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Cancer Modeling-on-a-Chip with Future Artificial Intelligence Integration.
Fetah, Kirsten Lee; DiPardo, Benjamin J; Kongadzem, Eve-Mary; Tomlinson, James S; Elzagheid, Adam; Elmusrati, Mohammed; Khademhosseini, Ali; Ashammakhi, Nureddin.
Afiliação
  • Fetah KL; Center for Minimally Invasive Therapeutics, University of California, Los Angeles, CA, 90095, USA.
  • DiPardo BJ; California NanoSystems Institute (CNSI), University of California, 570 Westwood Plaza, Los Angeles, CA, 90095, USA.
  • Kongadzem EM; Department of Bioengineering, University of California, Los Angeles, CA, 90095, USA.
  • Tomlinson JS; Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
  • Elzagheid A; School of Technology and Innovations, University of Vaasa, FI-65101, Vaasa, Finland.
  • Elmusrati M; Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
  • Khademhosseini A; Biotechnology Research Center, Libyan Authority for Research, Science and Technology, Tripoli, Libya.
  • Ashammakhi N; School of Technology and Innovations, University of Vaasa, FI-65101, Vaasa, Finland.
Small ; 15(50): e1901985, 2019 12.
Article em En | MEDLINE | ID: mdl-31724305
ABSTRACT
Cancer is one of the leading causes of death worldwide, despite the large efforts to improve the understanding of cancer biology and development of treatments. The attempts to improve cancer treatment are limited by the complexity of the local milieu in which cancer cells exist. The tumor microenvironment (TME) consists of a diverse population of tumor cells and stromal cells with immune constituents, microvasculature, extracellular matrix components, and gradients of oxygen, nutrients, and growth factors. The TME is not recapitulated in traditional models used in cancer investigation, limiting the translation of preliminary findings to clinical practice. Advances in 3D cell culture, tissue engineering, and microfluidics have led to the development of "cancer-on-a-chip" platforms that expand the ability to model the TME in vitro and allow for high-throughput analysis. The advances in the development of cancer-on-a-chip platforms, implications for drug development, challenges to leveraging this technology for improved cancer treatment, and future integration with artificial intelligence for improved predictive drug screening models are discussed.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Microfluídica / Modelos Biológicos / Neoplasias Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Microfluídica / Modelos Biológicos / Neoplasias Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article