ObjectivesTo systematically review the efficacy and safety of anti-inflammatory agents for patients with major depressive disorders.MethodsWe searched the literature to identify potentially relevant randomised controlled trials (RCTs) up to 1 January 2019. The primary outcome was efficacy, measured by mean changes in depression score from baseline to endpoint. Secondary outcomes included response and remission rates and quality of life (QoL). Safety was evaluated by incidence of classified adverse events. Heterogeneity was examined using the I2 and Q statistic. Pooled standard mean differences (SMDs) and risk ratios (RRs) were calculated. Subgroup meta-analyses were conducted based on type of treatment, type of anti-inflammatory agents, sex, sponsor type and quality of studies.ResultsThirty RCTs with 1610 participants were included in the quantitative analysis. The overall analysis pooling from 26 of the RCTs suggested that anti-inflammatory agents reduced depressive symptoms (SMD −0.55, 95% CI −0.75 to −0.35, I2=71%) compared with placebo. Higher response (RR 1.52, 95% CI 1.30 to 1.79, I2=29%) and remission rates (RR 1.79, 95% CI 1.29 to 2.49, I2=41%) were seen in the group receiving anti-inflammatory agents than in those receiving placebo. Subgroup analysis showed a greater reduction in symptom severity in both the monotherapy and adjunctive treatment groups. Subgroup analysis of non-steroidal anti-inflammatory drugs, omega-3 fatty acids, statins and minocyclines, respectively, disclosed significant antidepressant effects for major depressive disorder (MDD). For women-only trials, no difference in changes of depression severity was found between groups. Subanalysis stratified by sponsor type and study quality led to the same outcomes in favour of anti-inflammatory agents in both subgroups. Changes of QoL showed no difference between the groups. Gastrointestinal events were the only significant differences between groups in the treatment periods.ConclusionsResults of this systematic review suggest that anti-inflammatory agents play an antidepressant role in patients with MDD and are reasonably safe.

Due to the popularity of Deep Neural Network (DNN) models, we have witnessed extreme-scale DNN models with the continued increase of the scale in terms of depth and width. However, the extremely high memory requirements for them make it difficult to run the training processes on single many-core architectures such as a Graphic Processing Unit (GPU), which compels researchers to use model parallelism over multiple GPUs to make it work. However, model parallelism always brings very heavy additional overhead. Therefore, running an extreme-scale model in a single GPU is urgently required. There still exist several challenges to reduce the memory footprint for extreme-scale deep learning. To address this tough problem, we first identify the memory usage characteristics for deep and wide convolutional networks, and demonstrate the opportunities for memory reuse at both the intra-layer and inter-layer levels. We then present Layrub, a runtime data placement strategy that orchestrates the execution of the training process. It achieves layer-centric reuse to reduce memory consumption for extreme-scale deep learning that could not previously be run on a single GPU. Experiments show that, compared to the original Caffe, Layrub can cut down the memory usage rate by an average of 58.2% and by up to 98.9%, at the moderate cost of 24.1% higher training execution time on average. Results also show that Layrub outperforms some popular deep learning systems such as GeePS, vDNN, MXNet, and Tensorflow. More importantly, Layrub can tackle extreme-scale deep learning tasks. For example, it makes an extra-deep ResNet with 1,517 layers that can be trained successfully in one GPU with 12GB memory, while other existing deep learning systems cannot.

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