Categoria
Bolseiro
Unidade Orgânica
Department of Mechanical Engineering
Email
a.moreira@ua.pt
Ciência ID
4013-37AB-936E
ORCID iD
0009-0006-5117-7182

A. Catarina Moreira is a first-year PhD student at the University of Aveiro working on the Centre for Mechanical Engineering and Automation (TEMA/UA) and the Institute of Materials Aveiro (CICECO/UA), in collaboration with the spin off Cellularis Biomodels. Her research interests focus on the development of personalized and dynamic therapeutic strategies for cancer patients. In this context, her PhD project focuses on the design and biological testing of microfluidic tumor-on-a-chip platforms for 3D breast cancer models to study the in vitro delivery of dynamic electrochemotherapy. A key objective is the development of a dynamic, automated controller, inspired by nature-based algorithms, to achieve a more personalized and less empirical therapeutic delivery strategy. In parallel, she investigates innovative nanotherapeutic materials, including isotope-enriched systems for neutron capture therapy (NCT), with applications in brain and head-and-neck cancers. She has strong hands-on experience in cell culture, biochemical and molecular biology assays, immunoblotting, immunofluorescence, and imaging analysis, as well as in CAD and MATLAB software, 3D printing, and organic material synthesis and characterization. Catarina has participated in several national and international research initiatives, contributing to six scientific communications (five posters and one oral presentation) and authoring four scientific publications (three published and one submitted). Her research work has been recognized with awards at two scientific conferences. Additionally, she has been selected to participate in two international summer schools, further strengthening her interdisciplinary and international research training. Overall, Catarina is committed to advancing interdisciplinary and translational biomedical solutions, combining engineering-driven platform development, biological validation, and computational approaches to support the development of more effective and targeted cancer therapies.

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