长柄梭罗细胞毒活性部位的化学成分研究

目的 对长柄梭罗Reevesia longipetiolata树皮具细胞毒活性的醋酸乙酯部分化学成分进行研究。方法 采用常压、加压硅胶柱色谱、Sephadex LH-20凝胶柱色谱、高效液相色谱进行分离和纯化，通过理化和波谱分析方法鉴定化合物结构。结果 从其醋酸乙酯部分分离得到5个化合物，分别鉴定为β-谷甾醇(β-sitosterol，I)、胡萝卜苷(daucosterol，Ⅱ)、白桦脂酸(betulinic acid，Ⅲ)、羽扇豆醇(lupeol，Ⅳ)和( )-儿茶素[( )-catechin，V]。结论 5个化合物均为首次从该属植物中分得，并分别讨论了它们的细胞毒活性。     

黄春林教授治疗老年慢性肾功能衰竭经验介绍

介绍黄春林教授运用中医药治疗老年慢性肾功能衰竭的经验。黄教授认为脾肾亏虚、湿浊瘀阻是本病的基本病理因素，虚实并见则是本病的发病特点，及早诊断是有效治疗本病的前提。并阐述了中药肾衰康方在治疗本病中的作用机制及大黄的使用技巧。     

孟文瑞《春脚集》学术特色

清代医家孟文瑞撰《春脚集》,共四卷,是一部综合性方剂类著作,临床实用性强。卷一至卷三以人体部位分部设篇,再按病证列方;卷四以内、外、妇、儿分设专篇选方。集历代成方、验方484首。《春脚集》将人体部位共分为十七部,方便读者按图索骥,根据病位、症状检索方剂,便于临床用药。全书选方体现了中医独具特色的“简、便、验”精髓。     

Inhibition of Erythroid Differentiation of Human Leukemia K562 Cells by N-acetylcysteine and Ascorbic Acid through Downregulation of ROS

正This study investigated the effects of N-acetylcysteine(NAC)and ascorbic acid(AA)on hemin-induced K562 cell erythroid differentiation and the role of reactive oxygen species(ROS)in this process.Hemin increased ROS levels in a concentration-dependent manner,whereas NAC and AA had opposite effects.Both NAC and AA eliminated transient increased ROS levels after hemin treatment,inhibited hemin-induced     

Major flavonoid constituents and short-term effects of Chun Mee tea in rats

Chun Mee tea is a kind of green tea produced in China mainly for export purposes. Foam quantity is usually used as an index for evaluating the quality of Chun Mee tea. In the current study, we compared the concentrations of total saponin and flavonoids between foamy and low-foam Chun Mee tea. Our research confirmed that the total saponin and O-glycosylated flavonoid concentrations were related to the foam quantity of Chun Mee teas. We also studied the short-term safety effects of extract supplementation with foamy and low-foam Chun Mee tea in rats by routine blood tests and analysis of liver and kidney function, and blood lipids. Our results showed that both types of tea extract supplementations did not cause any observable adverse effects or impair either liver or kidney function. Additionally, this study confirmed the beneficial effects of Chun Mee tea extract supplementation on the decrease of total plasma cholesterol.     

Density estimation using deep generative neural networks

Density estimation is one of the fundamental problems in both statistics and machine learning. In this study, we propose Roundtrip, a computational framework for general-purpose density estimation based on deep generative neural networks. Roundtrip retains the generative power of deep generative models, such as generative adversarial networks (GANs) while it also provides estimates of density values, thus supporting both data generation and density estimation. Unlike previous neural density estimators that put stringent conditions on the transformation from the latent space to the data space, Roundtrip enables the use of much more general mappings where target density is modeled by learning a manifold induced from a base density (e.g., Gaussian distribution). Roundtrip provides a statistical framework for GAN models where an explicit evaluation of density values is feasible. In numerical experiments, Roundtrip exceeds state-of-the-art performance in a diverse range of density estimation tasks.

Let p(·) be a density on a n-dimensional Euclidean space χ. The task of density estimation is to estimate p(·) based on a set of independently and identically distributed data points {xi}i=1Ndrawn from this density.Traditional density estimators such as histograms (1, 2) and kernel density estimators (KDEs) (3, 4) typically perform well only in low dimension. Recently, neural network-based approaches were proposed for density estimation and yielded promising results in problems with high-dimensional data points such as images. There are mainly two families of such neural density estimators: autoregressive models (5–7) and normalizing flows (8–11). Autoregression-based neural density estimators decompose the density into the product of conditional densities based on probability chain rule p(x)=∏ip(xi|x1:i−1). Each conditional probability p(xi|x1:i−1) is modeled by a parametric density (e.g., Gaussian or mixture of Gaussian), of which the parameters are learned by neural networks. Density estimators based on normalizing flows represent x as an invertible transformation of a latent variable z with known density, where the invertible transformation is a composition of a series of simple functions whose Jacobian is easy to compute. The parameters of these component functions are then learned by neural networks.As suggested in ref. 12, both of these are special cases of the following general framework. Given a differentiable and invertible mapping G:Rn→Rn and a base density pzz, the density of x=G(z) can be represented using the change of variable rule as follows:pxx=pzz|detJz|−1,[1]where Jz=∂Gz/∂zT is the Jacobian matrix of function G(·) at point z. Density estimation at x can be solved if the base density pzz is known and the determinant of Jacobian matrix is feasible to calculate. To achieve this, previous neural density estimators have to impose heavy constraints on the model architecture. For example, refs. 7, 10, and 12 require the Jacobian to be triangular, ref. 13 constructed low rank perturbations of a diagonal matrix as the Jacobian, and ref. 14 proposed a circular convolution where the Jacobian is a circulant matrix. These strong constraints diminish the expressiveness of neural networks, which may lead to poor performance. For example, autoregressive neural density estimators based on learning p(xi|x1:i−1) are naturally sensitive to the order of the features. Moreover, the change of variable rule is not applicable when the domain dimension in base density differs from target density. However, experiences from deep generative models [e.g., GAN (15) and VAE (16)] suggested that it is often desirable to use a latent space of smaller dimension than the data space.To overcome the limitations above, we propose a neural density estimator called Roundtrip. Our approach is motivated by recent advances in deep generative neural networks (15, 17, 18). Roundtrip differs from previous neural density estimators in two ways. 1) It allows the direct use of a deep generative network to model the transformation from the latent variable space to the data space, while previous neural density estimators use neural networks only to learn the parameters in the component functions that are used for building up an invertible transformation. 2) It can efficiently model data densities that are concentrated near learned manifolds, which is difficult to achieve by previous approaches as they require the latent space to have the same dimension as the data space. Importantly, we also provide methods, based on either importance sampling and Laplace approximation, for the pointwise evaluation of the density estimate. We summarize our major contributions in this study as follows: 1) We propose a general-purpose neural density estimator based on deep generative models, which requires less restrictive model assumptions compared to previous neural density estimators. 2) We show that the principle in previous neural density estimators can be regarded as a special case in our Roundtrip framework. 3) We demonstrate state-of-the-art performance of Roundtrip model through a series of experiments, including density estimation tasks in simulations as well as in real data applications ranging from image generation to outlier detection.     

甘欣锦辨治滤泡性淋巴瘤经验

甘欣锦主任医师认为,滤泡性淋巴瘤乃本虚标实之证,以正气不足为本,痰毒瘀邪为标。在临床上,针对未达到西医治疗指征的滤泡性淋巴瘤患者,甘老师以“养正积自消”为治疗原则,扶正贯穿于治疗的始终,并加用抗肿瘤药物,以达扶正抗瘤之效。甘老师多采用补益药物充养先后天之本,从而改善肿瘤患者的虚损,以达阴平阳秘、气血调畅、正气充足的状态。甘老师常以主方为基础结合治疗变证的方法改善局部症状,并减轻放化疗、靶向治疗所带来的不良反应。临证时,在中医辨证的基础上结合辨病分层治疗,调节扶正与解毒的主次。另外,对于出现的各种变证当灵活化裁以改善症状,通过中医的治疗手段改善患者生活质量,延长患者生存时间。     

广东紫珠地上部分的化学成分

目的：研究马鞭草科紫珠属植物广东紫珠（Callicarpa kwangtungensis Chun）地上部分的化学成分。方法：应用硅胶柱层析和Sephadex LH-20柱层析及重结晶等方法从广东紫珠的地上部分分离其化学成分，通过波谱解析和理化性质对结构进行鉴定。结果：分离得到13个化合物，5个黄酮：5，4’-二羟基-3，7，3’-三甲氧基黄酮（5，4’-dihydroxy-3，7，3’trimethoxyflavone，1）、鼠李秦素（rhamnatin，2）、华良姜素（kumatakenin，3）、岳桦素（ermanine，4）、velutin（5）；3个三萜：齐墩果酸（oleanolic acid，6）、熊果酸（ursolic acid，7）、白桦酸（betulinic acid，8）；3个酚酸：水杨酸（salicylic acid，9）、丁香酸（syringicacid？10）、异香草酸（isovanilli cacid，11）；2个甾醇：胡萝卜孝（daucosterol，12）、β-谷甾醇（β-sitosterol，13）。结论：化合物1～5、8和儿为首次从紫珠属植物中分得，化合物10和12为首次从广东紫珠中首次分得。     

明代六部综合性医书的传日及其影响

明代传日医书众多,以《医书大全》、《南北经验医方大成》、《玉机微义》、《医学正传》、《医学入门》、《万病回春》等综合性医书,传播最快,影响最大。它们促进了16世纪以降日本开始对中国医籍的大量翻刻;为日本当时医师培养提供了便捷教本;丰富了道三学派的理论与实践,从而为日本医学中兴增添了砖瓦。     

Comorbidities Can Predict Mortality of Kidney Transplant Recipients: Comparison With the Charlson Comorbidity Index

Background

Comorbid conditions are important in the survival of kidney transplant recipients. The weights assigned to comorbidities to predict survival may vary based on the type of index disease and advances in the management of comorbidities. We aimed to develop a modified Charlson comorbidity index (CCI) in renal allograft recipients (mCCI-KT), thereby improving risk stratification for mortality.