The abusive use of antimicrobial compounds and the associated appearance of antimicrobial resistant strains are a major threat to human health. An improved antimicrobial administration involves a faster diagnosis and detection of resistances. Antimicrobial susceptibility testing (AST) are the reference techniques for this purpose, relying mainly in the use of culture techniques. The long time required for analysis and the lack of reproducibility of these techniques have fostered the development of high-throughput AST methods, including electrochemical biosensors. In this review, recent electrochemical methods used in AST have been revised, with particular attention on those used for the evaluation of new drug candidates. The role of nanomaterials in these biosensing platforms has also been questioned, inferring that it is of minor importance compared to other applications.
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For the first time the use of nanoparticles as carriers of an enzymatic substrate immobilized inside nanoporous alumina membranes is proposed with the aim of amplifying the nanochannel blocking produced and, consequently, improving the efficiency of an enzyme determination through enzymatic cleavage. Streptavidin-modified polystyrene nanoparticles (PSNPs) are proposed as carrier agents, contributing to the steric and the electrostatic blockage due to the charge they present at different pH values. Electrostatic blockage is the predominant effect that governs the blockage in the interior of the nanochannel and is dependent not just in the charge inside the channel, but also in the polarity of the redox indicator used. Hence, the effect of using negatively charged ([Fe(CN)6]4−) and positively charged ([Ru(NH3)6]3+) redox indicator ions is studied for the first time. Under the optimum conditions, matrix-metalloproteinase 9 (MMP-9) is detected at clinically relevant levels (100–1200 ng/mL) showing a detection limit of 75 ng/mL and a quantification limit of 251 ng/mL with good reproducibility (RSD: 8%) and selectivity, also showing an excellent performance in real samples with acceptable recovery percentages (in the range around 80–110%). Overall, our approach represents a cheap and fast sensing methodology of great potential in point-of-care diagnostics.
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