ABSTRACT
Sepsis constitutes a major cause of death in intensive care units. Patients present a dysregulated response to infection, which progresses through different pro/anti-inflammatory phases. The lack of tools to monitor their response constrains the therapeutic approaches. Here, we evaluated a test based on plasma protein fingerprints acquired by MALDI-TOF-mass spectrometry and supervised/unsupervised algorithms to discriminate the different immunological stages of sepsis in lipopolysaccharide-induced murine models with encouraging results. Moreover, our predictive models through machine learning algorithms were able to discriminate the different groups with a sensitivity of up to 95.7% and a specificity of 90.9% depending on the selected peaks number. Potential individual biomarkers associated with each phase were also analysed. Our data reveal the potential of plasma peptidome analysis by MALDI-TOF-mass spectrometry as a highly relevant strategy for sepsis patient stratification that could contribute to therapeutic decisions, depending on the immunological phase that the patient is undergoing.
Competing Interest Statement
The authors have declared no competing interest.