This paper was designed to observe the colocalization of 11beta-HSD1 and GR, and its significance in the rat hippocampus. Immunocytochemical dual-staining showed that not only 11beta-HSD1 but also GR immunoreactive substances were present in the cultured rat hippocampal neurons. Moreover, they were colocalized in the same hippocampal neuron. Synthetic glucocorticoid dexamethasone (DEX) up-regulated the protein expression and activity of 11beta-HSD1 in the cultured hippocampal neurons, as determined by Western blot and thin layer chromatography (TLC) respectively. The transfection of PC12 cells with the plasmid containing promoter sequence of 11beta-HSD1 gene and the reporter gene of CAT enzyme was conducted. DEX up-regulated the reporter gene expression in the system described above. The up-regulation of 11beta-HSD1 and reporter gene expression induced by DEX were both blocked by GR antagonist RU38486. Our study suggests that the colocalization of 11beta-HSD1 and GR in the hippocampus may be implicated in the up-regulation of 11beta-HSD1 expression by glucocorticoids combining to its promoter region, which in turn produces more biologically active glucocorticoids necessary for the binding of low affinity of GR.
11-beta-Hydroxysteroid Dehydrogenases ; genetics ; metabolism ; Animals ; Animals, Newborn ; Dexamethasone ; pharmacology ; Gene Expression Regulation ; Hippocampus ; cytology ; metabolism ; Mifepristone ; pharmacology ; Neurons ; chemistry ; metabolism ; PC12 Cells ; Promoter Regions, Genetic ; Rats ; Receptors, Glucocorticoid ; genetics ; metabolism ; Transfection

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.