MassIVE MSV000094486

Partial Public PXD051271

Population scale proteomics enables adaptive digital twin modelling in sepsis

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Description

1364 plasma proteome samples taken at time-of-admission to the emergency department from patients suspected of sepsis. Used initially with the following abstract: Sepsis is one of the leading causes of mortality in the world. Currently, the heterogeneity of sepsis makes it challenging to determine the molecular mechanisms that define the syndrome. Here, we leverage population scale proteomics to analyze a well-defined cohort of 1364 blood samples taken at time-of-admission to the emergency department from patients suspected of sepsis. We identified panels of proteins using explainable artificial intelligence that predict clinical outcomes and applied these panels to reduce high-dimensional proteomics data to a low-dimensional interpretable latent space (ILS). Using the ILS, we constructed an adaptive digital twin model that accurately predicted organ dysfunction, mortality, and early-mortality-risk patients using only data available at time-of-admission. In addition to being highly effective for investigating sepsis, this approach supports the flexible incorporation of new data and can generalize to other diseases to aid in translational research and the development of precision medicine. [doi:10.25345/C5MC8RT18] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: sepsis ; DIA ; diaPASEF ; plasma

Contact

Principal Investigators:
(in alphabetical order) 		Adam Linder, Division of Infection Medicine, Department of Clinical Sciences, Faculty of Medicine, Lund University, Sweden
Johan Malmstrom, Division of Infection Medicine, Department of Clinical Sciences, Faculty of Medicine, Lund University, Sweden
Submitting User: arnscott
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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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