Application of proteomic technologies to discover and identify biomarkers for periodontal diseases in gingival crevicular fluid: A review

Periodontal disease is a bacterial infection that destroys the gingiva and surrounding tissues of the oral cavity. In recent years, studies have shown a definite association between periodontal disease and other inflammatory conditions of the body. High-throughput analysis of proteins has become possible with the development of MS technology. This breakthrough in proteome technology enables comparative studies of comprehensive protein expression and identification of protein. In case of periodontal disease, proteome analysis using 2DE, as well as gel-free methods, has been reported. As a fluid lying in close proximity to periodontal tissue, the gingival crevicular fluid (GCF) is the principal target in the search for biomarkers of periodontal disease, because its protein composition may reflect the disease pathophysiology. Biochemical marker analysis of GCF is effective for objective diagnosis in the early and advanced stages of periodontal disease. Increasing numbers of recent reports have provided evidence that the proteomic approach is a promising tool for the discovery and identification of biochemical markers of periodontal disease. This search is of continuing interest in the field of experimental and clinical periodontal disease research. In this article, we summarize recent comprehensive proteomic studies aimed at discovering and identifying biomarkers of periodontal disease in GCF.     

Proteomics in human cancer research

Proteomics is now widely employed in the study of cancer. Many laboratories are applying the rapidly emerging technologies to elucidate the underlying mechanisms associated with cancer development, progression, and severity in addition to developing drugs and identifying patients who will benefit most from molecular targeted compounds. Various proteomic approaches are now available for protein separation and identification, and for characterization of the function and structure of candidate proteins. In spite of significant challenges that still exist, proteomics has rapidly expanded to include the discovery of novel biomarkers for early detection, diagnosis and prognostication (clinical application), and for the identification of novel drug targets (pharmaceutical application). To achieve these goals, several innovative technologies including 2-D-difference gel electrophoresis, SELDI, multidimensional protein identification technology, isotope-coded affinity tag, solid-state and suspension protein array technologies, X-ray crystallography, NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulation have evolved, and are being used in different combinations. This review provides an overview of the field of proteomics and discusses the key proteomic technologies available to researchers. It also describes some of the important challenges and highlights the current pharmaceutical and clinical applications of proteomics in human cancer research.     

Quantitative proteomics for identification of cancer biomarkers

Quantitative proteomics can be used for the identification of cancer biomarkers that could be used for early detection, serve as therapeutic targets, or monitor response to treatment. Several quantitative proteomics tools are currently available to study differential expression of proteins in samples ranging from cancer cell lines to tissues to body fluids. 2-DE, which was classically used for proteomic profiling, has been coupled to fluorescence labeling for differential proteomics. Isotope labeling methods such as stable isotope labeling with amino acids in cell culture (SILAC), isotope-coded affinity tagging (ICAT), isobaric tags for relative and absolute quantitation (iTRAQ), and (18) O labeling have all been used in quantitative approaches for identification of cancer biomarkers. In addition, heavy isotope labeled peptides can be used to obtain absolute quantitative data. Most recently, label-free methods for quantitative proteomics, which have the potential of replacing isotope-labeling strategies, are becoming popular. Other emerging technologies such as protein microarrays have the potential for providing additional opportunities for biomarker identification. This review highlights commonly used methods for quantitative proteomic analysis and their advantages and limitations for cancer biomarker analysis.     

Proteomic analysis of multiple myeloma: current status and future perspectives

Multiple myeloma (MM) is a malignant plasma cell neoplasm that accounts for slightly more than 10% of all hematologic cancers and remains incurable. The major challenge remains the identification of better diagnosis and prognostic biomarkers. The advent of proteomic technologies creates new opportunities and challenges for those seeking to gain greater understanding of MM. Although there is a limited number of proteomic studies to date in MM, those performed highlight the potential impact of these technologies in our understanding of MM pathogenesis and the identification of novel therapeutic targets. In this review, we introduce the proteomic technologies available for the study of MM, summarize results of the published proteomic studies on MM, and discuss the novel developments and applications for the analysis of protein PTM in MM. The application of proteomic technologies will be valuable to better understand the pathogenesis of MM and may in the future open novel avenues in the treatment of MM.     

On the uniqueness of prediction error models for systems with noisy input-output data

This paper addresses the uniqueness problem of the prediction error (PE) identification for a class of linear systems with noisy input and output data. Necessary and sufficient conditions are derived for the corresponding PE loss function to have (asymptotically) a unique global minimum. The results indicate that a PE algorithm may give very bad parameter estimates for systems not satisfying these conditions. Such a possibility is illustrated by a numerical example. While the PE method is used as a vehicle for illustration, the derived conditions for global uniqueness (or identifiability) apply to any consistent estimation method based on second-order data.     

Proteomic profiling of 13 paired ductal infiltrating breast carcinomas and non-tumoral adjacent counterparts

According to recent statistics, breast cancer remains one of the leading causes of death among women in Western countries. Breast cancer is a complex and heterogeneous disease, presently classified into several subtypes according to their cellular origin. Among breast cancer histotypes, infiltrating ductal carcinoma represents the most common and potentially aggressive form. Despite the current progress achieved in early cancer detection and treatment, including the new generation of molecular therapies, there is still need for identification of multiparametric biomarkers capable of discriminating between cancer subtypes and predicting cancer progression for personalized therapies. One established step in this direction is the proteomic strategy, expected to provide enough information on breast cancer profiling. To this aim, in the present study we analyzed 13 breast cancer tissues and their matched non-tumoral tissues by 2-DE. Collectively, we identified 51 protein spots, corresponding to 34 differentially expressed proteins, which may represent promising candidate biomarkers for molecular-based diagnosis of breast cancer and for pattern discovery. The relevance of these proteins as factors contributing to breast carcinogenesis is discussed.     

Proteomics in chronic kidney disease: The issues clinical nephrologists need an answer for

A growing number of patients are recognised to have chronic kidney disease (CKD). However, only a minority will progress to end-stage renal disease requiring dialysis or transplantation. Currently available diagnostic and staging tools frequently fail to identify those at higher risk of progression or death. Furthermore within specific disease entities there are shortcomings in the prediction of the need for therapeutic interventions or the response to different forms of therapy. Kidney and urine proteomic biomarkers are considered as promising diagnostic tools to predict CKD progression early in diabetic nephropathy, facilitating timely and selective intervention that may reduce the related health-care expenditures. However, independent groups have not validated these findings and the technique is not currently available for routine clinical care. Furthermore, there are gaps in our understanding of predictors of progression or need for therapy in non-diabetic CKD. Presumably, a combination of tissue and urine biomarkers will be more informative than individual markers. This review identifies clinical questions in need of an answer, summarises current information on proteomic biomarkers and CKD, and describes the European Kidney and Urine Proteomics initiative that has been launched to carry out a clinical study aimed at identifying urinary proteomic biomarkers distinguishing between fast and slow progressors among patients with biopsy-proven primary glomerulopathies.     

基于数据挖掘技术的加壳PE程序识别方法

恶意代码大量快速的繁衍使得恶意代码自动化检测成为必然趋势,加壳程序识别是恶意代码分析的一个必要步骤。为识别加壳可执行程序,提出一种基于数据挖掘技术的自动化加壳程序识别方法,该方法提取和选取可移植可执行(PE)特征,使用分类算法检测PE文件是否加壳。测试结果表明,在使用J48分类器时加壳文件识别率为98.7%。     

On persistent excitations for the identification of switched linear dynamical systems over finite fields

This paper discusses the issue of the Persistent Excitation (PE) conditions in the context of identification for dynamical systems defined over a finite field. The work is motivated by the fact that the asymptotical property of the PE conditions for dynamical systems defined over the field of real numbers is no longer valid in the case of systems defined over finite fields. The special class of switched linear discrete-time systems for which the mode is assumed to be unknown is considered. A necessary and sufficient condition that provides the minimum amount of data required for the identification is first proposed. Next, a necessary condition is derived that gives the structural condition the system must satisfy, regardless of the availability of data. Finally, some computational aspects are discussed and examples are given to illustrate the validity of the proposed results.     

SEREX, Proteomex, AMIDA, and beyond: Serological screening technologies for target identification

Despite the great body of knowledge about the aetiology, pathogenesis, risk factors, and associated molecular processes, cancer remains a prime health concern. Over the past decades scientific and medical research focused on the identification of biomarkers and target molecules for the diagnosis and therapy of cancer. Such markers may allow for improved and early diagnosis, as well as for immunotherapeutic approaches for cancer treatment. A plethora of technologies dedicated to the identification of target molecules was developed including those relying on a humoral response against tumour-associated antigens (TAA) in diseased individuals. As for other diseases, cancers elicit immune responses that result in the induction of T and B lymphocytes specific for tumour-associated proteins, largely self-antigens, but also those comprising viral and bacterial proteins. Cancer-specific serum antibodies are of great use for the isolation and subsequent identification of their cognate antigens. The present review will concentrate on three major serological target identification methods, i.e. SEREX, Proteomex, and AMIDA, concluding with a summary of the milestones in the clinical advancement and applications of serological TAA.     

Nitric oxide-mediated protein modification in cardiovascular physiology and pathology

Nitric oxide (NO) is a key regulator of cardiovascular functions including the control of vascular tone, anti-inflammatory properties of the endothelium, cardiac contractility, and thrombocyte activation and aggregation. Numerous experimental data support the view that NO not only acts via cyclic guanosine monophosphate (cGMP)-dependent mechanisms but also modulates protein function by nitrosation, nitrosylation, glutathiolation, and nitration, respectively. To understand how NO regulates all of these diverse biological processes on the molecular level a comprehensive assessment of NO-mediated cGMP-dependent and independent targets is required. Novel proteomic approaches allow the simultaneous identification of large quantities of proteins modified in an NO-dependent manner and thereby will considerably deepen our understanding of the role NO plays in cardiovascular physiology and pathophysiology.     

On the persistency of excitation in radial basis function networkidentification of nonlinear systems

Considers radial basis function (RBF) network approximation of a multivariate nonlinear mapping as a linear parametric regression problem. Linear recursive identification algorithms applied to this problem are known to converge, provided the regressor vector sequence has the persistency of excitation (PE) property. The main contribution of this paper is formulation and proof of PE conditions on the input variables. In the RBF network identification, the regressor vector is a nonlinear function of these input variables. According to the formulated condition, the inputs provide PE, if they belong to domains around the network node centers. For a two-input network with Gaussian RBF that have typical width and are centered on a regular mesh, these domains cover about 25% of the input domain volume. The authors further generalize the proposed solution of the standard RBF network identification problem and study affine RBF network identification that is important for affine nonlinear system control. For the affine RBF network, the author formulates and proves a PE condition on both the system state parameters and control inputs.     

Dynamic proteome in enigmatic preeclampsia: an account of molecular mechanisms and biomarker discovery

The coevolution of genomics and proteomics has led to advancements in the field of diagnosis and molecular mechanisms of disease. Proteomics is now stepping into the field of obstetrics, where early diagnosis of pregnancy complication such as preeclampsia (PE) is imperative. PE is a multifactorial disease characterized by hypertension with proteinuria, which is a leading cause of maternal and neonatal morbidity and mortality occurring in 5-7% of pregnancies worldwide. This review discusses the probable molecular mechanisms that lead to PE and summarizes the proteomics research carried out in understanding the pathogenicity of PE, and for identifying the candidate biomarker for diagnosis of the disease.     

On parameter convergence in least squares identification and adaptive control

Least squares estimation is appealing in performance and robustness improvements of adaptive control. A strict condition termed persistent excitation (PE) needs to be satisfied to achieve parameter convergence in least squares estimation. This paper proposes a least squares identification and adaptive control strategy to achieve parameter convergence without the PE condition. A modified modeling error that utilizes online historical data together with instant data is constructed as additional feedback to update parameter estimates, and an integral transformation is introduced to avoid the time derivation of plant states in the modified modeling error. On the basis of these results, a regressor filtering–free least squares estimation law is proposed to guarantee exponential parameter convergence by an interval excitation condition, which is much weaker than the PE condition. And then, an identification‐based indirect adaptive control law is proposed to establish exponential stability of the closed‐loop system under the interval excitation condition. Illustrative results considering both identification and control problems have verified the effectiveness and superiority of the proposed approach.     

Toward a better understanding of preeclampsia: Comparative proteomic analysis of preeclamptic placentas

Preeclampsia (PE), a pregnancy-specific syndrome of hypertension, proteinuria, and other systemic disturbances, is a state of widespread endothelial dysfunction secondary to defective placentation. Morphologically, the current data displayed degenerative and apoptotic changes in the mitochondria and villous trophoblasts of preeclamptic placenta. To reveal the superimposing alterations in placental proteins that might explain the pathophysiology of PE, we performed 2-DE MALDI-TOF MS/MS proteomics analysis of differentially expressed placental proteins with placenta from eight normal and eight preeclamptic pregnancies. The identified proteins were confirmed by Western blot analysis. We also performed morphologic evaluation of preeclamptic placentas under both electron and light microscopy. The results disclosed the marked overexpression of chaperonin 60, GST, VDAC, ERp29, and cathepsin D in PE. These proteomics findings clearly suggest the possible cellular battle against mitochondria-originated oxidative stress during PE that either end up with recovery or apoptosis. These results provide a better understanding of proteomic alterations and may help in clarification of stress-related changes in preeclamptic placentas.     

Challenges of EDI adoption for electronic trading in the London Insurance Market

This paper describes an in-depth case study, carried out over the period 1993–1997, of the attempts made to introduce an EDI service, LIMNET EPS, to support the placement of risk in the London Insurance Market. The research approach adopts a process-based methodology and develops a theoretical basis on the cultural assumptions of technology which draws on technological frames and structural culture. The main body of the paper then describes and analyses the initiation, development and adoption challenges of the LIMNET EPS service. It is concluded that there are significant organizational and social issues which need to be addressed in the adoption of EPS across this market. Specifically, our analysis suggests that the low levels of EPS adoption are related to incongruences in the cultural assumptions held by key market participants in three domains: the nature of technological change, the nature of business transaction, and the importance of market institutions. Finally, we believe our theoretical basis may be valuable in research and practice to assist in the early identification of new evolving forms of electronic commerce.     

Uniqueness of prediction error estimates of multivariable moving average models

In prediction error (PE) identification of the parameter estimates is given by the global minimum of a scalar-valued function of the innovation sample covariance matrix. It may happen that the loss function has multiple local minimum points so that a numerical search routine can fail to find the global minimum. Such a situation, usually referred to as lack of uniqueness of the estimates, was experienced in practice and also theoretically examined for various model structures. A unique minimum of the criterion is also crucial for convergence of recursive PE algorithms. In this paper multivariable moving average (MA) models are considered. It is proved that for such models any reasonable PE criterion has asymptotically a unique stationary point. Furthermore it is shown that this stationary point is a (global) minimum which corresponds to the true parameter vector. This extends the result known for univariate MA models to the multivariate case.     

A calibration method for odometry of mobile robots based on the least-squares technique: theory and experimental validation

For a mobile robot, odometry calibration consists of the identification of a set of kinematic parameters that allow reconstructing the vehicle's absolute position and orientation starting from the wheels' encoder measurements. This paper develops a systematic method for odometry calibration of differential-drive mobile robots. As a first step, the kinematic equations are written so as to underline linearity in a suitable set of unknown parameters; thus, the least-squares method can be applied to estimate them. A major advantage of the adopted formulation is that it provides a quantitative measure of the optimality of a test motion; this can be exploited to drive guidelines on the choice of the test trajectories and to evaluate accuracy of a solution. The proposed technique has been experimentally validated on two different mobile robots and, in one case, compared with other existing approaches; the obtained results confirm the effectiveness of the proposed calibration method.