The diagnosis of hospital-acquired pneumonia remains challenging. We hypothesized that analysis of volatile organic compounds (VOCs) in exhaled breath could be used to diagnose pneumonia or the presence of pathogens in the respiratory tract in intubated and mechanically-ventilated intensive care unit patients. In this prospective, single-centre, cross-sectional cohort study breath from mechanically ventilated patients was analysed using gas chromatography-mass spectrometry. Potentially relevant VOCs were selected with a p-value < 0.05 and an area under the receiver operating characteristics curve (AUROC) above 0.7. These VOCs were used for principal component analysis and partial least square discriminant analysis (PLS-DA). AUROC was used as a measure of accuracy. Ninety-three patients were included in the study. Twelve of 145 identified VOCs were significantly altered in patients with pneumonia compared to controls. In colonized patients, 52 VOCs were significantly different. Partial least square discriminant analysis classified patients with modest accuracy (AUROC: 0.73 (95% confidence interval (CI): 0.57–0.88) after leave-one-out cross-validation). For determining the colonization status of patients, the model had an AUROC of 0.69 (95% CI: 0.57–0.82) after leave-one-out cross-validation. To conclude, exhaled breath analysis can be used to discriminate pneumonia from controls with a modest to good accuracy. Furthermore breath profiling could be used to predict the presence and absence of pathogens in the respiratory tract. These findings need to be validated externally.

Introduction: Asthma is a heterogeneous condition, characterised by chronic inflammation of the airways, typically managed with inhaled bronchodilators and corticosteroids. In the case of uncontrolled asthma, oral corticosteroids (OCSs) are often prescribed. Good adherence and inhalation technique are associated with improved outcomes; however, it is difficult to monitor appropriate drug intake and effectiveness in individual patients. Exhaled breath contains thousands of volatile organic compounds (VOCs) that reflect changes in the body's chemistry and may be useful for monitoring drug pharmacokinetics/pharmacodynamics. We aimed to investigate the association of exhaled VOCs in severe asthma patients from the U-BIOPRED cohort (by gas chromatography coupled with time-of-flight mass spectrometry) with urinary levels of salbutamol and OCSs (by liquid chromatography coupled with high-resolution mass spectrometry).Methods: Samples were collected at baseline and after 12–18 months of follow-up. Statistical analysis was based on univariate and multivariate modelling, followed by area under the receiver operating characteristic curve (AUC) calculation. Results were verified through longitudinal replication and independent validation.Results: Data were available for 78 patients (baseline n=48, replication n=30 and validation n=30). Baseline AUC values were 82.1% (95% CI 70.4–93.9%) for salbutamol and 78.8% (95% CI 65.8–91.8%) for OCS. These outcomes could be adequately replicated and validated. Additional regression analysis between qualified exhaled VOCs and urinary concentrations of salbutamol and prednisone showed statistically significant correlations (p<0.01).Conclusion: We have linked exhaled VOCs to urinary detection of salbutamol and OCSs. This merits further development of breathomics into a point-of-care tool for therapeutic drug monitoring.

Digital video capturing is getting popular with the decreasing price of camcorders and the increasing availability of devices with embedded video cameras such as digital-still cameras, mobile phones and PDAs. While a raw home video is considered as visually non-appealing, having multiple recordings of the same event provides the opportunity to combine audio and video segments from different cameras for improving quality and aesthetics. Mixing content from different recordings requires precise synchronization among the recordings. In most present applications, synchronization is done manually and considered as a very tedious task. In this paper, we propose a novel automated synchronization approach based on detecting and matching audio and video features extracted from the recorded content. We assess experimentally three realizations of this approach on a common data set and make recommendations on the usability of the different realizations in practical use cases. The realizations have no limitations on the number and movement of the cameras. Moreover, they are robust against various ambient noises and audio-visual artifacts occurring during the recordings.