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Carlos Molina Cateriano

Institutions of which they are part

Head of group
Stroke research
Vall Hebron Institut de Recerca

Carlos Molina Cateriano

Institutions of which they are part

Head of group
Stroke research
Vall Hebron Institut de Recerca

Projects

UMBRELLA: Unleashing a CoMprehensive, Holistic and Patient Centric Stroke Management for a Better, Rapid, AdvancEd and PersonaLised Stroke Diagnosis, TreAtment and Outcome Prediction

IP: Carlos Molina Cateriano
Collaborators: Olalla Pancorbo Rosal, David Rodriguez Luna, Estela Sanjuan Menendez, Carolina Paredes Deiros, Giorgio Colangelo, Laura Rivera Sanchez, Mª Ángeles Muchada López
Funding agency: EUROPEAN COMMISSION
Funding: 1856250
Reference: UMBRELLA_HE_IHI2024
Duration: 01/10/2024 - 30/09/2029

EAST-STROKE: Early treatment of Atrial fibrillation for Stroke prevention Trial in acute STROKE

IP: Carlos Molina Cateriano
Collaborators: Olalla Pancorbo Rosal, Carole Conforti
Funding agency: EUROPEAN COMMISSION
Funding: 1131945
Reference: EASTSTROKE_HE-HLTH-2024
Duration: 01/12/2024 - 30/11/2030

NexTOLL: A Phase IIb Clinical Trial to evaluate the neuroprotective effect of ApTOLL in stroke patients

IP: Marc Ribó Jacobi
Collaborators: Marta Aurora Rubiera Del Fueyo, Jorge Pagola Pérez Blanca, Carlos Molina Cateriano, Mª Ángeles Muchada López
Funding agency: Ministerio de Ciencia e Innovación-MICINN
Funding: 498435
Reference: CPP2022-009778
Duration: 09/03/2023 - 08/03/2026

Ministerio de Ciencia

POC4TRIAGE: Point-of-Care Devices for Urgent Care Triage

IP: Carlos Molina Cateriano
Collaborators: Noelia Rodríguez Villatoro, Carole Conforti, David Rodriguez Luna
Funding agency: EUROPEAN COMMISSION
Funding: 672750
Reference: POC4TRIAGE_HE-HLTH2023
Duration: 01/07/2024 - 30/06/2028

Related news

The Stroke Research and the Neurovascular Diseases groups lead innovative research projects in both clinical and translational areas.

The team will develop a new medical tool to facilitate the removal of blood clots in blood vessels after a stroke in a safer and more effective manner.

A retrospective study has been carried out with data from 11,000 patients to train artificial intelligence algorithms and adapt the Nora mobile application to different patient profiles.

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