Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0220723 (PCA)
 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The use of the undecapeptide cyclosporine and the macrolide tacrolimus as immunosuppressants in transplantation medicine and for the therapy of immune diseases often provokes side effects, among the most important one is neurotoxicity. Changes in the cellular metabolism of glial cells (C6 rat glioma), neuronal cells (N1E-115 mouse neuroblastoma) and primary glia cells (isolated from rats) after addition of cyclosporine and tacrolimus were investigated using 1H-, 13C- and 31P-NMR spectroscopy in vitro. Cells were exposed to various concentrations of the drugs from 3 h to 42 days. The immunosuppressants (cyclosporine IC50 : 55 mumol/l; tacrolimus IC50 : 47 mumol/l) inhibited cell proliferation in a concentration- and time-dependent fashion. Multinuclear NMR studies of PCA extracts of drug-treated cells showed a significant deterioration in the energy status (a decreasing level of PCr : -46 +/- 11%; an increasing NDP/NTP ratio: +136 +/- 4% and an increasing level of Pi : +248 +/- 15%; mean +/- standard deviation). It also showed decreasing concentrations of major cell metabolites like NAA (-59 +/- 12%) in neuroblastoma cells and myo-inositol (-47 +/- 6%) in glia cells compared with untreated controls. Immunosuppressive treatment caused a large reduction of taurine (-36 +/- 12%) and glutamate (-68 +/- 10%) in all cell cultures, whereas intermediates of phospholipid biosynthesis (PE: +59 +/- 13%; PC: +127 +/- 27%;) and breakdown (GPE: +215 +/- 24%; GPC: +245 +/- 17%) increased. No significant differences were observed between the two immunosuppressants. The toxic effects of immunosuppressants on cell cultures are in line with MRI studies of brain oedema observed in patients under immunosuppressive treatment.
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PMID:Evaluation of the effects of immunosuppressants on neuronal and glial cells in vitro by multinuclear magnetic resonance spectroscopy. 897 22

We describe the optimal high-level postprocessing of single-voxel (1)H magnetic resonance spectra and assess the benefits and limitations of automated methods as diagnostic aids in the detection of recurrent brain tumor. In a previous clinical study, 90 long-echo-time single-voxel spectra were obtained from 52 patients and classified during follow-up (30/28/32 normal/non-progressive tumor/tumor). Based on these data, a large number of evaluation strategies, including both standard resonance line quantification and algorithms from pattern recognition and machine learning, were compared in a quantitative evaluation. Results from linear and non-linear feature extraction, including ICA, PCA and wavelet transformations, and also the data from resonance line quantification were combined systematically with different classifiers such as LDA, chemometric methods (PLS, PCR), support vector machines and ensemble methods. Classification accuracy was assessed using a leave-one-out cross-validation scheme and the area under the curve (AUC) of the receiver operator characteristic (ROC). A regularized linear regression on spectra with binned channels reached 91% classification accuracy compared with 83% from quantification. Interpreting the loadings of these regressions, we find that lipid and lactate signals are too unreliable to be used in a simple machine rule. Choline and NAA are the main source of relevant information. Overall, we find that fully automated pattern recognition algorithms perform as well as, or slightly better than, a manually controlled and optimized resonance line quantification.
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PMID:Optimal classification of long echo time in vivo magnetic resonance spectra in the detection of recurrent brain tumors. 1664 60

Depressive disorders cause large socioeconomic effects influencing not only the patients themselves but also their family and broader community as well. To better understand the physiologic factors underlying depression, in this study, we performed metabolomics analysis, an omics technique that comprehensively analyzes small molecule metabolites in biological samples. Specifically, we utilized high-resolution magic-angle spinning-1H-NMR (HRMAS-1H-NMR) spectroscopy to comprehensively analyze the changes in metabolites in the hippocampal tissue of rats exposed to chronic stress (CS) via multi-step principal component analysis (multi-step PCA). The rats subjected to CS exhibited obvious depression-like behaviors. High correlations were observed between the first principal component (PC1) score in the score plot obtained using multi-step PCA and measurements from depression-like behavioral testing (body weight, sucrose preference test, and open field test). Alanine, glutamate, glutamine, and aspartate levels in the hippocampal tissue were significantly lower, whereas N-acetylaspartate, myo-inositol, and creatine were significantly higher in the CS group compared to the control (non-CS) group. As alanine, glutamate, and glutamine are known to be involved in energy metabolism, especially in the tricarboxylic acid cycle, chronic exogenous stress may have induced abnormalities in energy metabolism in the brains of the rats. The results suggest that N-acetylaspartate and creatine levels may have increased in order to complement the loss of energy-producing activity resulting from the development of the depression-like disorder. Multi-step PCA therefore allowed an exploration of the degree of depression-like symptoms as represented by changes in intrinsic metabolites.
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PMID:High-Resolution Magic-Angle Spinning-1H-NMR Spectroscopy-Based Metabolic Profiling of Hippocampal Tissue in Rats with Depression-Like Symptoms. 2826 Jul 22