Herbal components of Japanese Kampo medicines exert laxative actions in colonic epithelium cells via activation of BK and CFTR channels

Japanese Kampo medicines Junchoto and Mashiningan are mixtures of numerous herbal plant extracts and empirically known to exert laxative actions by stimulating fluid secretion in the colonic epithelium. However, it is unknown which and how the herbal components of these crude Kampo drugs are effective to stimulate ion effluxes causing fluid secretion. Here, we selected four herbal components of Junchoto and Mashiningan, Mashinin (MSN), Kyonin (KYN), Tonin (TON), and Daio (DIO), which are putatively laxatives, and examined their effects on the ion channel activity of human colonic epithelial Caco-2 cells. Patch clamp analyses revealed that MSN activated whole-cell current characteristics of the cystic fibrosis transmembrane conductance regulator (CFTR) channel, whereas KYN, TON, and DIO activated the large-conductance and voltage-activated K+ (BK) channel. Furthermore, electronic cell sizing showed that MSN induced secretory volume decrease (SVD) sensitivity to a CFTR blocker, whereas TON, KYN, and DIO induced SVD sensitivity to a K+ channel blocker. In conclusion, MSN and TON, KYN, and DIO promote fluid secretion from colonic epithelial cells by activating CFTR and BK channels. Thus, Japanese Kampo medicines, Junchoto and Mashiningan, exert anti-constipation actions by inducing KCl efflux through the combined actions of CFTR- and BK-stimulating herbal components.

Fast and Near-Optimal Timing-Driven Cell Sizing under Cell Area and Leakage Power Constraints Using a Simplified Discrete Network Flow Algorithm

We propose a timing-driven discrete cell-sizing algorithm that can address total cell size and/or leakage power constraints. We model cell sizing as a “discretized” mincost network flow problem, wherein available sizes of each cell are modeled as nodes. Flow passing through a node indicates the choice of the corresponding cell size, and the total flow cost reflects the timing objective function value corresponding to these choices. Compared to other discrete optimization methods for cell sizing, our method can obtain near-optimal solutions in a time-efficient manner. We tested our algorithm on ISCAS’85 benchmarks, and compared our results to those produced by an optimal dynamic programming- (DP-) based method. The results show that compared to the optimal method, the improvements to an initial sizing solution obtained by our method is only 1% (3%) worse when using a 180 nm (90 nm) library, while being 40–60 times faster. We also obtained results for ISPD’12 cell-sizing benchmarks, under leakage power constraint, and compared them to those of a state-of-the-art approximate DP method (optimal DP runs out of memory for the smallest of these circuits). Our results show that we are only 0.9% worse than the approximate DP method, while being more than twice as fast.