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Antidepressive agents are one of the fastest-growing classes of prescribed drugs. However, the effects of antidepressive agents on bone density are controversial. The aim of this meta-analysis is to evaluate the state of research on the relationship between the use of tricyclic antidepressants (TCAs) or selective serotonin reuptake inhibitors (SSRIs) and bone mineral density (BMD) in women. The database searched was Pubmed. The meta-analysis included human studies in women fulfilling the following criteria: (i) an assessment of bone mineral density in the lumbar spine, the femoral neck or the total hip; (ii) a comparison of the BMD of depressed individuals using antidepressive agents (SSRIs or TCAs), and a control group that did not use antidepressive agents; (iii) measurement of BMD using dual-energy X-ray absorptiometry (DXA); and (iv) calculations of the mean BMD and standard deviation or standard error. Four studies were identified, which, in total, included 934 women using antidepressive agents and 5767 non-using individuals. The results showed that no significant negative composite weighted mean effect sizes were identified for the comparisons between SSRI users and non-users. Similarly, no significant negative composite weighted mean effect sizes were identified for the comparisons between TCA users and non-users, indicating similar BMD in SSRI or TCA users and non-users. The meta-analysis shows that the association between antidepressant medication and bone mineral density has not been extensively researched. Only four studies fulfilled the inclusion criteria. The global result of the literature review and meta-analysis was that the use of antidepressive agents was not associated with lower or higher BMD. This result applies to both SSRIs and TCAs and to all measurement locations (lumbar spine, femoral neck and total hip).
Antidepressant medication (ADM) and psychotherapy are effective treatments for major depressive disorder (MDD). It is unclear, however, if treatments differ in their effectiveness at the symptom level and whether symptom information can be utilised to inform treatment allocation. The present study synthesises comparative effectiveness information from randomised controlled trials (RCTs) of ADM versus psychotherapy for MDD at the symptom level and develops and tests the Symptom-Oriented Therapy (SOrT) metric for precision treatment allocation.
Knowledge-driven text mining is becoming an important research area for identifying pharmacogenomics target genes. However, few of such studies have been focused on the pharmacogenomics targets of adverse drug events (ADEs). The objective of the present study is to build a framework of knowledge integration and discovery that aims to support pharmacogenomics target predication of ADEs. We integrate a semantically annotated literature corpus Semantic MEDLINE with a semantically coded ADE knowledgebase known as ADEpedia using a semantic web based framework. We developed a knowledge discovery approach combining a network analysis of a protein-protein interaction (PPI) network and a gene functional classification approach. We performed a case study of drug-induced long QT syndrome for demonstrating the usefulness of the framework in predicting potential pharmacogenomics targets of ADEs.
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