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D Yaffe

Publications and source records attributed to D Yaffe.

79 records · Page 5Linked to original sources
PubMedDate not supplied

Hepatic infarction in preeclampsia as part of the HELLP syndrome: CT appearance.

We describe the computed tomographic (CT) findings of hepatic infarctions in two preeclamptic pregnant women. These infarcts were part of the HELLP syndrome (hemolysis, elevated liver function tests, and low platelets count). In both cases, CT disclosed features characteristic of multiple nonenhancing, low-attenuation, peripheral lesions with vessels coursing through and a mottled appearance. The recognition of such CT findings in liver disease associated with preeclampsia can establish the correct diagnosis.

Adult↗
PubMedDate not supplied

Neural network based temperature-dependent quantitative structure property relations (QSPRs) for predicting vapor pressure of hydrocarbons.

A neural network based quantitative structure-property relationship (QSPR) was developed for the vapor pressure-temperature behavior of hydrocarbons based on a data set for 274 compounds. The optimal QSPR model was developed based on a 7-29-1 back-propagation neural network architecture using valance molecular connectivity indices (1chi(v), 3chi(v), 4chi(v)), molecular weight, and temperature as input parameters. The average absolute errors in vapor pressure predictions for the test, validation, and overall data sets were 8.2% (0.036 log P units or 23.2 kPA), 9.2% (0.039 log P units or 26.8 kPA), and 10.7% (0.046 log P units or 31.1 kPA), respectively. The performance of the QSPR for temperature-dependent vapor pressure, which was developed from a simple set of molecular descriptors, displayed accuracy of better than or well within the range of other available estimation methods.

Journal Article↗
PubMedDate not supplied

A fuzzy ARTMAP based on quantitative structure-property relationships (QSPRs) for predicting aqueous solubility of organic compounds.

Quantitative structure-property relationships (QSPRs) for estimating aqueous solubility of organic compounds at 25 degrees C were developed based on a fuzzy ARTMAP and a back-propagation neural networks using a heterogeneous set of 515 organic compounds. A set of molecular descriptors, developed from PM3 semiempirical MO-theory and topological descriptors (first-, second-, third-, and fourth-order molecular connectivity indices), were used as input parameters to the neural networks. Quantum chemical input descriptors included average polarizability, dipole moment, resonance energy, exchange energy, electron-nuclear attraction energy, and nuclear-nuclear (core-core) repulsion energy. The fuzzy ARTMAP/QSPR correlated aqueous solubility (S, mol/L) for a range of -11.62 to 4.31 logS with average absolute errors of 0.02 and 0.14 logS units for the overall and validation data sets, respectively. The optimal 11-13-1 back-propagation/QSPR model was less accurate, for the same solubility range, and exhibited larger average absolute errors of 0.29 and 0.28 logS units for the overall and validation sets, respectively. The fuzzy ARTMAP-based QSPR approach was shown to be superior to other back-propagation and multiple linear regression/QSPR models for aqueous solubility of organic compounds.

Journal Article↗