A spectroscopic study of the interaction of isoflavones with human serum albumin

Genistein and daidzein, the major isoflavones present in soybeans, possess a wide spectrum of physiological and pharmacological functions. The binding of genistein to human serum albumin (HSA) has been investigated by equilibrium dialysis, fluorescence measurements, CD and molecular visualization. One mole of genistein is bound per mole of HSA with a binding constant of 1.5 +/- 0.2 X 10(5) m(-1). Binding of genistein to HSA precludes the attachment of daidzein. The ability of HSA to bind genistein is found to be lost when the tryptophan residue of albumin is modified with N-bromosuccinimide. At 27 degrees C (pH 7.4), van't Hoff's enthalpy, entropy and free energy changes that accompany the binding are found to be -13.16 kcal.mol(-1), -21 cal.mol(-1)K(-1) and -6.86 kcal.mol(-1), respectively. Temperature and ionic strength dependence and competitive binding measurements of genistein with HSA in the presence of fatty acids and 8-anilino-1-naphthalene sulfonic acid have suggested the involvement of both hydrophobic and ionic interactions in the genistein-HSA binding. Binding measurements of genistein with BSA and HSA, and those in the presence of warfarin and 2,3,5-tri-iodobenzoic acid and Forster energy transfer measurements have been used for deducing the binding pocket on HSA. Fluorescence anisotropy measurements of daidzein bound and then displaced with warfarin, 2,3,5-tri-iodobenzoic acid or diazepam confirm the binding of daidzein and genistein to subdomain IIA of HSA. The ability of HSA to form ternery complexes with other neutral molecules such as warfarin, which also binds within the subdomain IIA pocket, increases our understanding of the binding dynamics of exogenous drugs to HSA.

A New Iterative FIR Filter for Image and Video Restoration

Image and video filtering is a key image-processing task in computer vision especially in noisy environment. In most of the cases the noise source is unknown and hence possess a major difficulty in the filtering operation. In this paper we present an error-correction based learning approach for iterative filtering. A new FIR filter is designed in which the filter coefficients are updated based on Widrow-Hoff rule. Unlike the standard filter the proposed filter has the ability to remove noise without the a priori knowledge of the noise. Experimental result shows that the proposed filter efficiently removes the noise and preserves the edges in the image. We demonstrate the capability of the proposed algorithm by testing it on standard images infected by Gaussian noise and on a real time video containing inherent noise. Experimental result shows that the proposed filter is better than some of the existing standard filters