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The occurrences of dilated Virchow-Robin spaces (dVRSs) and inflammatory factors were closely related with development of depression and antidepressants. The present study indicated accumulation of inflammatory factors in dVRSs played a practical role in behaviors and the treatment-resistant in rats with depression. Eighty-one male Wistar rats were exposed to chronic unpredictable mild stress (CUMS) until the model was successfully established. According to the detection of dVRSs on 7 T structural magnetic resonance imaging (MRI), rats were divided into two groups (45 CUMSnon-dVRSs rats and 36 CUMSdVRSs rats). Subsequently, different antidepressant treatment (nontreat, aspirin, fluoxetine, fluoxetine+aspirin) was given to the two groups, respectively. Sucrose preference test (SPT), forced swim test (FST) and morris water maze (MWM) was used to test the behaviors of rats. ELISA and Western blot was used to determine the levels of inflammatory factors. Compared with the negative control (NC) group, there were obvious behavioral abnormalities and increased inflammatory factors in the CUMSnon-dVRSs rats. Interestingly, these changes were more significant in the CUMSdVRSs rats. In addition, the efficacy of fluoxetine was only found in CUMSnon-dVRSs rats rather than CUMSdVRSs rats. Meanwhile, behaviors and levels of inflammatory factor in hippocampus were ameliorated in the aspirin group of CUMSdVRSs rats, and these effects were significantly enhanced in the fluoxetine+aspirin group. The dVRSs in the hippocampus might exacerbate depression-like behaviors and the effects of antidepressant treatment in depressed rats by increasing the levels of inflammatory factors. [ABSTRACT FROM AUTHOR]

Background: Although several pharmacological treatment options for depression are currently available, a large proportion of patients still do not achieve a complete remission or respond adequately to the initial antidepressant prescribed for reasons that remain relatively unknown. This study explored the application of serum biomarkers to the predict the efficacy of escitalopram for treating depression, to guide clinical drug selection.Method: In this study, 306 patients suffering from depression were treated with escitalopram (10 mg) for 6 weeks. After 6 weeks of treatment, the patients were divided into an escitalopram-sensitive group (ES, n = 172) and an escitalopram-insensitive group (EIS, n = 134) according their HAMD-24 scores after 6 weeks of treatment. Serum samples from all participants were collected on the first day, and 10 different serum biomarkers were analysed. Data from 100 patients in the ES group and 100 patients in the EIS group were then used to build a logistic regression model, and a receiver operating characteristic (ROC) curve was drawn. To validate the accuracy of our model, another 72 patients in the ES group and 34 patients in the EIS group were studied.Results: Of the 10 selected serum biomarkers, 4 were screened to build the regression model. BDNF, FGF-2, TNF-α and 5-HT. The regression equation was Z = 1/[1 + e-(-5.065+0.145 (BDNF)+0.029 (FGF-2)-0.368 (TNF-α)+0.813 (5-HT))], and the 4 biomarkers-combined detection achieved an AUC (area under the ROC curve) of 0.929 and a predictive accuracy of 88.70%.Limitation: Decision support tools based on our combined biomarker prediction models hold comparatively great promises; however, they need to be validated on a much larger scales than current studies provide.Conclusion: The logistic regression model and ROC curves based of the serum biomarkers used in this study provide a more reliable means to predict the efficacy of escitalopram in patients with depression, and provide clinical evidence for drug selection. [ABSTRACT FROM AUTHOR]