Regulation of neovascularization by S-glutathionylation via the Wnt5a/sFlt-1 pathway

S-glutathionylation occurs when reactive oxygen or nitrogen species react with protein-cysteine thiols. Glutaredoxin-1 (Glrx) is a cytosolic enzyme which enzymatically catalyses the reduction in S-glutathionylation, conferring reversible signalling function to proteins with redox-sensitive thiols. Glrx can regulate vascular hypertrophy and inflammation by regulating the activity of nuclear factor κB (NF-κB) and actin polymerization. Vascular endothelial growth factor (VEGF)-induced endothelial cell (EC) migration is inhibited by Glrx overexpression. In mice overexpressing Glrx, blood flow recovery, exercise function and capillary density were significantly attenuated after hindlimb ischaemia (HLI). Wnt5a and soluble Fms-like tyrosine kinase-1 (sFlt-1) were enhanced in the ischaemic-limb muscle and plasma respectively from Glrx transgenic (TG) mice. A Wnt5a/sFlt-1 pathway had been described in myeloid cells controlling retinal blood vessel development. Interestingly, a Wnt5a/sFlt-1 pathway was found also to play a role in EC to inhibit network formation. S-glutathionylation of NF-κB components inhibits its activation. Up-regulated Glrx stimulated the Wnt5a/sFlt-1 pathway through enhancing NF-κB signalling. These studies show a novel role for Glrx in post-ischaemic neovascularization, which could define a potential target for therapy of impaired angiogenesis in pathological conditions including diabetes.

Rotation invariant iris feature extraction using Gaussian Markov random fields with non-separable wavelet

Rotation invariance is important for an iris recognition system since changes of head orientation and binocular vergence may cause eye rotation. The conventional methods of iris recognition cannot achieve true rotation invariance. They only achieve approximate rotation invariance by rotating the feature vector before matching or unwrapping the iris ring at different initial angles. In these methods, the complexity of the method is increased, and when the rotation scale is beyond the certain scope, the error rates of these methods may substantially increase. In order to solve this problem, a new rotation invariant approach for iris feature extraction based on the non-separable wavelet is proposed in this paper. Firstly, a bank of non-separable orthogonal wavelet filters is used to capture characteristics of the iris. Secondly, a method of Markov random fields is used to capture rotation invariant iris feature. Finally, two-class kernel Fisher classifiers are adopted for classification. Experimental results on public iris databases show that the proposed approach has a low error rate and achieves true rotation invariance. © 2010.