The Identification of Different Lung Diseases by Analysis of Volatile Organic Compounds in Breath Samples

The investigators assume that by analysis of different volatile organic compounds in the breath, using nanotechnology, the investigators will be able to identify a unique respiratory signature of different diseases including asthma, chronic obstructive pulmonary disease (COPD) and pulmonary hypertension.

Five groups of patients will be included in the study; each group will include 50-75 patients:

Group -1 - Healthy volunteer, will use as a control. Group -2 - Patients diagnosed as having diastolic heart failure with normal pulmonary artery pressure by echocardiography.

Group - 3 - Patients diagnosed with diastolic heart failure and out of proportion pulmonary hypertension confirm by right heart failure.

Group - 4 - Patients diagnosed with chronic obstructive lung disease. Group- 5 Patients diagnosed with asthma Demographic and clinical data will be collected for each patient. Exhaled alveolar air will be collected from each test groups. Samples will be collected using a breath collection method developed according to the recommendations of the American Thoracic Society, which effectively avoids artifacts and systematic errors. Two bags will be collected from each person tested: One for gas chromatograph analysis, and another one for analysis with the sensor array.

In order to achieve a artificial nose that has high sensitivity towards the unique breath markers of patients with specific disease, we will follow a 5-phase approach. In phase-1 we will collect suitable breath samples from each patient and compare the patient data to age-adjusted healthy controls. In phase-2 we will analyze the collected breath samples with the electronic nose setup. These breath samples are our training set. In phase-3 we will carry out auxiliary chemical analysis, using gas-chromatography linked with mass spectrometry of the breath samples under different aspects. Phase-4 will aim at the improvement of our electronic nose setup and will be conducted in parallel to the first three phases. The main steps of this phase will include:

* Improvement of the performance of the constituent sensors in terms of sensitivity and selectivity to the specific diseases biomarkers. The main parameter for sensor improvement will be the choice of the organic functionalities of the nanomaterials composes the sensors.
* Optimization of the choice of sensors in the array. For choosing the sensors we do not have to physically replace them, but can carry out the statistical analysis of the output of particular sub-groups of sensors in the array, instead of the output of all sensors. Based on the results of the gas chromatograph mass spectrometer chemical analysis, we will improve and optimize our sensor array so as to achieve: (i) maximum sensitivity to the breath biomarkers of the studied diseases and their stage dependent concentration profiles; (ii) minimum sensitivity to non-disease related changes of the chemical composition of the breath and (iii) minimum sensitivity to the major ingredients of the breath, such as water vapor. Technically, we aim at good reproducibility of the sensor fabrication. We will attempt to improve separation between the test groups by more sophisticated statistical treatment of the collected data. Towards the end of this proof-of-concept study we will compare the performance of our sensor array to the diagnosis according to clinical symptoms. The comparison will be done in terms of true positive, true negative, false positive, false negative, sensitivity and specificity.

Eligibility Criteria:
Inclusion Criteria

1. Diagnosis of asthma according to the American Thoracic Society guidelines.
2. Diagnosis of chornic obstructive pulmonary disease according to the American Thoracic Society, guidelines.
3. Diagnosis of pulmoanry hypertension according to the American Thoracic Society guidelins
4. Age-18-85 year old

Exclusion criteria

1. known carriers of infectious diseases: HIV, hepatitis B, C
2. pregnancy
3. History of lung cancer