{"id":3641,"date":"2022-06-30T17:01:41","date_gmt":"2022-06-30T15:01:41","guid":{"rendered":"https:\/\/marte.i3a.es\/?p=3641"},"modified":"2023-03-16T18:59:18","modified_gmt":"2023-03-16T16:59:18","slug":"elena-garcia-gonzalez","status":"publish","type":"post","link":"https:\/\/marte.i3a.es\/es\/elena-garcia-gonzalez\/","title":{"rendered":"Elena Garc\u00eda Gonz\u00e1lez"},"content":{"rendered":"
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    Investigadores<\/a><\/h3>\n

    Elena Garc\u00eda Gonz\u00e1lez<\/strong><\/h1>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/li>\n\t\t<\/ul>\t\t\t\t
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      Tel\u00e9fono:<\/strong> +34 976 765 500 ext 2156<\/p>\n

      Email:<\/strong> egarciag@salud.aragon.es<\/a><\/p>\n

      Direcci\u00f3n:<\/strong> c\/ Padre Arrupe sn, pl 3, Hospital Universitario Miguel Servet, S\u00b0 Bioqu\u00edmica Cl\u00ednica – Zaragoza (Spain)<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>\n\n

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      Elena Garc\u00eda Gonz\u00e1lez, Licenciada en Ciencias Qu\u00edmicas por la Universidad de Oviedo (1996 - 2001). Facultativo Especialista de \u00c1rea, especialidad Bioqu\u00edmica Cl\u00ednica desde el 2005. Miembro del grupo M.A.R.T.E. desde 2014.<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>

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      13 registros<\/span> «<\/a> ‹<\/a> 1 de 3 ›<\/a> »<\/a> <\/div><\/div>

      2023<\/h3>

      Garc\u00eda-Poyo, M. Carmen; B\u00e9rail, Sylvain; Ronzani, Anne Laure; Rello, Luis; Garc\u00eda-Gonz\u00e1lez, Elena; Nakadi, Fl\u00e1vio V.; Aramend\u00eda, Maite; Resano, Javier; Resano, Mart\u00edn; P\u00e9cheyran, Christophe<\/p>

      Cu fractionation, isotopic analysis, and data processing via machine learning: new approaches for the diagnosis and follow up of Wilson's disease via ICP-MS<\/a> Art\u00edculo de revista<\/span> <\/p>

      En: <\/span>J. Anal. At. Spectrom., <\/span>vol. 38, <\/span>iss. 1, <\/span>pp. 229-242, <\/span>2023<\/span>.<\/p>

      Resumen<\/a><\/span> | Enlaces<\/a><\/span> | BibTeX<\/a><\/span><\/p>

      @article{D2JA00267A,
      \r\ntitle = {Cu fractionation, isotopic analysis, and data processing via machine learning: new approaches for the diagnosis and follow up of Wilson's disease via ICP-MS},
      \r\nauthor = {M. Carmen Garc\u00eda-Poyo and Sylvain B\u00e9rail and Anne Laure Ronzani and Luis Rello and Elena Garc\u00eda-Gonz\u00e1lez and Fl\u00e1vio V. Nakadi and Maite Aramend\u00eda and Javier Resano and Mart\u00edn Resano and Christophe P\u00e9cheyran},
      \r\nurl = {http:\/\/dx.doi.org\/10.1039\/D2JA00267A},
      \r\ndoi = {10.1039\/D2JA00267A},
      \r\nyear  = {2023},
      \r\ndate = {2023-01-01},
      \r\nurldate = {2023-01-01},
      \r\njournal = {J. Anal. At. Spectrom.},
      \r\nvolume = {38},
      \r\nissue = {1},
      \r\npages = {229-242},
      \r\npublisher = {The Royal Society of Chemistry},
      \r\nabstract = {Information 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., \u03b465Cu 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.},
      \r\nkeywords = {},
      \r\npubstate = {published},
      \r\ntppubtype = {article}
      \r\n}
      \r\n<\/pre><\/div>
      Cerrar<\/a><\/p><\/div>
      Information 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., \u03b465Cu 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.<\/div>
      Cerrar<\/a><\/p><\/div>