Cell migration depends on the physical state of fibronectin, fibrillar vs. globular, which can be controlled by engineering biomaterials.

Fibronectin fibrillogenesis is the physiological process by which cells elaborate a fibrous FN matrix. Poly(ethyl acrylate), PEA, has been described to induce a similar process upon simple adsorption of fibronectin (FN) from a protein solution -in the absence of cells -leading to the so-called material-driven fibronectin fibrillogenesis. Poly(methyl acrylate), PMA, is a polymer with very similar chemistry to PEA, on which FN is adsorbed keeping the globular conformation of the protein in solution. We have used radical polymerisation to synthesise copolymers with controlled EA/MA ratio seeking to modulate the degree of FN fibrillogenesis. The physico-chemical properties of the system were studied using dynamicmechanical analysis, differential scanning calorimetry and water contact angle. Both the degree of FN fibrillogenesis and the availability of the integrin binding region of FN directly depend on the percentage of EA in the copolymer, whereas the same total amount of FN was adsorbed regardless the EA/MA ratio. Cell morphology adhesion and differentiation of murine C2C12 were shown to depend on the degree of FN fibrillogenesis previously attained on the material surface. Myogenic differentiation was enhanced on the copolymers with higher EA content, i.e. more interconnected FN fibrils.

In this work, we propose MAGES 4.0, a novel Software Development Kit (SDK) to accelerate the creation of collaborative medical training applications in VR/AR. Our solution is essentially a low-code metaverse authoring platform for developers to rapidly prototype high-fidelity and high-complexity medical simulations. MAGES breaks the authoring boundaries across extended reality, since networked participants can also collaborate using different virtual/augmented reality as well as mobile and desktop devices, in the same metaverse world. With MAGES we propose an upgrade to the outdated 150-year-old master-apprentice medical training model. Our platform incorporates, in a nutsell, the following novelties: a) 5G edge-cloud remote rendering and physics dissection layer, b) realistic real-time simulation of organic tissues as soft-bodies under 10ms, c) a highly realistic cutting and tearing algorithm, d) neural network assessment for user profiling and, e) a VR recorder to record and replay or debrief the training simulation from any perspective.THE MEDICAL METAVERSE, despite the inflated expectations, is steadily, albeit quietly, being created [15]. Along with it, many technical questions remain, including "who will build the medical metaverse and how?" Building such an ecosystem from few stakeholders would require Computer Graphics and Applications

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