Infections caused by Mycobacterium tuberculosis kill 1.8 million people each year and in 2015 10.4 million new cases occurred. Conventional diagnostic methods based on patient production of sputum (e.g. culture, nucleic acid amplification and smear microscopy) are not relevant for many with an HIV co-infection or children due to low sensitivity and inability to produce the sample. For infected individuals, PET/CT imaging can be used for evaluating M. tuberculosis disease severity. Although this technique provides information related to disease extent, reactivation risk and response to treatment, the procedure is costly and accompanied by substantial radiation dose and cancer risk. The non-invasive analysis of exhaled breath metabolites represent an alternative to conventional techniques for the diagnosis and evaluation of bacterial lung infections, including those caused by M. tuberculosis.
In the present work, a system was designed for the reproducible collection and concentration of breath metabolites from nonhuman primates in a BSL-3 facility. This system was used for the collection and evaluation of breath from a cohort of animals prior to and throughout a time course postinfection with M. tuberculosis. Through the use of advanced machine learning techniques, breath metabolites were selected that can be used to discriminate preinfection and postinfection animals as well as to stratify animals based on infection severity, which was quantified through PET/CT imaging. Selected breath metabolites were compared to those produced by M. tuberculosis in isolated culture.
Here we show that breath analysis can provide information regarding M. tuberculosis diagnosis and disease evaluation. This approach has potential to improve current diagnostic methods.
|Advisor:||Hill, Jane E.|
|Commitee:||Ackerman, Margie, Flynn, JoAnne, Griswold, Karl|
|School Location:||United States -- New Hampshire|
|Source:||DAI-B 79/12(E), Dissertation Abstracts International|
|Subjects:||Engineering, Biomedical engineering|
|Keywords:||Breath, Machine learning, Tuberculosis|
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