866 research outputs found

    Pseudohyperkalemia in Patients with Chronic Lymphocytic Leukemia

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    Pseudohyperkalemia occurs occasionally in patients with extreme leukocytosis. Increased white blood cell fragility coupled with mechanical stress is felt to be causal. Serum and plasma potassium levels have been both associated with pseudohyperkalemia. Whole blood potassium determination will usually verify the correct diagnosis. It is important to diagnose this condition early so that patients are not inappropriately treated. Two patients with chronic lymphocytic leukemia and extreme leukocytosis are presented, one with pseudohyperkalemia and one with probable pseudohyperkalemia, and diagnostic considerations are discusse

    Use of Hypertonic Continuous Venovenous Hemodiafiltration to Control Intracranial Hypertension in an End-Stage Renal Disease Patient

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    Continuous venovenous hemodiafiltration (CVVHDF) using solutions designed to maintain hypernatremia is described in an end-stage renal disease (ESRD) patient with cerebral edema (CE) due to an intracerebral hemorrhage (ICH). Hypernatremia was readily achieved and maintained without complication. CVVHDF should be considered as an alternative treatment option in ESRD patients with cerebral edema who require hypertonic saline therapy

    Communications Biophysics

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    Contains reports on four research projects.National Institutes of Health (Grant 5 PO1 GM14940-05)National Aeronautics and Space Administration (Grant NGL 22-009-304)National Institutes of Health (Grant 5 TO1 GM01555-05)Boston City Hospital Purchase Order 10656B-D Electrodyne Division, Becton Dickinson and Compan

    Inhibition in multiclass classification

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    The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

    Inhibition in multiclass classification

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    The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

    Transplantation for renal failure secondary to enteric hyperoxaluria: a case report

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    Enteric hyperoxaluria can lead to renal failure. There have only been a few reports of renal transplantation as treatment of endstage renal disease secondary to enteric hyperoxaluria and results have been mixed. This report describes a patient with Crohn's disease who developed chronic renal failure from enteric hyperoxaluria. He subsequently had a successful renal transplant without any post-operative oxalate related complications and has satisfactory renal function almost three years later. Aggressive pre-transplant hemodialysis was not done. The literature associated with renal transplantation for enteric hyperoxaluria is reviewed

    Multiclass classification of microarray data samples with a reduced number of genes

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    <p>Abstract</p> <p>Background</p> <p>Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained.</p> <p>Results</p> <p>A novel bound on the maximum number of genes that can be handled by binary classifiers in binary mediated multiclass classification algorithms of microarray data samples is presented. The bound suggests that high-dimensional binary output domains might favor the existence of accurate and sparse binary mediated multiclass classifiers for microarray data samples.</p> <p>Conclusions</p> <p>A comprehensive experimental work shows that the bound is indeed useful to induce accurate and sparse multiclass classifiers for microarray data samples.</p

    A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli

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    Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production. As a consequence, for any given protein, genetically identical organisms living in a constant environment will contain different amounts of that particular protein, resulting in different phenotypes. This phenomenon is known as “phenotypic noise.” In bacterial systems, previous studies have shown that, for specific genes, both transcriptional and translational processes affect phenotypic noise. Here, we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes, using data for over 60% of all promoters in Escherichia coli. We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise. We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E. coli. In contrast to previous results in eukaryotes, we find no association between promoter-mediated noise and gene expression plasticity. These results are consistent with the hypothesis that, in bacteria, natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alon
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