BackgroundElectronic noses are composites of nanosensor arrays. Numerous studies showed their potential to detect lung cancer from breath samples by analysing exhaled volatile compound pattern (“breathprint”). Expiratory flow rate, breath hold and inclusion of anatomic dead space may influence the exhaled levels of some volatile compounds; however it has not been fully addressed how these factors affect electronic nose data. Therefore, the aim of the study was to investigate these effects.Methods37 healthy subjects (44 ± 14 years) and 27 patients with lung cancer (60 ± 10 years) participated in the study. After deep inhalation through a volatile organic compound filter, subjects exhaled at two different flow rates (50 ml/sec and 75 ml/sec) into Teflon-coated bags. The effect of breath hold was analysed after 10 seconds of deep inhalation. We also studied the effect of anatomic dead space by excluding this fraction and comparing alveolar air to mixed (alveolar + anatomic dead space) air samples. Exhaled air samples were processed with Cyranose 320 electronic nose.ResultsExpiratory flow rate, breath hold and the inclusion of anatomic dead space significantly altered “breathprints” in healthy individuals (p < 0.05), but not in lung cancer (p > 0.05). These factors also influenced the discrimination ability of the electronic nose to detect lung cancer significantly.ConclusionsWe have shown that expiratory flow, breath hold and dead space influence exhaled volatile compound pattern assessed with electronic nose. These findings suggest critical methodological recommendations to standardise sample collections for electronic nose measurements.

Evening and morning exhaled volatile compound patterns are different in OSA. This might affect the ability of electronic noses to identify this disorder. Overnight alterations in volatile substances need to be taken into account during exhaled breath measurements in OSA.

In this study, we aimed to identify novel genes involved in experimental and human asthma, importance of which has not yet been recognized. In an ovalbumin-induced murine model of asthma, we applied microarray gene expression analysis at different time points after allergen challenges. Advanced statistical methods were used to relate gene expression changes to cellular processes and to integrate our results into multiple levels of information available in public databases. At 4 h after the first allergen challenge, gene expression pattern reflected mainly an acute, but non-atopic, inflammatory response and strong chemotactic activity. At 24 h after the third allergen challenge, gene set enrichment analysis revealed significant over-representation of gene sets corresponding to T(h)2-type inflammation models. Among the top down-regulated transcripts, an anti-oxidant enzyme, paraoxonase-1 (PON1), was identified. In human asthmatic patients, we found that serum PON1 activity was reduced at exacerbation, but increased parallel with improving asthma symptoms. PON1 gene polymorphisms did not influence the susceptibility to the disease. Our observations suggest that an altered PON1 activity might be involved in the pathogenesis of asthma, and serum PON1 level might be used for following up the effect of therapy.

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