Aliaksei S Vasilevich

TL;DR: Machine learning is used to successfully build a model that correlates cell attachment and phenotype with a selection of descriptors, illustrating that materials can potentially be designed to modulate inflammatory responses for future applications in the fight against foreign body rejection of medical devices.

TL;DR: High-throughput experimentation within the field of materiobiology enables the elucidation of the relationships between biomaterial properties and biological behavior and thereby serves as a potential tool for accelerating the development of high-performance biomaterials as mentioned in this paper.

TL;DR: It is discovered that mesenchymal stem cells exposed to both micro-topographies and TGF-β2 display synergistic induction of SMAD phosphorylation and transcription of the TGF -β target genes SCX, a-SMA, and SOX9, and pharmacological perturbations revealed that Rho/ROCK/SRF signaling is required for this synergistic response.

TL;DR: An approach to identify a universal set of genes that regulate the material-induced phenotypical response of human mesenchymal stem cells will allow designing implants that can actively regulate cellular, molecular signalling through cell shape.

TL;DR: In this paper, the authors assessed the extent to which the topographical design space of the TopoChip can be expanded using natural surfaces and demonstrated that natural surfaces extend the design space, which coincides with distinct morphological and focal adhesion profiles in mesenchymal stem cells and Pseudomonas aeruginosa colonization.