白首乌化学成分的研究

自白首乌主要品种耳叶牛皮消(Cynanchum auriculatum Royle ex Wight)根中首次分得三个已知甾体酯型甙元:告达亭(caudatin),开德甙元(kidjolanin),萝藦甙元(Metaplexigenin)和一种新的二苯酮衍生物(Ⅳ),经光谱分析推定其结构为2,6,2′,5′-四羟基,3-乙酰基,6′-甲基二苯酮,命名白首乌二苯酮。     

Optimizing the human learnability of abstract network representations

Precisely how humans process relational patterns of information in knowledge, language, music, and society is not well understood. Prior work in the field of statistical learning has demonstrated that humans process such information by building internal models of the underlying network structure. However, these mental maps are often inaccurate due to limitations in human information processing. The existence of such limitations raises clear questions: Given a target network that one wishes for a human to learn, what network should one present to the human? Should one simply present the target network as-is, or should one emphasize certain parts of the network to proactively mitigate expected errors in learning? To investigate these questions, we study the optimization of network learnability in a computational model of human learning. Evaluating an array of synthetic and real-world networks, we find that learnability is enhanced by reinforcing connections within modules or clusters. In contrast, when networks contain significant core–periphery structure, we find that learnability is best optimized by reinforcing peripheral edges between low-degree nodes. Overall, our findings suggest that the accuracy of human network learning can be systematically enhanced by targeted emphasis and de-emphasis of prescribed sectors of information.

From a young age, humans demonstrate the capacity to learn the relationships between concepts (1–3). During the learning process, humans are exposed to discrete chunks of information that combine and interconnect to form cognitive maps that can be represented as complex networks (4–9). These chunks of information often appear in a natural sequential order, such as words in language, notes in music, and abstract concepts in stories and classroom lectures (10–14). Further, these sequences are encoded in the brain as networks, with links between items reflecting observed transitions (see refs. 15–18 for empirical studies and 19 for a recent review). Broadly, the fact that many different types of information exhibit temporal order (and therefore network structure) motivates investigations into the processes that underlie the human learning of transition networks (8, 19, 20).To understand the network-learning process, recent studies have investigated how humans internally construct abstract representations of associations (21–23). Using a variety of approaches, from computational models to artificial neural networks, such studies have consistently found that the mind builds network representations by integrating information over time. Such integration enables humans to compress exact sequences of experienced events into broader, but less precise, representations of context (24). These mental representations allow learners to make better generalizations about new information, at the cost of accuracy (22). Here, we focus on one particular modeling approach that accounts for the temporal integration and inaccuracies inherent in human learning. In particular, we build upon a maximum-entropy model, which posits that the mind learns a network representation of the world in a manner guided by a tradeoff between accuracy and complexity (21, 25). Specifically, in order to conserve mental resources, humans will tend to reduce the complexity of their representations at the cost of accuracy by allowing for errors during the learning process.While inaccuracies in human learning can aid flexibility across contexts, they present fundamental obstacles for the human comprehension of transition networks. Thus, a clear question emerges: What strategies should be employed to most effectively communicate the structure of a network to an inaccurate human learner? Prior studies of animal communication and behavior have demonstrated the utility of exaggerating the presentation of certain signals to receivers in offsetting erroneous information processing (26, 27). Similarly, one could imagine that, by emphasizing some features of a network over others, one may be able to correct for errors in human learning. Such an approach of targeted modulation of emphasis may be helpful not only in learning a whole network, but also in optimally learning particularly challenging parts of a network. In fact, humans show consistent difficulties in learning certain motifs in networks, such as the connections between modules (21, 28–30). Taken together, these observations suggest that disproportionately weighting specific network features that are difficult to learn may facilitate human network learning.     

用差示扫描量热法及激光拉曼光谱研究足叶乙甙对二棕榈酰磷脂酰胆碱脂质体膜流动性影响

本文用DSC和激光拉曼光谱研究抗癌药物足叶乙甙(4-去甲基表鬼臼毒素-β-D-乙叉吡喃葡萄糖甙,简称VP 16-213)与二棕榈酰磷脂酰胆碱(DPPC)脂质体的作用。VP 16-213分子掺入DPPC脂质体双层中,不但使相转变温度向高温移动,而且吸热峰的半高宽度随VP 16-213浓度增加而变宽。其Raman光谱在频率2850 cm~(-1)处的C-H键对称伸缩振动亦随着药物浓度增加而减弱。这些结果表明VP 16-213分子是定域在脂双层中DPPC分子链的C_1～C_9亚甲基区域,使脂质体的有序性提高而流动性降低。     

白首乌化学成分的研究

自白首乌主要品种耳叶牛皮消(Cynanchum auriculatum Royle ex Wight)根中首次分得三个已知甾体酯型甙元:告达亭(caudatin),开德甙元(kidjolanin),萝藦甙元(Metaplexigenin)和一种新的二苯酮衍生物(Ⅳ),经光谱分析推定其结构为2,6,2′,5′-四羟基,3-乙酰基,6′-甲基二苯酮,命名白首乌二苯酮。     

急性心肌梗死后CD34+干细胞自体动员的意义

目的:已有理论提出急性心肌梗死后骨髓和外周血中的CD34 干细胞具有自身动员的潜能,观察这一潜能的变化特征及其对心肌梗死组织再生能力的影响。方法:实验于2004-09/2005-02在阜外心血管病医院完成。①实验动物:雄性SD大鼠40只,随机数字表法分为心肌梗死组、假手术组,20只/组。②实验方法:心肌梗死组大鼠采用冠状动脉结扎法建立心肌梗死模型。心电图ST段抬高或有室性心律出现,前壁心肌呈苍白色为造模成功。假手术组仅作开胸手术,前降支不予结扎。③实验评估:于心肌梗死后3,7,14,28d,流式细胞仪检测骨髓和外周血中CD34 干细胞的含量。用免疫组化方法检测梗死心肌组织中的Ki67细胞和毛细血管数量。结果:①外周血及骨髓CD34 干细胞含量的变化:心肌梗死组外周血中的CD34 干细胞数量于造模后3d开始上升,7d后明显高于假手术组(P<0.01),至14,28d时逐渐回落至假手术组水平(P>0.05)。心肌梗死组骨髓中的CD34 干细胞数量于造模后各时间点始终无明显变化(P>0.05)。②组织学评定:心肌梗死组梗死区Ki67细胞和毛细血管数量于造模后3d开始增多,7d时明显多于非梗死区(P<0.05);至14,28d梗死区Ki67细胞数量明显少于造模后7d(P<0.05),毛细血管数量的减少不明显(P>0.05)。免疫组化染色显示少数Ki67细胞分化为血管内皮细胞,未见向心肌细胞分化。③相关性分析:梗死区Ki67细胞、毛细血管数量于造模后7d与外周血中CD34 干细胞数量呈显著正相关(r=0.913,P=0.021;r=0.887,P=0.035)。结论:机体CD34 干细胞的自体动员、增殖反应的潜能随急性心肌梗死时间的延长而逐渐减弱,自体动员的干细胞功能尚不足以达到修复梗死心肌组织的效果。     

继续医学教育活动的有效性

继续医学教育的目的是通过向医生提供最新的医学知识和技能,使医生在其整个职业生涯中,一直保持较高的医疗水平.但目前还没有充足的证据能证明继续医学教育活动的有效性,以及在继续医学教育活动中哪些教育方法和技术能最有效地传播和记忆医学知识.为了全面、系统地评估继续医学教育活动的有效性,以及了解不同的教育手段对医生知识、态度、技能、临床表现和临床效果的作用.美国约翰·霍普金斯大学医学院、美国医疗保健研究与质量管理署和美国胸科医师学会的专家共同进行了一项系统回顾性研究.