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A hyaluronic acid-degrading enzyme (hyaluronidase; HAase) is involved in tumor growth and inflammation, and consequently, HAase inhibitors have received recent attention as potential pharmaceuticals. Previous studies have discovered a wide range of inhibitors; however, unfortunately, most of them are dissimilar to the original ligand hyaluronic acid, and their mode of inhibition remains ambiguous or seems promiscuous. This situation presents an urgent need for readily available and highly reliable assay systems identifying the promiscuous inhibitory properties of HAase inhibitors. We have previously proposed a unique method to identify promiscuous nonspecific binding inhibitors of HAase by using the DMSO-perturbing effect. Here, to obtain mechanistic insights into the DMSO-perturbing assay, we studied the addition effect of 11 water-compatible chemicals on HAase inhibitory assay. Intriguingly, the perturbing property was found to be highly specific to DMSO. Furthermore, kinetic analyses described characteristic description of the perturbing property of DMSO: DMSO displayed entropy-driven interactions with HAase, whereas nonperturbing agents such as ethanol and urea exhibited enthalpy-driven interactions. The enthalpy-driven tight interactions of ethanol and urea with HAase would lead to the irreversible denaturation of the enzymes, while the entropy-driven weak interactions caused structural and catalytic perturbation, generating nonproductive but nondenatured states of enzymes, that are key species of the perturbation assay. With these mechanistic understandings in hand, the present assay will enable rapid and reliable identification of HAase inhibitors with certain pharmaceutical potential.

The synchronization is very important technology in the field of Cellular Neural Networks (CNNs) due to its various applications from the biological, environmental to communication points of view. This paper deals with the parametric synchronization phenomena in large-scaled oscillating CNN. When we determine adequate parameters for the amplitude and frequency in the large-scaled oscillating CNNs with non-oscillating cells, the CNN oscillates in various ways depending on their conditions. In this paper, we have exhibited that both multi-amplitude and multi-phase synchronization are generated by their parameters. To demonstrate them, we simulated multi-dimensional CNN of 4 × 4 (16cells), 8 × 8 (64cells) and 16 × 16 (256cells) and checked the synchronization phenomena. Each cell has an inductor L, a capacitorC and a complex nonlinear conductance as i=f(v). We find that the synchronization phenomena are similarly occurred from the small networks to the larger one. We give a basic theoretical viewpoint for interesting numerical experiment results for the synchronization phenomena in this paper.