Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/125898
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cano, Ainara | - |
dc.contributor.author | Mariño, Zoe | - |
dc.contributor.author | Millet Aguilar-Galindo, Òscar | - |
dc.contributor.author | Martínez Arranz, Ibon | - |
dc.contributor.author | Navasa, Miquel | - |
dc.contributor.author | Falcón Pérez, Juan Manuel | - |
dc.contributor.author | Pérez Cormenzana, Miriam | - |
dc.contributor.author | Caballeria Rovira, Joan | - |
dc.contributor.author | Embade, Nieves | - |
dc.contributor.author | Forns, Xavier | - |
dc.contributor.author | Bosch, Jaume | - |
dc.contributor.author | Castro, Azucena | - |
dc.contributor.author | Mato, José M. | - |
dc.date.accessioned | 2018-11-07T16:47:30Z | - |
dc.date.available | 2018-11-07T16:47:30Z | - |
dc.date.issued | 2017-09-05 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | http://hdl.handle.net/2445/125898 | - |
dc.description.abstract | Liver fibrosis must be evaluated in patients with hepatitis C virus (HCV) after liver transplantation because its severity affects their prognosis and the recurrence of HCV. Since invasive biopsy is still the gold standard to identify patients at risk of graft loss from rapid fibrosis progression, it becomes crucial the development of new accurate, non-invasive methods that allow repetitive examination of the patients. Therefore, we have developed a non-invasive, accurate model to distinguish those patients with different liver fibrosis stages. Two hundred and three patients with HCV were histologically classified (METAVIR) into five categories of fibrosis one year after liver transplantation. In this cross-sectional study, patients at fibrosis stages F0-F1 (n = 134) were categorised as "slow fibrosers" and F2-F4 (n = 69) as "rapid fibrosers". Chloroform/methanol serum extracts were analysed by reverse ultra-high performance liquid chromatography coupled to mass spectrometry. A diagnostic model was built through linear discriminant analyses. An algorithm consisting of two sphingomyelins and two phosphatidylcholines accurately classifies rapid and slow fibrosers after transplantation. The proposed model yielded an AUROC of 0.92, 71% sensitivity, 85% specificity, and 84% accuracy. Moreover, specific bile acids and sphingomyelins increased notably along with liver fibrosis severity, differentiating between rapid and slow fibrosers. | - |
dc.format.extent | 10 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isformatof | Reproducció del document publicat a: https://doi.org/10.1038/s41598-017-10807-y | - |
dc.relation.ispartof | Scientific Reports, 2017, vol. 7, num. 1, p. 10497 | - |
dc.relation.uri | https://doi.org/10.1038/s41598-017-10807-y | - |
dc.rights | cc-by (c) Cano, Ainara et al., 2017 | - |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es | - |
dc.source | Articles publicats en revistes (Medicina) | - |
dc.subject.classification | Trasplantament hepàtic | - |
dc.subject.classification | Hepatitis C | - |
dc.subject.classification | Cirrosi hepàtica | - |
dc.subject.classification | Diagnòstic molecular | - |
dc.subject.other | Hepatic transplantation | - |
dc.subject.other | Hepatitis C | - |
dc.subject.other | Hepatic cirrhosis | - |
dc.subject.other | Molecular diagnosis | - |
dc.title | A Metabolomics Signature Linked To Liver Fibrosis In The Serum Of Transplanted Hepatitis C Patients | - |
dc.type | info:eu-repo/semantics/article | - |
dc.type | info:eu-repo/semantics/publishedVersion | - |
dc.identifier.idgrec | 675950 | - |
dc.date.updated | 2018-11-07T16:47:30Z | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
dc.identifier.pmid | 28874799 | - |
Appears in Collections: | Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer) Articles publicats en revistes (Medicina) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
675950.pdf | 1.76 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License