Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study.

BACKGROUND: Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed. METHODS: Patients with lung cancer, as well as healthy control and diseased control groups, were prospectively recruited from two referral centers between 2019 and 2022. Deep learning models for detecting lung cancer with eNose breathprint were developed using training cohort from one site and then tested on cohort from the other site. Semi-Supervised Domain-Generalized (Semi-DG) Augmentation (SDA) and Noise-Shift Augmentation (NSA) methods with or without fine-tuning was applied to improve performance. RESULTS: In this study, 231 participants were enrolled, comprising a training/validation cohort of 168 individuals (90 with lung cancer, 16 healthy controls, and 62 diseased controls) and a test cohort of 63 individuals (28 with lung cancer, 10 healthy controls, and 25 diseased controls). The model has satisfactory results in the validation cohort from the same hospital while directly applying the trained model to the test cohort yielded suboptimal results (AUC, 0.61, 95% CI: 0.47─0.76). The performance improved after applying data augmentation methods in the training cohort (SDA, AUC: 0.89 [0.81─0.97]; NSA, AUC:0.90 [0.89─1.00]). Additionally, after applying fine-tuning methods, the performance further improved (SDA plus fine-tuning, AUC:0.95 [0.89─1.00]; NSA plus fine-tuning, AUC:0.95 [0.90─1.00]). CONCLUSION: Our study revealed that deep learning models developed for eNose breathprint can achieve cross-site validation with data augmentation and fine-tuning. Accordingly, eNose breathprints emerge as a convenient, non-invasive, and potentially generalizable solution for lung cancer detection. CLINICAL TRIAL REGISTRATION: This study is not a clinical trial and was therefore not registered.

Single and multiple breath nitrogen washout compared with the methacholine test in patients with suspected asthma and normal spirometry.

BACKGROUND: Methods used to assess ventilation heterogeneity through inert gas washout have been standardised and showed high sensitivity in diagnosing many respiratory diseases. We hypothesised that nitrogen single or multiple breath washout tests, respectively nitrogen single breath washout (N2SBW) and nitrogen multiple breath washout (N2MBW), may be pathological in patients with clinical suspicion of asthma but normal spirometry. Our aim was to assess whether N2SBW and N2MBW are associated with methacholine challenge test (MCT) results in this population. We also postulated that an alteration in SIII at N2SBW could be detected before the 20% fall of forced expiratory volume in the first second (FEV1) in MCT. STUDY DESIGN AND METHODS: This prospective, observational, single-centre study included patients with suspicion of asthma with normal spirometry. Patients completed questionnaires on symptoms and health-related quality-of-life and underwent the following lung function tests: N2SBW (SIII), N2MBW (Lung clearance index (LCI), Scond, Sacin), MCT (FEV1 and sGeff) as well as N2SBW between each methacholine dose. RESULTS: 182 patients were screened and 106 were included in the study, with mean age of 41.8±14 years. The majority were never-smokers (58%) and women (61%). MCT was abnormal in 48% of participants, N2SBW was pathological in 10.6% at baseline and N2MBW abnormality ranged widely (LCI 81%, Scond 18%, Sacin 43%). The dose response rate of the MCT showed weak to moderate correlation with the subsequent N2SBW measurements during the provocation phases (ρ 0.34-0.50) but no correlation with N2MBW. CONCLUSIONS: Both MCT and N2 washout tests are frequently pathological in patients with suspicion of asthma with normal spirometry. The weak association and lack of concordance across the tests highlight that they reflect different but not interchangeable pathological pathways of the disease.

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.

Efficient detection of nitric oxide a biomarker associated with COVID19 via N, P co-doped C60 fullerene: a computational study.

CONTEXT: Coronavirus (COVID-19) is a novel respiratory viral infection, causing a relatively large number of deaths especially in people who underly lung diseases such as chronic obstructive pulmonary and asthma, and humans are still suffering from the limited testing capacity. In this article, a solution is proposed for the detection of COVID-19 viral infections through the analysis of exhaled breath gasses, i.e., nitric oxide, a prominent biomarker released by respiratory epithelial, as a non-invasive and time-saving approach. Here, we designed a novel and low-cost N and P co-doped C60 fullerene-based breathalyzer for the detection of NO gas exhaled from the respiratory epithelial cells. This breathalyzer shows a quick response to the detection of NO gas by directly converting NO to NO2 without passing any energy barrier (0 kcal/mol activation energy). The recovery time of breathalyzer is very short (0.98 × 103 s), whereas it is highly selective for NO sensing in the mixture of CO2 and H2O gasses. The study provides an idea for the synthesis of low-cost (compared to previously reported Au atom decorated nanostructure and metal-based breathalyzer), efficient, and highly selective N and P co-doped C60 fullerene-based breathalyzer for COVID-19 detection. METHODS: The geometries of N and P-doped systems and gas molecules are simulated using spin-polarized density functional theory calculations.

Predicting Helicobacter pylori infection from endoscopic features.

BACKGROUND: Helicobacter pylori infection, prevalent in more than half of the global population, is associated with various gastrointestinal diseases, including peptic ulcers and gastric cancer. The effectiveness of early diagnosis and treatment in preventing gastric cancer highlights the need for improved diagnostic methods. This study aimed to develop a simple scoring system based on endoscopic findings to predict H. pylori infection. METHODS: A retrospective analysis was conducted on 1,007 patients who underwent upper gastrointestinal endoscopy at Asan Medical Center from January 2019 to December 2021. Exclusion criteria included prior H. pylori treatment, gastric surgery, or gastric malignancies. Diagnostic techniques included rapid urease and 13C-urea breath tests, H. pylori culture, and assessment of endoscopic features following the Kyoto gastritis classification. A new scoring system based on endoscopic findings including regular arrangement of collecting venules (RAC), nodularity, and diffuse or spotty redness was developed for predicting H. pylori infection, utilizing logistic regression analysis in the development set. RESULTS: The scoring system demonstrated high predictive accuracy for H. pylori infection in the validation set. Scores of 2 and 3 were associated with 96% and 99% infection risk, respectively. Additionally, there was a higher prevalence of diffuse redness and sticky mucus in cases where the initial H. pylori eradication treatment failed. CONCLUSION: Our scoring system showed potential for improving diagnostic accuracy in H. pylori infection. H. pylori testing should be considered upon spotty redness, diffuse redness, nodularity, and RAC absence on endoscopic findings as determined by the predictive scoring system.

Features of exocrine pancreatic insufficiency in patients with non-alcoholic fatty liver disease in combination with type 2 diabetes and COVID-19.

OBJECTIVE: Aim: The aim of the research was to study the features of pancreatic exocrine insufficiency (EPI) in patients with nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (DM) at COVID-19. PATIENTS AND METHODS: Materials and Methods: 72 patients with NAFLD and COVID-19 were examined. The patients have been divided into two groups: group 1 included 42 patients with NAFLD and insulin resistance (IR); group 2 consisted of 30 patients with NAFLD in the combination with type 2 DM. EPI was detected by 13С-mixed triglyceride breath test (13С-MTBT) in all the patients. RESULTS: Results: The result of 13С-MTBT indicates EPI in the examined subjects of the 2 group. A significant decrease in the maximum concentration of 13СО2 between 150 and 210 min was also diagnosed in group 1 patients. research (up to 8.2 ± 0.9% - p < 0.05), however, the total concentration of 13СО2 at the end of 360 min. the study reached only 27.7 ± 1.1% (p < 0.05). CONCLUSION: Conclusions: Based on the results of laboratory-instrumental methods of research, patients with NAFLD and type 2 diabetes with COVID-19 were diagnosed with severe EPI. The results of 13С-MTBT in NAFLD and IR with COVID-19 indicate a decrease in the functional reserves of the pancreas and the formation of its EPI.

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

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

Unravelling the origin of isoprene in the human body-a forty year Odyssey.

In the breath research community's search for volatile organic compounds that can act as non-invasive biomarkers for various diseases, hundreds of endogenous volatiles have been discovered. Whilst these systemic chemicals result from normal and abnormal metabolic activities or pathological disorders, to date very few are of any use for the development of clinical breath tests that could be used for disease diagnosis or to monitor therapeutic treatments. The reasons for this lack of application are manifold and complex, and these complications either limit or ultimately inhibit the analytical application of endogenous volatiles for use in the medical sciences. One such complication is a lack of knowledge on the biological origins of the endogenous volatiles. A major exception to this is isoprene. Since 1984, i.e. for 40 years, it has been generally accepted that the pathway to the production of human isoprene, and hence the origin of isoprene in exhaled breath, is through cholesterol biosynthesis via the mevalonate (MVA) pathway within the liver. However, various studies between 2001 and 2012 provide compelling evidence that human isoprene is produced in skeletal muscle tissue. A recent multi-omic investigation of genes and metabolites has revealed that this proposal is correct by showing that human isoprene predominantly results from muscular lipolytic cholesterol metabolism. Despite the overwhelming proof for a muscular pathway to isoprene production in the human body, breath research papers still reference the hepatic MVA pathway. The major aim of this perspective is to review the evidence that leads to a correct interpretation for the origins of human isoprene, so that the major pathway to human isoprene production is understood and appropriately disseminated. This is important, because an accurate attribution to the endogenous origins of isoprene is needed if exhaled isoprene levels are to be correctly interpreted and for assessing isoprene as a clinical biomarker.

Establishing breath as a biomarker platform-take home messages from the Breath Biopsy Conference 2023.

The annual Breath Biopsy Conference hosted by Owlstone Medical gathers together the leading experts, early career researchers, and physicians working with breath as a biomarker platform for clinical purposes. The current topics in breath research are discussed and presented, and an overarching topical theme is identified and discussed as part of an expert panel to close the conference. The profiling of normal breath composition and the establishment of standards for analyzing breath compared to background signal were two important topics that were major focuses of this conference, as well as important innovative progress that has been made since last year, including the development of a non-invasive breath test for lung cancer and liver disease. This meeting report offers an overview of the key take-home messages from the various presentations, posters, and discussions from the conference.

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.

Fructose malabsorption and fructan malabsorption are associated in patients with irritable bowel syndrome.

BACKGROUND: Food malabsorption and intolerance is implicated in gastrointestinal symptoms among patients with irritable bowel syndrome (IBS). Key triggers include fructose and fructan. Prior studies examined fructose and fructan malabsorption separately in IBS patients. None have concurrently assessed both within the same patient group. We aimed to investigate the association between fructose and fructan malabsorption in the same patients with IBS using hydrogen breath testing (HBT). METHODS: We retrospectively identified patients with IBS who underwent fructose and fructan HBTs and abstracted their results from the electronic medical record. Fructose and fructan HBTs were performed by administering a 25 g fructose solution or 10 g fructan solution, followed by breath hydrogen readings every 30 min for 3 h. Patients were positive for fructose or fructan malabsorption if breath hydrogen levels exceeded 20 ppm. RESULTS: Of 186 IBS patients, 71 (38.2%) were positive for fructose malabsorption and 91 (48.9%) were positive for fructan malabsorption. Of these patients, 42 (22.6%) were positive for fructose malabsorption and fructan malabsorption. Positive fructose HBT readings were significantly associated with positive fructan HBT readings (p = 0.0283). Patients positive for fructose malabsorption or fructan malabsorption had 1.951 times higher odds of testing positive for the other carbohydrate. CONCLUSIONS: Our results reveal a clinically significant association between fructose malabsorption and fructan malabsorption in patients with IBS. Fructan malabsorption should be assessed in patients with fructose malabsorption, and vice versa. Further studies are required to identify the mechanisms underlying our findings.

Measurement uncertainty of ethyl alcohol concentration in the exhaled breath of drivers and determination of sobriety at the time of incident. / Niepewnosc pomiaru stezenia alkoholu etylowego w powietrzu wydychanym kierowców oraz ustalenia stanu trzezwosci w chwili zdarzenia.

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.

Oxygen Vacancy-Rich Bimetallic Au@Pt Core-Shell Nanosphere-Functionalized Electrospun ZnFe2O4 Nanofibers for Chemiresistive Breath Acetone Detection.

Sensitive and selective acetone detection is of great significance in the fields of environmental protection, industrial production, and individual health monitoring from exhaled breath. To achieve this goal, bimetallic Au@Pt core-shell nanospheres (BNSs) functionalized-electrospun ZnFe2O4 nanofibers (ZFO NFs) are prepared in this work. Compared to pure NFs-650 analogue, the ZFO NFs/BNSs-2 sensor exhibits a stronger mean response (3.32 vs 1.84), quicker response/recovery speeds (33 s/28 s vs 54 s/42 s), and lower operating temperature (188 vs 273 °C) toward 0.5 ppm acetone. Note that an experimental detection limit of 30 ppb is achieved, which ranks among the best cases reported thus far. Besides the demonstrated excellent repeatability, humidity-enhanced response, and long-term stability, the selectivity toward acetone is remarkably improved after BNSs functionalization. Through material characterizations and DFT calculations, all these improvements could be attributed to the boosted oxygen vacancies and abundant Schottky junctions between ZFO NFs and BNSs, and the synergistic catalytic effect of BNSs. This work offers an alternative strategy to realize selective subppm acetone under high-humidity conditions catering for the future requirements of noninvasive breath diabetes diagnosis in the field of individual healthcare.

The Fractional exhaled Nitric Oxide (FeNO)- test as add-on test in the diagnostic work-up of asthma: a study protocol.

BACKGROUND: Asthma is a common disease characterized by chronic inflammation of the lower airways, bronchial hyperactivity, and (reversible) airway obstruction. The Global Initiative of Asthma Guideline recommends a flowchart to diagnose asthma with first-step spirometry with reversibility and a bronchial challenge test (BPT) with histamine or methacholine as a second step [1]. The BPT is considered burdensome, time-consuming for patients and staff, can cause side effects, and is expensive. In addition, this test strongly encumbers lung function capacity. Elevated Nitric Oxide (NO) is associated with airway eosinophilic inflammation in asthma patients and can be measured in exhaled air with the Fractional exhaled (Fe) NO-test. This low-burden FeNO-test could be used as an 'add-on' test in asthma diagnostics [2, 3]. METHODS AND ANALYSIS: This multi-center prospective study (Trial number: NCT06230458) compares the 'standard asthma diagnostic work-up' (spirometry with reversibility and BPT) to the 'new asthma diagnostics work-up' (FeNO-test as an intermediate step between the spirometry with reversibility and the BPT), intending to determine the impact of the FeNO-based strategy, in terms of the number of avoided BPTs, cost-effectiveness and reduced burden to the patient and health care. The cost reduction of incorporating the FeNO-test in the new diagnostic algorithm will be established by the number of theoretically avoided BPT. The decrease in burden will be studied by calculating differences in the Visual Analogue Scale (VAS) -score and Asthma Quality of Life Questionnaire (AQLQ) -score after the BPT and FeNO-test with an independent T-test. The accuracy of the FeNO-test will be calculated by comparing the FeNO-test outcomes to the (gold standard) BPTs outcomes in terms of sensitivity and specificity. The intention is to include 171 patients. ETHICS AND DISSEMINATION: The local medical ethics committee approved the proposed study and is considered a low-burden and risk-low study. The local medical ethics committee registration number: R23.005. STRENGTHS AND LIMITATIONS OF THIS STUDY: Strengths: This is the first study that investigates the value of the FeNO-test (cut off ≥ 50 ppb) as an add-on test, to determine the impact of the FeNO-based strategy, in terms of the number of avoided BPTs, cost-effectiveness, and reduced burden on the patient and health care. LIMITATIONS: High FeNO levels may also be observed in other diseases such as eosinophilic chronic bronchitis and allergic rhinitis. The FeNO-test can be used to rule in a diagnosis of asthma with confidence, however, due to the poor sensitivity it is not suitable to rule out asthma.

Enhancing diagnosis of benign lesions and lung cancer through ensemble text and breath analysis: a retrospective cohort study.

Early diagnosis of lung cancer (LC) can significantly reduce its mortality rate. Considering the limitations of the high false positive rate and reliance on radiologists' experience in computed tomography (CT)-based diagnosis, a multi-modal early LC screening model that combines radiology with other non-invasive, rapid detection methods is warranted. A high-resolution, multi-modal, and low-differentiation LC screening strategy named ensemble text and breath analysis (ETBA) is proposed that ensembles radiology report text analysis and breath analysis. In total, 231 samples (140 LC patients and 91 benign lesions [BL] patients) were screened using proton transfer reaction-time of flight-mass spectrometry and CT screening. Participants were randomly assigned to a training set and a validation set (4:1) with stratification. The report section of the radiology reports was used to train a text analysis (TA) model with a natural language processing algorithm. Twenty-two volatile organic compounds (VOCs) in the exhaled breath and the prediction results of the TA model were used as predictors to develop the ETBA model using an extreme gradient boosting algorithm. A breath analysis model was developed based on the 22 VOCs. The BA and TA models were compared with the ETBA model. The ETBA model achieved a sensitivity of 94.3%, a specificity of 77.3%, and an accuracy of 87.7% with the validation set. The radiologist diagnosis performance with the validation set had a sensitivity of 74.3%, a specificity of 59.1%, and an accuracy of 68.1%. High sensitivity and specificity were obtained by the ETBA model compared with radiologist diagnosis. The ETBA model has the potential to provide sensitivity and specificity in CT screening of LC. This approach is rapid, non-invasive, multi-dimensional, and accurate for LC and BL diagnosis.

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.

User authentication system based on human exhaled breath physics.

This work, in a pioneering approach, attempts to build a biometric system that works purely based on the fluid mechanics governing exhaled breath. We test the hypothesis that the structure of turbulence in exhaled human breath can be exploited to build biometric algorithms. This work relies on the idea that the extrathoracic airway is unique for every individual, making the exhaled breath a biomarker. Methods including classical multi-dimensional hypothesis testing approach and machine learning models are employed in building user authentication algorithms, namely user confirmation and user identification. A user confirmation algorithm tries to verify whether a user is the person they claim to be. A user identification algorithm tries to identify a user's identity with no prior information available. A dataset of exhaled breath time series samples from 94 human subjects was used to evaluate the performance of these algorithms. The user confirmation algorithms performed exceedingly well for the given dataset with over 97% true confirmation rate. The machine learning based algorithm achieved a good true confirmation rate, reiterating our understanding of why machine learning based algorithms typically outperform classical hypothesis test based algorithms. The user identification algorithm performs reasonably well with the provided dataset with over 50% of the users identified as being within two possible suspects. We show surprisingly unique turbulent signatures in the exhaled breath that have not been discovered before. In addition to discussions on a novel biometric system, we make arguments to utilise this idea as a tool to gain insights into the morphometric variation of extrathoracic airway across individuals. Such tools are expected to have future potential in the area of personalised medicines.

Effect of overweight/obesity on relationship between fractional exhaled nitric oxide and airway hyperresponsiveness in Chinese elderly patients with asthma.

Purpose: This retrospective study investigates the influence of overweight and obesity status on pulmonary function, airway inflammatory markers, and airway responsiveness in elderly asthma patients. Methods: Patients with asthma older than 65 years old who completed a bronchial provocation test (BPT) or bronchial dilation test (BDT) and a fractional exhaled nitric oxide (FeNO) test between December 2015 and June 2020 were identified retrospectively for this study. All of the patients were categorized into overweight/obesity and non-obesity groups based on their BMI. Pulmonary function test (PFT) and FeNO measurements were accomplished according to the 2014 recommendations of the Chinese National Guidelines of Pulmonary Function Test and American Thoracic Society/European Respiratory Society recommendations, respectively. Results: A total of 136 patients with an average age of 71.2 ± 5.40 years were identified. The average BMI was 23.8 ± 3.63, while the value of FeNO was 42.3 ± 38.4 parts per billion (ppb). In contrast to the non-obesity group, which had a value of 48.8 ± 43.1 ppb for FeNO, the overweight/obesity group had a significant lower value of 35.4 ± 31.4 ppb. There was no significant difference in the proportion of individuals with high airway hyperresponsiveness between the overweight/obesity and non-obesity groups (96 patients in total). Multiple linear regression analysis established an inverse correlation between FeNO and Provocation concentration causing a 20% fall in FEV1(PC20) but excluded significant relationships with age and BMI. The model's R is 0.289, and its p value is 0.045. Conclusion: The elderly Chinese Han asthmatics with overweight/obesity had lower FeNO levels than those with non-obese according to our findings. In addition, the FeNO level was inversely correlated between FeNO levels and PC20 in elderly asthmatics.

Exhaled breath condensate contains extracellular vesicles (EVs) that carry miRNA cargos of lung tissue origin that can be selectively purified and analyzed.

Lung diseases, including lung cancer, are rising causes of global mortality. Despite novel imaging technologies and the development of biomarker assays, the detection of lung cancer remains a significant challenge. However, the lung communicates directly with the external environment and releases aerosolized droplets during normal tidal respiration, which can be collected, stored and analzsed as exhaled breath condensate (EBC). A few studies have suggested that EBC contains extracellular vesicles (EVs) whose microRNA (miRNA) cargos may be useful for evaluating different lung conditions, but the cellular origin of these EVs remains unknown. In this study, we used nanoparticle tracking, transmission electron microscopy, Western blot analyses and super resolution nanoimaging (ONi) to detect and validate the identity of exhaled EVs (exh-EVs). Using our customizable antibody-purification assay, EV-CATCHER, we initially determined that exh-EVs can be selectively enriched from EBC using antibodies against three tetraspanins (CD9, CD63 and CD81). Using ONi we also revealed that some exh-EVs harbour lung-specific proteins expressed in bronchiolar Clara cells (Clara Cell Secretory Protein [CCSP]) and Alveolar Type II cells (Surfactant protein C [SFTPC]). When conducting miRNA next generation sequencing (NGS) of airway samples collected at five different anatomic levels (i.e., mouth rinse, mouth wash, bronchial brush, bronchoalveolar lavage [BAL] and EBC) from 18 subjects, we determined that miRNA profiles of exh-EVs clustered closely to those of BAL EVs but not to those of other airway samples. When comparing the miRNA profiles of EVs purified from matched BAL and EBC samples with our three tetraspanins EV-CATCHER assay, we captured significant miRNA expression differences associated with smoking, asthma and lung tumor status of our subjects, which were also reproducibly detected in EVs selectively purified with our anti-CCSP/SFTPC EV-CATCHER assay from the same samples, but that confirmed their lung tissue origin. Our findings underscore that enriching exh-EV subpopulations from EBC allows non-invasive sampling of EVs produced by lung tissues.