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This study showed that pioglitazone could be a tolerable and effective adjunctive therapy for improving depressive symptoms in bipolar disorder without type 2 diabetes or metabolic syndrome.

Celecoxib is an effective adjuvant therapy in the treatment of manic episodes (without psychotic features) of bipolar mood disorder. The mood-stabilizing role of the drug might be mediated via its action on the inflammatory cascade.

Patients with the HIV infection are at high risk for developing depression. The aim of this study was to investigate the safety and efficacy of antidepressant effects of minocycline on HIV patients with depression. Forty-six HIV patients, with mild-to-moderate depression and a Hamilton Depression Rating Scale (HDRS) up to 18, participated in a parallel, randomized, double-blind, placebo-controlled trial and underwent 6 weeks of treatment with either minocycline (100 mg twice daily) or placebo in the same manner. Patients were assessed using HDRS at baseline and at weeks 3 and 6. The primary outcome measure was to evaluate the efficacy of minocycline in improving depressive symptoms. General linear model repeated measures showed significant effect for time × treatment interaction on the HDRS score during the trial course [F(2, 88)=7.50, P=0.001]. There was no significant difference between the two groups regarding adverse events. No serious adverse event was reported. The administration of 100 mg minocycline twice daily seems to be safe and effective in improving depressive symptoms in HIV/AIDS patients with mild-to-moderate depression.

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The results of the current study suggest that celecoxib could be a tolerable and effective adjunctive treatment for more rapid and more satisfying improvements in OCD symptoms.

Deep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to “learn” from annotated data. Deep-learning models require large, diverse training datasets for optimal model convergence. The effort to curate these datasets is widely regarded as a barrier to the development of deep-learning systems. We developed RIL-Contour to accelerate medical image annotation for and with deep-learning. A major goal driving the development of the software was to create an environment which enables clinically oriented users to utilize deep-learning models to rapidly annotate medical imaging. RIL-Contour supports using fully automated deep-learning methods, semi-automated methods, and manual methods to annotate medical imaging with voxel and/or text annotations. To reduce annotation error, RIL-Contour promotes the standardization of image annotations across a dataset. RIL-Contour accelerates medical imaging annotation through the process of annotation by iterative deep learning (AID). The underlying concept of AID is to iteratively annotate, train, and utilize deep-learning models during the process of dataset annotation and model development. To enable this, RIL-Contour supports workflows in which multiple-image analysts annotate medical images, radiologists approve the annotations, and data scientists utilize these annotations to train deep-learning models. To automate the feedback loop between data scientists and image analysts, RIL-Contour provides mechanisms to enable data scientists to push deep newly trained deep-learning models to other users of the software. RIL-Contour and the AID methodology accelerate dataset annotation and model development by facilitating rapid collaboration between analysts, radiologists, and engineers.

Aim:The aim of the present randomized, doubleblind, placebo-controlled, 8-week trial was to assess the efficacy and tolerability of riluzole augmentation of fluvoxamine in treatment of patients with moderate to severe obsessive-compulsive disorder.Methods: Patients were randomized into two parallel groups to receive fluvoxamine plus placebo or fluvoxamine plus riluzole (50 mg twice daily). All patients, regardless of their treatment group, received fluvoxamine at 100 mg/day for the initial 4 weeks of the study followed by 200 mg/day of fluvoxamine for the rest of the trial course. A total of 50 patients (25 in each group) were evaluated for response to treatment using the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) at baseline and at weeks 4, 8 and 10. Side-effects were recorded using predesigned checklists in each visit. Repeatedmeasure analysis of variance showed a significant effect for time × treatment interaction in the Y-BOCS total score and a significant effect for time Conclusion:Riluzole may be of clinical use as an adjuvant agent to fluvoxamine in treatment of moderate to severe obsessive-compulsive disorder.