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For: Cortes M, Pareja E, García-Cañaveras JC, Donato MT, Montero S, Mir J, Castell JV, Lahoz A. Metabolomics discloses donor liver biomarkers associated with early allograft dysfunction. J Hepatol 2014;61:564-74. [PMID: 24798621 DOI: 10.1016/j.jhep.2014.04.023] [Cited by in Crossref: 40] [Cited by in F6Publishing: 35] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
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8 Roca M, Alcoriza MI, Garcia-Cañaveras JC, Lahoz A. Reviewing the metabolome coverage provided by LC-MS: Focus on sample preparation and chromatography-A tutorial. Anal Chim Acta 2021;1147:38-55. [PMID: 33485584 DOI: 10.1016/j.aca.2020.12.025] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
9 García-Cañaveras JC, Jiménez N, Gómez-Lechón MJ, Castell JV, Donato MT, Lahoz A. LC-MS untargeted metabolomic analysis of drug-induced hepatotoxicity in HepG2 cells. Electrophoresis 2015;36:2294-302. [PMID: 26031481 DOI: 10.1002/elps.201500095] [Cited by in Crossref: 24] [Cited by in F6Publishing: 22] [Article Influence: 3.4] [Reference Citation Analysis]
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11 Zhi Y, Sun Y, Jiao Y, Pan C, Wu Z, Liu C, Su J, Zhou J, Shang D, Niu J, Hua R, Yin P. HR-MS Based Untargeted Lipidomics Reveals Characteristic Lipid Signatures of Wilson's Disease. Front Pharmacol 2021;12:754185. [PMID: 34880754 DOI: 10.3389/fphar.2021.754185] [Reference Citation Analysis]
12 Bruinsma BG, Sridharan GV, Weeder PD, Avruch JH, Saeidi N, Özer S, Geerts S, Porte RJ, Heger M, van Gulik TM, Martins PN, Markmann JF, Yeh H, Uygun K. Metabolic profiling during ex vivo machine perfusion of the human liver. Sci Rep 2016;6:22415. [PMID: 26935866 DOI: 10.1038/srep22415] [Cited by in Crossref: 48] [Cited by in F6Publishing: 51] [Article Influence: 8.0] [Reference Citation Analysis]
13 Xu J, Casas-Ferreira AM, Ma Y, Sen A, Kim M, Proitsi P, Shkodra M, Tena M, Srinivasan P, Heaton N, Jassem W, Legido-Quigley C. Lipidomics comparing DCD and DBD liver allografts uncovers lysophospholipids elevated in recipients undergoing early allograft dysfunction. Sci Rep 2015;5:17737. [PMID: 26635289 DOI: 10.1038/srep17737] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 1.9] [Reference Citation Analysis]
14 Huang H, Li B, Song J, Ye G, Tang X, Qu T, Yan L, Wen T, Li B, Wang W, Wu H, Xu M, Yang J, Luo Y. Can ultrasound elastography assess liver quality in brain-dead donors and predict early allograft dysfunction after transplantation? Acad Radiol 2021;28 Suppl 1:S112-7. [PMID: 34756817 DOI: 10.1016/j.acra.2020.10.030] [Reference Citation Analysis]
15 Salviano MEM, Lima AS, Tonelli IS, Correa HP, Chianca TCM. Primary liver graft dysfunction and non-function: integrative literature review. Rev Col Bras Cir 2019;46:e2039. [PMID: 31017176 DOI: 10.1590/0100-6991e-20192039] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
16 Attard JA, Dunn WB, Mergental H, Mirza DF, Afford SC, Perera MTPR. Systematic Review: Clinical Metabolomics to Forecast Outcomes in Liver Transplantation Surgery. OMICS 2019;23:463-76. [PMID: 31513460 DOI: 10.1089/omi.2019.0086] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 2.3] [Reference Citation Analysis]
17 Bonneau E, Tétreault N, Robitaille R, Boucher A, De Guire V. Metabolomics: Perspectives on potential biomarkers in organ transplantation and immunosuppressant toxicity. Clinical Biochemistry 2016;49:377-84. [DOI: 10.1016/j.clinbiochem.2016.01.006] [Cited by in Crossref: 29] [Cited by in F6Publishing: 27] [Article Influence: 4.8] [Reference Citation Analysis]
18 Lee DD, Croome KP, Shalev JA, Musto KR, Sharma M, Keaveny AP, Taner CB. Early allograft dysfunction after liver transplantation: an intermediate outcome measure for targeted improvements. Ann Hepatol. 2016;15:53-60. [PMID: 26626641 DOI: 10.5604/16652681.1184212] [Cited by in Crossref: 47] [Cited by in F6Publishing: 50] [Article Influence: 7.8] [Reference Citation Analysis]
19 García-Cañaveras JC, Peris-Díaz MD, Alcoriza-Balaguer MI, Cerdán-Calero M, Donato MT, Lahoz A. A lipidomic cell-based assay for studying drug-induced phospholipidosis and steatosis. Electrophoresis 2017;38:2331-40. [PMID: 28512733 DOI: 10.1002/elps.201700079] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 2.4] [Reference Citation Analysis]
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21 Perera MT, Higdon R, Richards DA, Silva MA, Murphy N, Kolker E, Mirza DF. Biomarker differences between cadaveric grafts used in human orthotopic liver transplantation as identified by coulometric electrochemical array detection (CEAD) metabolomics. OMICS. 2014;18:767-777. [PMID: 25353146 DOI: 10.1089/omi.2014.0094] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 1.3] [Reference Citation Analysis]
22 Mas VR. Ischemia/reperfusion, does temperature matter? Laboratory perspective. Liver Transpl 2015;21 Suppl 1:S1-5. [PMID: 26334928 DOI: 10.1002/lt.24322] [Reference Citation Analysis]
23 Cano A, Mariño Z, Millet O, Martínez-Arranz I, Navasa M, Falcón-Pérez JM, Pérez-Cormenzana M, Caballería J, Embade N, Forns X, Bosch J, Castro A, Mato JM. A Metabolomics Signature Linked To Liver Fibrosis In The Serum Of Transplanted Hepatitis C Patients. Sci Rep 2017;7:10497. [PMID: 28874799 DOI: 10.1038/s41598-017-10807-y] [Cited by in Crossref: 14] [Cited by in F6Publishing: 10] [Article Influence: 2.8] [Reference Citation Analysis]
24 Huang H, Sun Z, Pan H, Chen M, Tong Y, Zhang J, Chen D, Su X, Li L. Serum metabolomic signatures discriminate early liver inflammation and fibrosis stages in patients with chronic hepatitis B. Sci Rep 2016;6:30853. [PMID: 27498553 DOI: 10.1038/srep30853] [Cited by in Crossref: 10] [Cited by in F6Publishing: 14] [Article Influence: 1.7] [Reference Citation Analysis]
25 García-Cañaveras JC, Castell JV, Donato MT, Lahoz A. A metabolomics cell-based approach for anticipating and investigating drug-induced liver injury. Sci Rep 2016;6:27239. [PMID: 27265840 DOI: 10.1038/srep27239] [Cited by in Crossref: 42] [Cited by in F6Publishing: 37] [Article Influence: 7.0] [Reference Citation Analysis]
26 Zhang A, Carroll C, Raigani S, Karimian N, Huang V, Nagpal S, Beijert I, Porte RJ, Yarmush M, Uygun K, Yeh H. Tryptophan Metabolism via the Kynurenine Pathway: Implications for Graft Optimization during Machine Perfusion. J Clin Med 2020;9:E1864. [PMID: 32549246 DOI: 10.3390/jcm9061864] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
27 Sui W, Gan Q, Liu F, Ou M, Wang B, Liao S, Lai L, Chen H, Yang M, Dai Y. Dynamic Metabolomics Study of the Bile Acid Pathway During Perioperative Primary Hepatic Carcinoma Following Liver Transplantation. Ann Transplant 2020;25:e921844. [PMID: 32572018 DOI: 10.12659/AOT.921844] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
28 Sun R, Zhao H, Huang S, Zhang R, Lu Z, Li S, Wang G, Aa J, Xie Y. Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy. Front Pharmacol 2021;12:760474. [PMID: 34916939 DOI: 10.3389/fphar.2021.760474] [Reference Citation Analysis]
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35 García-cañaveras JC, López S, Castell JV, Donato MT, Lahoz A. Extending metabolome coverage for untargeted metabolite profiling of adherent cultured hepatic cells. Anal Bioanal Chem 2016;408:1217-30. [DOI: 10.1007/s00216-015-9227-8] [Cited by in Crossref: 26] [Cited by in F6Publishing: 23] [Article Influence: 4.3] [Reference Citation Analysis]
36 Bruinsma BG, Avruch JH, Sridharan GV, Weeder PD, Jacobs ML, Crisalli K, Amundsen B, Porte RJ, Markmann JF, Uygun K, Yeh H. Peritransplant Energy Changes and Their Correlation to Outcome After Human Liver Transplantation. Transplantation 2017;101:1637-44. [PMID: 28230641 DOI: 10.1097/TP.0000000000001699] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 2.4] [Reference Citation Analysis]
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39 Jehl F, Muret K, Bernard M, Boutin M, Lagoutte L, Désert C, Dehais P, Esquerré D, Acloque H, Giuffra E, Djebali S, Foissac S, Derrien T, Pitel F, Zerjal T, Klopp C, Lagarrigue S. An integrative atlas of chicken long non-coding genes and their annotations across 25 tissues. Sci Rep 2020;10:20457. [PMID: 33235280 DOI: 10.1038/s41598-020-77586-x] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]