{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["9(6)"],"submitter":["Liang Q"],"pubmed_abstract":["High-throughput metabolic profiling technology has been used for biomarker discovery and to reveal underlying metabolic mechanisms. Sepsis-induced myocardial dysfunction (SMD) is a common complication in sepsis patients, and severely affects their quality of life. However, the pathogenesis of SMD is currently unclear, and there has been inadequate basic research. In this study, metabolic profiling was explored by liquid chromatography/mass spectrometry (LC/MS) combined with chemometrics and bioinformatic analysis. The global metabolome data were analyzed using chemometrics analysis including principal component analysis and partial least squares discriminant analysis for significant metabolites. Variable importance for projection values obtained utilizing a pattern recognition method were "],"journal":["RSC advances"],"pagination":["3351-3358"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9087870"],"repository":["biostudies-literature"],"pubmed_title":["High-throughput metabolic profiling, combined with chemometrics and bioinformatic analysis reveals functional alterations in myocardial dysfunction."],"pmcid":["PMC9087870"],"pubmed_authors":["Liu H","Li X","Hairong P","Yang Y","Sun P","Du C","Liang Q"],"additional_accession":[]},"is_claimable":false,"name":"High-throughput metabolic profiling, combined with chemometrics and bioinformatic analysis reveals functional alterations in myocardial dysfunction.","description":"High-throughput metabolic profiling technology has been used for biomarker discovery and to reveal underlying metabolic mechanisms. Sepsis-induced myocardial dysfunction (SMD) is a common complication in sepsis patients, and severely affects their quality of life. However, the pathogenesis of SMD is currently unclear, and there has been inadequate basic research. In this study, metabolic profiling was explored by liquid chromatography/mass spectrometry (LC/MS) combined with chemometrics and bioinformatic analysis. The global metabolome data were analyzed using chemometrics analysis including principal component analysis and partial least squares discriminant analysis for significant metabolites. Variable importance for projection values obtained utilizing a pattern recognition method were ","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Jan","modification":"2025-04-04T07:44:28.743Z","creation":"2025-04-04T07:44:28.743Z"},"accession":"S-EPMC9087870","cross_references":{"pubmed":["35548688"],"doi":["10.1039/c8ra07572g"]}}