Eugenol, a natural constituent of a number of aromatic plants and their essential oil fractions, has several biological effects. However, its protective effects against endothelial injury remain unclarified. This study investigates how eugenol affects human umbilical vein endothelial cells (HUVECs) dysfunction mediated by oxidized low density lipoprotein (oxLDL). Our results showed that the suppression of endothelial NO synthase (eNOS) expression, enhancement of adhesion molecules (ICAM, VCAM, and E-selectin) expression, and adherence of monocytic THP1 cells caused by a non-cytotoxic concentration (100 microg/ml) of oxLDL were ameliorated following a eugenol treatment (12.5-100 microM) in HUVECs. Eugneol also inhibited the reactive oxygen species (ROS) generation, intracellular calcium accumulation, and the subsequent mitochondrial membrane potential collapse, cytochrome c release and caspase-3 activation induced by oxLDL. The cytotoxicity and apoptotic features induced by a cytotoxic concentration (200 microg/ml) of oxLDL was also attenuated by eugenol. Our results suggest that eugenol may protect against the oxLDL-induced dysfunction in endothelial cells.
The employment of liquid chromatography‐mass spectrometry (LC‐MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC‐MS‐untargeted and targeted metabolomics. To improve the sensitivity of low‐abundance or poor‐ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC‐MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC‐MS metabolomics to accelerate metabolite identification and data‐processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC‐MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.