Oncocytic tumors are a distinctive class of proliferative lesions composed of cells with a striking degree of mitochondrial hyperplasia that are particularly frequent in the thyroid gland. To understand whether specific mitochondrial DNA (mtDNA) mutations are associated with the accumulation of mitochondria, we sequenced the entire mtDNA in 50 oncocytic lesions (45 thyroid tumors of epithelial cell derivation and 5 mitochondrion-rich breast tumors) and 52 control cases (21 nononcocytic thyroid tumors, 15 breast carcinomas, and 16 gliomas) by using recently developed technology that allows specific and reliable amplification of the whole mtDNA with quick mutation scanning. Thirteen oncocytic lesions (26%) presented disruptive mutations (nonsense or frameshift), whereas only two samples (3.8%) presented such mutations in the nononcocytic control group. In one case with multiple thyroid nodules analyzed separately, a disruptive mutation was found in the only nodule with oncocytic features. In one of the five mitochondrion-rich breast tumors, a disruptive mutation was identified. All disruptive mutations were found in complex I subunit genes, and the association between these mutations and the oncocytic phenotype was statistically significant (P ‫؍‬ 0.001). To study the pathogenicity of these mitochondrial mutations, primary cultures from oncocytic tumors and corresponding normal tissues were established. Electron microscopy and biochemical and molecular analyses showed that primary cultures derived from tumors bearing disruptive mutations failed to maintain the mutations and the oncocytic phenotype. We conclude that disruptive mutations in complex I subunits are markers of thyroid oncocytic tumors.oncocytic tumors ͉ heteroplasmy ͉ homoplasmy ͉ damaging mutation ͉ microenvironment

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A method is proposed for solving the two key problems facing quantum neural networks: introduction of nonlinearity in the neuron operation and efficient use of quantum superposition in the learning algorithm. The former is indirectly solved by using suitable Boolean functions. The latter is based on the use of a suitable nonlinear quantum circuit. The resulting learning procedure does not apply any optimization method. The optimal neural network is obtained by applying an exhaustive search among all the possible solutions. The exhaustive search is carried out by the proposed quantum circuit composed of both linear and nonlinear components. © 2009 John Wiley & Sons, Ltd

TC is active in AGC with a high response rate in a multicentric trial. Despite its hematotoxicity, this regimen is well tolerated and can be recycled as originally planned in 78% of the cases. These results may serve as basis for further developments of docetaxel containing regimens in this disease.