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Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm's achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.

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Different techniques have been proposed to measure antibiotic levels within the lung parenchyma; however, their use is limited because they are invasive and associated with adverse effects. We explore whether beta-lactam antibiotics could be measured in exhaled breath condensate collected from heat and moisture exchange filters (HMEFs) and correlated with the concentration of antibiotics measured from bronchoalveolar lavage (BAL). We designed an observational study in patients undergoing mechanical ventilation, which required a BAL to confirm or discard the diagnosis of pneumonia. We measured and correlated the concentration of beta-lactam antibiotics in plasma, epithelial lining fluid (ELF), and exhaled breath condensate collected from HMEFs. We studied 12 patients, and we detected the presence of antibiotics in plasma, ELF, and HMEFs from every patient studied. The concentrations of antibiotics were very heterogeneous over the population studied. The mean antibiotic concentration was 293.5 (715) ng/mL in plasma, 12.3 (31) ng/mL in ELF, and 0.5 (0.9) ng/mL in HMEF. We found no significant correlation between the concentration of antibiotics in plasma and ELF (R2 = 0.02, p = 0.64), between plasma and HMEF (R2 = 0.02, p = 0.63), or between ELF and HMEF (R2 = 0.02, p = 0.66). We conclude that beta-lactam antibiotics can be detected and measured from the exhaled breath condensate accumulated in the HMEF from mechanically ventilated patients. However, no correlations were observed between the antibiotic concentrations in HMEF with either plasma or ELF.

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OBJECTIVE(S): To investigate the predictive performances of exhaled breath volatile organic compounds (VOCs) for development of bronchopulmonary dysplasia (BPD) in infants born preterm. METHODS: Exhaled breath was collected from infants born <30 weeks' gestation at days 3 and 7 of life. Ion fragments detected by gas chromatography-mass spectrometry analysis were used to derive and internally validate a VOC prediction model for moderate or severe BPD at 36 weeks of postmenstrual age. We tested the predictive performance of the National Institute of Child Health and Human Development (NICHD) clinical BPD prediction model with and without VOCs. RESULTS: Breath samples were collected from 117 infants (mean gestation 26.8 ± 1.5 weeks). Thirty-three percent of the infants developed moderate or severe BPD. The VOC model showed a c-statistic of 0.89 (95% CI 0.80-0.97) and 0.92 (95% CI 0.84-0.99) for the prediction of BPD at days 3 and 7, respectively. Adding the VOCs to the clinical prediction model in noninvasively supported infants resulted in significant improvement in discriminative power on both days (day 3: c-statistic 0.83 vs 0.92, P value .04; day 7: c-statistic 0.82 vs 0.94, P value .03). CONCLUSIONS: This study showed that VOC profiles in exhaled breath of preterm infants on noninvasive support in the first week of life differ between those developing and not developing BPD. Adding VOCs to a clinical prediction model significantly improved its discriminative performance.

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The objective of this work was to evaluate the use of an electronic nose and chemometric analysis to discriminate global patterns of volatile organic compounds (VOCs) in breath of postCOVID syndrome patients with pulmonary sequelae. A cross-sectional study was performed in two groups, the group 1 were subjects recovered from COVID-19 without lung damage and the group 2 were subjects recovered from COVID-19 with impaired lung function. The VOCs analysis was executed using a Cyranose 320 electronic nose with 32 sensors, applying principal component analysis (PCA), Partial Least Square-Discriminant Analysis, random forest, canonical discriminant analysis (CAP) and the diagnostic power of the test was evaluated using the ROC (Receiver Operating Characteristic) curve. A total of 228 participants were obtained, for the postCOVID group there are 157 and 71 for the control group, the chemometric analysis results indicate in the PCA an 84% explanation of the variability between the groups, the PLS-DA indicates an observable separation between the groups and 10 sensors related to this separation, by random forest, a classification error was obtained for the control group of 0.090 and for the postCOVID group of 0.088 correct classification. The CAP model showed 83.8% of correct classification and the external validation of the model showed 80.1% of correct classification. Sensitivity and specificity reached 88.9% (73.9%-96.9%) and 96.9% (83.7%-99.9%) respectively. It is considered that this technology can be used to establish the starting point in the evaluation of lung damage in postCOVID patients with pulmonary sequelae.

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The quarrying is considered a precarious occupation with high toxicity, is an informal economic activity that employs low technology, limited protection, and poses a risk to workers and their families. In quarrying, silica dust is generated and there is also occupational exposure to significant mixtures of pneumotoxic pollutants, including mineral dust (crystalline silica, carbon or cement, polycyclic aromatic hydrocarbons (PAHs), solvents, and others, which are aggravated by the lack of use of protective equipment, causing irreversible damage to the worker's respiratory health. Thus, the objective of this work focused on the evaluation of the respiratory health of artisan stonemasons in San Luis Potosí, Mexico through the study of exhaled breath condensate (EBC) (pH, pro-inflammatory cytokines) as well as the study of the exposure to pollutants present in the work area (PAHs, toluene, and 2.5 µm particulate matter) through biomarkers of exposure (hippuric acid and hydroxylated metabolites of PAHs). The results show the presence of crystalline SiO2 in 100% of the samples analyzed; the PM2.5 concentrations were 5 to 10 times the permitted levels. Regarding exposure to PAHs, all the stonemasons presented urine concentrations of at least 5 of the OH-PAHs evaluated; 9-OH-FLU occurred at higher concentrations of 171.2 (122.7-279.4) µg L-1; hippuric acid, which was present in 100% of the workers evaluated in concentrations of 283.4 (27.72-1119) mg L-1, 100% of which were above the values established for occupational scenarios. The pH values obtained for the EBC samples were presented at an average of 7.07 (6.33-7.66). Pro-inflammatory cytokines were present in 86.1% of the study population. The cytokine that was found in higher concentrations was IL-2, with a mean of 178.01 pg mL-1 and 3124.01 pg mL-1 for the pH < 7 and pH > 7 groups, respectively. Some correlations between the cytokines and the exposure biomarkers were presented. Stonemasons are highly exposed to pneumotoxic pollutants and markers of inflammation at the pulmonary level; in addition, a high risk of developing silicosis. Quarrying should be addressed as a carcinogenic activity, which would imply the design of monitoring and control strategies for these pollutants that our country currently lacks, particularly in precarious occupations. It is necessary to develop strategies to protect the health of precarious workers.

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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% (PC1 = 95.9%, PC2 = 1.0%, PC3 = 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.

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Tuberculosis (TB) is one of the highest infectious burdens worldwide. An excess of inflammation and inadequate antioxidant defense mechanisms are believed to lead to chronic inflammation and lung damage in tuberculosis (TB). However, circulating metabolites do not always replicate lung-associated biomarkers that define the pathobiology of the disease. The objective of this study was to determine the utility of exhaled breath condensate (EBC), a non-invasive and straightforward sample, to evaluate alveolar space-derived metabolites of redox state and inflammation. We assessed the levels of exhaled oxidant/antioxidant parameters (8-isoprostane, MDA, GSH), inflammatory markers, such as nucleosomes, cytokines (IL-2, IL-4, IL-6 and IL-8, IL-10, GM-CSF, TNF-α, and IFN-γ) and lipid mediators (PGE2, LTB4, RvD1, and Mar1), in patients with recently diagnosed pulmonary TB and healthy controls' EBC and serum. The TB patients showed 36% lower GSH levels, and 2-, 1.4-, 1.1-, and 50-fold higher levels of 8-isoprostanes, nucleosomes, IL-6, and LTB4, respectively, in EBC. There was no correlation between EBC and serum, highlighting the importance of measuring local biomarkers. Quantitation of local inflammatory molecules and redox states in EBC would help find biomarkers useful for pharmacological and follow-up studies in pulmonary tuberculosis.

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BACKGROUND: We identified a global chemical pattern of volatile organic compounds in exhaled breath capable of discriminating between COVID-19 patients and controls (without infection) using an electronic nose. METHODS: The study focused on 42 SARS-CoV-2 RT-qPCR positive subjects as well as 42 negative subjects. Principal component analysis indicated a separation of the study groups and provides a cumulative percentage of explanation of the variation of 98.3%. RESULTS: The canonical analysis of principal coordinates model shows a separation by the first canonical axis CAP1 (r2 = 0.939 and 95.23% of correct classification rate), the cut-off point of 0.0089; 100% sensitivity (CI 95%:91.5-100%) and 97.6% specificity (CI 95%:87.4-99.9%). The predictive model usefulness was tested on 30 open population subjects without prior knowledge of SARS-CoV-2 RT-qPCR status. Of these 3 subjects exhibited COVID-19 suggestive breath profiles, all asymptomatic at the time, two of which were later shown to be SARS-CoV-2 RT-qPCR positive. An additional subject had a borderline breath profile and SARS-CoV-2 RT-qPCR positive. The remaining 27 subjects exhibited healthy breath profiles as well as SARS-CoV-2 RT-qPCR test results. CONCLUSIONS: In all, the use of olfactory technologies in communities with high transmission rates as well as in resource-limited settings where targeted sampling is not viable represents a practical COVID-19 screening approach capable of promptly identifying COVID-19 suspect patients and providing useful epidemiological information to guide community health strategies in the context of COVID-19.

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PURPOSE: The purpose of this study was to compare pulmonary and plasma markers of oxidative stress and inflammation after concentric and eccentric cycling bouts in individuals with chronic obstructive pulmonary disease (COPD). METHODS: Ten patients with moderate COPD level (68.3 ± 9.1 years; forced expiratory volume in 1 s = 68.6 ± 20.4% of predicted) performed 30 min of moderate-intensity concentric (CONC-M: 50% maximum concentric cycling power output; POmax) and eccentric cycling (ECC-M: 50% POmax), and high-intensity eccentric cycling (ECC-H: 100% POmax) in a randomised order. Cardiometabolic demand was monitored during cycling. Indirect markers of muscle damage were assessed before, immediately after, 24 and 48 h after cycling (muscle strength, muscle soreness and creatine kinase activity). Plasma oxidative stress (malondialdehyde: MDA), antioxidant (glutathione peroxidase activity: GPx) and inflammatory markers (IL-6, TNF-α) were measured before and 5 min after cycling. Exhaled breath condensate (EBC) samples were collected before and 15 min after cycling and analysed for hydrogen peroxide (H2O2), nitrites (NO2-) and pH. RESULTS: Cardiometabolic demand was 40-50% lesser for ECC-M than CONC-M and ECC-H. Greater muscle damage was induced after ECC-H than ECC-M and CONC-M. MDA decreased immediately after CONC-M (- 28%), ECC-M (- 14%), and ECC-H (- 17%), while GPx remained unchanged. IL-6 increased only after ECC-H (28%), while TNF-α remained unchanged after exercise. Pulmonary H2O2, NO2- and pH remained unchanged after exercise. CONCLUSION: These results suggest that only moderate muscle damage and inflammation were induced after high-intensity eccentric cycling, which did not induce pulmonary or plasmatic increases in markers of oxidative stress. TRIAL REGISTRATION NUMBER: Trial registration number: DRKS00009755.

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BACKGROUND: Analysis of volatile organic compounds (VOCs) in exhaled breath has been proposed as a screening method that discriminates between disease and healthy subjects, few studies evaluate whether these chemical fingerprints are specific when compared between diseases. We evaluated global VOCs and their discrimination capacity in chronic obstructive pulmonary disease (COPD), lung cancer, breast cancer and healthy subjects by chemoresistive sensors and chemometric analysis. METHODS: A cross-sectional study was conducted with the participation of 30 patients with lung cancer, 50 with breast cancer, 50 with COPD and 50 control subjects. Each participant's exhaled breath was analyzed with the electronic nose. A multivariate analysis was carried: principal component analysis (PCA) and, canonical analysis of principal coordinates (CAP). Twenty single-blind samples from the 4 study groups were evaluated by CAP. RESULTS: A separation between the groups of patients to the controls was achieved through PCA with explanations of >90% of the data and with a correct classification of 100%. In the CAP of the 4 study groups, discrimination between the diseases was obtained with 2 canonical axes with a correct general classification of 91.35%. This model was used for the prediction of the single-blind samples resulting in correct classification of 100%. CONCLUSIONS: The application of chemoresistive gas sensors and chemometric analysis can be used as a useful tool for a screening test for lung cancer, breast cancer and COPD since this equipment detects the set of VOCs present in the exhaled breath to generate a characteristic chemical fingerprint of each disease.

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This article presents a database which was obtained by acquiring measurements through a multisensory device called Electronic Nose (E-nose) based on a matrix of metal oxide sensors, in order to discriminate and classify a group of people affected by the respiratory disease Chronic Obstructive Pulmonary Disease (COPD), smokers and healthy control people through exhaled breath analysis. The database consists of 4 groups of measurements which were acquired through the E-nose system: 10 control samples (healthy people), 20 samples of people with COPD, 4 samples of smokers and 10 air samples, where in each group two samples of exhaled breath per person were acquired giving a total of 78 samples (40 from COPD, 20 from control, 8 from smokers and 10 from the air).

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AIMS: Exhaled breath acetone (EBA) has been described as a new biomarker of heart failure (HF) diagnosis. EBA concentration increases according to severity of HF and is associated with poor prognosis, especially in acute decompensated HF. However, there are no data on chronic HF patients. The aim is to evaluate the role of EBA for predicting cardiac and overall mortality in chronic HF patients. METHODS AND RESULTS: In GENIUS-HF cohort, chronic patients were enrolled between August 2012 and December 2014. All patients had left ventricular ejection fraction ≤ 50%, and the diagnosis was established according to Framingham criteria. After consent, patients were submitted to clinical evaluation and exhaled breath collection. EBA identification and quantitative determination were done by spectrophotometry. The clinical characteristics associated with acetone were identified. All participants were followed for 18 months to assess cardiac and overall mortality. Around 700 participants were enrolled in the current analysis. Patients were 55.4 ± 12.2 years old, 67.6% male patients, and 81% New York Heart Association I/II with left ventricular ejection fraction of 32 ± 8.6%. EBA median concentration was 0.6 (0.3-1.2) ug/L. Acetone levels increased with the number of symptoms of HF and were associated with right HF signs/symptoms and liver biochemical changes. EBA at highest quartile (EBA > 1.2ug/L) was associated with a significantly worse prognosis (log rank test, P < 0.001). Cox proportional multivariable regression model revealed that EBA > 1.20ug/L was an independent predictor of cardiac (P = 0.011) and overall (P = 0.010) mortality in our population. CONCLUSIONS: This study shows that EBA levels reflect clinical HF features, especially right HF signs/symptoms. EBA is an independent predictor of cardiac and overall mortality in chronic HF patients.

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PURPOSE: The analysis of breath-print, has been proposed as an attractive alternative to investigate possible biomarkers of Chronic Obstructive Pulmonary Disease (COPD). The aim of the present study was to discriminate between healthy subjects, patients with COPD associated with smoking (COPD-S) and patients with COPD associated with household air pollution (COPD-HAP). METHODS: A cross-sectional study of 294 participants was conducted, 88 with smoking associated COPD, 28 associated with HAP and 178 healthy subjects. Breath-print analysis was performed by using the Cyranose 320 electronic nose. Group data were evaluated by Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA) and Support Vector Machine (SVM) and the test's diagnostic power by means of ROC (Receiver Operating Characteristic) curves. RESULTS: The results indicated that the breath-print of patients with COPD is different from the one of healthy subjects explaining a variability of 93.8% with a correct prediction of 97.8% and correct classification of 100%,also positive and negative predictive value of 96.5 and 100% respectively. Furthermore, the breath-print of exhaled breath from patients with COPD-S and COPD-HAP does not present any difference. CONCLUSIONS: The breath-print of exhaled breath from patients with COPD-S and COPD-HAP does not present any difference, which demonstrates that the breath-print is related to the disease and not to causality. With these results, the analysis of the breath-print of COPD is proposed as an alternative for a screening method in future clinical applications.

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An analytical method to identify volatile organic compounds (VOCs) in the exhaled breath from patients with a diagnosis of chronic obstructive pulmonary disease (COPD) using a ultrafast gas chromatography system equipped with an electronic nose detector (FGC eNose) has been developed. A prospective study was performed in 23 COPD patients and 33 healthy volunteers; exhalation breathing tests were performed with Tedlar bags. Each sample was analyzed by FCG eNose and the identification of VOCs was based on the Kovats index. Raw data were reduced by principal component analysis (PCA) and canonical discriminant analysis [canonical analysis of principal coordinates (CAP)]. The FCG eNose technology was able to identify 17 VOCs that distinguish COPD patients from healthy volunteers. At all stages of PCA and CAP the discrimination between groups was obvious. Chemical prints were correctly classified up to 82.2%, and were matched with 78.9% of the VOCs detected in the exhaled breath samples. Receiver operating characteristic curve analysis indicated the sensitivity and specificity to be 96% and 91%, respectively. This pilot study demonstrates that FGC eNose is a useful tool to identify VOCs as biomarkers in exhaled breath from COPD patients. Further studies should be performed to enhance the clinical relevance of this quick and ease methodology for COPD diagnosis.

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PURPOSE: The respiratory redox-state of swimmers can be affected by chronic exposures to chlorinated pools, and the effects of different exercises on it are unknown. Our aim was to compare two exercises performed at high-intensity and under habitual environmental conditions (swimming indoor vs. running outdoor) on the production of pro-oxidants (hydrogen peroxide and nitrite) and pH in exhaled breath condensate (EBC) and spirometry parameters in competitive swimmers chronically exposed to chlorinated pools. METHODS: Seventeen men and women (mean age ± SD = 21 ± 2 years) swam 3.5 km in an indoor pool treated with Cl2, and after 2-weeks, they ran 10 km outdoors. The pHEBC, [H2O2]EBC, [NO2-]EBC, [NO2-]EBC/[NO2-]Plasma and spirometry parameters were analyzed pre-exercise and 20 min and 24 h after exercise ended. RESULTS: Two mixed models were applied to compare EBC parameters between swimming and running. Lower levels of [H2O2]EBC and [NO2-]EBC (p = 0.008 and p = 0.018, respectively) were found 24-h post-swimming, and the same trend was observed for [NO2-]EBC/[NO2-]Plasma (p = 0.062). Correlations were found in both exercises between pre-exercise levels of pHEBC, [H2O2]EBC, [NO2-]EBC, and [NO2-]EBC/[NO2-]Plasma and their changes (Δ) after 24-h as well as between [H2O2]EBC and [NO2-]EBC for basal levels and for changes after 24 h. A relationship was also found for running exercise between pulmonary ventilation and changes after 24 h in [H2O2]EBC. Spirometry data were unaffected in both types of exercise. CONCLUSION: In competitive swimmers, at 24-h acute post-exercise follow-up, swimming decreased and running increased pro-oxidant biomarkers of pulmonary origin, without changes in lung function.

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INTRODUCTION: Tuberculosis (TB) is the leading cause of death due to an infectious disease worldwide. Especially in low-income countries, new diagnostic techniques that are accessible, inexpensive and easy-to-use, are needed to shorten transmission time and initiate treatment earlier. OBJECTIVE: We conducted a study with a handheld, point-of-care electronic nose (eNose) device to diagnose TB through exhaled breath. SETTING: This study includes a total of 110 patients and visitors of an expert centre of respiratory diseases in Asunción, Paraguay. TB diagnosis was established by culture of Mycobacterium tuberculosis complex and compared with the eNose results in two phases. RESULTS: The calibration phase, including only culture confirmed TB cases versus healthy people, demonstrated a sensitivity and specificity of 91% and 93% respectively. The confirmation phase, including all participants, showed a sensitivity of 88% and a specificity of 92%. The eNose showed high acceptance rate among participants, and was easy to operate. CONCLUSION: The eNose resulted in a powerful technique to differentiate between healthy people and TB patients. Its comfort, speed and usability promise great potential in vulnerable groups, in remote areas and hospital settings to triage patients with suspicion of TB.

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AIM: To set up and test the feasibility of a handmade apparatus adapted for exhaled breath condensate (EBC) collection in medium-sized animals. MATERIALS AND METHODS: The apparatus was produced using an 18-mm thick u-shaped borosilicate glass. The u-shaped tube body is 25 cm in diameter, and the horizontal portions are 12 cm in diameter. The base consists of a tube joint 14/20 or 14 mm thick by 20 cm in diameter, and has a length of 5 cm. This has a hole that is plugged for condensate flow to a 1.5 mL polypropylene microtube that stores the condensate during collection. Was placed inside a styrofoam box and immersed in crushed ice and salt to ensure cooling. The temperature was monitored and maintained throughout the collection at -10°C. One of the outputs of the u-shaped tube was connected to the expiratory limb of the ventilator. RESULTS: An experimental model of ALI, induced by oleic acid (OA) was adopted to determine the concentration of biomarkers of oxidative stress: malondialdehyde (MDA), glutathione (GSH), and nitrite/nitrate (NOx). The proposed model allows measurement of NOx, MDA, and GSH. However, the NOx and MDA levels in the EBC were not significant. It was only possible to observe an upward trend, which suggests a temporal evolution of the presence of these markers in the EBC. CONCLUSION: The EBC collection method adapted is effective to generate sufficient content that allows to determine the levels of different biomarkers, such as NOx, MDA, and GSH, that are involved in oxidative and inflammatory stress processes during respiratory diseases.

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Heart failure (HF) is a clinical condition that presents high morbidity and mortality and is one of the main reasons for hospital admissions all over the world. Although biochemical processes that occur in the body during heart failure are known, this syndrome is still associated to poor prognosis. Exhaled breath analysis has emerged as a promising noninvasive tool in different clinical conditions and, recently, it has been also tested in patients with HF. This review presents the main breath HF biomarkers, which reflect metabolic changes that occur in this complex syndrome. It also discusses the diagnostic and prognostic value of exhaled breath compounds for HF and makes a short description of the main technologies involved in this analysis. Some perspectives on the area are presented as well.

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OBJECTIVE: The aim of the present study was to assess nasal mucociliary clearance, mucus properties and inflammation in smokers and subjects enrolled in a Smoking Cessation Program (referred to as quitters). METHOD: A total of 33 subjects with a median (IQR) smoking history of 34 (20-58) pack years were examined for nasal mucociliary clearance using a saccharine transit test, mucus properties using contact angle and sneeze clearability tests, and quantification of inflammatory and epithelial cells, IL-6 and IL-8 concentrations in nasal lavage fluid. Twenty quitters (mean age: 51 years, 9 male) were assessed at baseline, 1 month, 3 months and 12 months after smoking cessation, and 13 smokers (mean age: 52 years, 6 male) were assessed at baseline and after 12 months. Clinicaltrials.gov: NCT02136550. RESULTS: Smokers and quitters showed similar demographic characteristics and morbidities. At baseline, all subjects showed impaired nasal mucociliary clearance (mean 17.6 min), although 63% and 85% of the quitters demonstrated significant nasal mucociliary clearance improvement at 1 month and 12 months, respectively. At 12 months, quitters also showed mucus sneeze clearability improvement (∼26%), an increased number of macrophages (2-fold) and no changes in mucus contact angle or cytokine concentrations. CONCLUSION: This study showed that smoking cessation induced early improvements in nasal mucociliary clearance independent of mucus properties and inflammation. Changes in mucus properties were observed after only 12 months of smoking cessation.

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Exercise promotes pulmonary oxidative imbalance. In this regard, some evidence has been obtained from the study of exhaled breath condensate (EBC) during urban races, in which the factors involved in the occurrence of this process are still not characterized. In this paper, under laboratory conditions, both the role of time of exercise on the generation of pro-oxidants (H2O2, NO2 (-)) and pH have been assessed in EBC of 16 under-trained subjects who completed three tests of cycloergometric exercise at low intensity (30 % of VO2 max) with a duration of 10, 30, and 90 min. Samples were obtained as follows: immediately before and at 80 min post exertion in each test. In the 90-min test, an increase in H2O2, NO2 (-) concentration in EBC at 80 min post exertion with no changes in the pH was observed. Total O2 consumption and total ventilation weakly correlated with the changes in H2O2 and NO2 (-). In conclusion, the concentration of pro-oxidants in the EBC depends on the duration of the exercise when it is performed at low intensity under laboratory conditions.

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