ArticleRSC advances2019
High-throughput metabolic profiling, combined with chemometrics and bioinformatic analysis reveals functional alterations in myocardial dysfunction.
Article in RSC advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Utilizing omics technologies in the investigation of sepsis-induced cardiomyopathy.International journal of cardiology. Heart & vasculature · 2024Review
- Identifying Significant Metabolic Pathways Using Multi-Block Partial Least-Squares Analysis.Journal of proteome research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
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 used to identify potential biomarkers. The differential metabolites were putatively identified using the metabolome database and bioinformatics analysis was conducted
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.