Thomas Bjarnsholt

The paper uses machine learning to predict tobramycin susceptibility in Pseudomonas aeruginosa biofilms using multiple analytical methods, achieving high accuracy with MALDI-TOF MS and Raman spectroscopy.

The paper investigates how biofilms and core pathogens like Bacteroides fragilis and Fusobacterium nucleatum influence the tumor microenvironment and immune phenotype in colorectal cancer.

This study characterizes the proteomic and microbial composition of wound slough and integrates these with clinical data to predict wound healing outcomes.

The paper describes a method to separate bacterial cultures into single cells and biofilm aggregates using mesh filters, validated by microscopy and DNA quantification.

The paper identifies two compounds that inhibit quorum sensing in Pseudomonas aeruginosa by inducing the MexEF-oprN efflux pump, reducing QS-regulated virulence factors.