, the collected proteins were eluted, digested, and identified using nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS). The adsorption of exhaled proteins was evaluated, showing that modified gold foil is an effective material for collecting exhaled proteins. Various endogenous proteins were successfully identified from exhaled breath, many of which can be potential biomarkers for disease diagnosis. By coupling the newly developed mask sampler with nano-LC-MS/MS, human exhaled proteins were successfully collected and identified. Our results show that the mask sampler is wearable, simple, and convenient, and the method is noninvasive for investigating disease diagnosis and human health.

Ultra-sensitive analysis of exhaled biomarkers in ozone-exposed mice via PAI-TOFMS assisted with machine learning algorithms

Ground-level ozone ranks sixth among common air pollutants. It worsens lung diseases like asthma, emphysema, and chronic bronchitis. Despite recent attention from researchers, the link between exhaled breath and ozone-induced injury remains poorly understood. This study aimed to identify novel exhaled biomarkers in ozone-exposed mice using ultra-sensitive photoinduced associative ionization time-of-flight mass spectrometry and machine learning. Distinct ion peaks for acetonitrile (m/z 42, 60, and 78), butyronitrile (m/z 70, 88, and 106), and hydrogen sulfide (m/z 35) were detected. Integration of tissue characteristics, oxidative stress-related mRNA expression, and exhaled breath condensate free-radical analysis enabled a comprehensive exploration of the relationship between ozone-induced biological responses and potential biomarkers. Under similar exposure levels, C57BL/6 mice exhibited pulmonary injury characterized by significant inflammation, oxidative stress, and cardiac damage. Notably, C57BL/6 mice showed free radical signals, indicating a distinct susceptibility profile. Immunodeficient non-obese diabetic Prkdc-/-/Il2rg-/- (NPI) mice exhibited minimal biological responses to pulmonary injury, with little impact on the heart. These findings suggest a divergence in ozone-induced damage pathways in the two mouse types, leading to alterations in exhaled biomarkers. Integrating biomarker discovery with comprehensive biopathological analysis forms a robust foundation for targeted interventions to manage health risks posed by ozone exposure.

Polycrystalline hollow MOF derived Co3O4 semiconductor to achieve room-temperature ammonia detection in human exhaled breath

Functional gas-sensing devices, on their portable and non-invasive merits, have stimulated a new exploring in clinical detection of gaseous biomarkers for critical diseases such as hepatopathy and nephropathy. Metal oxide semiconductor (MOS) sensors owing to their high sensitivity and facile manufacture appears to be the most promising for integration into illness surveillance devices. However, the high operating temperature of MOS poses great challenge for their realistic application. Herein, MOF-derived Co3O4 sensor series are prepared by facile manipulation of the morphological structure and crystalline state of their maternal ZIF-67. It shows that polycrystalline hollow ZIF-67 (PH-ZIF-67) derived PH-Co3O4 exhibits the optimum response and selectivity to trace NH3 at room-temperature sensing. PH-Co3O4 has a low limit of detection and well moisture resistance, which further behaves a significant quantitative response to actual exhaled breath of patients with hepatic encephalopathy. Furthermore, the scientific link between performance enhancement and structural adjustment is revealed by XPS, EPR, etc. It is found that the construction of the polycrystalline structure increases the oxygen vacancies generated during calcination, and additionally, the hollow structure provides more chemisorbed oxygen on the extra inner surface, which coupled to enhance the number of reactive oxygen sites responsible for the response improvement.

Analysis of exhaled breath condensate in patients with asthma and recurrent wheezing

Wheezing is the most common clinical symptom of bronchial obstructive syndrome. The functions of pulmonary surfactant (PS) depend directly on the level of total phospholipids (TP). This can be used to assess the integrity of the cell membrane structure.&#x0D; Objective. To determine the level of total phospholipids and calcium in exhaled breath condensate in young children with recurrent wheezing and asthma.&#x0D; Materials and methods. The study included 77 patients divided into 3 groups. Group 1 included 30 patients (20 girls and 10 boys) with no more than 2 episodes of recurrent wheezing in their lifetime; Group 2 -10 patients (6 boys and 4 girls) with more than 3 episodes of recurrent wheezing, and 37 patients (15 girls, 22 boys) with asthma. The control group consisted of 20 conditionally healthy children (11 girls, 9 boys). All patients were aged from 3 months to 6 years. The determination of phospholipid levels and calcium (Ca) in the exhaled breath condensate was performed in dynamics - during the period of clinical manifestations and remission.&#x0D; Results. The level of phospholipids in the exhaled breath condensate was the highest in patients of all groups at the stage of clinical manifestations compared with the period of remission, while its lowest level was noted in the control group. During the peak of the disease, the level of Ca in the exhaled breath condensate was significantly lower compared to the period of remission. The control group had the highest level.&#x0D; Conclusions. The exhaled breath condensate's high level of phospholipids in the first days of the disease confirms cell damage in the presence of inflammation. Additionally, the exhaled breath condensate's Ca level is lowest during this period, possibly indicating its involvement in cell damage

Boosting acetone response of p-type Co3O4 sensor via Sn and Ni co-doping for diabetes diagnosis

The hole accumulation layer (HAL) configuration of p-type oxides (including Co3O4) in ambient air causes intrinsically low gas response and hinders their promising applications in exhaled gas analysis. Herein, Sn and Ni co-doping has been proposed to trigger the response of chemiresistive Co3O4 sensor toward acetone (biomarker of diabetes). Via incorporating 1 at% Sn and 0.5 at% Ni doping (Co2.95Sn0.03Ni0.02O4), the response to 100 ppm acetone has been boosted ∼2 orders (from 1.24 to 125.6) at 70 °C, the limit of detection (LoD) has been reduced ∼4 times (from 47.9 to 12.4 ppb), the optimal operation temperature has been decreased from 130 °C to ∼70 °C. Various characterizations suggest that co-doping induced abundant surface asymmetric oxygen vacancy defects (Co-□-Ni, Sn-□-Ni), which facilitate the catalytic oxidation of acetone molecules at relatively low operation temperature. In addition to excellent reproducibility and long-term stability, Co2.95Sn0.03Ni0.02O4 sensor could also operate under highly humid air background and reliably detect acetone concentration in exhaled breath at 150 °C, opening the opportunity for the practical application of p-type oxide sensors for diabetes diagnosis.