BUSCADOR RECOLECTA

Computational 3D models of human atria and torso with encoded anatomical and functional heterogeneity. The finite element model of the atria is a multi-layer mesh with a homogeneous wall thickness between 600 and 900 μm (754.893 nodes and 515.010 elements), built with linear hexahedral elements and with regular spatial resolution of 300 µm. The model consists of: 21 regions with 53 sub-divisions representing the detailed description of fibre orientation; electrophysiological heterogeneity represented by 8 cellular models with differences in the action potential properties; and conduction velocities and anisotropy ratios tuned to each atrial region. The torso was modelled using a dataset obtained from the online open repository at the Centre for Integrative Biomedical Computing (CBIC) from University of Utah. The torso model has 190.804 nodes and 1.149.531 tetrahedral elements with a spatial resolution of 0.6 mm. It has 6 different regions: lungs, bones, liver, ventricle, blood pools, and flesh.

This repository contains four Excel 2017 files named “APD.xlsx”, “QT.xlsx”, “Tx_APD.xlsx” and “Tx_QT.xlsx”. APD.xlsx and Tx_APD.xlsx files contain three sheets (called “Epi”, “Mid” and “Endo”), whereas the rest contain only one sheet (called “QT”). These files contain the results of simulations performed in the O’Hara-Rudy model (ORd) [1] of human ventricular cells (206.766 simulations) and tissue (7072 simulations), which consist of APD90 and QT intervals as well as a new index called Tx extracted from both APD and QT values. References: [1]. O’Hara, T.; Virág, L.; Varró, A.; Rudy, Y. Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation. PLoS Comput. Biol. 2011, 7 (5), e1002061. [2]. Romero L., Cano J., Gomis-Tena J., Trenor B., Sanz F., Pastor M., Saiz J. In silico QT and APD Prolongation Assay for Early Screening of Drug-Induced Proarrhythmic Risk. Journal of Chemical Information and Modeling. 2017 (Currently in revision).

This repository contains the Matlab functions used in [1] and six Excel files named “Tx matrix KrKsCaL.xlsx”, “Tx matix KrNaLCaL.xlsx”, “TqNet matrix KrKsCaL.xlsx”, “TqNet matrix KrNaLCaL.xlsx”, "Ttirang matrix KrKsCaL.xlsx" and “Ttirang matrix KrNaLCaL.xlsx”. These files contain the value of three arrhythmogenic indices proposed in [1] (Tx, TqNet and Ttriang) resulting from simulations of drug effects on the action potential. The simulations were performed in a modified version of the O’Hara-Rudy model (ORd) [2] of human endocardial ventricular cells (492.536 simulations). References: [1]. Llopis J., Gomis-Tena J., Cano J., Romero L., Saiz J., Trenor B. In-Silico Classifiers for Assessment of Drug Proarrhythmicity. 2020 (Currently under revision) [2]. O’Hara, T.; Virág, L.; Varró, A.; Rudy, Y. Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation. PLoS Comput. Biol. 2011, 7 (5), e1002061., The authors would like to strongly thank Dr. Manuel Pastor from Research Programme on Biomedical Informatics (GRIB), at Universitat Pompeu Fabra (Barcelona, Spain) for its review of the manuscript and expert opinions and suggestions. This work was partially supported by the Dirección general de Política Científica de la Generalitat Valencia (PROMETEU2016/088), Primeros Proyectos de Investigación (PAID-06-18), Vicerrectorado de Investigación, Innovación y Transferencia de la Universitat Politècnica de València (UPV), València, Spain as well as Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 from the Ministerio de Economía, Industria y Competitividad of Spain (DPI2016-75799-R) and AEI/FEDER, UE. JL is being funded by the Ministerio de Ciencia, Innovación y Universidades for the Formación de Profesorado Universitario (grant reference: FPU18/01659).

[EN] This dataset contains the evolution with time of the following measured variables: relative Norfloxacin concentration, relative total organic carbon Concentration and cell potential These variables was obtained for 3 different anodic materials, CuO (ceramic anode with Cupper as sintering aid), BDD and BCE (basic ceramic electrode, based on SnO2 doped with Sb2O3); and two different electrochemical reactors with and without membrane, EMR and OCR, respectively. All the experiments were under an applied current of 400 mA and at room temperature., The authors want to express their gratitude to the Ministerio de Economía y Competitividad (Spain) and the FEDER funds, which financially support the project RTI2018-101341-B-C21.

The present dataset is the database and image database used to train and test the Convolutional Neural Network models of the scientific paper: Deep Learning algorithm, based on convolutional neural networks, for electrical equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy