Moleculer characterization of severe allergic asthma patients via exhaled breath volatile organic compounds(Ege University Translational Pulmonology Research Group [EgeTPRG])
Bildiriler > Özet Bildiri · 2017
Yazarlar: Özlem GÖKSEL, Levent PELİT, Füsun PELİT, Burak ORDİN, Arif GÜRSOY, Durmus OZDEMIR, Nil ERTAS, Tuncay GÖKSEL
Monitoring Airway Disease, İtalya, Uluslararası, Sözlü Sunum, Özet Bildiri, 2017-2017, Wos, cilt 50, ss PA4762-PA4762
DOI: 10.1183/1393003.congress-2017.pa4762
Özet
A minority of patients with asthma have still uncontrolled symptoms despite proper intensive treatment. Better defining severe allergic asthma at moleculer level by easy, non-invasive exhale breath analysis would be a feasible alternative to improve characterization of this group. Methods: Severe allergic asthma patients under step 5 treatment with Anti-IgE according to GINA 2016 included in this prospective case-control study, along with healthy controls. Breath samples were collected via our lab-made sample collector where the VOC content was enriched on a polythiophene solid phase microextraction (SPME)fibers.VOC profile from SPME fibers was analyzed by inserting into injection port of a gas chromatography coupled with mass detector(GC-MS)allowing thermal desorption. Commercial Carboxen/Polydimethylsiloxane SPME fiber was also used for control. Multidimensional data mining approaches were used to interpret the data Results: A total of 41 severe asthma patients along with 45 healthy controls were recruited during past 18 months. Neurol Network (NN) classification algorithm in WEKA system gave the best result to discriminate severe allergic asthma patients than the healthy controls with sensitivity 95.6%, specificity of 95.8% and a success rate of 88.6% in the training set Conclusion: We have completed one and half year with our specially designed home-made exhale breathe sample collector and analyzing system. Analyzing of exhaled breath samples with this system can easily discriminate severe asthma patients than healthy controls with significant different signal level of VOCs mixture in NN classification algorithm.