Ela Sauerborn

This paper develops a method to link plasmid-encoded AMR genes to their bacterial hosts using nanopore sequencing methylation data, achieving high accuracy in clinical samples.

This study verifies nanopore sequencing for CRE surveillance by comparing it to routine diagnostics and determining required sequencing depths for various clinical tasks.

The paper develops a machine learning model to detect pOXA-48-carrying Enterobacterales using MALDI-TOF MS spectra, achieving high accuracy across different cohorts.

This study uses long-read nanopore sequencing to analyze carbapenem resistance dynamics in a hospital setting, revealing plasmid spread and environmental reservoirs.

This paper introduces a computational framework using nanopore sequencing and deep learning to infer microbial viability from raw signal data, showing high accuracy in controlled and real-world applications.

This paper demonstrates that nanopore sequencing can effectively monitor air microbiomes by analyzing microbial diversity in controlled and natural environments.