The efficacy and safety of 10 mg/day vortioxetine compared to placebo for adult major depressive disorder: a meta-analysis

Background: There is a growing interest in vortioxetine in major depressive disorder (MDD). Objectives: This meta-analysis aimed to assess the efficacy and safety of 10 mg/day (mg/d) vortioxetine compared to placebo for MDD in adult.Methods: Eight randomly controlled trials (RCTs) about the treatment of 10 mg/d vortioxetine in adult patients with MDD were identified and 2354 patients were included in meta-analysis.Results: According to the results, 10 mg/d vortioxetine showed significant differences in response rates (OR=1.88, 95% CI=1.40-2.53, P<0.0001), remission rates (OR=1.54, 95% CI=1.27-1.86, P<0.00001), change from baseline in Montgomery-As- berg Depression Rating Scale (MADRS) total score (SMD=-3.50, 95%CI=-4.83 to -2.17, P<0.00001), clinical global Impres- sion-Global Improvement (CGI-I) total score (SMD=-3.40, 95% CI=-4.69 to -2.11, P<0.00001), and change from baseline in Sheehan Disability Scale (SDS) total score (SMD=-2.09, 95% CI=-2.64 to -1.55, P<0.00001). But 10 mg/d vortioxetine was easier induced nausea (OR=4.18, 95% CI=3.21-5.44, P<0.00001) and constipation (OR=1.88, 95% CI=1.14 to 3.09, P=0.01).Conclusion: 10 mg/d vortioxetine was more effective, but easily induced nausea and constipation when compared to placebo for MDD in adult.Keywords: Vortioxetine, major depressive disorder, meta-analysis.

Effective Control of Bioelectricity Generation from a Microbial Fuel Cell by Logical Combinations of pH and Temperature

In this study, a microbial fuel cell (MFC) with switchable power release is designed, which can be logically controlled by combinations of the most physiologically important parameters such as “temperature” and “pH.” Changes in voltage output in response to temperature and pH changes were significant in which voltage output decreased sharply when temperature was lowered from 30°C to 10°C or pH was decreased from 7.0 to 5.0. The switchability of the MFC comes from the microbial anode whose activity is affected by the combined medium temperature and pH. Changes in temperature and pH cause reversible activation-inactivation of the bioanode, thus affecting the activity of the entire MFC. With temperature and pH as input signals, an AND logic operation is constructed for the MFC whose power density is controlled. The developed system has the potential to meet the requirement of power supplies producing electrical power on-demand for self-powered biosensors or biomedical devices

Effective Control of Bioelectricity Generation from a Microbial Fuel Cell by Logical Combinations of pH and Temperature

In this study, a microbial fuel cell (MFC) with switchable power release is designed, which can be logically controlled by combinations of the most physiologically important parameters such as “temperature” and “pH.” Changes in voltage output in response to temperature and pH changes were significant in which voltage output decreased sharply when temperature was lowered from 30°C to 10°C or pH was decreased from 7.0 to 5.0. The switchability of the MFC comes from the microbial anode whose activity is affected by the combined medium temperature and pH. Changes in temperature and pH cause reversible activation-inactivation of the bioanode, thus affecting the activity of the entire MFC. With temperature and pH as input signals, an AND logic operation is constructed for the MFC whose power density is controlled. The developed system has the potential to meet the requirement of power supplies producing electrical power on-demand for self-powered biosensors or biomedical devices

ALUM: Adversarial Data Uncertainty Modeling from Latent Model Uncertainty Compensation

It is critical that the models pay attention not only to accuracy but also to the certainty of prediction. Uncertain predictions of deep models caused by noisy data raise significant concerns in trustworthy AI areas. To explore and handle uncertainty due to intrinsic data noise, we propose a novel method called ALUM to simultaneously handle the model uncertainty and data uncertainty in a unified scheme. Rather than solely modeling data uncertainty in the ultimate layer of a deep model based on randomly selected training data, we propose to explore mined adversarial triplets to facilitate data uncertainty modeling and non-parametric uncertainty estimations to compensate for the insufficiently trained latent model layers. Thus, the critical data uncertainty and model uncertainty caused by noisy data can be readily quantified for improving model robustness. Our proposed ALUM is model-agnostic which can be easily implemented into any existing deep model with little extra computation overhead. Extensive experiments on various noisy learning tasks validate the superior robustness and generalization ability of our method. The code is released at https://github.com/wwzjer/ALUM.Comment: 10 pages, 5 figure

Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions

Large-scale Pretrained Language Models~(LLMs), such as ChatGPT and GPT4, have shown strong abilities in multilingual translations, without being explicitly trained on parallel corpora. It is interesting how the LLMs obtain their ability to carry out translation instructions for different languages. In this paper, we present a detailed analysis by finetuning a multilingual pretrained language model, XGLM-7B, to perform multilingual translation following given instructions. Firstly, we show that the multilingual LLMs have stronger translation abilities than previously demonstrated. For a certain language pair, the performance depends on both the language families and the amount of data used in the pretraining phase. Secondly, we find that LLMs' ability to carry out translation instructions relies on the understanding of translation instruction and the alignment among different languages. With proper enhancement, LLMs could perform the translation task well even for those language pairs unseen during the instruction tuning phase

The efficacy and safety of 10 mg/day vortioxetine compared to placebo for adult major depressive disorder: a meta-analysis

Background: There is a growing interest in vortioxetine in major depressive disorder (MDD). Objectives: This meta-analysis aimed to assess the efficacy and safety of 10 mg/day (mg/d) vortioxetine compared to placebo for MDD in adult. Methods: Eight randomly controlled trials (RCTs) about the treatment of 10 mg/d vortioxetine in adult patients with MDD were identified and 2354 patients were included in meta-analysis. Results: According to the results, 10 mg/d vortioxetine showed significant differences in response rates (OR=1.88, 95% CI=1.40-2.53, P<0.0001), remission rates (OR=1.54, 95% CI=1.27-1.86, P<0.00001), change from baseline in Montgomery-Asberg Depression Rating Scale (MADRS) total score (SMD=-3.50, 95%CI=-4.83 to -2.17, P<0.00001), clinical global Impression-Global Improvement (CGI-I) total score (SMD=-3.40, 95% CI=-4.69 to -2.11, P<0.00001), and change from baseline in Sheehan Disability Scale (SDS) total score (SMD=-2.09, 95% CI=-2.64 to -1.55, P<0.00001). But 10 mg/d vortioxetine was easier induced nausea (OR=4.18, 95% CI=3.21-5.44, P<0.00001) and constipation (OR=1.88, 95% CI=1.14 to 3.09, P=0.01). Conclusion: 10 mg/d vortioxetine was more effective, but easily induced nausea and constipation when compared to placebo for MDD in adult. DOI: https://dx.doi.org/10.4314/ahs.v19i1.48 Cite as: Zheng J, Wang Z, E L. The efficacy and safety of 10 mg/day vortioxetine compared to placebo for adult major depressive disorder: a meta-analysis. Afri Health Sci. 2019;19(1). 1716-1726. https://dx.doi. org/10.4314/ ahs. v19i1.4