Measuring Compounds in Exhaled Air to Detect Alzheimer's Disease and Parkinson's Disease.

BACKGROUND: Alzheimer's disease (AD) is diagnosed based upon medical history, neuropsychiatric examination, cerebrospinal fluid analysis, extensive laboratory analyses and cerebral imaging. Diagnosis is time consuming and labour intensive. Parkinson's disease (PD) is mainly diagnosed on clinical grounds. OBJECTIVE: The primary aim of this study was to differentiate patients suffering from AD, PD and healthy controls by investigating exhaled air with the electronic nose technique. After demonstrating a difference between the three groups the secondary aim was the identification of specific substances responsible for the difference(s) using ion mobility spectroscopy. Thirdly we analysed whether amyloid beta (Aß) in exhaled breath was causative for the observed differences between patients suffering from AD and healthy controls. METHODS: We employed novel pulmonary diagnostic tools (electronic nose device/ion-mobility spectrometry) for the identification of patients with neurodegenerative diseases. Specifically, we analysed breath pattern differences in exhaled air of patients with AD, those with PD and healthy controls using the electronic nose device (eNose). Using ion mobility spectrometry (IMS), we identified the compounds responsible for the observed differences in breath patterns. We applied ELISA technique to measure Aß in exhaled breath condensates. RESULTS: The eNose was able to differentiate between AD, PD and HC correctly. Using IMS, we identified markers that could be used to differentiate healthy controls from patients with AD and PD with an accuracy of 94%. In addition, patients suffering from PD were identified with sensitivity and specificity of 100%. Altogether, 3 AD patients out of 53 participants were misclassified. Although we found Aß in exhaled breath condensate from both AD and healthy controls, no significant differences between groups were detected. CONCLUSION: These data may open a new field in the diagnosis of neurodegenerative disease such as Alzheimer's disease and Parkinson's disease. Further research is required to evaluate the significance of these pulmonary findings with respect to the pathophysiology of neurodegenerative disorders.

Exhaled breath analysis for lung cancer detection using ion mobility spectrometry.

BACKGROUND: Conventional methods for lung cancer detection including computed tomography (CT) and bronchoscopy are expensive and invasive. Thus, there is still a need for an optimal lung cancer detection technique. METHODS: The exhaled breath of 50 patients with lung cancer histologically proven by bronchoscopic biopsy samples (32 adenocarcinomas, 10 squamous cell carcinomas, 8 small cell carcinomas), were analyzed using ion mobility spectrometry (IMS) and compared with 39 healthy volunteers. As a secondary assessment, we compared adenocarcinoma patients with and without epidermal growth factor receptor (EGFR) mutation. RESULTS: A decision tree algorithm could separate patients with lung cancer including adenocarcinoma, squamous cell carcinoma and small cell carcinoma. One hundred-fifteen separated volatile organic compound (VOC) peaks were analyzed. Peak-2 noted as n-Dodecane using the IMS database was able to separate values with a sensitivity of 70.0% and a specificity of 89.7%. Incorporating a decision tree algorithm starting with n-Dodecane, a sensitivity of 76% and specificity of 100% was achieved. Comparing VOC peaks between adenocarcinoma and healthy subjects, n-Dodecane was able to separate values with a sensitivity of 81.3% and a specificity of 89.7%. Fourteen patients positive for EGFR mutation displayed a significantly higher n-Dodecane than for the 14 patients negative for EGFR (p<0.01), with a sensitivity of 85.7% and a specificity of 78.6%. CONCLUSION: In this prospective study, VOC peak patterns using a decision tree algorithm were useful in the detection of lung cancer. Moreover, n-Dodecane analysis from adenocarcinoma patients might be useful to discriminate the EGFR mutation.

An approach to automated frequency-domain feature extraction in nuclear magnetic resonance spectroscopy.

For the analysis of metabolite systems, nuclear magnetic resonance (NMR) spectroscopy has become an important quantitative monitoring technology. Automated quantitation methods are highly desired and mainly characterized by the tasks of model selection and parameter approximation. This paper proposes a promising automated two stage approach in the frequency-domain, in which signaling peaks are first identified and filtered from noise based on curvature properties of the spectrum, and then proportionally approximated based on the analytical solution of a Lorentz-function. Remarkably, in opposition to common least-squares approaches, the proposed approximation scheme does not rely on partial derivatives, and furthermore, the runtime is independent to the number of spectral datapoints. Simulations provide promising empirical evidence for successful peak selection and parameter approximation, with the results for the latter highly outperforming the Levenberg-Marquardt algorithm in terms of error minimization and robustness.

Detection of volatile metabolites of Escherichia coli by multi capillary column coupled ion mobility spectrometry.

Detection and immediate quantification of microbial metabolic activities is of high interest in fields as diverse as biotechnology and infection biology. Interestingly, the most direct signals of microbial metabolism, the evolution of volatile metabolites, is largely ignored in the literature, and rather, metabolite concentrations in the microbial surrounding or even disruptive methods for intracellular metabolite measurements (i.e., metabolome analysis) are favored. Here, the development of a multi capillary column coupled ion mobility spectrometer (MCC-IMS) was described for the detection of volatile organic compounds from microbes and the MCC-IMS was used for characterization of metabolic activity of growing Escherichia coli. The MCC-IMS chromatogram of the microbial culture off-gas of the acetone-producing E. coli strain BL21 pLB4 revealed four analytes that positively correlated with growth, which were identified as ethanol, propanone (acetone), heptan-2-one, and nonan-2-one. The occurrence of these analytes was cross-validated by solid-phase micro-extraction coupled with gas chromatography mass spectrometry analysis. With this information in hand, the dynamic relationship between the E. coli biomass concentration and the metabolite concentrations in the headspace was measured. The results suggest that the metabolic pathways of heptan-2-one and nonan-2-one synthesis are regulated independent of each other. It is shown that the MCC-IMS in-line off-gas analysis is a simple method for real-time detection of microbial metabolic activity and discussed its potential for application in metabolic engineering, bioprocess control, and health care.