GuideView: structured multi-modal delivery of clinical guidelines.

GuideView is a system designed for structured, multi-modal delivery of clinical guidelines. Clinical instructions are presented simultaneously in voice, text, pictures or video or animations. Users navigate using mouse-clicks and voice commands. An evaluation study performed at a medical simulation laboratory found that voice and video instructions were rated highly.

Multiple independent genetic factors at NOS1AP modulate the QT interval in a multi-ethnic population.

Extremes of electrocardiographic QT interval are associated with increased risk for sudden cardiac death (SCD); thus, identification and characterization of genetic variants that modulate QT interval may elucidate the underlying etiology of SCD. Previous studies have revealed an association between a common genetic variant in NOS1AP and QT interval in populations of European ancestry, but this finding has not been extended to other ethnic populations. We sought to characterize the effects of NOS1AP genetic variants on QT interval in the multi-ethnic population-based Dallas Heart Study (DHS, n = 3,072). The SNP most strongly associated with QT interval in previous samples of European ancestry, rs16847548, was the most strongly associated in White (P = 0.005) and Black (P = 3.6 x 10(-5)) participants, with the same direction of effect in Hispanics (P = 0.17), and further showed a significant SNP x sex-interaction (P = 0.03). A second SNP, rs16856785, uncorrelated with rs16847548, was also associated with QT interval in Blacks (P = 0.01), with qualitatively similar results in Whites and Hispanics. In a previously genotyped cohort of 14,107 White individuals drawn from the combined Atherosclerotic Risk in Communities (ARIC) and Cardiovascular Health Study (CHS) cohorts, we validated both the second locus at rs16856785 (P = 7.63 x 10(-8)), as well as the sex-interaction with rs16847548 (P = 8.68 x 10(-6)). These data extend the association of genetic variants in NOS1AP with QT interval to a Black population, with similar trends, though not statistically significant at P<0.05, in Hispanics. In addition, we identify a strong sex-interaction and the presence of a second independent site within NOS1AP associated with the QT interval. These results highlight the consistent and complex role of NOS1AP genetic variants in modulating QT interval.

IDENTIFICATION AND CHARACTERIZATION OF DISTINCT POPULATIONS OF CLONOGENIC BONE MARROW STROMAL CELLS CAPABLE OF TRANSFERRING THE HEMATOPOIETIC MICROENVIRONMENT IN VIVO AND SUPPORTING LT-HSCs IN VITRO

Bone marrow (BM) stromal cells are ascribed two key functions, 1) stem cells for non-hematopoietic tissues (MSC) and 2) as components of the hematopoietic stem cell niche. Current approaches studying the stromal cell system in the mouse are complicated by the low yield of clonogenic progenitors (CFU-F). Given the perivascular location of MSC in BM, we developed an alternative methodology to isolate MSC from mBM. An intact ‘plug’ of bone marrow is expelled from bones and enzymatically disaggregated to yield a single cell suspension. The recovery of CFU-F (1917.95+199) reproducibly exceeds that obtained using the standard BM flushing technique (14.32+1.9) by at least 2 orders of magnitude (P<0.001; N = 8) with an accompanying 196-fold enrichment of CFU-F frequency. Purified BM stromal and vascular endothelial cell populations are readily obtained by FACS. A detailed immunophenotypic analysis of lineage depleted BM identified PDGFRαβPOS stromal cell subpopulations distinguished by their expression of CD105. Both subpopulations retained their original phenotype of CD105 expression in culture and demonstrate MSC properties of multi-lineage differentiation and the ability to transfer the hematopoietic microenvironment in vivo. To determine the capacity of either subpopulation to support long-term multi-lineage reconstituting HSCs, we fractionated BM stromal cells into either the LinNEGPDGFRαβPOSCD105POS and LINNEGPDGFRαβPOSCD105LOW/- populations and tested their capacity to support LT-HSC by co-culturing each population with either 1 or 10 HSCs for 10 days. Following the 10 day co-culture period, both populations supported transplantable HSCs from 10 cells/well co-cultures demonstrating high levels of donor repopulation with an average of 65+23.6% chimerism from CD105POS co-cultures and 49.3+19.5% chimerism from the CD105NEG co-cultures. However, we observed a significant difference when mice were transplanted with the progeny of a single co-cultured HSC. In these experiments, CD105POS co-cultures (100%) demonstrated long-term multi- lineage reconstitution, while only 4 of 8 mice (50%) from CD105NEG -single HSC co-cultures demonstrated long-term reconstitution, suggesting a more limited expansion of functional stem cells. Taken together, these results demonstrate that the PDGFRαβCD105POS stromal cell subpopulation is distinguished by a unique capacity to support the expansion of long-term reconstituting HSCs in vitro.

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.