Effectiveness of Moderate-Intensity Aerobic Water Exercise during Pregnancy on Quality of Life and Postpartum Depression: A Multi-Center, Randomized Controlled Trial

Background: The global prevalence of postpartum depression is about 20%. This disease has serious consequences for women, their infants, and their families. The aim of this randomized clinical trial was to analyze the effectiveness and safety of a moderate-intensity aerobic water exercise program on postpartum depression, sleep problems, and quality of life in women at one month after delivery. Methods: This was a multi-center, parallel, randomized, evaluator blinded, controlled trial in a primary care setting. Pregnant women (14–20 weeks gestational age) who had low risk of complications and were from five primary care centers in the area covered by the obstetrics unit of Son Llatzer Hospital (Mallorca, Spain) were invited to participate. A total of 320 pregnant women were randomly assigned to two groups, an intervention group (moderate aquatic aerobic exercise) and a control group (usual prenatal care). One month after birth, sleep quality (MOS sleep), quality of life (EQ-5D), and presence of anxiety or depression (EPDS) were recorded. Findings: Women in the intervention group were less likely to report anxiety or depression on the EQ5D (11.5% vs. 22.7%; p < 0.05) and had a lower mean EPDS score (6.1 ± 1.9 vs. 6.8 ± 2.4, p < 0.010). The two groups had no significant differences in other outcomes, maternal adverse events, and indicators of the newborn status. Conclusion: Moderate-intensity aquatic exercise during pregnancy decreased postpartum anxiety and depressive symptoms in mothers and was safe for mothers and their newborns.

Automatic filtering and substantiation of drug safety signals

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions.

Adverse drug reactions (ADRs) constitute a major cause of morbidity and mortality worldwide. Due to the relevance of ADRs for both public health and pharmaceutical industry, it is important to develop efficient ways to monitor ADRs in the population. In addition, it is also essential to comprehend why a drug produces an adverse effect. To unravel the molecular mechanisms of ADRs, it is necessary to consider the ADR in the context of current biomedical knowledge that might explain it. Nowadays there are plenty of information sources that can be exploited in order to accomplish this goal. Nevertheless, the fragmentation of information and, more importantly, the diverse knowledge domains that need to be traversed, pose challenges to the task of exploring the molecular mechanisms of ADRs. We present a novel computational framework to aid in the collection and exploration of evidences that support the causal inference of ADRs detected by mining clinical records. This framework was implemented as publicly available tools integrating state-of-the-art bioinformatics methods for the analysis of drugs, targets, biological processes and clinical events. The availability of such tools for in silico experiments will facilitate research on the mechanisms that underlie ADR, contributing to the development of safer drugs.