Self-microemulsifying drug delivery system of patchoulic alcohol to improve oral bioavailability in rats / 中国中药杂志

<p><b>OBJECTIVE</b>To improve the oral bioavailability of patchoulic alcohol in rats by using self-microemulsifying drug delivery systems (SMEDDS).</p><p><b>METHOD</b>Patchoulic alcohol was separated and purified from patchoulic oil, and the SMEDDSs including patchoulic alcohol or patchoulic oil were optimized by pseudo-ternary phase diagrams via central composite design-response surface methodology. Pharmacokinetics of both SMEDDSs and patchoulic alcohol itself in rats were investigated.</p><p><b>RESULT</b>The patchoulic alcohol SMEDDS (Cremophor EL-Tween 80-PEG 400-isopropyl myristate-patchoulic alcohol, 2:2:0.8:1.95:0.65) was considered as the optimized formulation. The mean drop size of the system was 30. 1 nm after diluted 100 folds in water. The average self-microemulsifying time was 142 s. Patchoulic alcohol SMEDDS and patchoulic oil SMEDDS showed no signficant difference in Tmax compared with patchoulic alcohol with around 60 minutes, while the AUCs of both SMEDDSs (2001 745.6 +/- 329 663.6) and (1594 005.6 +/- 280 150.3) microg x min x L(-1) were significantly higher than that of patchoulic alcohol (1 163 153.3 +/- 232 324.3) microg x min x L(-1).</p><p><b>CONCLUSION</b>SMEDDS can effectively improve the oral bioavailability of patchoulic alcohol in rats.</p>

Extraction of single-trial event-related potentials by means of ARX modeling and independent component analysis / 生物医学工程学杂志

The present paper focused on the extraction of event-related potentials on a single sweep under extremely low S/N ratio. Two methods that can efficiently remove spontaneous EEG, ocular artifacts and power line interference were presented based on ARX modeling and independent component analysis (ICA). The former method applied ARX model to the measured compound signal that extensively contained the three kinds of ordinary noises mentioned above, and used ARX algorithm for parametric identification. The latter decomposed the signal by means of independent component analysis. Besides, some of ICA's important decomposing characters and its intrinsic causality were pointed out definitely. According to the practical situation, some modification on FastICA algorithm was also given, so as to implement auto-adaptive mapping of decomposed results to ERP component. Through simulation, both the two ways are proved to be highly capable of signal extraction and S/N ratio improving.