Neurogenic responses mediated by vanilloid receptor-1 (TRPV1) are blocked by the high affinity antagonist, iodo-resiniferatoxin

(1) Stimulation of the vanilloid receptor-1 (TRPV1) results in the activation of nociceptive and neurogenic inflammatory responses. Poor specificity and potency of TRPV1 antagonists has, however, limited the clarification of the physiological role of TRPV1. (2) Recently, iodo-resiniferatoxin (I-RTX) has been reported to bind as a high affinity antagonist at the native and heterologously expressed rat TRPV1. Here we have studied the ability of I-RTX to block a series of TRPV1 mediated nociceptive and neurogenic inflammatory responses in different species (including transfected human TRPV1). (3) We have demonstrated that I-RTX inhibited capsaicin-induced mobilization of intracellular Ca(2+) in rat trigeminal neurons (IC(50) 0.87 nM) and in HEK293 cells transfected with the human TRPV1 (IC(50) 0.071 nM). (4) Furthermore, I-RTX significantly inhibited both capsaicin-induced CGRP release from slices of rat dorsal spinal cord (IC(50) 0.27 nM) and contraction of isolated guinea-pig and rat urinary bladder (pK(B) of 10.68 and 9.63, respectively), whilst I-RTX failed to alter the response to high KCl or SP. (5) Finally, in vivo I-RTX significantly inhibited acetic acid-induced writhing in mice (ED(50) 0.42 micro mol kg(-1)) and plasma extravasation in mouse urinary bladder (ED(50) 0.41 micro mol kg(-1)). (6) In in vitro and in vivo TRPV1 activated responses I-RTX was approximately 3 log units and approximately 20 times more potent than capsazepine, respectively. This high affinity antagonist, I-RTX, may be an important tool for future studies in pain and neurogenic inflammatory models.

Complex-valued b-spline neural networks for modelling and inverse of wiener systems

Communication signal processing applications often involve complex-valued (CV) functional representations for signals and systems. CV artificial neural networks have been studied theoretically and applied widely in nonlinear signal and data processing [1–11]. Note that most artificial neural networks cannot be automatically extended from the real-valued (RV) domain to the CV domain because the resulting model would in general violate Cauchy-Riemann conditions, and this means that the training algorithms become unusable. A number of analytic functions were introduced for the fully CV multilayer perceptrons (MLP) [4]. A fully CV radial basis function (RBF) nework was introduced in [8] for regression and classification applications. Alternatively, the problem can be avoided by using two RV artificial neural networks, one processing the real part and the other processing the imaginary part of the CV signal/system. A even more challenging problem is the inverse of a CV