仙人掌药用成分的提取及其镇痛作用的实验研究

目的对仙人掌药用成分的提取工艺及镇痛作用进行探讨。方法采用乙醇提取,醋酸乙酯等有机溶剂萃取,经硅胶柱层分离,得到淡黄色物质,并对该物质进行了化学定性分析和小鼠镇痛作用实验。结果经化学定性分析,表明这种药用成分中含有黄酮类物质,其镇痛作用与阿司匹林和生理盐水比较:扭体法2χ分别为5.19,9.68(P<0.05或0.001);温浴法P<0.01或P<0.001。结论仙人掌植物中含有黄酮类物质,对小鼠具有显著的镇痛作用。     

中药标准化提取物应用建议及产业化思考

“现代中药”是我国实施中药现代化而催生的具体产业方向。中药标准化提取物是现代中药的一种产品形式,中药提取过程是中药现代化的重要技术环节,提取物在国际市场上的应用是中药国际化的先行者和铺路石。中药提取物应是中药的一种产品方式。建议国家主管部门的产业发展规划与工作部署中应提高对涉及中药生产过程控制(中药材种植、提取工艺、装备及辅料应用)产业化项目的关注度,积极促进以中药标准化提取物投料的现代中药和保健品的开发研究和生产销售。     

论空间技术在药用植物研究上的应用

中药现代化的发展需要空间技术的应用。国外空间植物学的研究已经由传统的利用空间条件进行育种、观察染色体变化实验阶段发展到植物生长与人类空间生存一体化研究阶段。我国空间植物学的研究还处于起始阶段 ,实验多在空间育种方面 ,对返回地面的材料进行了较为深入的生长发育、生理生化、遗传变异等基础研究。关于植物搭载设备的研究还比较薄弱 ,植物在太空生长方面的研究还处于空白状态。我国空间技术在药用植物上的应用研究有一定的特色和优势。小型生物舱可以用来搭载药用植物的种子 ,种子太空飞行后可通过仪器检测将种子区分成微重力组和太空射线击中组 ,但遗传育种以及有效成分变化方面的研究明显不足。未来的空间药用植物学研究一方面要探讨药用植物在空间生命支持系统中的作用 ,另一方面还应探讨中药在宇航员飞行过程中的保健作用。     

生化过程工程与中药现代化

在生化工程20余年的研究工作基础上。结合中药现代化开展了一些技术、材料与设备的研究开发工作。研究植物细胞、组织、器官大规模培养增殖技术，应用于细胞代谢产物、生物转化和人工育种及发根，实现了工厂化生产，并成功研制多种类型的生物反应器；在原有化学工程提取分离技术的基础上，发展了反应分离耦合、微波辅助提取等新技术，实现了高效浸出，并且节能、节水；此外，又发展了包括反胶团萃取、一步三相萃取青霉素、泡沫分级分离、膜分离、高速逆流色谱分离纯化等新技术。高速逆流色谱技术是一种没有固相载体的液一液多级逆流萃取技术，避免了不可逆吸附，已成功用于多种天然产物的分析和分离，作为研究中药指纹图谱的新方法具有很好的精密度和重现性。高效液相色谱、气相色谱、毛细管电泳、质谱等技术在指纹图谱研究中发挥了重要作用。与此同时，还研制了多种分离介质，并在药物的修饰与包埋方面做了大量工作。另外，在海洋药物研究领域，创造了连续培养连续采收的新流程、新技术．进行了转基因藻的培养。用于生产基因工程产品。     

转基因植物疫苗研究进展

转基因植物疫苗作为一种新型的基因工程疫苗，和其它疫苗相比，具有廉价性、可食性等优点。在最近的研究中，越来越多的抗原蛋白在植物中得到了表达，进入了动物和人体实验；而且，植物表达体系进一步多样化，抗原蛋白的表达量和免疫原性得到很大的提高。本文就这些进展进行了综述。     

9种试点食药物质的安全性评价及试点风险监测现况

按照传统既是食品又是中药材的物质简称为食药物质,传统中医药常使用食药物质来防治疾病或养生保健。随着我国大健康产业的迅速发展,食药物质成为健康领域关注的热点。近年来,随着食药物质品种的增加,研发产品的创新种类和应用增多,拟新增食药物质的安全性问题成为关注重点。现综述国家卫生健康委员会和市场监督管理总局开展的按照传统既是食品又是中药材物质管理试点工作的9种试点食药物质的食用与药用价值、安全性和不良反应、国内部分省份风险监测内容和方案,以期为9种试点食药物质的食品安全风险监测工作的开展提供参考,为食药物质的合理应用和资源开发提供一定的依据。     

基于通用DNA条形码序列的黄精属药用植物分子鉴定

目的 分析4个通用植物DNA条形码序列(trnH-psbA、matK、rbcL和ITS2)及其组合对黄精属药用植物的物种鉴定分辨率,挖掘适用于黄精属种间鉴定的高分辨率分子标记。方法 以《中国药典》2020年版中收录的黄精属药用植物黄精Polygonatum sibiricum、滇黄精P. kingianum、多花黄精P. cyrtonema、玉竹P. odorati及其地方常见同属替代品、混伪品共12种79个野生个体为对象,将4个通用DNA条形码序列独立、联合分析,评估其种间、种内变异情况,并分别基于建树法(tree-based method)和PWG距离法(PWG-distance method)评估不同条形码及其组合的物种鉴定分辨率。结果 ITS2序列扩增成功率低,trnH-psbA、matK、rbcL序列的引物在黄精属植物中通用性较好;3组叶绿体序列的种间变异依次为matK＞trnH-psbA＞rbcL,种内变异差异不显著,种间、种内遗传距离无明显的Barcoding gap;各条形码独立及联合分析的物种鉴定分辨率普遍偏低,其中,组合条形码trnH-psbA＋matK＋rbcL在建树法分析中的分辨率最高,为25%,trnH-psbA＋rbcL在距离法分析中的分辨率最高,为50%。结论 4个通用DNA条形码序列及其组合都并非黄精属药用植物不同种间有效区分鉴定的理想分子标记,但多序列联合分析能在一定程度上提高物种鉴定成功率。     

Predicting species’ range limits from functional traits for the tree flora of North America

Using functional traits to explain species’ range limits is a promising approach in functional biogeography. It replaces the idiosyncrasy of species-specific climate ranges with a generic trait-based predictive framework. In addition, it has the potential to shed light on specific filter mechanisms creating large-scale vegetation patterns. However, its application to a continental flora, spanning large climate gradients, has been hampered by a lack of trait data. Here, we explore whether five key plant functional traits (seed mass, wood density, specific leaf area (SLA), maximum height, and longevity of a tree)—indicative of life history, mechanical, and physiological adaptations—explain the climate ranges of 250 North American tree species distributed from the boreal to the subtropics. Although the relationship between traits and the median climate across a species range is weak, quantile regressions revealed strong effects on range limits. Wood density and seed mass were strongly related to the lower but not upper temperature range limits of species. Maximum height affects the species range limits in both dry and humid climates, whereas SLA and longevity do not show clear relationships. These results allow the definition and delineation of climatic “no-go areas” for North American tree species based on key traits. As some of these key traits serve as important parameters in recent vegetation models, the implementation of trait-based climatic constraints has the potential to predict both range shifts and ecosystem consequences on a more functional basis. Moreover, for future trait-based vegetation models our results provide a benchmark for model evaluation.In 1895 the Danish plant ecologist Eugen Warming defined for the first time the objectives of a functional plant biogeography, when he expressed the need “to investigate the problems concerning the economy of plants, the demands that they make on their environment, and the means that they use to use the surrounding conditions….” He already envisioned how to tackle this: “This subject leads us into deep morphological, anatomical, and physiological investigations; […] it is very difficult, yet very alluring; but only in few cases can its problems be satisfactorily solved at the present time” (1).Since Warming’s days plant science has progressed beyond the study of just a “few cases.” For more than a century now, botanists and plant ecologists have collected data on morphological, anatomical, and physiological traits (2, 3), and have mapped the distributions of tens of thousands of plant species (e.g., Global Biodiversity Information Facility, www.gbif.org). In addition, climatologists and soil scientists have provided us with high-resolution global maps of the plant’s surrounding condition. With this it has now become feasible to analyze the functional underpinnings of plant distributions for entire regional floras across large-scale environmental gradients (4). It is well established that on regional and global scales, climate determines the distribution not only of plant species but also of form and function (5, 6) because it constitutes the overall physical constraint under which plants must establish and reproduce, before biotic interactions may modulate plant fitness. Plants have evolved a multitude of adaptations to climatic constraints, which are expressed in the diversity of their functional traits. These allow them to tolerate climate extremes such as summer drought or low winter temperatures. In other words, the climate range occupied by plants should be predictable from their functional traits.Current species distribution models (SDMs) (7) use correlations between current climate and species distributions, so-called climate envelopes. Even modern dynamic global vegetation models (DGVMs) (8) capable of representing carbon acquisition, water balance, and competitive interactions of plant functional types (PFTs) in great mechanistic detail, still incorporate empirical climate envelopes to constrain PFT distributions. This obvious lack of mechanism is an important limitation when such models are used to predict vegetation shifts under future climate scenarios, especially under novel combinations of climate variables (8). Here, we introduce a unique approach—the “double quantile” approach (Fig. 1 and see Linking Traits to Climate Ranges)—that allows us to predict species distribution limits from functional plant traits. Although still empirical at heart, this approach has distinct advantages: (i) The very nature of the traits emerging as suitable predictors of species distribution limits sheds light on the biological mechanisms. Accordingly, below we are able to put forward concrete hypotheses of the biological underpinnings of trait–climate limit relationships. (ii) Functional traits serve as a common currency across species and thus provide the basis for assimilating the behavior of many species into a single generic predictive framework. (iii) Because this approach replaces idiosyncrasy by generality, the handshake with process-oriented models is greatly facilitated as will be discussed below.Open in a separate windowFig. 1.(A) Species are distributed along climatic gradients and occupy species-specific climate ranges, which can be characterized by three measures: the upper limit (red squares), the lower limit (blue squares), and the median (black squares) for which the highest species’ occurrence probability is suggested. (B) To explore the response of the climate range measures to traits, we related them separately against the traits using linear quantile regression analysis. We estimated the upper quantiles for the upper limits, the lower quantiles for the lower limits, and the median quantile for the median; a solid line indicates a slope significantly different from zero (increasing or decreasing) and a dotted line represents a nonsignificant slope. The area between the outermost regression lines represents the possible climate range species can occupy across their trait values whereas areas outside these lines describe no-go areas. (C) We distinguish three types of response patterns: (i) one-sided constraint, i.e., significant slope at only one limit (the upper or the lower one); (ii) two-sided constraint with reverse slopes at both limits; and (iii) constant shift with aligned slopes at both limits.Here, we explore the potential of five functional traits—specific leaf area (SLA), wood density, maximum height, seed mass, and tree longevity—to explain the climate range limits and mean climate preferences of 250 North American tree species covering a temperature gradient from the boreal to the subtropics and a gradient from 65 to 3,000 mm of annual precipitation. Although there has been a first attempt to incorporate trait information in SDMs (9), we present here a unique study using plant functional traits to predict their limiting effect on species’ climate ranges at a taxonomic and climatic scale relevant for DGVMs. We chose to present the relationship between traits and species climate range limits from a trait perspective to highlight their potential for predicting species’ climate niches as a holistic measure of plant performance in response to climate. Unlike previous studies, our double quantile approach places an emphasis on the responses of species-specific climate ranges at the potentially stressful ends of climate gradients, where strong effects of functional traits on range limits can be expected.

Functional Traits: Selection and Relevance.

The five traits represent key functions defining plant strategy axes related to the fundamental tradeoffs of resource acquisition and reproduction (10, 11) and are thus indicative of life history, mechanical, and physiological mechanisms. Furthermore, some of these traits are frequently used as parameters in DGVMs (2). Because these traits vary across climatic gradients (12, 13), they are ideally suited to gain insight into processes shaping tree distributions at continental scales and at the same time to improve predictions on ecosystem functions under climate change. SLA is a key trait of the leaf economic spectrum (14) and defines a species’ resource use strategy from acquisitive to conservative. It is related to growth rate under different climatic conditions (15) and reflects tradeoffs in species’ shade and drought tolerances (16). Wood density is related to the efficiency and safety of water transport (17) and represents a tradeoff between mechanical strength and vertical growth. It is strongly correlated with growth and mortality rates (12). Maximum height describes the maximum recorded height of a species and quantifies species’ carbon gain strategy via light capture (18); it is related to successional status, shade tolerance and responds to gradients in precipitation on a global scale (19). Seed mass correlates positively with seedling survival rates under hazardous conditions during seedling establishment (11) and negatively with dispersal distance and the number of seeds produced per unit energy invested (20). Maximum tree longevity determines species responses to disturbance (21), compensates for reduced fecundity or juvenile survival (22), and relates to defensive investment (23).

Linking Traits to Climate Ranges.

We derive a tree species’ climate range from its natural geographic distribution (24). We use a set of eight bioclimatic variables (Methods) which represent dominant climatic gradients over North America and are widely used in climatic niche modeling (7, 25). To define a species’ climate range (Fig. 1A) we estimate for each bioclimatic variable the lower (5th quantile) and upper limits (95th quantile) and the median (50th quantile) across a species’ distribution range. Using linear quantile regression analysis (26), we regress across all species the three species-specific range measures against each of the five traits separately estimating the lower (10th, 5th), the upper (90th, 95th) and median (50th) regression quantiles, respectively (Fig. 1B). Thus, the 50th quantile regression lines fit to the medians (black line and squares in Fig. 1B) and describe how the mean realized climate niche depends on the trait values. The lower and upper quantile regression lines fit to the lower and upper limits (blue line and squares and red line and squares, respectively). In this double quantile approach, the outer regression lines enclose an area corresponding to the climate range the pool of 250 North American tree species can occupy across the range of their trait values (Fig. 1B). At the same time it identifies “no-go areas” which cannot be occupied by trees with a given trait value. The delineated areas can attain three possible shapes: (i) the area is wedge-shaped when there is a one-sided constraint, i.e., only one outer quantile represents a climatic extreme requiring a trait adaptation. (ii) The area has the form of an acute-angled triangle, when there is a two-sided constraint leading to reverse responses of the outer quantiles. Both triangular shapes, i and ii, imply that the possible climate range of the species pool changes with a given trait value (see Fig. 1C for examples). (iii) The area can have a rhomboid shape when the two-sided constraints are aligned. This implies a shift in the mean climate preference, but no change in the potential climate range per trait value.     

基于高通量测序技术研究药食两用薏苡仁中污染真菌多样性

目的:调查药食两用薏苡仁中污染真菌多样性,为其安全使用提供参考依据。方法:收集薏苡仁样品18批,提取真菌DNA并扩增ITS2序列,基于Illumina MiSeq PE250平台进行高通量测序。结果:共检测到4门18纲44目99科149属的真菌,子囊菌门Ascomycota是最优势菌门,镰刀菌属Fusarium(3.05%~60.32%)是属水平最优势属,其次是曲霉属Aspergillus(2.20%~45.44%)、白僵菌属Beauveria(0.07%~63.21%)、链格孢属Alternaria(0.80%~11.92%)、Arachnomyces(0.03%~39.36%)和青霉属Penicillium(0.24%~8.03%)。此外,共检测到5种潜在产毒真菌,分别是烟曲霉A.fumigatus、土曲霉A.terreus、梨孢镰刀菌F.poae、囊状青霉P.capsulatum和展青霉P.paxilli。结论:高通量测序技术可以快速有效地检测薏苡仁中污染真菌种类,为薏苡仁污染真菌毒素提供风险预警。