Enhancing Micropipette Aspiration with Artificial Intelligence Analysis

Abarca Ortega, Aldo Fabián ORCID: https://orcid.org/0000-0002-1600-8555, González Bermúdez, Blanca ORCID: https://orcid.org/0000-0002-1266-0917 and Plaza Baonza, Gustavo Ramón ORCID: https://orcid.org/0000-0002-5555-5498 (2024). Enhancing Micropipette Aspiration with Artificial Intelligence Analysis. "Biophysical Journal", v. 123 (n. 17); pp. 2860-2868. ISSN 0006-3495. https://doi.org/10.1016/j.bpj.2024.04.006.

Descripción

Título: Enhancing Micropipette Aspiration with Artificial Intelligence Analysis
Autor/es:
Tipo de Documento: Artículo
Título de Revista/Publicación: Biophysical Journal
Fecha: Septiembre 2024
ISSN: 0006-3495
Volumen: 123
Número: 17
Materias:
ODS:
Palabras Clave Informales: micropipette aspiration; artificial intelligence; cell mechanobiology; biomaterials
Escuela: E.T.S.I. Caminos, Canales y Puertos (UPM)
Departamento: Ciencia de los Materiales
Grupo Investigación UPM: Materiales Estructurales Avanzados y Nanomateriales
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The micropipette-aspiration technique is commonly used in the field of mechanobiology, offering a variety of measurement types. To extract biophysical parameters from the experiments, numerical analysis is required. Although previous works have developed techniques for the partial automation of these analyses, these approaches are relatively time consuming for the researchers. In this article, we describe the development and application of an artificial-intelligence tool for the completely automatic analysis of micropipette-aspiration experiments. The use of this tool is compared with previous methods and the impressive reduction in the time required for these analyses is discussed. The new tool opens new possibilities for the micropipette-aspiration technique by enabling dealing with large numbers of experiments and real-time measurements.

Proyectos asociados

Tipo
Código
Acrónimo
Responsable
Título
Comunidad de Madrid
CM/P2018/NMT-4443
Tec4Bio
Gustavo R. Plaza
Nuevas Tecnologías Aplicadas al Estudio de Nanomáquinas Biológicas
Comunidad de Madrid
S2022-BMD-7236
MINA-CM
Gustavo V. Guinea
Madrid Innovative Neurotech Alliance (MINA-CM)

Más información

ID de Registro: 87841
Identificador DC: https://oa.upm.es/87841/
Identificador OAI: oai:oa.upm.es:87841
URL Portal Científico: https://portalcientifico.upm.es/es/ipublic/item/10247154
Identificador DOI: 10.1016/j.bpj.2024.04.006
URL Oficial: https://www.sciencedirect.com/science/article/pii/...
Depositado por: Dr. Blanca de los Reyes González Bermúdez
Depositado el: 13 Feb 2025 15:24
Ultima Modificación: 16 Ene 2026 11:07