Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform.

Antimicrobial resistance (AMR) is a major public health threat, reducing treatment options for infected patients. AMR is promoted by a lack of access to rapid antibiotic susceptibility tests (ASTs). Accelerated ASTs can identify effective antibiotics for treatment in a timely and informed manner.

Investigating nanomotion-based technology (Resistell AST) for rapid antibiotic susceptibility testing among adult patients admitted to a tertiary-care hospital with Gram-negative bacteraemia: protocol for a prospective, observational, cross-sectional, single-arm study.

Introduction: Effective treatment of bloodstream infections (BSIs) is relying on rapid identification of the causing pathogen and its antibiotic susceptibility. Still, most commercially available antibiotic susceptibility testing (AST) methods are based on monitoring bacterial growth, thus impacting the time to results. The Resistell AST is based on a new technology measuring the nanomotion caused by physiologically active bacterial cells and detecting the changes in nanomotion caused by the exposure to a drug.

Identification and Characterization of Mediators of Fluconazole Tolerance in .

is an important human pathogen and a major concern in intensive care units around the world. infections are associated with a high mortality despite the use of antifungal treatments. One of the causes of therapeutic failures is the acquisition of antifungal resistance by mutations in the genome.

Defining the frontiers between antifungal resistance, tolerance and the concept of persistence.

A restricted number of antifungal agents are available for the therapy of fungal diseases. With the introduction of epidemiological cut-off values for each agent in important fungal pathogens based on the distribution of minimal inhibitory concentration (MIC), the distinction between wild type and drug-resistant populations has been facilitated. Antifungal resistance has been described for all currently available antifungal agents in several pathogens and most of the associated resistance mechanisms have been deciphered at the molecular level.

Adaptation of a Gaussia princeps Luciferase reporter system in Candida albicans for in vivo detection in the Galleria mellonella infection model.

For the past 10 years, mini-host models and in particular the greater wax moth Galleria mellonella have tended to become a surrogate for murine models of fungal infection mainly due to cost, ethical constraints and ease of use. Thus, methods to better assess the fungal pathogenesis in G. mellonella need to be developed.

Examining the virulence of Candida albicans transcription factor mutants using Galleria mellonella and mouse infection models.

The aim of the present study was to identify Candida albicans transcription factors (TFs) involved in virulence. Although mice are considered the gold-standard model to study fungal virulence, mini-host infection models have been increasingly used. Here, barcoded TF mutants were first screened in mice by pools of strains and fungal burdens (FBs) quantified in kidneys.