Postpartum depression is a common complication of childbearing and up to 12 months postpartum. This study aimed to determine the prevalence of postpartum depressive mood (PDM) in China by performing a meta-analysis of published studies. Studies that reported the prevalence of PDM in China were identified by searching the PubMed, Embase, CNKI, and CQVIP databases. Three thousand, one hundred, and two articles were obtained, and after careful evaluation, 26 studies were finally included in the meta-analysis. The combined studies included a total of 7618 cases with 1621 cases of PDM. The studies were assessed on the basis of heterogeneity testing and the potential for publication bias. Stata software 11.0 was used to perform the meta-analysis. The random-effect model showed that the prevalence of PDM was 21% with a 95% confidence interval (CI) of 17-25%. PDM was the highest 0 to 1.5 months after delivery. PDM levels decreased to 10.4% (95% CI 9.7-11.1%, P < 0.001) after publication bias were corrected. Sensitivity analyses evaluated the stability of our results and showed no significant change when any single study was excluded. Subgroup analyses showed that region, instruments used, cut-off score, and time points for depression assessment were positively associated with PDM prevalence. The prevalence of PDM differed among regions, with South Central China and East China exhibiting the lowest prevalence. The prevalence was higher in regions with poor economic development, suggesting that more attention should be devoted to Southwest and North China and that the prevalence of PDM should be evaluated 0 to 1.5 months after delivery.

Introduction: Self-medication with antibiotics (SMA) is common among university students in low and middle-income countries (LMICs). However, there has been no meta-analysis and systematic review in the population. Methodology: A literature search was conducted using PubMed, Embase and Web of Science for the period from January 2000 to July 2018. Only observational studies that had SMA among university students from LMICs were included. A random-effects model was applied to calculate the pooled effect size with 95% confidence interval (CI) due to the expected heterogeneity (I2 over 50%). Results: The pooled prevalence of SMA of overall included studies was 46.0% (95% CI: 40.3% to 51.8%). Africa had the highest pooled prevalence of SMA among university students (55.30%), whereas South America had the lowest prevalence (38.3%). Among individual LMICs, the prevalence of SMA among university students varied from as low as 11.1% in Brazil to 90.7% in Congo. Conclusions: The practice of SMA is a widespread phenomenon among university students in LMICs and is frequently associated with inappropriate use. Effective interventions such as medication education and stricter governmental regulation concerning antibiotic use and sale are required to be established in order to deal with SMA properly.

Aim To determine the efficacy of Internet‐based interventions in decreasing the prevalence of postpartum depression in perinatal women. Design This review was conducted according to the standards outlined in the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses statement. Methods We performed a systematic meta‐analysis of randomized controlled trials on the efficacy of Internet‐based interventions for postpartum depression. Studies (2008–2018) were identified through a search conducted on PubMed, EMBASE and the Cochrane Library. Risk ratios or weighted mean differences with 95% confidence intervals were calculated using a fixed‐effects model or a random‐effects model. Stata software 11.0 was used to perform the meta‐analysis. Results Most of the seven eligible studies were randomized controlled trials. The random‐effects model indicated that Internet‐based interventions significantly improved postpartum depression (d = 0.642, N = 7). Attrition rates ranged from 4.5%–86.9% and from 0%–87.1% for the intervention and control groups, respectively.

Modern information technology services largely depend on cloud infrastructures to provide their services. These cloud infrastructures are built on top of Datacenter Networks (DCNs) constructed with high-speed links, fast switching gear, and redundancy to offer better flexibility and resiliency. In this environment, network traffic includes long-lived (elephant) and short-lived (mice) flows with partitioned/aggregated traffic patterns. Although SDN-based approaches can efficiently allocate networking resources for such flows, the overhead due to network reconfiguration can be significant. With limited capacity of Ternary Content-Addressable Memory (TCAM) deployed in an OpenFlow enabled switch, it is crucial to determine which forwarding rules should remain in the flow table and which rules should be processed by the SDN controller in case of a table-miss on the SDN switch. This is needed in order to obtain the flow entries that satisfy the goal of reducing the long-term control plane overhead introduced between the controller and the switches. To achieve this goal, we propose a machine learning technique that utilizes two variations of Reinforcement Learning (RL) algorithms—the first of which is a traditional RL-based algorithm, while the other is deep reinforcement learning-based. Emulation results using the RL algorithm show around 60% improvement in reducing the long-term control plane overhead and around 14% improvement in the table-hit ratio compared to the Multiple Bloom Filters (MBF) method, given a fixed size flow table of 4KB.