Immobilization of cell-binding peptides on poly-ε-caprolactone film surface to biomimic the peripheral nervous system.

Cell-material interactions are crucial for cell adhesion and proliferation on biomaterial surfaces. Immobilization of biomolecules leads to the formation of biomimetic substrates, improving cell response. We introduced RGD (Arg-Gly-Asp) sequences on poly-ε-caprolactone (PCL) film surfaces using thiol chemistry to enhance Schwann cell (SC) response. XPS elemental analysis indicated an estimate of 2-3% peptide functionalization on the PCL surface, comparable with carbodiimide chemistry. Contact angle was not remarkably reduced; hence, cell response was only affected by chemical cues on the film surface. Adhesion and proliferation of Schwann cells were enhanced after PCL modification. Particularly, RGD immobilization increased cell attachment up to 40% after 6 h of culture. It was demonstrated that SC morphology changed from round to very elongated shape when surface modification was carried out, with an increase in the length of cellular processes up to 50% after 5 days of culture. Finally RGD immobilization triggered the formation of focal adhesion related to higher cell spreading. In summary, this study provides a method for immobilization of biomolecules on PCL films to be used in peripheral nerve repair, as demonstrated by the enhanced response of Schwann cells.

Representations of uncertainty in sensorimotor control.

Uncertainty is ubiquitous in our sensorimotor interactions, arising from factors such as sensory and motor noise and ambiguity about the environment. Setting it apart from previous theories, a quintessential property of the Bayesian framework for making inference about the state of world so as to select actions, is the requirement to represent the uncertainty associated with inferences in the form of probability distributions. In the context of sensorimotor control and learning, the Bayesian framework suggests that to respond optimally to environmental stimuli the central nervous system needs to construct estimates of the sensorimotor transformations, in the form of internal models, as well as represent the structure of the uncertainty in the inputs, outputs and in the transformations themselves. Here we review Bayesian inference and learning models that have been successful in demonstrating the sensitivity of the sensorimotor system to different forms of uncertainty as well as recent studies aimed at characterizing the representation of the uncertainty at different computational levels.