Integration of Women Veterans into VA Quality Improvement Research Efforts: What Researchers Need to Know

The Department of Veterans Affairs (VA) and other federal agencies require funded researchers to include women in their studies. Historically, many researchers have indicated they will include women in proportion to their VA representation or pointed to their numerical minority as justification for exclusion. However, women’s participation in the military—currently 14% of active military—is rapidly changing veteran demographics, with women among the fastest growing segments of new VA users. These changes will require researchers to meet the challenge of finding ways to adequately represent women veterans for meaningful analysis. We describe women veterans’ health and health-care use, note how VA care is organized to meet their needs, report gender differences in quality, highlight national plans for women veterans’ quality improvement, and discuss VA women’s health research. We then discuss challenges and potential solutions for increasing representation of women veterans in VA research, including steps for implementation research

Predicting P-Glycoprotein-Mediated Drug Transport Based On Support Vector Machine and Three-Dimensional Crystal Structure of P-glycoprotein

Human P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter that confers resistance to a wide range of chemotherapeutic agents in cancer cells by active efflux of the drugs from cells. P-gp also plays a key role in limiting oral absorption and brain penetration and in facilitating biliary and renal elimination of structurally diverse drugs. Thus, identification of drugs or new molecular entities to be P-gp substrates is of vital importance for predicting the pharmacokinetics, efficacy, safety, or tissue levels of drugs or drug candidates. At present, publicly available, reliable in silico models predicting P-gp substrates are scarce. In this study, a support vector machine (SVM) method was developed to predict P-gp substrates and P-gp-substrate interactions, based on a training data set of 197 known P-gp substrates and non-substrates collected from the literature. We showed that the SVM method had a prediction accuracy of approximately 80% on an independent external validation data set of 32 compounds. A homology model of human P-gp based on the X-ray structure of mouse P-gp as a template has been constructed. We showed that molecular docking to the P-gp structures successfully predicted the geometry of P-gp-ligand complexes. Our SVM prediction and the molecular docking methods have been integrated into a free web server (http://pgp.althotas.com), which allows the users to predict whether a given compound is a P-gp substrate and how it binds to and interacts with P-gp. Utilization of such a web server may prove valuable for both rational drug design and screening

Factors Associated with Work Participation and Work Functioning in Depressed Workers: A Systematic Review

Background Depression is associated with negative work outcomes such as reduced work participation (WP) (e.g., sick leave duration, work status) and work functioning (WF) (e.g., loss of productivity, work limitations). For the development of evidence-based interventions to improve these work outcomes, factors predicting WP and WF have to be identified. Methods This paper presents a systematic literature review of studies identifying factors associated with WP and WF of currently depressed workers. Results A total of 30 studies were found that addressed factors associated with WP (N = 19) or WF (N = 11). For both outcomes, studies reported most often on the relationship with disorder-related factors, whereas personal factors and work-related factors were less frequently addressed. For WP, the following relationships were supported: strong evidence was found for the association between a long duration of the depressive episode and work disability. Moderate evidence was found for the associations between more severe types of depressive disorder, presence of co-morbid mental or physical disorders, older age, a history of previous sick leave, and work disability. For WF, severe depressive symptoms were associated with work limitations, and clinical improvement was related to work productivity (moderate evidence). Due to the cross-sectional nature of about half of the studies, only few true prospective associations could be identified. Conclusion Our study identifies gaps in knowledge regarding factors predictive of WP and WF in depressed workers and can be used for the design of future research and evidence-based interventions. We recommend undertaking more longitudinal studies to identify modifiable factors predictive of WP and WF, especially work-related and personal factors