000121366 001__ 121366
000121366 005__ 20250116144525.0
000121366 0247_ $$2doi$$a10.1039/D2JA00267A
000121366 0248_ $$2sideral$$a131669
000121366 037__ $$aART-2023-131669
000121366 041__ $$aeng
000121366 100__ $$aGarcía-Poyo, M. Carmen
000121366 245__ $$aCu fractionation, isotopic analysis, and data processing via machine learning: new approaches for the diagnosis and follow up of Wilson's disease via ICP-MS
000121366 260__ $$c2023
000121366 5060_ $$aAccess copy available to the general public$$fUnrestricted
000121366 5203_ $$aInformation about Cu fractionation and Cu isotopic composition can be paramount when investigating Wilson's disease (WD). This information can provide a better understanding of the metabolism of Cu. Most importantly, it may provide an easy way to diagnose and to follow the evolution of WD patients. For such purposes, protocols for Cu determination and Cu isotopic analysis via inductively coupled plasma mass spectrometry were investigated in this work, both in bulk serum and in the exchangeable copper (CuEXC) fractions. The CuEXC protocol provided satisfactory recovery values. Also, no significant mass fractionation during the whole analytical procedure (CuEXC production and/or Cu isolation) was detected. Analyses were carried out in controls (healthy persons), newborns, patients with hepatic disorders, and WD patients. While the results for Cu isotopic analysis are relevant (e.g., δ65Cu values were lower for both WD patients under chelating treatment and patients with hepatic problems in comparison with those values obtained for WD patients under Zn treatments, controls, and newborns) to comprehend Cu metabolism and to follow up the disease, the parameter that can help to better discern between WD patients and the rest of the patients tested (non-WD) was found to be the REC (relative exchangeable Cu). In this study, all the WD patients showed a REC higher than 17%, while the rest showed lower values. However, since establishing a universal threshold is complicated, machine learning was investigated to produce a model that can differentiate between WD and non-WD samples with excellent results (100% accuracy, albeit for a limited sample set). Most importantly, unlike other ML approaches, our model can also provide an uncertainty metric to indicate the reliability of the prediction, overall opening new ways to diagnose WD.
000121366 536__ $$9info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/PGC2018-093753-B-I00$$9info:eu-repo/grantAgreement/ES/FEDER/Interreg POCTEFA 176-16-DBS$$9info:eu-repo/grantAgreement/ES/DGA/E43-20R$$9info:eu-repo/grantAgreement/ES/DGA-ESF/T58-20R$$9info:eu-repo/grantAgreement/ES/AEI-FEDER/PID2019-105660RB-C21
000121366 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000121366 590__ $$a3.1$$b2023
000121366 592__ $$a0.722$$b2023
000121366 591__ $$aSPECTROSCOPY$$b9 / 44 = 0.205$$c2023$$dQ1$$eT1
000121366 593__ $$aSpectroscopy$$c2023$$dQ2
000121366 591__ $$aCHEMISTRY, ANALYTICAL$$b37 / 106 = 0.349$$c2023$$dQ2$$eT2
000121366 593__ $$aAnalytical Chemistry$$c2023$$dQ2
000121366 594__ $$a6.2$$b2023
000121366 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000121366 700__ $$aBérail, Sylvain
000121366 700__ $$aRonzani, Anne Laure
000121366 700__ $$aRello, Luis
000121366 700__ $$aGarcía-González, Elena
000121366 700__ $$aNakadi, Flávio V.
000121366 700__ $$0(orcid)0000-0002-3916-9992$$aAramendía, Maite$$uUniversidad de Zaragoza
000121366 700__ $$0(orcid)0000-0002-7532-2720$$aResano, Javier$$uUniversidad de Zaragoza
000121366 700__ $$0(orcid)0000-0002-7450-8769$$aResano, Martín$$uUniversidad de Zaragoza
000121366 700__ $$aPécheyran, Christophe
000121366 7102_ $$12009$$2750$$aUniversidad de Zaragoza$$bDpto. Química Analítica$$cÁrea Química Analítica
000121366 7102_ $$15007$$2035$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Arquit.Tecnología Comput.
000121366 773__ $$g1, 34 (2023), 229-242$$pJ. anal. at. spectrom.$$tJournal of Analytical Atomic Spectrometry$$x0267-9477
000121366 8564_ $$s1899610$$uhttps://zaguan.unizar.es/record/121366/files/texto_completo.pdf$$yPostprint
000121366 8564_ $$s1609024$$uhttps://zaguan.unizar.es/record/121366/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000121366 909CO $$ooai:zaguan.unizar.es:121366$$particulos$$pdriver
000121366 951__ $$a2025-01-16-14:42:54
000121366 980__ $$aARTICLE