RESUMO
Many previous studies have shown reduced glucose uptake in the ischemic brain. In contrast, in a permanent unilateral common carotid artery occlusion (UCCAO) mouse model, our pilot experiments using 18F-fluorodeoxyglucose positron emission tomography (FDG PET) revealed that a subset of mice exhibited conspicuously high uptake of glucose in the ipsilateral hemisphere at 1 week post-occlusion (asymmetric group), whereas other mice showed symmetric uptake in both hemispheres (symmetric group). Thus, we aimed to understand the discrepancy between the two groups. Cerebral blood flow and histological/metabolic changes were analyzed using laser Doppler flowmetry and immunohistochemistry/Western blotting, respectively. Contrary to the increased glucose uptake observed in the ischemic cerebral hemisphere on FDG PET (p<0.001), cerebral blood flow tended to be lower in the asymmetric group than in the symmetric group (right to left ratio [%], 36.4±21.8 vs. 58.0±24.8, p=0.059). Neuronal death was observed only in the ischemic hemisphere of the asymmetric group. In contrast, astrocytes were more activated in the asymmetric group than in the symmetric group (p<0.05). Glucose transporter-1, and monocarboxylate transporter-1 were also upregulated in the asymmetric group, compared with the symmetric group (p<0.05, respectively). These results suggest that the increased FDG uptake was associated with relatively severe ischemia, and glucose transporter-1 upregulation and astrocyte activation. Glucose metabolism may thus be a compensatory mechanism in the moderately severe ischemic brain.
RESUMO
PURPOSE: The aim of this study was to develop a bioinformatics software and to test it in serum samples of papillary thyroid cancer using mass spectrometry (SELDI-TOF-MS). MATERIALS AND METHODS: Development of 'Protein analysis' software performing decision tree analysis was done by customizing C4.5. Sixty-one serum samples from 27 papillary thyroid cancer, 17 autoimmune thyroiditis, 17 controls were applied to 2 types of protein chips, CM10 (weak cation exchange) and IMAC3 (metal binding - Cu). Mass spectrometry was performed to reveal the protein expression profiles. Decision trees were generated using 'Protein analysis' software, and automatically detected biomarker candidates. Validation analysis was performed for CM10 chip by random sampling. RESULTS: Decision tree software, which can perform training and validation from profiling data, was developed. For CM10 and IMAC3 chips, 23 of 113 and 8 of 41 protein peaks were significantly different among 3 groups (p<0.05), respectively. Decision tree correctly classified 3 groups with an error rate of 3.3% for CM10 and 2.0% for IMAC3, and 4 and 7 biomarker candidates were detected respectively. In 2 group comparisons, all cancer samples were correctly discriminated from non-cancer samples (error rate = 0%) for CM10 by single node and for IMAC3 by multiple nodes. Validation results from 5 test sets revealed SELDI-TOF-MS and decision tree correctly differentiated cancers from non-cancers (54/55, 98%), while predictability was moderate in 3 group classification (36/55, 65%). CONCLUSION: Our in-house software was able to successfully build decision trees and detect biomarker candidates, therefore it could be useful for biomarker discovery and clinical follow up of papillary thyroid cancer.