Proteomics in diagnostic neuropathology.
ABSTRACT In the "postgenome" era, attention has turned to the proteome as a source of complementary diagnostic and prognostic information. Recent advances in imaging mass spectrometry (IMS) uses matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS) to acquire up to 1,000 individual protein signals within the molecular weight range of 2,000 to over 100,000 in specific areas of tissue sections. The systematic investigation of these sections permits creation of specific molecular weight images (ion density maps) for each signal detected. Analysis of these images can reveal a collection of unique protein changes, or a "protein signature", of diagnostic and prognostic value. These signatures may also afford assessment of disease progression and tissue response to treatments. Combined with histology and molecular genetic analyses, new proteomic techniques should refine subclassifications and provide defining information about the pathogenesis of many central and peripheral nervous system diseases.
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ABSTRACT: This chapter examines the problems of dealing with trending type data when there is uncertainty over whether or not we really have unit roots in the data. This uncertainty is practical – for many macroeconomic and financial variables theory does not imply a unit root in the data however unit root tests fail to reject. This means that there may be a unit root or roots close to the unit circle. We first examine the differences between results using stationary predictors and nonstationary or near nonstationary predictors. Unconditionally, the contribution of parameter estimation error to expected loss is of the same order for stationary and nonstationary variables despite the faster convergence of the parameter estimates. However expected losses depend on true parameter values.We then review univariate and multivariate forecasting in a framework where there is uncertainty over the trend. In univariate models we examine trade-offs between estimators in the short and long run. Estimation of parameters for most models dominates imposing a unit root. It is for these models that the effects of nuisance parameters in the models is clearest. For multivariate models we examine forecasting from cointegrating models as well as examine the effects of erroneously assuming cointegration. It is shown that inconclusive theoretical implications arise from the dependence of forecast performance on nuisance parameters. Depending on these nuisance parameters imposing cointegration can be more or less useful for different horizons. The problem of forecasting variables with trending regressors – for example, forecasting stock returns with the dividend–price ratio – is evaluated analytically. The literature on distortion in inference in such models is reviewed. Finally, forecast evaluation for these problems is discussed.Handbook of Economic Forecasting.
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ABSTRACT: The increasing application of proteomic methods to biomedical research is providing us with important new information; it holds particular promise in advancing basic and clinical renal research, but whether proteomics can ever become a routine diagnostic tool in nephrology is still uncertain. Currently, proteomic techniques are used by many groups in the search for “biomarkers” of disease, especially kidney disease, because of the ready availability of urine as an “end-product” of renal function. However, the question as to whether any disease-specific biomarkers exist or can be identified by proteomics is also uncertain. A growing application of proteomics in biomedical research is to understand the mechanism(s) of disease. This brief review is selective; in it we consider examples of proteomic studies of human urine for biomarkers, others that have explored renal physiology, and still others that have begun to probe the proteome of organelles. No single approach is sufficiently comprehensive, and the pooled application of proteomics to renal research will undoubtedly improve our understanding of renal function and enable us to explore in more detail subcellular structures, and to characterize cellular processes at the molecular level. When combined with other techniques in renal research, proteomics, and related analytical methods could prove indispensable in modeling renal function, and perhaps also in diagnosis and management of renal disease.PROTEOMICS - CLINICAL APPLICATIONS 11/2008; 2(12):1564 - 1574. · 1.81 Impact Factor
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ABSTRACT: The use of proteomic approaches in investigating diseases is continuing to expand and has started to provide answers to substantial gaps in our understanding of disease pathogenesis as well as in the development of effective strategies for the early diagnosis and treatment of diseases. Biophysical techniques form a crucial part of the advanced proteomic techniques currently used and include mass spectrometry and protein separation techniques, such as two-dimensional gel electrophoresis and liquid chromatography. The application of biophysical proteomic techniques in the study of disease includes delineation of altered protein expression, not only at the whole-cell or tissue levels, but also in subcellular structures, protein complexes, and biological fluids. These techniques are also being used for the discovery of novel disease biomarkers, exploration of the pathogenesis of diseases, development of new diagnostic methodologies, and identification of new targets for therapeutics. Proteomic techniques also have the potential for accelerating drug development through more effective strategies for evaluating a specific drug’s therapeutic effects and toxicity. This article discusses the application of biophysical proteomic techniques in delineating cardiovascular disease and other diseases, as well as the limitations and future research directions required for these techniques to gain greater acceptance and have a larger impact.Biophysical Reviews 06/2012; 4(2).