基于组合药物网络的疾病关联性分析

组合药物在复杂疾病的治疗中形成了多靶点,多环节上的密切联系,对疾病的治疗效果也可达到单种药物治疗意想不到的效果。组合药物中各单药功能各异但联用后治疗效果更佳,说明所对应疾病之间可能存在某种关系。通过研究疾病间关联关系,可能会发现治疗某种疾病的新靶标,从而在新药的研发中取得新的进展。本文以DCDB(组合药物数据库)中的药物组合为数据源构建组合药物网络,并通过网络聚类算法得到了33个独立且内部联系紧密的药物模块。其中7组药物模块所包含的组合药物用于治疗两种或两种以上疾病,说明这些疾病之间存在一定的关联关系。对这些关系进行论证,结果表明,组合药物网络是发现疾病关联关系的一种有效手段。     

基于复杂网络的脑疾病功能网络研究进展

脑功能连接能够反映脑区间的相互联系状况,目前已成为脑功能研究的主要方法。复杂网络方法源于图论分析,可以对功能连接所构网络进行量化分析,提供多种量化指标。本文介绍了当前复杂网络的多种基本概念,及其在几种典型脑部疾病上的应用情况。     

基于病例组合指标的医生疾病治疗费用差异研究

目的 应用诊断相关分组项目中的病例组合指标（CMI）分析医生对某一疾病患者的治疗方式及费用差异。方法 选取某三级甲等医院胸外科肺恶性肿瘤疾病患者住院病例的住院首页信息（ICD-C34）,提取不同患者疾病诊断的CMI值。采用非参数检验、Logistic分析病种的住院费用影响因素,包括医生,CMI等,比较参与治疗该病的四位医生之间差异。结果 影响患者住院费用的主要因素是不同医生、CMI、住院日和患者年龄;不同医生诊治的患者的 CMI无统计学差异,以住院费用为因变量,医生和CMI为自变量的Logistic分析结果表明CMI对住院费用具有影响。结论 CMI有可能作为一项重要指标用于评价医生对某一疾病治疗的合理性和工作质量。     

基于乒乓算法的复杂疾病标志物识别

目的:生物标志物是标识系统、器官、组织等改变或可能发生改变的生化指标,具有非常广泛的临床应用。本文希望从高通量数据出发,提出一种新的研究复杂疾病标志物的方法。方法:以"组学"数据为研究对象,利用乒乓算法构建lnc RNA-mRNA交互网络,通过随机游走算法计算选出复杂疾病的生物标志物,并将其与t检验结果比较。结果:将本文方法运用于食管癌标志物的识别,得出与食管癌发生和发展过程相关的14个lnc RNA(CCAT1、MEG3、Snhg1、MALAT1、HOTAIR、UCA1、PVT1、CASC9、LOC100130476、TUG1、BC200、POU6F2-AS2、TP73-AS1和ZEB1-AS1)和12个mRNA(SPARC、CMTM7、Sph K1、NANOG、LOXL2、HMGCS2、FZD7、PTOV1、CADM1、CTHRC1、MGMT和RECK)。对比显示,识别出t检验未识别出的4个lnc RNA(BC200、POU6F2-AS2、TP73-AS1和ZEB1-AS1)和3个mRNA(CADM1、Sph K1和RECK)。结论:该方法能够更有效的预测复杂疾病相关的标志物。     

罕见疾病及孤儿药物研究现状及进展

文中简述了罕见疾病的定义、发病原因、分类,总结了国内外罕见疾病研究和孤儿药物研发的现状,分析了生物技术的研究手段在罕见疾病药物研发方面的应用,进一步阐述了罕见疾病研究和孤儿药物开发的必要性和紧迫性,对推动科学技术的进步和人类健康事业的发展具有深远的意义。     

基于复杂网络的蛋白质结构域组进化分析

结构域重组与序列复制、变异一起,推动了生命的进化。文章应用复杂网络理论比较分析了不同复杂程度的真核生物体中蛋白质结构域组的进化规律。结果表明大量的结构域(约34%)被基因组共享,而结构域的相邻二元组合却具有很大的物种特异性。结构域组合网络呈现无尺度特性,其幂率分布及平均连接度在一定程度上反映了物种的复杂性;网络的聚集系数远高于相同度分布的随机网络(P=0.0096),聚集系数与度呈现幂率分布,这说明网络服从模块化层次式组织规律。最后以人类基因组为例,初步探索了网络模块与功能的关系,发现网络模块中的结构域具有不同程度的功能一致性。     

复杂疾病关联研究中的若干问题

关联研究广泛应用于阐述心血管疾病、2型糖尿病、原发性高血压和肥胖等人类复杂疾病的遗传学基础。文中就关联研究中混杂的识别与控制、候选基因的选择、中间表型的应用、单体型分析方法的应用,以及结果的判定等问题进行了讨论。人群分层是关联研究混杂的主要来源之一。选择患者亲属做对照、基因组对照和选择遗传背景较为一致的隔离人群都可以减少混杂。候选基因的选择可以基于与疾病间的生物学联系或是该基因与疾病某已知相关基因的同源性。适当的应用中间表型和单体型分析方法可以增加关联研究有意义发现的机会。本文认为,优化研究设计、足够的样本含量、正确选择对照,结合先进的数据分析方法,关联研究必将为困扰人类的常见疾病的易感性研究发挥更大的作用。     

基于数据挖掘和网络药理学研究强直性脊柱炎的用药规律及高频药物组合的分子作用机制CSCD

基于数据挖掘及网络药理学分析方法,总结上海中医药大学附属光华医院风湿免疫科医师运用中药治疗强直性脊柱炎(ankylosing spondylitis,AS)的用药规律。并对出现频率最高的药对进行网络药理学分析,进一步阐释其药理作用机制,为中药治疗AS提供一定的参考。本研究以上海中医药大学附属光华医院为主要研究中心,采用横断面调查研究方法,对就诊于光华医院风湿免疫门诊及住院部的AS患者进行信息采集,收集患者的基本信息、中药治疗处方等数据资料,并依托现代计算机信息技术,采用数据挖掘方法进行分析,并对得到的高频药对进行网络药理学分析。收集到中药处方200个,统计中药236味,使用频率≥1%的中药有狗脊、白术、独活等28味,主要是补虚药、清热药、活血化瘀药和祛风湿药;关联规则后得到频数≥40的药物组合33个,其中狗脊-续断排名最高。网络药理学分析结果显示狗脊-续断共含有12个有效成分,对应作用蛋白430个;药物作用于疾病的主要基因包括TNF、EGFR、VEGFA、STAT3、HSP90AA1、PTGS2、MMP2、MMP9、IL2等。基因本体论(GO)生物过程分析和京都基因和基因组百科全书(KEGG)通路分析显示主要参与炎症调控反应和配体激活的转录因子活性等多项进程,并通过癌症通路、Toll样受体信号通路、神经营养素信号通路和T细胞受体信号通路等多条通路作用于AS。上海中医药大学附属光华医院在治疗AS上采用补虚强脊为主,兼以清热、活血化瘀、祛风除湿的标本兼治方法,为进一步应用中医药治疗AS提供参考;网络药理分析表明狗脊-续断中β-谷甾醇、山奈酚等成分可能通过TNF、VEGFA、STAT3、PTGS2、MMP9、IL2等靶点作用于癌症通路、Toll样受体信号通路和T细胞受体信号通路等,从而发挥治疗AS的作用。表明中医药治疗疾病具有多成分、多靶点、多通路的特点,为后续临床应用提供了理论基础。     

RNA干扰药物用于疾病治疗的安全性问题

RNA干扰(RNA interference,RNAi)是指由双链RNA介导的序列特异的转录后基因沉默.RNAi技术因其特异性.高效性而备受青睐,在疾病治疗方面表现出广阔的应用前景.然而,近年来的研究发现,基于RNAi技术开发的药物存在安全性问题,主要包括引起免疫反应,产生脱靶效应、以及存在竞争微RNA通路等.这些问题的突破将使RNAi技术获得更加广泛的应用.     

基于复杂网络的长三角城市对外服务群落结构研究

城市群已成为国家参与全球竞争与国际分工的重要空间载体,其空间组织形式正从个体城市集聚、等级性的中心地结构向多中心、嵌套式的群落结构演变。为刻画长三角地区城市在不同要素层面下形成的多层次群落结构及其状态,借助复杂网络分析工具,从城市群落的节点特征、垂直和水平结构、不同群落结构间的相互关联三方面实证分析长三角地区3类(生产性、生活性、公共性)对外服务流的网络结构特征。研究显示:1)长三角城市节点服务功能分化,节点层级性分异显著,生产和生活性服务网络的节点规模呈"长尾"分布,公共性服务网络的节点规模相对均衡;2)垂直结构上,3类对外服务网络的网络密度、网络效率、流量占比和空间分布各不相同;水平结构上,初步形成对外服务网络的专业化分工格局,部分城市突破区域界线,呈跨地域集聚组团的态势。3)较强的结构关联性存在于生产性和生活性对外服务网络之间,两者在中低度值的城市节点上具有一致性,呈联动发展格局;公共性对外服务网络与前两者的节点度值分异较大,促进了整体群落服务功能结构的丰富和完善。基于复杂网络的群落研究可以从多维结构的分析中寻求城市群落的分工协作和共生,为当前多核心、网络化的城市空间组织与规划提供科学参考。     

Combination therapeutics in complex diseases

The biological redundancies in molecular networks of complex diseases limit the efficacy of many single drug therapies. Combination therapeutics, as a common therapeutic method, involve pharmacological intervention using several drugs that interact with multiple targets in the molecular networks of diseases and may achieve better efficacy and/or less toxicity than monotherapy in practice. The development of combination therapeutics is complicated by several critical issues, including identifying multiple targets, targeting strategies and the drug combination. This review summarizes the current achievements in combination therapeutics, with a particular emphasis on the efforts to develop combination therapeutics for complex diseases.     

A two-step drug repositioning method based on a protein-protein interaction network of genes shared by two diseases and the similarity of drugs

The present study proposed a two-step drug repositioning method based on a protein-protein interaction (PPI) network of twodiseases and the similarity of the drugs prescribed for one of the two. In the proposed method, first, lists of disease related geneswere obtained from a meta-database called Genotator. Then genes shared by a pair of diseases were sought. At the first step of themethod, if a drug having its target(s) in the PPI network, the drug was deemed a repositioning candidate. Because targets of manydrugs are still unknown, the similarities between the prescribed drugs for a specific disease were used to infer repositioningcandidates at the second step. As a first attempt, we applied the proposed method to four different types of diseases: hypertension,diabetes mellitus, Crohn disease, and autism. Some repositioning candidates were found both at the first and second steps.     

Network biology bridges the gaps between quantitative genetics and multi-omics to map complex diseases

With advances in high-throughput sequencing technologies, quantitative genetics approaches have provided insights into genetic basis of many complex diseases. Emerging in-depth multi-omics profiling technologies have created exciting opportunities for systematically investigating intricate interaction networks with different layers of biological molecules underlying disease etiology. Herein, we summarized two main categories of biological networks: evidence-based and statistically inferred. These different types of molecular networks complement each other at both bulk and single-cell levels. We also review three main strategies to incorporate quantitative genetics results with multi-omics data by network analysis: (a) network propagation, (b) functional module-based methods, (c) comparative/dynamic networks. These strategies not only aid in elucidating molecular mechanisms of complex diseases but can guide the search for therapeutic targets.     

An entropy-based approach for testing genetic epistasis underlying complex diseases

The genetic basis of complex diseases is expected to be highly heterogeneous, with complex interactions among multiple disease loci and environment factors. Due to the multi-dimensional property of interactions among large number of genetic loci, efficient statistical approach has not been well developed to handle the high-order epistatic complexity. In this article, we introduce a new approach for testing genetic epistasis in multiple loci using an entropy-based statistic for a case-only design. The entropy-based statistic asymptotically follows a χ² distribution. Computer simulations show that the entropy-based approach has better control of type I error and higher power compared to the standard χ² test. Motivated by a schizophrenia data set, we propose a method for measuring and testing the relative entropy of a clinical phenotype, through which one can test the contribution or interaction of multiple disease loci to a clinical phenotype. A sequential forward selection procedure is proposed to construct a genetic interaction network which is illustrated through a tree-based diagram. The network information clearly shows the relative importance of a set of genetic loci on a clinical phenotype. To show the utility of the new entropy-based approach, it is applied to analyze two real data sets, a schizophrenia data set and a published malaria data set. Our approach provides a fast and testable framework for genetic epistasis study in a case-only design.     

Brain functional network modeling and analysis based on fMRI: a systematic review

In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer’s disease, Parkinson’s disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as small-world, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions.     

复配益生菌用于危重症抗生素相关性腹泻患者的营养治疗评价研究

目的 探讨复配益生菌制剂治疗危重症患者抗生素相关性腹泻的临床效果。方法 选取新疆维吾尔自治区人民医院重症监护室2015年12月至2016年12月住院并诊断为抗生素相关性腹泻的患者80例,随机分为观察组和对照组。两组患者均给予基础治疗加营养治疗。在营养治疗方面,对照组患者给予常规营养治疗,观察组患者在常规营养治疗的基础上加用复配益生菌制剂,30 g/d,连用6 d。观察两组患者在治疗前、治疗后第3天、第6天的肝功能,包括谷丙转氨酶（ALT）、谷草转氨酶（AST）,肾功能（血肌酐、尿素氮）,营养状况（白蛋白、总蛋白）,炎症指标（C反应蛋白、降钙素原）的变化,并进行临床疗效及安全性评定。结果 治疗6 d后,观察组患者肝功能、肾功能较对照组明显改善,差异有统计学意义（P0.05）;治疗后第3天、第6天观察组患者炎症指标显著低于对照组,两组相比差异有统计学意义（P<0.05）。观察组患者临床总有效率为95.0%,明显高于对照组的72.5%,差异有统计学意义（Z=2.437,P<0.05）。结论 复配益生菌制剂调节机体肠道菌群,恢复体内微生态平衡,对治疗危重症患者抗生素相关性腹泻疗效显著,安全性好,能有效改善危重症患者营养状况、肝肾功能,提高机体免疫力,减少炎症反应,从而提高危重症患者的预后,值得广泛推广应用。     

Ancestral processes for non-neutral models of complex diseases

We consider non-neutral models for unlinked loci, where the fitness of a chromosome or individual is not multiplicative across loci. Such models are suitable for many complex diseases, where there are gene-interactions. We derive a genealogical process for such models, called the complex selection graph (CSG). This coalescent-type process is related to the ancestral selection graph, and is derived from the ancestral influence graph by considering the limit as the recombination rate between loci gets large. We analyse the CSG both theoretically and via simulation. The main results are that the gene-interactions do not produce linkage disequilibrium, but do produce dependencies in allele frequencies between loci. For small selection rates, the distributions of the genealogy and the allele frequencies at a single locus are well-approximated by their distributions under a single locus model, where the fitness of each allele is the average of the true fitnesses of that allele with respect to the distribution of alleles at other loci.     

Design and analysis of drug combination experiments

In this paper we present and discuss a novel, simple and easy to implement parametric modeling approach to assess synergy. An extended three parameter log-logistic model is used to analyse the data and calculate confidence intervals of the interaction indices. In addition the model corrects for the bias due to plate-location effects. The analysis is performed with PROC NLMIXED and SAS-code is provided. The approach is illustrated using data coming from an oncology study in which the inhibition effect of a combination of two compounds is studied using 96-well plates and a fixed-ratio design.     

某院感染科病房住院患者常见病原菌分布及耐药性分析

目的 对感染科病房住院患者临床常见病原菌的分布及其耐药性进行分析,为临床预防和治疗感染性疾病提供依据。方法 回顾性分析中国医科大学附属第一医院感染科病房2012年1月至2016年12月住院患者体液及组织样本中分离的病原菌,对其耐药情况进行分析。结果 5年中感染科共分离出非重复病原菌1 266株,其中革兰阴性菌786株,占62.09%,分离率居前3位的是大肠埃希菌、肺炎克雷伯菌以及铜绿假单胞菌,分别占17.22%、15.24%和10.58%;革兰阳性菌480株,占37.91%,分离率居前3位的是屎肠球菌、草绿色链球菌以及金黄色葡萄球菌,分别占9.79%、7.50%和6.00%。产超广谱β-内酰胺酶（ESBLs）大肠埃希菌和肺炎克雷伯菌检出率分别为66.7%和28.8%。大肠埃希菌对亚胺培南和美罗培南的耐药率分别为0.95%和3.79%,肺炎克雷伯菌对亚胺培南和美罗培南的耐药率分别为2.80%和2.80%。结论 我院感染科病房住院患者感染病原菌以革兰阴性菌为主,为有效的控制和避免耐药菌感染的发生,临床应根据药敏试验结果合理应用抗菌药物。