75 research outputs found

    Barnase as a New Therapeutic Agent Triggering Apoptosis in Human Cancer Cells

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    RNases are currently studied as non-mutagenic alternatives to the harmful DNA-damaging anticancer drugs commonly used in clinical practice. Many mammalian RNases are not potent toxins due to the strong inhibition by ribonuclease inhibitor (RI) presented in the cytoplasm of mammalian cells.In search of new effective anticancer RNases we studied the effects of barnase, a ribonuclease from Bacillus amyloliquefaciens, on human cancer cells. We found that barnase is resistant to RI. In MTT cell viability assay, barnase was cytotoxic to human carcinoma cell lines with half-inhibitory concentrations (IC(50)) ranging from 0.2 to 13 microM and to leukemia cell lines with IC(50) values ranging from 2.4 to 82 microM. Also, we characterized the cytotoxic effects of barnase-based immunoRNase scFv 4D5-dibarnase, which consists of two barnase molecules serially fused to the single-chain variable fragment (scFv) of humanized antibody 4D5 that recognizes the extracellular domain of cancer marker HER2. The scFv 4D5-dibarnase specifically bound to HER2-positive cells and was internalized via receptor-mediated endocytosis. The intracellular localization of internalized scFv 4D5-dibarnase was determined by electronic microscopy. The cytotoxic effect of scFv 4D5-dibarnase on HER2-positive human ovarian carcinoma SKOV-3 cells (IC(50) = 1.8 nM) was three orders of magnitude greater than that of barnase alone. Both barnase and scFv 4D5-dibarnase induced apoptosis in SKOV-3 cells accompanied by internucleosomal chromatin fragmentation, membrane blebbing, the appearance of phosphatidylserine on the outer leaflet of the plasma membrane, and the activation of caspase-3.These results demonstrate that barnase is a potent toxic agent for targeting to cancer cells

    Behavioral Corporate Finance: An Updated Survey

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    Dynamic Susceptibility Contrast-MRI Quantification Software Tool: Development and Evaluation

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    Relative cerebral blood volume (rCBV) is a magnetic resonance imaging biomarker that is used to differentiate progression from pseudoprogression in patients with glioblastoma multiforme, the most common primary brain tumor. However, calculated rCBV depends considerably on the software used. Automating all steps required for rCBV calculation is important, as user interaction can lead to increased variability and possible inaccuracies in clinical decision-making. Here, we present an automated tool for computing rCBV from dynamic susceptibility contrast-magnetic resonance imaging that includes leakage correction. The entrance and exit bolus time points are automatically calculated using wavelet-based detection. The proposed tool is compared with 3 Food and Drug Administration-approved software packages, 1 automatic and 2 requiring user interaction, on a data set of 43 patients. We also evaluate manual and automated white matter (WM) selection for normalization of the cerebral blood volume maps. Our system showed good agreement with 2 of the 3 software packages. The intraclass correlation coefficient for all comparisons between the same software operated by different people was >0.880, except for FuncTool when operated by user 1 versus user 2. Little variability in agreement between software tools was observed when using different WM selection techniques. Our algorithm for automatic rCBV calculation with leakage correction and automated WM selection agrees well with 2 out of the 3 FDA-approved software packages

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