Effect of stents coated with a combination of sirolimus and alpha-lipoic acid in a porcine coronary restenosis model

The aim of this study was to evaluate antiproliferative sirolimus- and antioxidative alpha-lipoic acid (ALA)-eluting stents using biodegradable polymer [poly-l-lactic acid (PLA)] in a porcine coronary overstretch restenosis model. Forty coronary arteries of 20 pigs were randomized into four groups in which the coronary arteries had a bare metal stent (BMS, n = 10), ALA-eluting stent with PLA (AES, n = 10), sirolimus-eluting stent with PLA (SES, n = 10), or sirolimus- and ALA-eluting stent with PLA (SAS, n = 10). A histopathological analysis was performed 28 days after the stenting. The ALA and sirolimus released slowly over 30 days. There were no significant differences between groups in the injury or inflammation score; however, there were significant differences in the percent area of stenosis (56.2 ± 11.78 % in BMS vs. 51.5 ± 12.20 % in AES vs. 34.7 ± 7.23 % in SES vs. 28.7 ± 7.30 % in SAS, P < 0.0001) and fibrin score [1.0 (range 1.0–1.0) in BMS vs. 1.0 (range 1.0–1.0) in AES vs. 2.0 (range 2.0–2.0) in SES vs. 2.0 (range 2.0–2.0) in SAS, P < 0.0001] between the four groups. The percent area of stenosis based on micro-computed tomography corresponded with the restenosis rates based on histopathological stenosis in different proportions in the four groups (54.8 ± 7.88 % in BMS vs. 50.4 ± 14.87 % in AES vs. 34.5 ± 7.22 % in SES vs. 28.9 ± 7.22 % in SAS, P < 0.05). SAS showed a better neointimal inhibitory effect than BMS, AES, and SES at 1 month after stenting in a porcine coronary restenosis model. Therefore, SAS with PLA can be a useful drug combination for coronary stent coating to suppress neointimal hyperplasia.     

生物模板法合成锂离子电池电极材料研究进展

锂离子电池是一类极具潜力的新型二次化学储能器件,被广泛应用于便携式电子设备、电动交通工具和智能电网等领域。高性能电极材料的设计和合成是获得高能量密度、长循环寿命、高安全性锂离子电池的关键。文章针对锂离子电池电极材料存在制备工艺复杂、结构难以控制、活性物质利用率低、循环稳定性和倍率性能差等问题,从生物资源高效利用角度出发,结合生物材料尺寸均匀、形态多变、结构精密、环境友好等优点,综述了生物模板法合成锂离子电池电极材料的研究进展,并对该领域的发展方向进行了展望。     

Transformation of a thin-walled solid model into a surface model via solid deflation

A simplified geometric model with lower dimensionality, such as a mid-surface model, is often preferred over a detailed solid model for the analysis process, if the analysis results are not seriously impacted. In order to derive a mid-surface model from a thin-walled solid model, in this paper, we propose a novel approach called the solid deflation method. In this method, a solid model is assumed to be created by using air to inflate a shell that comprises the surface of the solid model. First, the model is simplified by the removal of any detailed features whose absence would not alter its overall shape. Next, the solid model itself can be converted into a degenerate solid model with zero thickness. Finally, a surface model is generated by splitting large faces paired in the thinned solid model, selecting one face per pair for creating a sheet model, and sewing the selected faces. Using this method, a more practical and usable mid-surface model can be very efficiently generated from a solid model because it can circumvent not only the tedious trimming and extension processes of the medial axis transformation method but also the time-consuming patch joining process of the mid-surface abstraction approach.     

LINUX下UBI子系统的研究与应用

对Linux中基于MTD(Memory Technology Device)的UBI(Unsorted Block Images)子系统和基于UBI的UBIFS的二者内部工作机制和算法进行分析,在此基础上着重探讨UBIFS实现快速挂载与I/O的原理.在同一嵌入式Linux系统中分别使用直接基于MTD的JFFS2和基于UBI的UBIFS,对它们的挂载和I/O时间进行性能测试比对,证实UBIFS实现嵌入式Linux文件系统的快速挂载与读写.     

基于Mega169的空调控制器的设计

采用单片机技术,设计了一种车用空调控制器.控制器可对空调的各个部件进行控制,实现制冷制热、调整风量及控制风口的变换等操作.系统采用液晶显示屏显示,由程序控制实现各个环节的功能,方便了汽车驾驶员的操作.该设计具有很高的实用价值,可适用于现有的大多数汽车以及工程车.     

氡及其子体运移仿真系统开发

基于.net开发平台,借助C讨论了仿真系统的软件实现过程,简述了系统的功能模块及关键技术实现.实现了氡及其子体运移机制研究的计算机化.     

Predication based immune network for multimodal function optimization

For the problem of indeterminate direction of local search, lacking of efficient regulation mechanism between local search and global search and regenerating new antibodies randomly in the original optimization version of artificial immune network (opt-aiNet), this paper puts forward a novel predication based immune network (PiNet) to solve multimodal function optimization more efficiently, accurately and reliably. The algorithm mimics natural phenomenon in immune system such as clonal selection, affinity maturation, immune network, immune memory and immune predication. The proposed algorithm includes two main features with opt-aiNet. The information of antibodies in continuous generations is utilized to point out the direction of local search and to adjust the balance between local and global search. PiNet also employs memory cells to generate new antibodies with high affinities. Theory analysis and experiments on 10 widely used benchmark problems show that when compared with opt-aiNet method, PiNet algorithm is capable of improving search performance significantly in successful rate, convergence speed, search ability, solution quality and algorithm stability.