Machine learning identifies clinical sepsis phenotypes that translate to the plasma proteome

The identified clinical phenotypes reflected varying degrees of sepsis severity and were mirrored in the plasma proteome. Proteomic profiling offered novel insights into the molecular mechanisms underlying sepsis and enabled a deeper characterization of the identified phenotypes.

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Publication Details

Journal
Infection
Published
2025-08-28
DOI
https://doi.org/10.1007/s15010-025-02628-3
Citations
6
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
8.13

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article

Machine learning identifies clinical sepsis phenotypes that translate to the plasma proteome

Malte Bayer, Thilo von Groote, Thilo Bracht, Ulrich Limper et al.
6 citations
Infection
Sepsis Diagnosis and Treatment
8.13
article

Machine learning identifies clinical sepsis phenotypes that translate to the plasma proteome

Malte Bayer, Thilo von Groote, Thilo Bracht, Ulrich Limper, Stefan Ehrentraut, Dietrich Henzler, Matthias Unterberg, Alexander Zarbock, Tim Rahmel, K Käppler, Lars Palmowski, Helge Haberl, Katrin Marcus, Björn Koos, Katharina Rump, Michael Adamzik, Barbara Sitek, Alexander Wolf, Karin Schork, Hartmuth Nowak, Dominik Ziehe, Martin Eisenacher, Maike Weber
article en
6 citations

Abstract

The identified clinical phenotypes reflected varying degrees of sepsis severity and were mirrored in the plasma proteome. Proteomic profiling offered novel insights into the molecular mechanisms underlying sepsis and enabled a deeper characterization of the identified phenotypes.

InfectionVol. 53(6)
Witten/Herdecke University (DE), University Hospital Bonn (DE), University Hospitals of the Ruhr-University of Bochum (DE), University Hospital Münster (DE), Universitätsklinikum Knappschaftskrankenhaus Bochum (DE), Herford Hospital (DE), Ruhr University Bochum (DE)
Ruhr-Universität Bochum
Openalex Percentile: Top 2%
Sepsis Diagnosis and Treatment
8.13
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