Distribution of neutral prilocaine in a phospholipid bilayer: Insights from molecular dynamics simulations

In this work, we report a 20-ns constant pressure molecular dynamics simulation of prilocaine (PLC), in amine-amide local anesthetic, in a hydrated liquid crystal bilayer of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine. The partition of PLC induces the lateral expansion of the bilayer and a concomitant contraction in its thickness. PLC molecules are preferentially found in the hydrophobic acyl chains region, with a maximum probability at similar to 12 angstrom from the center of the bilayer (between the C(4) and C(5) methylene groups). A decrease in the acyl chain segmental order parameter, vertical bar S-CD vertical bar, compared to neat bilayers, is found, in good agreement with experimental H-2-NMR studies. The decrease in vertical bar S-CD vertical bar induced by PLC is attributed to a larger accessible volume per lipid in the acyl chain region. (C) 2008 Wiley Periodicals, Inc.

Preferential location of prilocaine and etidocaine in phospholipid bilayers: A molecular dynamics study

In this work, we report a 20-ns constant pressure molecular dynamics simulation of the uncharged form of two amino-amide local anesthetics (LA). etidocaine and prilocaine, present at 1:3 LA:lipid, molar ratio inside the membrane, in the hydrated liquid crystal bilayer phase of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine (POPC). Both LAs induced lateral expansion and a concomitant contraction in the bilayer thickness. A decrease in the acyl chain segment order parameter, -S(CD), compared to neat bilayers, was also observed. Besides, both LA molecules got preferentially located in the hydrophobic acyl chains region, with a maximum probability at similar to 12 and similar to 10 angstrom from the center of the bilayer for prilocaine and etidocaine, respectively. (C) 2009 Elsevier B.V. All rights reserved.

A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function

Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.