Optimizing Retrieval of Biospecimens Using the Curated Cancer Clinical Outcomes Database (C3OD)

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author's publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.To fully support their role in translational and personalized medicine, biorepositories and biobanks must continue to advance the annotation of their biospecimens with robust clinical and laboratory data. Translational research and personalized medicine require well-documented and up-to-date information, but the infrastructure used to support biorepositories and biobanks can easily be out of sync with the host institution. To assist researchers and provide them with accurate pathological, epidemiological, and bio-molecular data, the Biospecimen Repository Core Facility (BRCF) at the University of Kansas Medical Center (KUMC) merges data from medical records, the tumor registry, and pathology reports using the Curated Cancer Clinical Outcomes Database (C3OD). In this report, we describe the utilization of C3OD to optimally retrieve and dispense biospecimen samples using these 3 data sources and demonstrate how C3OD greatly increases the efficiency of obtaining biospecimen samples for the researchers.National Cancer Institute (NCI) Cancer Center Support Grant P30 CA168524Biostatistics and Informatics Shared Resource (BISR)Biospecimen Shared Resource (BSR

Yoga Asana Sessions Increase Brain GABA Levels: A Pilot Study

Objectives: The aim of this study was to compare changes in brain gamma-aminobutyric (GABA) levels associated with an acute yoga session versus a reading session. It was hypothesized that an individual yoga session would be associated with an increase in brain GABA levels. Design: This is a parallel-groups design. Settings/Location: Screenings, scan acquisitions, and interventions took place at medical school-affiliated centers. Subjects: The sample comprised 8 yoga practitioners and 11 comparison subjects. Interventions: Yoga practitioners completed a 60-minute yoga session and comparison subjects completed a 60-minute reading session. Outcome Measures: GABA-to-creatine ratios were measured in a 2-cm axial slab using magnetic resonance spectroscopic imaging immediately prior to and immediately after interventions. Results: There was a 27% increase in GABA levels in the yoga practitioner group after the yoga session (0.20 mmol/kg) but no change in the comparison subject group after the reading session ( -0.001 mmol/kg) (t = -2.99, df = 7.87, p = 0.018). Conclusions: These findings demonstrate that in experienced yoga practitioners, brain GABA levels increase after a session of yoga. This suggests that the practice of yoga should be explored as a treatment for disorders with low GABA levels such as depression and anxiety disorders. Future studies should compare yoga to other forms of exercise to help determine whether yoga or exercise alone can alter GABA levels

Performance on the Stroop predicts treatment compliance in cocaine-dependent individuals.

Treatment dropout is a problem of great prevalence and stands as an obstacle to recovery in cocaine-dependent (CD) individuals. Treatment attrition in CD individuals may result from impairments in cognitive control, which can be reliably measured by the Stroop color-word interference task. The present analyses contrasted baseline performance on the color-naming, word-reading, and interference subtests of the Stroop task in CD subjects who completed a cocaine treatment trial (completers: N=50) and those who dropped out of the trial before completion (non-completers: N=24). A logistic regression analysis was used to predict trial completion using three models with the following variables: the Stroop task subscale scores (Stroop model); the Hamilton depression rating scale (HDRS) scores (HDRS model); and both the Stroop task subscale scores and HDRS scores (Stroop and HDRS model). Each model was able to significantly predict group membership (completers vs non-completers) better than a model based on a simple constant (HDRS model p=0.02, Stroop model p=0.006, and Stroop and HDRS model p=0.003). Models using the Stroop preformed better than the HDRS model. These findings suggest that the Stroop task can be used to identify cocaine-dependent subjects at risk for treatment dropout. The Stroop task is a widely available, reliable, and valid instrument that can be easily employed to identify and tailor interventions of at risk individuals in the hope of improving treatment compliance