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.

Adaptive control of stiffness to stabilize hand position with large loads.

The goal of this work was to investigate stability in relation to the magnitude and direction of forces applied by the hand. The endpoint stiffness and joint stiffness of the arm were measured during a postural task in which subjects exerted up to 30% maximum voluntary force in each of four directions while controlling the position of the hand. All four coefficients of the joint stiffness matrix were found to vary linearly with both elbow and shoulder torque. This contrasts with the results of a previous study, which employed a force control task and concluded that the joint stiffness coefficients varied linearly with either shoulder or elbow torque but not both. Joint stiffness was transformed into endpoint stiffness to compare the effect on stability as endpoint force increased. When the joint stiffness coefficients were modeled as varying with the net torque at only one joint, as in the previous study, we found that hand position became unstable if endpoint force exceeded about 22 N in a specific direction. This did not occur when the joint stiffness coefficients were modeled as varying with the net torque at both joints, as in the present study. Rather, hand position became increasingly more stable as endpoint force increased for all directions of applied force. Our analysis suggests that co-contraction of biarticular muscles was primarily responsible for the increased stability. This clearly demonstrates how the central nervous system can selectively adapt the impedance of the arm in a specific direction to stabilize hand position when the force applied by the hand has a destabilizing effect in that direction.

How is somatosensory information used to adapt to changes in the mechanical environment?

Recent studies examining adaptation to unexpected changes in the mechanical environment highlight the use of position error in the adaptation process. However, force information is also available. In this chapter, we examine adaptation processes in three separate studies where the mechanical environment was changed intermittently. We compare the expected consequences of using position error and force information in the changes to motor commands following a change in the mechanical environment. In general, our results support the use of position error over force information and are consistent with current computational models of motor learning. However, in situations where the change in the mechanical environment eliminates position error the central nervous system does not necessarily respond as would be predicted by these models. We suggest that it is necessary to take into account the statistics of prior experience to account for our observations. Another deficiency in these models is the absence of a mechanism for modulating limb mechanical impedance during adaptation. We propose a relatively simple computational model based on reflex responses to perturbations which is capable of accounting for iterative changes in temporal patterns of muscle co-activation.

A neuro-inspired method for data rate limited feedback control

The nervous system implements a networked control system in which the plants take the form of limbs, the controller is the brain, and neurons form the communication channels. Unlike standard networked control architectures, there is no periodic sampling, and the fundamental units of communication contain little numerical information. This paper describes a novel communication channel, modeled after spiking neurons, in which the transmitter integrates an input signal and sends out a spike when the integral reaches a threshold value. The reciever then filters the sequence of spikes to approximately reconstruct the input signal. It is shown that for appropriate choices of channel parameters, stable feedback control over these spiking channels is possible. Furthermore, good tracking performance can be achieved. The data rate of the channel increases linearly with the size of the inputs. Thus, when placed in a feedback loop, small loop gains imply a low data rate. ©2010 IEEE.