Reagent and analyte ion hydrates in secondary electrospray ionization mass spectrometry (SESI‐MS), their equilibrium distributions and dehydration in an ion transfer capillary: Modelling and experiments

2021 ◽  
Vol 35 (7) ◽  
Author(s):  
Kseniya Dryahina ◽  
Suman Som ◽  
David Smith ◽  
Patrik Španěl
Keyword(s):  
Mass Spectrometry ◽  
Electrospray Ionization ◽  
Electrospray Ionization Mass Spectrometry ◽  
Ionization Mass Spectrometry ◽  
Ionization Mass ◽  
Ion Transfer ◽  
Equilibrium Distributions ◽  
Secondary Electrospray Ionization

scholarly journals Secondary electrospray ionization-mass spectrometry (SESI-MS) breathprinting of multiple bacterial lung pathogens, a mouse model study

2013 ◽  
Vol 114 (11) ◽  
pp. 1544-1549 ◽  
Author(s):  
Jiangjiang Zhu ◽  
Heather D. Bean ◽  
Jaime Jiménez-Díaz ◽  
Jane E. Hill
Keyword(s):  
Mass Spectrometry ◽  
Electrospray Ionization ◽  
Principal Components ◽  
Electrospray Ionization Mass Spectrometry ◽  
Bacterial Pneumonia ◽  
Ionization Mass Spectrometry ◽  
Ionization Mass ◽  
Secondary Electrospray Ionization ◽  
Lung Infections

Bacterial pneumonia is one of the leading causes of disease-related morbidity and mortality in the world, in part because the diagnostic tools for pneumonia are slow and ineffective. To improve the diagnosis success rates and treatment outcomes for bacterial lung infections, we are exploring the use of secondary electrospray ionization-mass spectrometry (SESI-MS) breath analysis as a rapid, noninvasive method for determining the etiology of lung infections in situ. Using a murine lung infection model, we demonstrate that SESI-MS breathprints can be used to distinguish mice that are infected with one of seven lung pathogens: Haemophilus influenzae, Klebsiella pneumoniae, Legionella pneumophila, Moraxella catarrhalis, Pseudomonas aeruginosa, Staphylococcus aureus, and Streptococcus pneumoniae, representing the primary causes of bacterial pneumonia worldwide. After applying principal components analysis, we observed that with the first three principal components (primarily comprised of data from 14 peaks), all infections were separable via SESI-MS breathprinting ( P < 0.0001). Therefore, we have shown the potential of this SESI-MS approach for rapidly detecting and identifying acute bacterial lung infections in situ via breath analysis.

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