Interferometric biosensors for rapid identification of nosocomial infections

Abstract

This doctoral Thesis is focusing on the development of a novel optical biosensor as an alternative technique for the identification of nosocomial infections in a faster way. This new tool will also facilitate the finding of the most effective treatment for each patient, reduce the nonspecific use of broad-spectrum antimicrobial drugs, and facilitate new antibiotic treatments. We propose the use of a novel nanophotonic sensor based on an interferometric transducer device, the Bimodal Waveguide device (BiMW) for the rapid, specific, highly sensitive and direct analysis of different pathogens associated to nosocomial infections and their multidrug resistant. First, we assessed and optimized different biofunctionalization strategies for an efficient immobilization of the required biorecognition receptors, which ensure a highly sensitive bacterial detection with enough selectivity and reproducibility, particularly suitable for the direct detection in complex matrices, such as urine and ascitic fluid. The optimized strategies were employed for the identification of various nosocomial pathogens such as Bacillus cereus, Escherichia coli, and Pseudomonas aeruginosa using antibodies as biorecognition elements. The detection of Escherichia coli was done in human ascitic fluid. Finally, the BiMW biosensor was employed to identify the multidrug-resistant bacteria such as: i) the identification of methicillin-resistant Staphylococcus aureus (MRSA) using a specific aptamer, which is able to discriminate among a susceptible one to antibiotic and a multidrug-resistant Staphylococcus, and (ii) the ultra-sensitive detection of multidrug-resistant E. coli genes without PCR amplification._x000D_ This Thesis takes advantage of the knowledge in photonics biosensors and bioanalytical methods in our Group in order to develop a powerful tool for the direct and effective identification of nosocomial pathogens and their antibiotic-resistance in a rapid and label-free scheme.

Date of Award	28 Mar 2017
Original language	English
Supervisor	Antonio Pedro Villaverde Corrales (Tutor) & Ana Belén González Guerrero (Director)