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Shedding Metabo'light' on the Search for Sepsis Biomarkers

Overview
Journal Crit Care
Specialty Critical Care
Date 2015 Jul 8
PMID 26148483
Citations 4
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Abstract

The clinical presentation of severe infection with generalized inflammation is similar, if not identical, to systemic inflammation induced by sterile tissue injury. Novel models and unbiased technologies are urgently needed for biomarker identification and disease profiling in sepsis. Here we briefly review the article of Kamisoglu and colleagues in this issue of Critical Care on comparing metabolomics data from different studies to assess whether responses elicited by endotoxin recapitulate, at least in part, those seen in clinical sepsis.

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References
1.
Artenstein A, Higgins T, Opal S . Sepsis and scientific revolutions. Crit Care Med. 2013; 41(12):2770-2. DOI: 10.1097/CCM.0b013e31829eb98f. View

2.
Arakawa K, Tomita M . Merging multiple omics datasets in silico: statistical analyses and data interpretation. Methods Mol Biol. 2013; 985:459-70. DOI: 10.1007/978-1-62703-299-5_23. View

3.
Yoon S, Han M, Jeong H, Lee C, Xia X, Lee D . Comparative multi-omics systems analysis of Escherichia coli strains B and K-12. Genome Biol. 2012; 13(5):R37. PMC: 3446290. DOI: 10.1186/gb-2012-13-5-r37. View

4.
Kaddurah-Daouk R, Kristal B, Weinshilboum R . Metabolomics: a global biochemical approach to drug response and disease. Annu Rev Pharmacol Toxicol. 2008; 48:653-83. DOI: 10.1146/annurev.pharmtox.48.113006.094715. View

5.
Bartel J, Krumsiek J, Theis F . Statistical methods for the analysis of high-throughput metabolomics data. Comput Struct Biotechnol J. 2014; 4:e201301009. PMC: 3962125. DOI: 10.5936/csbj.201301009. View