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Polyurethane-bases block copolymers (TPCUs) are block-copolymers with systematically varied soft and hard segments. They have been suggested to serve as material for chondral implants in joint regeneration. Such applications may require the adhesion of chondrocytes to the implant surface, facilitating cell growth while keeping their phenotype. Thus, aims of this work were (1) to modify the surface of soft biostable polyurethane-based model implants (TPCU and TSiPCU) with high-molecular weight hyaluronic acid (HA) using an optimized multistep strategy of immobilization, and (2) to evaluate bioactivity of the modified TPCUs in vitro. Our results show no cytotoxic potential of the TPCUs. HAbioactive molecules (Mw =700kDa) were immobilized onto the polyurethane surface via polyethylenimine (PEI) spacers, and modifications were confirmed by several characterization methods. Tests with porcine chondrocytes indicated the potential of the TPCU-HA for inducing enhanced cell proliferation.
In vitro, hydrogel-based ECMs for functionalizing surfaces of various material have played an essential role in mimicking native tissue matrix. Polydimethylsiloxane (PDMS) is widely used to build microfluidic or organ-on-chip devices compatible with cells due to its easy handling in cast replication. Despite such advantages, the limitation of PDMS is its hydrophobic surface property. To improve wettability of PDMS-based devices, alginate, a naturally derived polysaccharide, was covalently bound to the PDMS surface. This alginate then crosslinked further hydrogel onto the PDMS surface in desired layer thickness. Hydrogel-modified PDMS was used for coating a topography chip system and in vitro investigation of cell growth on the surfaces. Moreover, such hydrophilic hydrogel-coated PDMS is utilized in a microfluidic device to prevent unspecific absorption of organic solutions. Hence, in both exemplary studies, PDMS surface properties were modified leading to improved devices.
Digital light microscopy techniques are among the most widely used methods in cell biology and medical research. Despite that, the automated classification of objects such as cells or specific parts of tissues in images is difficult. We present an approach to classify confluent cell layers in microscopy images by learned deep correlation features using deep neural networks. These deep correlation features are generated through the use of gram-based correlation features and are input to a neural network for learning the correlation between them. In this work we wanted to prove if a representation of cell data based on this is suitable for its classification as has been done for artworks with respect to their artistic period. The method generates images that contain recognizable characteristics of a specific cell type, for example, the average size and the ordered pattern.