Gene expression of hypoxia-inducible factor (HIF), HIF regulators, and putative HIF targets in ventricle and telencephalon of Trachemys scripta acclimated to 21 °C or 5 °C and exposed to normoxia, anoxia or reoxygenation.

In anoxia-sensitive mammals, hypoxia inducible factor (HIF) promotes cellular survival in hypoxia, but also tumorigenesis. By comparison, anoxia-tolerant vertebrates likely need to circumvent a prolonged upregulation of HIF to survive long-term anoxia, making them attractive biomedical models for investigating HIF regulation. To lend insight into the role of HIF in anoxic Trachemys scripta ventricle and telencephalon, 21 °C- and 5 °C-acclimated turtles were exposed to normoxia, anoxia (24 h at 21 °C; 24 h or 14 d at 5 °C) or anoxia + reoxygenation and the gene expression of HIF-1α (hif1a) and HIF-2α (hif2a), two regulators of HIF, and eleven putative downstream targets of HIF quantified by qPCR. Changes in gene expression with anoxia at 21 °C differentially aligned with a circumvention of HIF activity. Whereas hif1a and hif2a expression was unaffected in ventricle and telencephalon, and BCL2 interacting protein 3 gene expression reduced by 30% in telencephalon, gene expression of vascular endothelial growth factor-A increased in ventricle (4.5-fold) and telencephalon (1.5-fold), and hexokinase 1 (2-fold) and hexokinase 2 (3-fold) gene expression increased in ventricle. At 5 °C, the pattern of gene expression in ventricle or telencephalon was unaltered with oxygenation state. However, cold acclimation in normoxia induced downregulation of HIF-1α, HIF-2α, and HIF target gene expression in telencephalon. Overall, the findings lend support to the postulation that prolonged activation of HIF is counterproductive for long-term anoxia survival. Nevertheless, quantification of the effect of anoxia and acclimation temperature on HIF binding activity and regulation at the protein level are needed to provide a strong scientific framework whereby new strategies for oxygen related pathologies can be developed.

A visual interactive analytic tool for filtering and summarizing large health data sets coded with hierarchical terminologies (VIADS).

BACKGROUND: Vast volumes of data, coded through hierarchical terminologies (e.g., International Classification of Diseases, Tenth Revision-Clinical Modification [ICD10-CM], Medical Subject Headings [MeSH]), are generated routinely in electronic health record systems and medical literature databases. Although graphic representations can help to augment human understanding of such data sets, a graph with hundreds or thousands of nodes challenges human comprehension. To improve comprehension, new tools are needed to extract the overviews of such data sets. We aim to develop a visual interactive analytic tool for filtering and summarizing large health data sets coded with hierarchical terminologies (VIADS) as an online, and publicly accessible tool. The ultimate goals are to filter, summarize the health data sets, extract insights, compare and highlight the differences between various health data sets by using VIADS. The results generated from VIADS can be utilized as data-driven evidence to facilitate clinicians, clinical researchers, and health care administrators to make more informed clinical, research, and administrative decisions. We utilized the following tools and the development environments to develop VIADS: Django, Python, JavaScript, Vis.js, Graph.js, JQuery, Plotly, Chart.js, Unittest, R, and MySQL. RESULTS: VIADS was developed successfully and the beta version is accessible publicly. In this paper, we introduce the architecture design, development, and functionalities of VIADS. VIADS includes six modules: user account management module, data sets validation module, data analytic module, data visualization module, terminology module, dashboard. Currently, VIADS supports health data sets coded by ICD-9, ICD-10, and MeSH. We also present the visualization improvement provided by VIADS in regard to interactive features (e.g., zoom in and out, customization of graph layout, expanded information of nodes, 3D plots) and efficient screen space usage. CONCLUSIONS: VIADS meets the design objectives and can be used to filter, summarize, compare, highlight and visualize large health data sets that coded by hierarchical terminologies, such as ICD-9, ICD-10 and MeSH. Our further usability and utility studies will provide more details about how the end users are using VIADS to facilitate their clinical, research or health administrative decision making.