Search

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]

Depression is a serious and potentially life-threatening mental disorder with unknown etiology. Emerging evidence shows that brain-derived neurotrophic factor (BDNF) and microRNAs (miRNAs) play critical roles in the etiology of depression. However, the molecular mechanisms are not fully understood. Expression of miR-182 and BDNF in the hippocampus were analyzed in a chronic unpredictable mild stress (CUMS) model. Male Wistar rats received bilateral intra-hippocampal infusions of BDNF- and miR-182-expressing (miR-182) or miR-182-silencers (si-miR-182) lentiviral vectors (LV). miR-182 upregulation was correlated with decreased BDNF expression in the hippocampus of a CUMS model. Accordingly, an anti-depressant like effect was observed when LV-BDNF or LV-si-miR-182 was injected into the hippocampus. Moreover, BDNF and its target gene cyclic AMP responsive element binding protein 1 (CREB1) decreased following LV-miR-182 injection and increased upon LV-si-miR-182 injection in rat hippocampus and cultured neuronal cells. In contrast, miR-182 overexpression exacerbated depression-like behaviors and decreased BDNF. Further, luciferase reporter evidence confirmed BDNF was a miR-182 target. Taken together, the current results reveal a potential molecular regulation of miR-182 on BDNF and the pronounced behavioral consequences of this regulation. [ABSTRACT FROM AUTHOR]

Background: Stressful life events (SLEs) are well-established proximal predictors of the onset of depression. However, the fundamental causes of interindividual differences in depression outcomes are poorly understood. This study addressed this depression susceptibility mechanism using a well-powered sample of adults living in China.Methods: Healthy participants with SLEs (n = 185; mean = 47.51 years, 49.73% female), drawn from a longitudinal study on the development of depression, underwent diffusion tensor imaging, interleukin-6 (IL-6) level measurement, and trimonthly standardized clinical and scale evaluations within a two-year period.Results: Receiver operating characteristic analyses indicated that reduced feeder connection and HIP.R nodal efficiency improved the predictive accuracy of post-SLEs depression (ORfeeder = 0.623, AUC = 0.869, P < 0.001; ORHIP = 0.459, AUC = 0.855, P < 0.001). The successfully established path analysis model confirmed the significant partial effect of SLEs-IL-6-white matter (WM) network differences-depression (onset and severity) (x2/8 = 1.453, goodness-of-fit [GFI] = 0.935, standard root-mean-square error of approximation [SRMR] = 0.024). Females, individuals with lower exercise frequency (EF) or annual household income (AHI) were more likely to have higher IL-6 level after SLEs (βint-female⁎SLEs = -0.420, P < 0.001; βint-exercise⁎SLEs = -0.412, P < 0.001; βint-income⁎SLEs = -0.302, P = 0.005).Limitations: The sample size was restricted due to the limited incidence rate and prospective follow-up design.Conclusions: Our results suggested that among healthy adults after SLEs, those who exhibited abnormal IL-6-WM differences were susceptible to developing depression. Females, lower AHI or EF might account for an increased risk of developing these abnormal IL-6-WM differences. [ABSTRACT FROM AUTHOR]

Repetitive Transcranial Magnetic Stimulation (rTMS) is an evidence-based treatment for depression. However, the patterns of response to this treatment modality are inconsistent. Whilst many people see a significant reduction in the severity of their depression following rTMS treatment, some patients do not. To support and improve patient outcomes, recent work is exploring the possibility of using Machine Learning to predict rTMS treatment outcomes. Our proposed model is the first to combine functional magnetic resonance imaging (fMRI) connectivity with deep learning techniques to predict treatment outcomes before treatment starts. Furthermore, with the use of Explainable AI (XAI) techniques, we identify potential biomarkers that may discriminate between rTMS responders and non-responders. Our experiments utilize 200 runs of repeated bootstrap sampling on two rTMS datasets. We compare performances between our proposed feedforward deep neural network against existing methods, and compare the average accuracy, balanced accuracy and F1-score on a held-out test set. The results of these experiments show that our model outperforms existing methods with an average accuracy of 0.9423, balanced accuracy of 0.9423, and F1-score of 0.9461 in a sample of 61 patients. We found that functional connectivity measures between the Subgenual Anterior Cingulate Cortex and Centeral Opercular Cortex are a key determinant of rTMS treatment response. This knowledge provides psychiatrists with further information to explore the potential mechanisms of responses to rTMS treatment. Our developed prototype is ready to be deployed across large datasets in multiple centres and different countries. [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]