Effect of the influenza A (H1N1) live attenuated intranasal vaccine on nitric oxide (FE _NO ) and other volatiles in exhaled breath

Abstract

For the 2009 influenza A (H1N1) pandemic, vaccination and infection control were the main modes of prevention. A live attenuated H1N1 vaccine mimics natural infection and works by evoking a host immune response, but currently there are no easy methods to measure such a response. To determine if an immune response could be measured in exhaled breath, exhaled nitric oxide (FENO) and other exhaled breath volatiles using selected ion flow tube mass spectrometry (SIFT-MS) were measured before and daily for seven days after administering the H1N1 2009 monovalent live intranasal vaccine (FluMist®, MedImmune LLC) in nine healthy healthcare workers (age 35 ± 7 years; five females). On day 3 after H1N1 FluMist® administration there were increases in FENO (MEAN±SEM: day 0 15 ± 3 ppb, day 3 19 ± 3 ppb; p < 0.001) and breath isoprene (MEAN±SEM: day 0 59 ± 15 ppb, day 3 99 ± 17 ppb; p = 0.02). MS analysis identified the greatest number of changes in exhaled breath on day 3 with 137 product ion masses that changed from baseline. The exhaled breath changes on day 3 after H1N1 vaccination may reflect the underlying host immune response. However, further work to elucidate the sources of the exhaled breath changes is necessary.

Analysis of breath volatile organic compounds as a noninvasive tool to diagnose nonalcoholic fatty liver disease in children

“…The SIFT-MS technology and instrument used in this study have previously been described elsewhere by our group and others [9][10][11].…”

Real-Time Detection of a Virus Using Detection Dogs

“…The researchers detected 12 and 6 VOC that were associated with bacterial and viral growth and identified 2 VOC that were differentiated between bacterial and viral infection ( 9 ). Lastly, Mashir et al ( 10 ) administered live attenuated H1N1 vaccine (FluMist ® ) to humans and demonstrated that exhaled breath VOC increased for 7 days after the vaccination. These studies suggest that unique VOC profiles associated with viral pathogens exist and that they may be detected in patients.…”

Ultrafast Screening of COVID-19 by Machine Learning Analysis of Exhaled NO

“…The approach consists of three parts: feature engineering, ML model training, and the output COVID-19 status probability. First, feature engineering is needed to evaluate the feature importance of classifying COVID- 19. The random forest method was used for feature engineering.…”

“…After at least 28 years of exploration, it was determined that the detection of FeNO could accurately predict lung cancer, asthma, scleroderma, sarcoidosis and other lung diseases [5][6][7][9][10][11][12][13][14][15][16][17] . After the severe acute respiratory syndrome coronavirus (SARS-CoV) outbreak in 2002-2003, relevant studies showed that the NO concentration in the organism changes with the degree of infection by a coronavirus 18,19 . For example, in the early stage of infection with SARS-CoV, activation of epithelial cells, such as those in alveoli, to produce cytokines results in the upregulation of inducible NO synthase, further increasing the NO concentration and thus playing an antiviral role in controlling SARS-CoV infection 20 .…”