The aim of this work was to investigate whether the oral bioavailability and brain regional distribution of (+)-catechin could be improved by utilizing elastic liposomes. Liposomes containing soy phosphatidylcholine, cholesterol, and Tween 80 in the presence of 15% ethanol were prepared by a thin-film method and subsequent sonication and extrusion. The size, zeta potential, and stability of the liposomes in simulated gastrointestinal (GI) media were characterized. The mean size of liposomes was 35-70 nm, which decreased with an increase in the Tween 80 concentration. The zeta potential of the system was about-15 mV. More than 80% of the (+)-catechin was entrapped in the aqueous core of liposomes produced with 1% Tween 80. Liposomes entrapping (+)-catechin remained stable in the presence of GI fluids, especially in simulated intestinal fluid. The liposomes showed suppressed and sustained release of (+)-catechin compared with that from an aqueous solution. The aqueous control and liposomes were orally administered to rats. The blood level of liposomal (+)-catechin was enhanced at a later stage after administration compared with the free control. In the experiment on the brain distribution, liposomes with elastic properties showed 2.9- and 2.7-fold higher (+)-catechin accumulations compared with the aqueous solution in the cerebral cortex and hippocampus, respectively. Greater compound accumulations with liposomes were also detected in the striatum and thalamus. The experimental results suggest that elastic liposomes may offer a promising strategy for improving (+)-catechin delivery via oral ingestion.

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We propose an in-place search algorithm for computing the exact solutions to the resource constrained scheduling problem. This algorithm supports operation chaining, pipelining and multicycling in the underlying scheduling problem. Based on two lower-bound estimation mechanisms that are capable of predicting the criterion values of search nodes represented by partially scheduled data flow graphs, the proposed algorithm can effectively prune the nonpromising search space and finds the optimum usually several times faster than existing techniques. As opposed to existing search-based scheduling techniques whose space complexity is squared or exponential in the search depth, our approach requires only a constant storage space during the traversal of the search tree. The low space complexity is accomplished by using a combination-generating algorithm, which leads our approach to visit search nodes in such a way that each one is obtained by making only a small change to its sibling without keeping any parent nodes in memory. Experimental results on several well known benchmarks with varying resource constraints show the effectiveness of the proposed algorithm.

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