β-Ar-ACETYL D-GALACTOSAMINIDASE IN BULK SEPARATED NEURONS AND NEUROPIL FROM RAT CEREBRAL CORTEX

Abstract— β- N -Acetyl D-galactosaminidase was studied in isolated neuronal and neuropil fractions from cerebral cortex and subcellular fractions derived from them. Although the enzyme activity evinced some latency properties, its subcellular distribution pattern was broader than that observed with other acid hydrolases. By contrast with nine other acid hydrolases, it was more active in neuropil than neuronal fractions (neuronal/neuropil activity ratio 0.63). This ratio was preserved in lysosomal subfractions derived from the isolated cell fractions. The data is taken as further evidence for the microheterogeneity of lysosomal particles from the brain.     

Glutamine synthetase regulation by dexamethasone,RU486, and compound A in astrocytes derived from aged mouse cerebral hemispheres is mediated via glucocorticoid receptor

Molecular and Cellular Biochemistry - Glucocorticoids (GCs) regulate astrocyte function, while glutamine synthetase (GS), an enzyme highly expressed in astrocytes, is one of the most remarkable...     

Metabolites in Blood for Prediction of Bacteremic Sepsis in the Emergency Room

A metabolomics approach for prediction of bacteremic sepsis in patients in the emergency room (ER) was investigated. In a prospective study, whole blood samples from 65 patients with bacteremic sepsis and 49 ER controls were compared. The blood samples were analyzed using gas chromatography coupled to time-of-flight mass spectrometry. Multivariate and logistic regression modeling using metabolites identified by chromatography or using conventional laboratory parameters and clinical scores of infection were employed. A predictive model of bacteremic sepsis with 107 metabolites was developed and validated. The number of metabolites was reduced stepwise until identifying a set of 6 predictive metabolites. A 6-metabolite predictive logistic regression model showed a sensitivity of 0.91(95% CI 0.69–0.99) and a specificity 0.84 (95% CI 0.58–0.94) with an AUC of 0.93 (95% CI 0.89–1.01). Myristic acid was the single most predictive metabolite, with a sensitivity of 1.00 (95% CI 0.85–1.00) and specificity of 0.95 (95% CI 0.74–0.99), and performed better than various combinations of conventional laboratory and clinical parameters. We found that a metabolomics approach for analysis of acute blood samples was useful for identification of patients with bacteremic sepsis. Metabolomics should be further evaluated as a new tool for infection diagnostics.