Hospital Clínico San Carlos de Madrid -ko ikertzaileekin lankidetzan egindako argitalpenak (11)

2024

  1. Development of bioactive solid-foam scaffolds from decellularized cartilage with chondrogenic and osteogenic properties

    Materials Today Bio, Vol. 28

  2. Printable Poly(3,4-ethylenedioxythiophene)-Based Conductive Patches for Cardiac Tissue Remodeling

    ACS Applied Materials and Interfaces , Vol. 16, Núm. 27, pp. 34467-34479

2021

  1. Human influenza A virus causes myocardial and cardiac-specific conduction system infections associated with early inflammation and premature death

    Cardiovascular research, Vol. 117, Núm. 3, pp. 876-889

2016

  1. Accurate quantification of atherosclerotic plaque volume by 3D vascular ultrasound using the volumetric linear array method

    Atherosclerosis, Vol. 248, pp. 230-237

2015

  1. Exercise triggers ARVC phenotype in mice expressing a disease-causing mutated version of human plakophilin-2

    Journal of the American College of Cardiology, Vol. 65, Núm. 14, pp. 1438-1450

2014

  1. Metabolomics reveals metabolite changes in acute pulmonary embolism

    Journal of Proteome Research, Vol. 13, Núm. 2, pp. 805-816

  2. β3 adrenergic receptor selective stimulation during ischemia/reperfusion improves cardiac function in translational models through inhibition of mPTP opening in cardiomyocytes

    Basic Research in Cardiology, Vol. 109, Núm. 4

2010

  1. Chitosan scaffolds for osteochondral tissue regeneration

    Journal of Biomedical Materials Research - Part A, Vol. 95, Núm. 4, pp. 1132-1141

  2. In vivo comparison of the effects of RHBMP-2 and RHBMP-4 in osteochondral tissue regeneration

    European Cells and Materials, Vol. 20, pp. 367-378

2004

  1. Colloidal dispersions of maghemite nanoparticles produced by laser pyrolysis with application as NMR contrast agents

    Journal of Physics D: Applied Physics, Vol. 37, Núm. 15, pp. 2054-2059

2003

  1. Computer-assisted enhanced volumetric segmentation magnetic resonance imaging data using a mixture of artificial neural networks

    Magnetic Resonance Imaging, Vol. 21, Núm. 8, pp. 901-912