Multiplex assays for biomarker research and clinical application: translational science coming of age

Over the last decade, translational science has come into the focus of academic medicine, and significant intellectual and financial efforts have been made to initiate a multitude of bench-to-bedside projects. The quest for suitable biomarkers that will significantly change clinical practice has become one of the biggest challenges in translational medicine. Quantitative measurement of proteins is a critical step in biomarker discovery. Assessing a large number of potential protein biomarkers in a statistically significant number of samples and controls still constitutes a major technical hurdle. Multiplexed analysis offers significant advantages regarding time, reagent cost, sample requirements and the amount of data that can be generated. The two contemporary approaches in multiplexed and quantitative biomarker validation, antibody-based immunoassays and MS-based multiple (or selected) reaction monitoring, are based on different assay principles and instrument requirements. Both approaches have their own advantages and disadvantages and therefore have complementary roles in the multi-staged biomarker verification and validation process. In this review, we discuss quantitative immunoassay and multiple reaction monitoring/selected reaction monitoring assay principles and development. We also discuss choosing an appropriate platform, judging the performance of assays, obtaining reliable, quantitative results for translational research and clinical applications in the biomarker field.     

SILAC for biomarker discovery

SILAC has been employed in MS-based proteomics for nearly a decade. This method is based on cells in culture metabolically incorporating isotope-coded essential amino acids and allows the quantification of global protein populations to identify characteristic changes. Variations of this technique developed over the years allow the application of SILAC not only to cell culture derived samples but also to tissues and human specimens, making this powerful technique amenable to clinically relevant samples. In this review, we provide an overview of different SILAC-derived methods and their use in the identification and development of biomarkers.     

Using lectins in biomarker research: Addressing the limitations of sensitivity and availability

Carbohydrates have fundamental roles throughout biology, yet they have not been as well studied as proteins and nucleic acids, in part due to limitations in the experimental tools. Improved methods for studying glycans could spur significant advances in the understanding and application of glycobiology. The use of affinity reagents, such as lectins and glycan-binding antibodies, is a valuable complement to methods involving mass spectrometry and chromatography. This article addresses two limitations that have prevented the broader experimental use of glycan-binding proteins: sensitivity and availability. The sensitivity limitation stems from the poor affinity that many glycan-binding proteins have as isolated analytical reagents. To address this problem, I propose making use of multivalent interactions between lectins and glycans, mimicking those frequently found in the biological setting. Recent experiments show that a practical technique for producing lectin multimers can significantly improve detection sensitivity. The second limitation, availability, is the difficulty of finding and obtaining glycan-binding proteins that recognize less common or arbitrarily defined glycan structures. To address this problem, I propose translating the wealth of existing glycan array data into a quantitative, searchable database of the specificities of glycan-binding proteins. Such a resource would allow us to more easily identify proteins with defined specificities and perform detailed comparisons between reagents. Solutions to these two limitations could lead to the more effective use of, and a broader range of, glycan-binding reagents.     

Quantitative proteomic approaches for biomarker discovery

The rapid advances in proteomic technologies have made possible systematic analysis of hundreds to thousands of proteins in clinical samples with the promise of uncovering novel protein biomarkers for various disease conditions. We will discuss in this review article current MS and protein chip-based quantitative proteomic approaches and their application in biomarker discovery. The emphasis will be placed on new quantification strategies employing stable isotopic labeling coupled with MS/MS, and antibody-based protein chips and nanodevices. The strength and weakness of each technology are briefly highlighted.     

Applications of protein microarrays for biomarker discovery

The search for new biomarkers for diagnosis, prognosis, and therapeutic monitoring of diseases continues in earnest despite dwindling success at finding novel reliable markers. Some of the current markers in clinical use do not provide optimal sensitivity and specificity, with the prostate cancer antigen (PSA) being one of many such examples. The emergence of proteomic techniques and systems approaches to study disease pathophysiology has rekindled the quest for new biomarkers. In particular the use of protein microarrays has surged as a powerful tool for large-scale testing of biological samples. Approximately half the reports on protein microarrays have been published in the last two years especially in the area of biomarker discovery. In this review, we will discuss the application of protein microarray technologies that offer unique opportunities to find novel biomarkers.     

Using data-independent,high-resolution mass spectrometry in protein biomarker research: Perspectives and clinical applications

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)—one of the data-independent strategies that have gained wide application of late.     

移动自组网拓扑发现策略的分析研究*

移动自组网是由移动节点自组织形成的无线网络,由于节点移动、无线通信等特点,构造它的拓扑结构比较困难。通过分析传统的拓扑发现策略,研究了移动自组网拓扑发现策略的最新进展,主要分成蚂蚁算法类和聚簇算法类两种类型,对每个类型中各种算法进行了分类论述,分析了优缺点并进行了综合比较,并讨论了未来需要解决的问题。     

对于网格计算工具包NetSolve的分析与研究

系统地研究了高性能计算的网格应用中间件NetSolve系统的结构和工作原理，并对服务器端进行了深入探讨，指出了现有实现存在的缺陷，对改进方案进行了分析。     

Body fluid proteomics: Prospects for biomarker discovery

Many diseases are caused by perturbations of cellular signaling pathways and related pathway networks as a result of genetic aberrations. These perturbations are manifested by altered cellular protein profiles in the fluids bathing tissue/organs (i.e., the tissue interstitial fluid, TIF). A major challenge of clinical chemistry is to quantitatively map these perturbed protein profiles - the so-called "signatures of disease" - using modern proteomic technologies. This information can be utilized to design protein biomarkers for the early detection of disease, monitoring disease progression and efficacy of drug action. Here, we discuss the use of body fluids in the context of prospective biomarker discovery, and the marked 1000-1500-fold dilution of body fluid proteins, during their passage from TIF to the circulatory system. Further, we discuss proteomics strategies aimed at depleting major serum proteins, especially albumin, in order to focus on low-abundance protein/peptides in plasma. A major limitation of depletion strategies is the removal of low-molecular weight protein/peptides which specifically bind major plasma proteins. We present a prototype model, using albumin, for understanding the multifaceted nature of biomarker research, highlighting the involvement of albumin in Alzheimer's disease. This model underscores the need for a system-level understanding for biomarker research and personalized medicine.     

McEliece公钥密码体制中问题的分析研究

使用线性分组码用于构造M公钥体制本质上就是使用线性分组码构造M公钥体制的安全.研究了其它线性分组码用于构造M公钥体制的可行性;分析了M公钥体制中G、S、P是保密的,实现随机选取G、S、P成为了建立M公钥体制的关键;分析了满秩矩阵S和置换矩阵P的随机产生问题,并得到了一些重要结果;这些结果不仅对M公钥体制是适用的,而且对其它纠错码体制和方案也同样是有用的.     

网络坦克作战系统基于MAS的决策过程分析与研究

网络坦克作战系统的决策过程是一个群组决策过程.系统决策过程实质上是各组成部分协作进行问题求解的过程.基于多Agent系统(MAS)的网络坦克作战系统决策是系统中多个Agent面向作战任务进行协作问题求解的过程.运用MAS技术对系统的决策过程进行了分析,提出了系统基于MAS的群组决策基本框架,给出了分布式的系统决策结构,并对决策过程进行了设计和详细分析.MAS协作规划有利于系统的群组决策.     

非时隙CSMA/CA性能分析与研究

通过对基于IEEE802.15.4的无线传感网络的非时隙CSMA/CA的算法进行分析,建立了适合于大部分无线传感网络实际应用的具有单步转移概率分布的离散马尔可夫链模型。并给出了在该模型下数据包延时、退避次数两个主要的网络性能评价指标的计算公式。最后通过仿真实验分析了CSMA/CA 算法的三个网络参数对系统性能的影响。     

网格性能评价问题分析与探讨

网格作为一种新兴的计算平台,其复杂程度远远超出了传统的分布式系统。因此,对其进行性能评价也变得更为复杂,面临许多新的困难。为了对网格性能评价有一个全面深刻的认识,通过分析网格与分布式系统的根本区别,并指出影响其性能的关键因素以及对其进行性能评价的特殊要求,从而明确了网格性能评价需要解决的新问题,并初步探讨了可能的解决方向或方案。     

Proteome analysis of body fluids for amyotrophic lateral sclerosis biomarker discovery

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder of motor neurons leading to death of the patients, mostly within 2–5 years after disease onset. The pathomechanism of motor neuron degeneration is only partially understood and therapeutic strategies based on mechanistic insights are largely ineffective. The discovery of reliable biomarkers of disease diagnosis and progression is the sine qua non of both the revelation of insights into the ALS pathomechanism and the assessment of treatment efficacies. Proteomic approaches are an important pillar in ALS biomarker discovery. Cerebrospinal fluid is the most promising body fluid for differential proteome analyses, followed by blood (serum, plasma), and even urine and saliva. The present study provides an overview about reported peptide/protein biomarker candidates that showed significantly altered levels in certain body fluids of ALS patients. These findings have to be discussed according to proposed pathomechanisms to identify modifiers of disease progression and to pave the way for the development of potential therapeutic strategies. Furthermore, limitations and advantages of proteomic approaches for ALS biomarker discovery in different body fluids and reliable validation of biomarker candidates have been addressed.     

Development of a model-based clinical sepsis biomarker for critically ill patients

Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48 h. Insulin sensitivity (S_I) is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to accurately identify insulin sensitivity in real-time.Hourly model-based insulin sensitivity S_I values were calculated from glycemic control data of 36 patients with sepsis. The hourly S_I is compared to the hourly sepsis score (ss) for these patients (ss = 0-4 for increasing severity). A multivariate clinical biomarker was also developed to maximize the discrimination between different ss groups. Receiver operator characteristic (ROC) curves for severe sepsis (ss ≥ 2) are created for both S_I and the multivariate clinical biomarker.Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% positive predictive value (PPV), and 98.3% negative predictive value (NPV) at an S_I cut-off value of 0.00013 L/mU/min. Multivariate clinical biomarker combining S_I, temperature, heart rate, respiratory rate, blood pressure, and their respective hourly rates of change achieves 73% sensitivity, 80% specificity, 8.4% PPV, and 99.2% NPV. Thus, the multivariate clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis. Examination of both inter- and intra-patient statistical distribution of this biomarker and sepsis score shows potential avenues to improve the positive predictive value.     

A nature-inspired biomarker for mental concentration using a single-channel EEG

Neural Computing and Applications - We developed a system for measuring the attentional process during the performance of specific activities. The proposed biomarker device is able to estimate the...     

面向方面编程的分析与研究

深入地分析了传统面向对象编程方式在项目开发中出现的问题,明确指出了面向对象建模技术的内在局限性,提出了一种新型的,实用的、面向方面的编程方式.在介绍面向方面编程(AOP)原理和思想的基础上深入分析了AOP的实质.详细讨论了采用AOP进行具体开发的步骤和实现过程.通过几个静态指标详细分析和评测了由AOP开发的一个项目实例,体现了采用AOP进行软件开发的强大优势.     

用于检测双星故障的RAIM算法分析与研究*

随着GPS等卫星导航技术的广泛应用,在实际导航中对接收机的导航定位精度,尤其对定位的有效性提出了越来越高的要求。目前对此较为有效的方法就是利用接收机自主完好性监视(RAIM, Receiver Autonomous Integrity Monitoring)进行保障,而且该方法日益凸显出其优越性,但目前对该方法的研究多基于单星故障的假设。随着新一代导航系统的完善以及组合导航技术的发展,双星甚至多星同时出现故障成为不可避免的话题。本文分析和比较了用于检测两颗卫星故障的偏差完好性风险(BIT, Bias Integrity Threat)法和保护限值(PL, Protection Level)法两种RAIM算法。通过数据仿真,对用不同方法检测卫星故障的可用性及故障检测率进行比较,并分析了两种方法存在差异的理论依据。通过分析比较,说明在未来接收机处理多颗卫星同时出现故障的情况时,如何更精确地描述统计检测量与定位偏差的关系以及如何处理不同测量噪声的影响是RAIM算法可用性及有效性的关键。