Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa genomes.

Background: Antimicrobial resistance (AMR) is an intensifying threat that requires urgent mitigation to avoid a post-antibiotic era. Pseudomonas aeruginosa represents one of the greatest AMR concerns due to increasing multi- and pan-drug resistance rates. Shotgun sequencing is gaining traction for in silico AMR profiling due to its unambiguity and transferability; however, accurate and comprehensive AMR prediction from P.

Rapid fluoroquinolone resistance detection in using mismatch amplification mutation assay-based real-time PCR.

Antimicrobial resistance (AMR) is an ever-increasing global health concern. One crucial facet in tackling the AMR epidemic is earlier and more accurate AMR diagnosis, particularly in the dangerous and highly multi-drug-resistant ESKAPE pathogen, . We aimed to develop two SYBR Green-based mismatch amplification mutation assays (SYBR-MAMAs) targeting GyrA T83I (248) and GyrA D87N, D87Y and D87H (259).

Express Yourself: Quantitative Real-Time PCR Assays for Rapid Chromosomal Antimicrobial Resistance Detection in Pseudomonas aeruginosa.

The rise of antimicrobial-resistant (AMR) bacteria is a global health emergency. One critical facet of tackling this epidemic is more rapid AMR diagnosis in serious multidrug-resistant pathogens like Pseudomonas aeruginosa. Here, we designed and then validated two multiplex quantitative real-time PCR (qPCR) assays to simultaneously detect differential expression of the resistance-nodulation-division efflux pumps MexAB-OprM, MexCD-OprJ, MexEF-OprN, and MexXY-OprM, the AmpC β-lactamase, and the porin OprD, which are commonly associated with chromosomally encoded AMR.

Taking the next-gen step: Comprehensive antimicrobial resistance detection from Burkholderia pseudomallei.

Background: Antimicrobial resistance (AMR) poses a major threat to human health. Whole-genome sequencing holds great potential for AMR identification; however, there remain major gaps in accurately and comprehensively detecting AMR across the spectrum of AMR-conferring determinants and pathogens.

Methods: Using 16 wild-type Burkholderia pseudomallei and 25 with acquired AMR, we first assessed the performance of existing AMR software (ARIBA, CARD, ResFinder, and AMRFinderPlus) for detecting clinically relevant AMR in this pathogen.