Dr. Andreas Wieser Profile

Dr. Andreas Wieser

at Ludwig-Maximilians-Univ. München

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Proceedings Article | 28 February 2020 Presentation + Paper

Deuterium uptake in combination with Raman spectroscopy as a tool to investigate antibiotic susceptibility of bacteria

D. Bauer, L. Qiu, K. Wieland, A.-C. Neumann-Cip, A. Wieser, G. Magistro, C. Stief, C. Haisch

Proceedings Volume 11223, 1122302 (2020) https://doi.org/10.1117/12.2553093

KEYWORDS: Bacteria, Raman spectroscopy, Statistical analysis, Diagnostics, Data modeling, Mode conditioning cables, Amplifiers, Chemical analysis, Analytics, Time metrology

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The evolution of multi-resistant bacteria poses a major threat to our healthcare system worldwide. To address this serious public health issue, the speed and sensitivity of state-of-the-art antibiotic susceptibility testing (AST) such as broth dilution and disc diffusion methods need to be significantly improved to provide better patient care. With the ultimate goal of developing a fast and reliable alternative that outperforms current state-of-the-art procedures, a high-throughput, generally– in terms of bug drug combinations – applicable analytical protocol was developed employing Raman spectroscopy as non-destructive analysis tool to monitor the deuterium uptake of metabolically active bacteria and additionally extract chemical-specific information for bacteria and/or contaminant identification. Our AST is based on two reference strains representing Gram-negative (E. coli) and Gram-positive bacteria (E. faecalis), treated with a total of four different antibiotics. Apart from high sensitivity and specificity, time is the most crucial parameter in clinical diagnosis. Hence, bulk analysis of highly concentrated samples was favored over a single-cell approach to allow timeefficient, straight-forward sample preparation and investigation. The developed AST also comprises a preincubation step (bug-drug incubation prior to the addition of D2O containing medium) which is shown to be key for the development of a reliable test comprising Gram-positive and Gram-negative bacteria. 52 clinical isolates typical for urinary tract infection causing pathogens were investigated in a semi-automated setting showing good agreement with state-of-the-art analytics.

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