Quantification of propionaldehyde in breath of patients after lung transplantation.

Oxygen-derived free radicals (ROS) have been identified to contribute significantly to ischemia-reperfusion (I/R) injury by initiating chain reactions with polyunsaturated membrane lipids (lipid peroxidation, LPO) resulting in the generation of several aldehydes and ketones. Due to their volatile nature these LPO products can be measured noninvasively in breath. We hypothesized that one of these markers, namely propionaldehyde, will be increased in lung and heart-lung transplant patients where severe oxidative stress due to I/R injury with early graft dysfunction represents one of the major postoperative complications resulting in prolonged ventilation and increased in-hospital morbidity and mortality. Expiratory air measurements for acetone, isoprene, and propionaldehyde were performed in seven patients after lung (n = 5) or heart-lung (n = 2) transplantation, ventilated patients (n = 12), and healthy volunteers (n = 17) using online ion-molecule reaction mass spectrometry. Increased concentrations of acetone (transplanted: 3812 [2347-12498]; ventilated: 1255 [276-1959]; healthy: 631 [520-784] ppbv; P < .001) and propionaldehyde (transplanted: 270 [70-424]; ventilated: 82 [41.8-142]; healthy: 1.7 [0.1-11.8] ppbv; P < .001) were found in expiratory air of transplanted and ventilated patients. Propionaldehyde resulting from spontaneous fragmentation of peroxides due to free radical-induced LPO after I/R injury in patients after lung or heart-lung transplantation can be quantified in expired breath.

Volatile compound profiling for the identification of Gram-negative bacteria by ion-molecule reaction-mass spectrometry.

AIMS: Fast and reliable methods for the early detection and identification of micro-organism are of high interest. In addition to established methods, direct mass spectrometry-based analysis of volatile compounds (VCs) emitted by micro-organisms has recently been shown to allow species differentiation. Thus, a large number of pathogenic Gram-negative bacteria, which comprised Acinetobacter baumannii, Enterobacter cloacae, Escherichia coli, Klebsiella oxytoca, Pseudomonas aeruginosa, Proteus vulgaris and Serratia marcescens, were subjected to headspace VC composition analysis using direct mass spectrometry in a low sample volume that allows for automation. METHODS AND RESULTS: Ion-molecule reaction-mass spectrometry (IMR-MS) was applied to headspace analysis of the above bacterial samples incubated at 37°C starting with 10(2) CFU ml(-1) . Measurements of sample VC composition were performed at 4, 8 and 24 h. Microbial growth was detected in all samples after 8 h. After 24 h, species-specific mass spectra were obtained allowing differentiation between bacterial species. CONCLUSIONS: IMR-MS provided rapid growth detection and identification of micro-organisms using a cumulative end-point model with a short analysis time of 3 min per sample. SIGNIFICANCE AND IMPACT OF THE STUDY: Following further validation, the presented method of bacterial sample headspace VC analysis has the potential to be used for bacteria differentiation.

Volatile organic compound analysis by ion molecule reaction mass spectrometry for Gram-positive bacteria differentiation.

Approximately 50 % of all clinically proven infections in critically ill patients are caused by Gram-positive bacteria. The timely and appropriate treatment of these infections is vital in order to avoid negative outcomes. Hence, fast and reliable methods are needed for the early detection and identification of microorganisms. Recently, direct mass spectrometry-based analysis of volatile organic compounds emitted by microorganisms has been employed to study Gram-negative bacteria. Here, we report a feasibility study of ion molecule reaction mass spectrometry (IMR-MS) for in vitro growth detection and species differentiation of selected Gram-positive bacteria that are frequently isolated in blood culture samples, namely, Enterococcus faecalis, Enterococcus faecium, Staphylococcus aureus, and Staphylococcus epidermidis. Ion molecule reaction mass spectrometry was used to analyze the headspace above cultures containing Gram-positive bacteria incubated at 37 °C starting with 10(2) colony-forming units (CFU)/ml. Measurements to determine the presence of volatile organic compounds were performed 4, 8, and 24 h after incubation, respectively. The detection of microbial growth was accomplished already after 8 h in cultures containing E. faecalis. After 24 h of incubation, characteristic mass spectra were obtained for all species. Processing these mass spectra by hierarchic clustering and principal component analysis (PCA) enabled us to differentiate between bacterial species. IMR-MS in conjunction with a cumulative end-point model provides the means for rapid growth detection and differentiation of Gram-positive bacteria on the species level, typically within an analysis time of less than 3 min per sample.

A new ensemble-based algorithm for identifying breath gas marker candidates in liver disease using ion molecule reaction mass spectrometry.

MOTIVATION: Alcoholic fatty liver disease (AFLD) and non-AFLD (NAFLD) can progress to severe liver diseases such as steatohepatitis, cirrhosis and cancer. Thus, the detection of early liver disease is essential; however, minimal invasive diagnostic methods in clinical hepatology still lack specificity. RESULTS: Ion molecule reaction mass spectrometry (IMR-MS) was applied to a total of 126 human breath gas samples comprising 91 cases (AFLD, NAFLD and cirrhosis) and 35 healthy controls. A new feature selection modality termed Stacked Feature Ranking (SFR) was developed to identify potential liver disease marker candidates in breath gas samples, relying on the combination of different entropy- and correlation-based feature ranking methods including statistical hypothesis testing using a two-level architecture with a suggestion and a decision layer. We benchmarked SFR against four single feature selection methods, a wrapper and a recently described ensemble method, indicating a significantly higher discriminatory ability of up to 10-15% for the SFR selected gas compounds expressed by the area under the ROC curve (AUC) of 0.85-0.95. Using this approach, we were able to identify unexpected breath gas marker candidates in liver disease of high predictive value. A literature study further supports top-ranked markers to be associated with liver disease. We propose SFR as a powerful tool for biomarker search in breath gas and other biological samples using mass spectrometry. AVAILABILITY: The algorithm SFR and IMR-MS datasets are available under http://biomed.umit.at/page.cfm?pageid=526.

A chemometric study on human breath mass spectra for biomarker identification in cystic fibrosis.

Alveolar breath samples from a small case-control study population have been collected and measured via ion-molecule reaction mass spectrometry, and a constructive statistical approach to the identification of volatile biomarkers has been formulated by applying multivariate statistical methods on the mass spectra. The nature of the data is such that the number of variables largely exceeds the observations, representing a typical experimental scenario when breath analysis is conducted using mass spectrometry. Principal components analysis has been performed on the high dimensional dataset of molecular abundances, providing evidence of case separation and reducing the number of functional discriminators by almost 90%. Afterwards, a deductive approach based on a binary regression was conducted on the reduced dataset, providing an entirely reliable case discrimination model exclusively depending on the concentrations in the breath mixture of 3 out of a total of 97 metabolites.

Molecular breath-gas analysis by online mass spectrometry in mechanically ventilated patients: a new software-based method of CO(2)-controlled alveolar gas monitoring.

Analysis of volatile organic compounds (VOCs) in exhaled breath offers diagnostic potential in research and clinical medicine. Mass spectrometry of expiratory air allows VOC measurements in a concentration range from parts per trillion to parts per million. For the reduction of dilution-related measurement errors due to dead space admixture, the precise identification of the end-expiratory phase of expiration is essential. We used a combination of two integrated MS systems consisting of a conventional MS capable of fast CO(2) tracing controlling a second, highly sensitive MS for the measurement of VOCs based on ion-molecule-reaction-MS (IMR-MS). This study intended to test the applicability of a software-based method of CO(2)-controlled alveolar breath-gas sampling in 12 ventilated patients using acetaldehyde, acetone, ethanol and isoprene as target VOCs (IMR-MS compound integration time 500 ms, cycle time 2 ms, measurement time 120 min). CO(2)-controlled versus mixed inspiratory/expiratory results are as follows: acetaldehyde 71* (61-133) versus 63 (47-87); acetone 544* (208-1174) versus 504 (152-950); ethanol 133 (99-166) versus 123 (108-185); isoprene 118* (69-253) versus 58 (44-112) (values in ppbv as medians with 25-75%; *p < 0.05 versus mixed inspiratory/expiratory values). The applied software-based CO(2)-controlled sampling method of expiratory air resulted in significant higher concentrations of acetaldehyde, acetone and isoprene.