000126944 001__ 126944
000126944 005__ 20241125101142.0
000126944 0247_ $$2doi$$a10.1016/j.bioelechem.2023.108510
000126944 0248_ $$2sideral$$a134439
000126944 037__ $$aART-2023-134439
000126944 041__ $$aeng
000126944 100__ $$0(orcid)0000-0002-6768-5177$$aBriz, P.$$uUniversidad de Zaragoza
000126944 245__ $$aTumor location on electroporation therapies by means of multi-electrode structures and machine learning
000126944 260__ $$c2023
000126944 5060_ $$aAccess copy available to the general public$$fUnrestricted
000126944 5203_ $$aElectroporation is a phenomenon produced in the cell membrane when it is exposed to high pulsed electric fields that increases its permeability. Among other application fields, this phenomenon can be exploited in a clinical environment for tumor ablation therapies. In this context to achieve optimum results, it is convenient to focus the treatment on the tumor tissue to minimize side effects. In this work, a pre-treatment tumor location method is developed, with the purpose of being able to precisely target the therapy. This is done by taking different impedance measurements with a multi-output electroporation generator in conjunction with a multi-electrode structure. Data are processed by means of a vector of independent artificial neural networks, trained and tested with simulation data, and validated with phantom gels. This algorithm proved to provide suitable accuracy in spite of the low electrode count compared to the number of electrodes of a standard electrical impedance tomography device.
000126944 536__ $$9info:eu-repo/grantAgreement/EUR/AEI/TED2021-129274B-I00$$9info:eu-repo/grantAgreement/ES/ISCIII/PI21-00440$$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2019-103939RB-I00$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PDC2021-120898-I00
000126944 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000126944 590__ $$a4.8$$b2023
000126944 592__ $$a0.705$$b2023
000126944 591__ $$aBIOCHEMISTRY & MOLECULAR BIOLOGY$$b67 / 313 = 0.214$$c2023$$dQ1$$eT1
000126944 593__ $$aBiophysics$$c2023$$dQ2
000126944 591__ $$aBIOLOGY$$b14 / 109 = 0.128$$c2023$$dQ1$$eT1
000126944 593__ $$aPhysical and Theoretical Chemistry$$c2023$$dQ2
000126944 591__ $$aBIOPHYSICS$$b10 / 77 = 0.13$$c2023$$dQ1$$eT1
000126944 593__ $$aMedicine (miscellaneous)$$c2023$$dQ2
000126944 591__ $$aELECTROCHEMISTRY$$b14 / 45 = 0.311$$c2023$$dQ2$$eT1
000126944 593__ $$aElectrochemistry$$c2023$$dQ2
000126944 594__ $$a9.1$$b2023
000126944 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000126944 700__ $$0(orcid)0000-0003-2848-170X$$aLópez-Alonso, B.$$uUniversidad de Zaragoza
000126944 700__ $$0(orcid)0000-0001-8399-4650$$aSarnago, H.$$uUniversidad de Zaragoza
000126944 700__ $$0(orcid)0000-0002-9655-5531$$aBurdío, J.M.$$uUniversidad de Zaragoza
000126944 700__ $$0(orcid)0000-0002-1284-9007$$aLucía, O.$$uUniversidad de Zaragoza
000126944 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000126944 773__ $$g154 (2023), 108510 [7 pp.]$$pBIOELECTROCHEMISTRY$$tBioelectrochemistry$$x1567-5394
000126944 8564_ $$s4649446$$uhttps://zaguan.unizar.es/record/126944/files/texto_completo.pdf$$yVersión publicada
000126944 8564_ $$s2573332$$uhttps://zaguan.unizar.es/record/126944/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000126944 909CO $$ooai:zaguan.unizar.es:126944$$particulos$$pdriver
000126944 951__ $$a2024-11-22-12:02:52
000126944 980__ $$aARTICLE