Development and optimization of diagnostic methods
Novel diagnostic approaches and omics-based technologies enable the rapid detection of resistant pathogens and support early, targeted treatment decisions through the analysis of resistance and virulence factors.
The use of data-driven predictive models and high-resolution diagnostics is intended to accelerate treatment decisions in clinical settings and, in the long term, curb the spread of multidrug-resistant pathogens. A prerequisite for this is a substantial reduction in diagnostic turnaround times, helping to minimize antibiotic exposure and the associated selection pressure.
Methodologically, we rely on highly characterized cohorts that allow for the collection and analysis of a broad spectrum of biological samples as well as clinical surveillance data. Building on these cohorts and the R-Net and Shield projects, we are developing innovative diagnostic approaches:
Lateral-flow-based rapid tests
These are simple strip-based rapid tests, similar to pregnancy tests, that provide results within minutes.
Clinical application of nanopore sequencing for rapid prediction of antibiotic susceptibility, virulence, and transmission dynamics
This is a portable sequencing technology that enables real-time analysis of genetic material from pathogens.
Analysis of immunological features that correlate with the host’s microbiome profiles as novel predictors of clinical outcomes
These analyses investigate immune responses and the composition of microorganisms in the body to improve prediction of disease outcomes.
Identification of biomarkers for infections with previously poorly defined diagnostic criteria
This approach focuses on identifying measurable biological signals that reliably indicate the presence of specific infections, for example urinary tract infections.
