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Issue 12, 2017
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Characterization of bacteria causing acute otitis media using Raman microspectroscopy

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Abstract

Otitis media (OM) is a prevalent disease that is the most frequent cause of acute physician visits and prescription of antibiotics for children. Current methods to diagnose OM and differentiate between the two main types of OM, acute otitis media (AOM) and otitis media with effusion (OME), rely on interpreting symptoms that may overlap between them. Since AOM requires antibiotic treatment and OME does not, there is a clinical need to distinguish between AOM and OME to determine whether antibiotic treatment is necessary and guide future prescriptions. We used an optical spectroscopy technique, Raman spectroscopy (RS), to identify and characterize the biochemical features of the three main pathogens that cause AOM in vitro. A Renishaw inVia confocal Raman microscope at 785 nm was used to spectrally investigate the Raman signatures of Haemophilus influenzae, Moraxella catarrhalis, and Streptococcus pneumoniae. Biochemical features or biomarkers important for classification of each bacterial species were identified and yielded a 97% accuracy of discrimination. To test the effectiveness of Raman-based bacterial classification in a clinical sample, human middle ear effusion (MEE) from patients affected by recurrent AOM was collected, cultured, and measured using RS. The probability of bacterial involvement from each of the three main bacteria that cause AOM was determined from the clinical MEE samples. These results suggest the potential of utilizing RS to aid in accurately diagnosing AOM and providing physicians with bacterial identification to guide treatment.

Graphical abstract: Characterization of bacteria causing acute otitis media using Raman microspectroscopy

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Article information


Submitted
14 Jan 2017
Accepted
25 Jan 2017
First published
20 Feb 2017

Anal. Methods, 2017,9, 1864-1871
Article type
Paper

Characterization of bacteria causing acute otitis media using Raman microspectroscopy

O. D. Ayala, C. A. Wakeman, I. J. Pence, C. M. O'Brien, J. A. Werkhaven, E. P. Skaar and A. Mahadevan-Jansen, Anal. Methods, 2017, 9, 1864
DOI: 10.1039/C7AY00128B

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