Dataset Information

Application of Predictive Modeling Tools for the Identification of Ocimum spp. Herbal Products

ABSTRACT: LC-MS files for three species of Ocimum, to evaluate the potential of controlled growth studies to predict classification of consumer-based samples.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Ocimum Tenuiflorum (ncbitaxon:204149) Ocimum Basilicum (ncbitaxon:39350) Ocimum Gratissimum (ncbitaxon:204144)

SUBMITTER: Josh J Kellogg

PROVIDER: MSV000094012 | MassIVE | Mon Feb 05 09:13:00 GMT 2024

REPOSITORIES: MassIVE

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:BACKGROUND. Atherosclerosis disease results from sustained lipid accumulation within the arterial walls and subsequent chronic inflammatory response, being the major responsible of adverse cardiovascular events with high mortality rates worldwide. An early identification of patients at risk of atherosclerotic occlusive events is crucial, in order to prevent further complications with appropriate therapies. Currently, the use of machine learning classification algorithms (MLCA) constitutes a promising alternative in biomedical research, allowing patients classification based on the integration of clinical, genomic and other individual information, which could enhance the application of precision medicine. METHODS. In order to identify discriminating markers of atherosclerosis, a high-throughput approach with six different MLCA was applied to evaluate the clinical information as well as the proteomic changes detected in the serum from hospitalized patients with carotid atherosclerotic stenosis (n:60), compared to diagnosed dyslipidemic patients (with subclinical atheromatous status, n:55) or healthy controls (n:66). RESULTS. The combined approach, considering clinical and individual proteomic data, provided a more accurate classification of patients than the clinical or proteomic analyses alone. Furthermore, a panel of 14 proteins were identified as highly discriminating markers between the groups: ACTB, APOB, B2MG, C4BPA, CO1A1, A1AG1, FIBA, FIBB, FIBG, GPV, MMP9, PCOC1, PLF4, TSP1. Turbidimetric assays validated the changes seen by proteomic analysis. CONCLUSIONS. Our results corroborate the potential of using MLCA in combination with clinical and proteomic data to provide optimal patients classification and enhance precision medicine approaches for atherosclerosis management. Furthermore, a panel of 14 proteins has been highlighted as a potential signature of atherosclerotic progression. Overall, our data addressed the need to orchestrate a multipathway therapy to prevent unwanted thrombotic events, which special emphasis on platelet activation, uncontrolled angiogenesis and intraplaque hemorrhage.

Dataset Information

Application of Predictive Modeling Tools for the Identification of Ocimum spp. Herbal Products

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets