1,720,997 research outputs found
Ultrastructural definition of apoptosis in heart failure
No full textCardiac myocytes die through apoptosis, oncosis, and autophagy. Apoptosis affects single cells and is morphologically characterized by nuclear fragmentation with generation of apoptotic bodies that can be seen either within dying cells or free in the interstitial spaces. Dead myocytes are removed by macrophages through phagocytosis without triggering inflammation. The circulating markers of myocyte necrosis are not increased by apoptosis. The morphologic changes of the induction and early execution phases are seen at electron microscopy while late fragmentation is visible on both light and electron microscopy. Immunoelectron microscopy provides combined functional and structural information showing cytochrome c immuno-labelling release from mitochondria, TUNEL labelling of apoptotic nuclei, annexin V translocation in the outer plasma cell layer. Oncosis is characterized by specific morphologic features that may coexist with apoptosis, especially in ischemic myocardium. Autophagy is a defense process that is associated with significant myocardial damage and necrosis when removal of the lysosomal content is impaired. Morphological features of apoptosis, oncosis, and autophagocytosis may coexist at the same time. Although dead myocytes showing characteristics of autophagy and apoptosis are rarely observed in human decompensated hearts, autophagic vacuoles, and early apoptotic changes may be seen more often in morphologically viable myocytes. Such features may occur in failing hearts of both ischemic and non-ischemic etiology. The shared mode of cardiac myocyte death in failing human hearts of different etiologies suggests that preservation of myocyte integrity may be possible by similar therapeutic strategies
Functional, structural, and genetic mitochondrial abnormalities in myocardial diseases
No full textMyocardial tissue is highly dependent on energy supplied by normal mitochondrial function. Therefore defects of energy production or utilization affect the heart in both syndromic and isolated disorders. Knowledge of the peculiar structural, functional, and genetic characteristics of mitochondria provides the basis for identification and classification of mitochondrial defects as well as for establishment of a diagnostic workup useful for related cardiac disorders. This review is therefore dedicated to the characteristics of normal mitochondria and the pathologic alterations of these organelles in various cardiovascular diseases
BICA and Random Subspace ensembles for DNA microarray-based diagnosis
No full textWe compare two ensemble methods to classify DNA microarray data. The methods use different strategies to face the course of dimensionality plaguing these data. One of them projects data along random coordinates, the other compresses them into independent boolean variables. Both result in random feature extraction
procedures, feeding SVMs as base learners for a majority voting ensemble classifier. The classification capabilities are comparable, degrading on instances that are acknowledged anomalous in the literature
BICA: a Boolean Independent Component Analysis Algorithm
Full text linkWe introduce a procedure for mapping general data records onto Boolean vectors, in the philosophy of ICA procedures. The task is demanded of a neural network with double duty: i) extracting a compressed version of the data in a tight hidden layer of a self-associative multilayer architecture, and ii) mapping it onto Boolean vectors that optimize an entropic target. We prove that the components of these vectors are approximately independent and appreciate their ability to preserve data information in a statistically driven solution of benchmark classification problem
BICA: a Boolean Independent Component Analysis Approach
No full textWe analyze the potentialities of an approach to represent general data records through Boolean vectors in the philosophy of ICA. We envisage these vectors at an intermediate step of a clustering procedure aimed at taking decisions from data. With a “divide et conquer” strategy we first look for a suitable representation of the data and then assign them to clusters. We assume a Boolean coding to be a proper representation of the input of the discrete function computing assignments. We demand the following of this coding: to preserve most information so as to prove appropriate independently of the particular clustering task; to be concise, in order to get understandable assignment rules; and to be sufficiently random, to prime statistical classification methods. In the paper we toss these properties in terms of entropic features and connectionist procedures, whose validation is checked on a series of benchmarks
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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