Machine Learning Analysis of the Cerebrovascular Thrombi Lipidome in Acute Ischemic Stroke.
OBJECTIVE: The aim of this study was to identify a signature lipid profile from cerebral thrombi in acute ischemic stroke (AIS) patients at the time of ictus. METHODS: We performed untargeted lipidomics analysis using liquid chromatography-mass spectrometry on cerebral thrombi taken from a nonprobab...
| Publicado en: | Journal of Neuroscience Nursing Vol. 55; no. 1; pp. 10 - 18 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Feb2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161203668&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161203668 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08880395 38Z jtl: Journal of Neuroscience Nursing issn: 08880395 maglogo: N pubinfo: dt: Feb2023 vid: 55 iid: 1 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 161203668 161203668 161203668 10.1097/JNN.0000000000000682 161203668 ppf: 10 ppct: 8 formats: tig: atl: Machine Learning Analysis of the Cerebrovascular Thrombi Lipidome in Acute Ischemic Stroke. aug: au: Martha, Sarah R. Levy, Samuel H. Federico, Emma Levitt, Michael R. Walker, Melanie affil: Samuel H. Levy, Department of Neurological Surgery and Stroke and Applied Neuroscience (SANS) Center, University of Washington, Seattle, WA. sug: subj: Machine Learning Ischemic Stroke Intracranial Thrombosis Metabolomics Human Chromatography, Liquid Mass Spectrometry Nonprobability Sample Convenience Sample Random Forest Biological Markers Research, Nursing ab: OBJECTIVE: The aim of this study was to identify a signature lipid profile from cerebral thrombi in acute ischemic stroke (AIS) patients at the time of ictus. METHODS: We performed untargeted lipidomics analysis using liquid chromatography-mass spectrometry on cerebral thrombi taken from a nonprobability, convenience sampling of adult subjects (≥18 years old, n = 5) who underwent thrombectomy for acute cerebrovascular occlusion. The data were classified using random forest, a machine learning algorithm. RESULTS: The top 10 metabolites identified from the random forest analysis were of the glycerophospholipid species and fatty acids. CONCLUSION: Preliminary analysis demonstrates feasibility of identification of lipid metabolomic profiling in cerebral thrombi retrieved from AIS patients. Recent advances in omic methodologies enable lipidomic profiling, which may provide insight into the cellular metabolic pathophysiology caused by AIS. Understanding of lipidomic changes in AIS may illuminate specific metabolite and lipid pathways involved and further the potential to develop personalized preventive strategies. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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