Paper
23 February 2009 Urinary tract infection diagnosis and response to antibiotics using Raman spectroscopy
Evdokia Kastanos, Alexandros Kyriakides, Katerina Hadjigeorgiou, Constantinos Pitris
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Abstract
Urinary tract infection diagnosis and antibiogram require a 48 hour waiting period using conventional methods. This results in ineffective treatments, increased costs and most importantly in increased resistance to antibiotics. In this work, a novel method for classifying bacteria and determining their sensitivity to an antibiotic using Raman spectroscopy is described. Raman spectra of three species of gram negative Enterobacteria, most commonly responsible for urinary tract infections, were collected. The study included 25 samples each of E.coli, Klebsiella p. and Proteus spp. A novel algorithm based on spectral ratios followed by discriminant analysis resulted in classification with over 94% accuracy. Sensitivity and specificity for the three types of bacteria ranged from 88-100%. For the development of an antibiogram, bacterial samples were treated with the antibiotic ciprofloxacin to which they were all sensitive. Sensitivity to the antibiotic was evident after analysis of the Raman signatures of bacteria treated or not treated with this antibiotic as early as two hours after exposure. This technique can lead to the development of new technology for urinary tract infection diagnosis and antibiogram with same day results, bypassing urine cultures and avoiding all undesirable consequences of current practice.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Evdokia Kastanos, Alexandros Kyriakides, Katerina Hadjigeorgiou, and Constantinos Pitris "Urinary tract infection diagnosis and response to antibiotics using Raman spectroscopy", Proc. SPIE 7169, Advanced Biomedical and Clinical Diagnostic Systems VII, 71690I (23 February 2009); https://doi.org/10.1117/12.808731
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Cited by 3 scholarly publications.
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KEYWORDS
Raman spectroscopy

Bacteria

Surface enhanced Raman spectroscopy

Statistical analysis

Pathogens

Photons

Resistance

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