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      Laser-assisted 3D bioprinting of exocrine pancreas spheroid models for cancer initiation study

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          Abstract

          <p class="first" id="d34952911e95">Pancreatic ductal adenocarcinoma (PDAC) is the most common malignancy of the pancreas. It has shown a poor prognosis and a rising incidence in the developed world. Other pathologies associated with this tissue include pancreatitis, a risk condition for pancreatic cancer. The onset of both pancreatitis and pancreatic cancer follows a common pattern: exocrine pancreatic acinar cells undergo a transdifferentiation to duct cells that triggers a 3D restructuration of the pancreatic tissue. However, the exact mechanism underlying this process remains partially undefined. Further understanding the cellular events leading to PDAC could open new avenues in the development of novel therapeutic approaches. Since current 2D cell culture models fail to mimic the tridimensional complexity of the pancreatic tissue, new in vitro models are urgently needed. Here, we generated 3D pancreatic cell spheroid arrays using laser-assisted bioprinting and characterized their phenotypic evolution over time through image analysis and phenotypic characterization. We show that these bioprinted spheroids, composed of both acinar and ductal cells, can replicate the initial stages of PDAC development. This bioprinted miniaturized spheroid-based array model should prove useful for the study of the internal and external factors that contribute to the formation of precursor PDAC lesions and to cancer progression, and may therefore shed light on future PDAC therapy strategies. </p>

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          (View ORCID Profile)
          Journal
          Biofabrication
          Biofabrication
          IOP Publishing
          1758-5090
          July 01 2020
          April 16 2020
          : 12
          : 3
          : 035001
          Article
          10.1088/1758-5090/ab7cb8
          32131058
          7350670d-5571-496d-9d6c-2e91a4d0c8d5
          © 2020

          http://iopscience.iop.org/info/page/text-and-data-mining

          http://iopscience.iop.org/page/copyright

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