| 1. |
- Aidas, Kestutis, et al.
(författare)
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Solvent effects on NMR isotropic shielding constants. A comparison between explicit polarizable discrete and continuum approaches
- 2007
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Ingår i: The Journal of Physical Chemistry Part A: Molecules, Spectroscopy, Kinetics, Environment and General Theory. - : American Chemical Society (ACS). - 1520-5215. ; 111:20, s. 4199-4210
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Tidskriftsartikel (refereegranskat)abstract
- The gas-to-aqueous solution shifts of the O-17 and C-13 NMR isotropic shielding constants for the carbonyl chromophore in formaldehyde and acetone are investigated. For the condensed-phase problem, we use the hybrid density functional theory/molecular mechanics approach in combination with a statistical averaging over an appropriate number of solute-solvent configurations extracted from classical molecular dynamics simulations. The PBE0 exchange-correlation functional and the 6-311++G(2d,2p) basis set are used for the calculation of the shielding constants. London atomic orbitals are employed to ensure gauge-origin independent results. The effects of the bulk solvent molecules are found to be crucial in order to calculate accurate solvation shifts of the shielding constants. Very good agreement between the computed and experimental solvation shifts is obtained for the shielding constants of acetone when a polarizable water potential is used. Supermolecular results based on geometry-optimized molecular structures are presented. We also compare the results obtained with the polarizable continuum model to the results obtained using explicit MM molecules to model the bulk solvent effect.
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| 2. |
- Gutte, Henrik, et al.
(författare)
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Automated interpretation of PET/CT images in patients with lung cancer.
- 2007
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Ingår i: Nuclear Medicine Communications. - 1473-5628. ; 28:2, s. 79-84
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To develop a completely automated method based on image processing techniques and artificial neural networks for the interpretation of combined [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) images for the diagnosis and staging of lung cancer. Methods: A total of 87 patients who underwent PET/CT examinations due to suspected lung cancer comprised the training group. The test group consisted of PET/CT images from 49 patients suspected with lung cancer. The consensus interpretations by two experienced physicians were used as the 'gold standard' image interpretation. The training group was used in the development of the automated method. The image processing techniques included algorithms for segmentation of the lungs based on the CT images and detection of lesions in the PET images. Lung boundaries from the CT images were used for localization of lesions in the PET images in the feature extraction process. Eight features from each examination were used as inputs to artificial neural networks trained to classify the images. Thereafter, the performance of the network was evaluated in the test set. Results: The performance of the automated method measured as the area under the receiver operating characteristic curve, was 0.97 in the test group, with an accuracy of 92%. The sensitivity was 86% at a specificity of 100%. Conclusions: A completely automated method using artificial neural networks can be used to detect lung cancer with such a high accuracy that the application as a clinical decision support tool appears to have significant potential.
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