958 resultados para Sections verticales
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Mode of access: Internet.
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This article reviews methods for quantifying the abundance of histological features in thin tissue sections of brain such as neurons, glia, blood vessels, and pathological lesions. The sampling methods by which quantitative measures can be obtained are described. In addition, methods are described for determining the spatial pattern of an object and for measuring the degree of spatial correlation between two or more histological features.
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The development of abnormal protein aggregates in the form of extracellular plaques and intracellular inclusions is a characteristic feature of many neurodegenerative diseases such as Alzheimer's disease (AD), Creutzfeldt-Jakob disease (CJD) and the fronto-temporal dementias (FTD). An important aspect of a pathological protein aggregate is its spatial topography in the tissue. Lesions may not be randomly distributed within a histological section but exhibit spatial pattern, a departure from randomness either towards regularity or clustering. Information on the spatial pattern of a lesion may be useful in elucidating its pathogenesis and in studying the relationships between different lesions. This article reviews the methods that have been used to study the spatial topography of lesions. These include simple tests of whether the distribution of a lesion departs significantly from random using randomized points or sample fields, and more complex methods that employ grids or transects of contiguous fields and which can detect the intensity of aggregation and the sizes, distribution and spacing of the clusters. The usefulness of these methods in elucidating the pathogenesis of protein aggregates in neurodegenerative disease is discussed.
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This article reviews the statistical methods that have been used to study the planar distribution, and especially clustering, of objects in histological sections of brain tissue. The objective of these studies is usually quantitative description, comparison between patients or correlation between histological features. Objects of interest such as neurones, glial cells, blood vessels or pathological features such as protein deposits appear as sectional profiles in a two-dimensional section. These objects may not be randomly distributed within the section but exhibit a spatial pattern, a departure from randomness either towards regularity or clustering. The methods described include simple tests of whether the planar distribution of a histological feature departs significantly from randomness using randomized points, lines or sample fields and more complex methods that employ grids or transects of contiguous fields, and which can detect the intensity of aggregation and the sizes, distribution and spacing of clusters. The usefulness of these methods in understanding the pathogenesis of neurodegenerative diseases such as Alzheimer's disease and Creutzfeldt-Jakob disease is discussed. © 2006 The Royal Microscopical Society.
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Histological features visible in thin sections of brain tissue, such as neuronal perikarya, blood vessels, or pathological lesions may exhibit a degree of spatial association or correlation. In neurodegenerative disorders such as AD, Pick's disease, and CJD, information on whether different types of pathological lesion are spatially correlated may be useful in elucidating disease pathogenesis. In the present article the statistical methods available for studying spatial association in histological sections are reviewed. These include tests of interspecific association between two or more histological features using χ2 contingency tables, measurement of 'complete' and 'absolute' association, and more complex methods that use grids of contiguous samples. In addition, the use of correlation matrices and stepwise multiple regression methods are described. The advantages and limitations of each method are reviewed and possible future developments discussed.
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The use of MS imaging (MSI) to resolve the spatial and pharmacodynamic distributions of compounds in tissues is emerging as a powerful tool for pharmacological research. Unlike established imaging techniques, only limited a priori knowledge is required and no extensive manipulation (e.g., radiolabeling) of drugs is necessary prior to dosing. MS provides highly multiplexed detection, making it possible to identify compounds, their metabolites and other changes in biomolecular abundances directly off tissue sections in a single pass. This can be employed to obtain near cellular, or potentially subcellular, resolution images. Consideration of technical limitations that affect the process is required, from sample preparation through to analyte ionization and detection. The techniques have only recently been adapted for imaging and novel variations to the established MSI methodologies will further enhance the application of MSI for pharmacological research.
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AIMS: To investigate multiple techniques for the preparation of solid tissue for polymerase chain reaction (PCR) analysis, and to identify the most simple techniques for routine use in the laboratory. METHODS: Techniques for the preparation of arterial tissue samples including homogenisation, ultrafiltration, and treatments involving proteinase K, Gene Clean, lectin, and Fe3+ specific chelators were evaluated using the PCR to amplify both Chlamydia pneumoniae and human DNA. RESULTS: Treatment with either Gene-Clean or lectin and the Fe3+ specific chelator deferoxamine mesylate removed PCR inhibitors from tissue homogenates. Homogenisation followed by GeneClean treatment resulted in the amplification of C pneumoniae DNA from within a section of atherosclerotic carotid artery, implying that C pneumoniae elementary bodies had been disrupted. In eight further clinical samples from patients not known to have C pneumoniae infection, human DNA was amplified and no cross contamination was observed between samples. These samples contained no evidence of C pneumoniae by PCR. CONCLUSIONS: A simple preparation of solid tissue for PCR analysis, involving homogenisation followed by GeneClean treatment has been developed, and is effective for the amplification of both C pneumoniae and human DNA.
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A method of determining the spatial pattern of any histological feature in sections of brain tissue which can be measured quantitatively is described and compared with a previously described method. A measurement of a histological feature such as density, area, amount or load is obtained for a series of contiguous sample fields. The regression coefficient (β) is calculated from the measurements taken in pairs, first in pairs of adjacent samples and then in pairs of samples taken at increasing degrees of separation between them, i.e. separated by 2, 3, 4,..., n units. A plot of β versus the degree of separation between the pairs of sample fields reveals whether the histological feature is distributed randomly, uniformly or in clusters. If the feature is clustered, the analysis determines whether the clusters are randomly or regularly distributed, the mean size of the clusters and the spacing of the clusters. The method is simple to apply and interpret and is illustrated using simulated data and studies of the spatial patterns of blood vessels in the cerebral cortex of normal brain, the degree of vacuolation of the cortex in patients with Creutzfeldt-Jacob disease (CJD) and the characteristic lesions present in Alzheimer's disease (AD). Copyright (C) 2000 Elsevier Science B.V.
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A method is described which enables the spatial pattern of discrete objects in histological sections of brain tissue to be determined. The method can be applied to cell bodies, sections of blood vessels or the characteristic lesions which develop in the brain of patients with neurodegenerative disorders. The density of the histological feature under study is measured in a series of contiguous sample fields arranged in a grid or transect. Data from adjacent sample fields are added together to provide density data for larger field sizes. A plot of the variance/mean ratio (V/M) of the data versus field size reveals whether the objects are distributed randomly, uniformly or in clusters. If the objects are clustered, the analysis determines whether the clusters are randomly or regularly distributed and the mean size of the clusters. In addition, if two different histological features are clustered, the analysis can determine whether their clusters are in phase, out of phase or unrelated to each other. To illustrate the method, the spatial patterns of senile plaques and neurofibrillary tangles were studied in histological sections of brain tissue from patients with Alzheimer's disease.
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Stereology and other image analysis methods have enabled rapid and objective quantitative measurements to be made on histological sections. These mesurements may include total volumes, surfaces, lengths and numbers of cells and blood vessels or pathological lesions. Histological features, however, may not be randomly distributed across a section but exhibit 'dispersion', a departure from randomness either towards regularity or aggregation. Information of population dispersion may be valuable not only in understanding the two-or three-dimensional structure but also in elucidating the pathogenesis of lesions in pathological conditions. This article reviews some of the statistical methods available for studying dispersion. These range from simple tests of whether the distribution of a histological faeture departs significantly from random to more complex methods which can detect the intensity of aggregation and the sizes, distribution and spacing of the clusters.
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The concept of an Expert System (ES) has been acknowledged as a very useful tool, but few studies have been carried out in its application to the design of cold rolled sections. This study involves primarily the use of an ES as a tool to improve the design process and to capture the draughtsman's knowledge. Its main purpose is to reduce substantially the time taken to produce a section drawing, thereby facilitating a speedy feedback to the customer. In order to communicate with a draughtsman, it is necessary to use sketches, symbolic representations and numerical data. This increases the complexity of programming an ES, as it is necessary to use a combination of languages so that decisions, calculations, graphical drawings and control of the system can be effected. A production system approach is used and a further step has been taken by introducing an Activator which is an autoexecute operation set up by the ES to operate an external program automatically. To speed up the absorption of new knowledge into the knowledge base, a new Learning System has been constructed. In addition to developing the ES, other software has been written to assist the design process. The section properties software has been introduced to improve the speed and consistency of calculating the section properties. A method of selecting or comparing the most appropriate section for a given specification is also implemented. Simple loading facilities have been introduced to guide the designer as to the loading capacity of the section. This research has concluded that the application of an ES is beneficial and with the activator approach, automated designing can be achieved. On average a complex drawing can be displayed on the screen in about 100 seconds, where over 95% of the initial section design time for repetitive or similar profile can be saved.