<?xml version="1.0" encoding="UTF-8"?>
<collection>
<dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:invenio="http://invenio-software.org/elements/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:identifier>doi:10.1115/1.4053143</dc:identifier><dc:language>eng</dc:language><dc:creator>Juste-Lanas, Yago</dc:creator><dc:creator>Guerrero, Pedro E.</dc:creator><dc:creator>Camacho-Gomez, Daniel</dc:creator><dc:creator>Hervas-Raluy, Silvia</dc:creator><dc:creator>García-Aznar, J.M.</dc:creator><dc:creator>Gómez-Benito, María José</dc:creator><dc:title>Confined cell migration and asymmetric hydraulic environments to evaluate the metastatic potential of cancer cells</dc:title><dc:identifier>ART-2022-125420</dc:identifier><dc:description>Metastasis, a hallmark of cancer development, is also the leading reason for most cancer-related deaths. Furthermore, cancer cells are highly adaptable to microenvironments and can migrate along pre-existing channel-like tracks of anatomical structures. However, more representative three-dimensional models are required to reproduce the heterogeneity of metastatic cell migration in vivo to further understand the metastasis mechanism and develop novel therapeutic strategies against it. Here, we designed and fabricated different microfluidic-based devices that recreate confined migration and diverse environments with asymmetric hydraulic resistances. Our results show different migratory potential between metastatic and nonmetastatic cancer cells in confined environments. Moreover, although nonmetastatic cells have not been tested against barotaxis due to their low migration capacity, metastatic cells present an enhanced preference to migrate through the lowest resistance path, being sensitive to barotaxis. This device, approaching the study of metastasis capability based on confined cell migration and barotactic cell decisions, may pave the way for the implementation of such technology to determine and screen the metastatic potential of certain cancer cells.</dc:description><dc:date>2022</dc:date><dc:source>http://zaguan.unizar.es/record/109410</dc:source><dc:doi>10.1115/1.4053143</dc:doi><dc:identifier>http://zaguan.unizar.es/record/109410</dc:identifier><dc:identifier>oai:zaguan.unizar.es:109410</dc:identifier><dc:relation>info:eu-repo/grantAgreement/ES/DGA-FEDER/RIS3-LMP74-18</dc:relation><dc:relation>info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE</dc:relation><dc:relation>This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE</dc:relation><dc:relation>info:eu-repo/grantAgreement/ES/MCIU/FPU17-03867</dc:relation><dc:relation>info:eu-repo/grantAgreement/ES/MICINN/RTI2018-094494-B-C21</dc:relation><dc:identifier.citation>JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME 144, 7 (2022), 074502 [29 pp.]</dc:identifier.citation><dc:rights>by</dc:rights><dc:rights>http://creativecommons.org/licenses/by/3.0/es/</dc:rights><dc:rights>info:eu-repo/semantics/openAccess</dc:rights></dc:dc>

</collection>