UHPLC-MS/MS法同时测定大鼠血浆中汉黄芩素、隐绿原酸和白杨素-7-O-葡萄糖醛酸苷含量

建立了UHPLC-MS/MS同时测定大鼠血浆中的汉黄芩素、隐绿原酸和白杨素-7-O-葡萄糖醛酸苷含量的方法。以芹菜苷为内标,采用ACQUITY UPLC HSS T3色谱柱(2.1 mm×100 mm, 1.8 μm),乙腈和0.1％甲酸溶液为流动相,流速为0.2 mL·min^-1。结果表明,汉黄芩素、隐绿原酸和白杨素-7-O-葡萄糖醛酸苷线性范围为1～1 000 ng·mL^-1(R2>0.99),最低定量浓度为1 ng·mL^-1,提取回收率、基质效应均符合生物样品分析要求。建立的方法灵敏、准确,可用于大鼠血浆中汉黄芩素、隐绿原酸和白杨素-7-O-葡萄糖醛酸苷的测定及其药代动力学研究。     

基因重组型枯草芽孢杆菌芽孢表面展示技术与应用

枯草芽孢杆菌是一种好氧型革兰氏阳性益生菌,在营养匮乏的环境下,可形成具有强抗逆性的芽孢。其芽孢独特的结构和生理功能使得芽孢表面展示技术相较于其他表面展示技术具有多种优势,构建的重组芽孢不但易纯化、回收率高,而且安全性好。近年来,枯草芽孢杆菌芽孢表面展示技术得到迅猛发展,已成功利用CotB、CotC、CotG、CotZ、CotA、OxdD、CotE、CotZ、CgeA等多种锚定蛋白将外源蛋白或多肽展示在芽孢表面,并应用于工业化酶、口服疫苗和药物、大分子量多聚体蛋白的生产以及环境污染的生物治理等领域。本文首先介绍了芽孢展示系统整合策略,并重点阐述了基因重组型枯草芽孢杆菌芽孢展示技术中涉及的锚定蛋白以及该技术在各领域的应用与展望。     

SPS预烧结制备超细晶梯度硬质合金

首先采用SPS预烧结和真空预烧结制备超细晶硬质合金,再经过梯度烧结使超细晶硬质合金表面形成梯度层,研究了不同预烧结方式对合金组织的影响,分析了预烧结后合金微观组织对超细晶硬质合金的梯度形成及晶粒生长的影响.结果表明,预烧结后合金的微观组织对梯度烧结后的梯度形成和晶粒生长有较大影响,经过SPS预烧结后的硬质合金进行梯度烧结后,可以获得梯度层厚度为 53μm,平均WC晶粒尺寸为0.3μm的超细晶梯度硬质合金.     

LC3真核表达载体构建及其在293T细胞中自噬诱导研究

【目的】构建pEGFP-N1-LC3真核表达载体,并将其转染进人胚肾细胞293T,通过Earle's盐平衡液(Balanced salt solution,EBSS)饥饿诱导观察细胞自噬现象。【方法】RT-PCR扩增LC3基因,并将其插入pEGFP-N1真核表达载体构建重组质粒。将重组质粒转染至人胚肾细胞293T,显微镜观察、Western blot检测GFP-LC3融合蛋白表达情况。对转染细胞进行Earle's盐平衡液饥饿诱导,通过Western blot验证自噬指标,激光共聚焦显微镜观察自噬泡形成。【结果】成功构建pEGFP-N1-LC3真核表达载体,并在293T细胞中成功表达目的蛋白,在进行Earle's盐平衡液饥饿诱导后观察到自噬泡的形成,Western blot检测到LC3-Ⅰ向LC3-Ⅱ的转化。【结论】本实验为研究自噬在癌细胞病理进程中的机制提供了实验素材。     

血浆sST2联合NT-proBNP对急诊心衰患者生存状况的预测价值

目的探讨血浆sST2联合NT-proBNP对急诊心衰患者生存状况的预测价值.方法选择急诊心衰患者130例,分为心功能Ⅱ级组、心功能Ⅲ~Ⅳ级组,对照组选择健康的体检者60例.对3组NT-proBNP及血浆sST2水平进行观察对比,对心功能不同分级患者NT-proBNP及血浆sST2水平进行比较,比较NT-proBNP,sST2,NTproBNP联合血浆sST2对心衰患者死亡状况的预测价值.结果随心功能分级递增,NT-proBNP水平逐渐升高,sST2曲线下面积(0.776)及NT-proBNP对死亡判定的ROC曲线与NT-proBNP曲线下面积(0.775)相比较没有明显差异,但二者联合曲线下面积为0.813,与NT-proBNP及sST2曲线下面积相比较差异显著.sST2联合NTproBNP进行心衰患者死亡状况预测时的敏感度为99.0%,特异度为93.2%.结论作为急诊心衰患者诊断、预后和风险评估的主要标志物,sST2联合NT-proBNP对患者生存状况进行评估可以很好地预测患者的生存状况,提高准确度和敏感度,值得在临床上推广应用.     

面向同源蛋白质探测的一种新型混合深度学习模型

根据蛋白质氨基酸链探测其同源蛋白质,进而预测蛋白质的功能,是生物信息学研究领域的一个重要挑战,也是众多生物医学研究领域的基础研究内容,有着重要的科研价值和广泛的应用需求。其研究难点在于:(1)如何学习对同源蛋白质预测有效、有用的蛋白质特征信息;(2)如何更好地运用蛋白质特征信息,实现同源蛋白质的探测与识别。为了解决同源蛋白质探测与识别研究中的关键难点,本文提出一种基于混合深度学习架构的同源蛋白质探测与识别模型(HDLM-PHP)。通过采用统一的"管道式"深度学习架构,将蛋白质特征学习和探测识别统一为一个整体,提高同源蛋白质探测与识别的效能。采用多组并行的深度卷积神经网络,学习蛋白质的各种属性信息,以期获得丰富的待检测蛋白质和靶蛋白质的高级相关性特征,并通过全连接方式使用多层RBM结构融合和精炼这些相关性特征为全局相关性特征。通过统一的深度网络连接方式,以探测和识别任务为导向,学习到对于同源蛋白质预测最有效、最全面的蛋白质特征信息。在标准数据集SCOPe上,对所提模型进行性能与效率评测,结果表明:本文提出的模型能有效地学习到符合任务导向的蛋白质特征数据,提升同源蛋白质探测与识别的准确度和召回率,优于现有的模型和算法。     

PBNA： An Improved Probabilistic Biological Network Alignment Method

Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment problem.However, it is worth noting that interactions in biological networks, like many other processes in the biological realm,are probabilistic events. Therefore, more accurate and better results can be obtained if biological networks are characterized by probabilistic graphs. This probabilistic information, however, increases difficulties in analyzing networks and only few methods can handle the probabilistic information. Therefore, in this paper, an improved Probabilistic Biological Network Alignment（PBNA） is proposed. Based on Iso Rank, PBNA is able to use the probabilistic information. Furthermore, PBNA takes advantages of Contributor and Probability Generating Function（PGF） to improve the accuracy of node similarity value and reduce the computational complexity of random variables in similarity matrix. Experimental results on dataset of the Protein-Protein Interaction（PPI） networks provided by Todor demonstrate that PBNA can produce some alignment results that ignored by the deterministic methods, and produce more biologically meaningful alignment results than Iso Rank does in most of the cases based on the Gene Ontology Consistency（GOC） measure. Compared with Prob method, which is designed exactly to solve the probabilistic alignment problem, PBNA can obtain more biologically meaningful mappings in less time.     

基于全基因组测序的粘绿木霉Gv29-8中分泌蛋白预测

粘绿木霉Gv29-8作为木霉属中重要的生防菌之一,对该菌分泌蛋白进行预测及其特征进行明确具有重要的理论意义。利用SignalP、ProtComp等预测程序对该菌中12427条蛋白质序列进行分泌蛋白找寻,并对上述分泌蛋白的氨基酸分布、信号肽长度大小及切割位点等性质进行分析。粘绿木霉含有分泌蛋白为377个,其氨基酸长度、信号肽长度与植物病原菌不同;信号肽切割位点属于A-X-A类型,与其他已经报道的植物病原真菌、卵菌中分泌蛋白信号肽切割位点一致。     

Bluff body flow-induced noise control with sliding plasma actuators

Sliding plasma actuators were used to control the flow-induced broadband noise radiating from a bluff body. The structure consisted of a cylinder and a torque link that was installed on the lee side of the cylinder. The objective was to reduce the noise generated during the cylinder wake impinging on the torque link. By manipulating cylinder wake with the externally-imposed body force generated by the plasma actuators, the flow-structure interaction was reduced, leading to an attenuation of the broadband noise up to 3 dB in overall sound pressure level at a free stream speed of 30 m/s.     

For whom the bell tolls? DING proteins in health and disease

DING proteins, identified mainly by their eponymous N-terminal sequences, are ubiquitous in living organisms. Amongst bacteria, they are common in pseudomonads, and have been characterised with respect to genetics and structure. They form part of a wider family of phosphate-binding proteins, with emerging roles in phosphate acquisition and pathogenicity. Many DING proteins have been isolated in eukaryotes, in which they have been associated with very diverse biological activities, often in the context of possible signalling roles. Disease states in which DING proteins have been implicated include rheumatoid arthritis, lithiasis, atherosclerosis, some tumours and tumour-associated cachexia, and bacterial and viral adherence. Complete genetic and structural characterisation of eukaryotic DING genes and proteins is still lacking, though the phosphate-binding site seems to be conserved. Whether as bacterial proteins related to bacterial pathogenicity, or as eukaryotic components of biochemical signalling systems, DING proteins require further study. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.