Dog peritoneal and pleural cavities as bioreactors to grow autologous vascular grafts

Objective: The purpose of this study was to grow artificial blood vessels for autologous transplantation as arterial interposition grafts in a large animal model (dog). Method and results: Tubing up to 250 mm long, either bare or wrapped in biodegradable polyglycolic acid (Dexon) or nonbiodegradable polypropylene (Prolene) mesh, was inserted in the peritoneal or pleural cavity of dogs, using minimally invasive techniques, and tethered at one end to the wall with a loose suture. After 3 weeks the tubes and their tissue capsules were harvested, and the inert tubing was discarded. The wall of living tissue was uniformly 1-1.5 mm thick throughout its length, and consisted of multiple layers of myofibroblasts and matrix overlaid with a single layer of mesothelium. The myofibroblasts stained for a-smooth muscle actin, vimentin, and desmin. The bursting strength of tissue tubes with no biodegradable mesh scaffolds was in excess of 2500 mm Hg, and the suture holding strength was 11.5 N, both similar to that in dog carotid and femoral arteries. Eleven tissue tubes were transplanted as interposition grafts into the femoral artery of the same dog in which they were grown, and were harvested after 3 to 6.5 months. Eight remained patent during this time. At harvest, their lumens were lined with endothelium-like cells, and wall cells stained for alpha-actin, smooth muscle myosin, desmin and smoothelin; there was also a thick adventitia containing vasa vasorum. Conclusion: Peritoneal and pleural cavities of large animals can function as bioreactors to grow myofibroblast tubes for use as autologous vascular grafts.

How does your model grow? An empirical investigation of the effects of ontological clarity and application domain size on query performance

Semantic data models provide a map of the components of an information system. The characteristics of these models affect their usefulness for various tasks (e.g., information retrieval). The quality of information retrieval has obvious important consequences, both economic and otherwise. Traditionally, data base designers have produced parsimonious logical data models. In spite of their increased size, ontologically clearer conceptual models have been shown to facilitate better performance for both problem solving and information retrieval tasks in experimental settings. The experiments producing evidence of enhanced performance for ontologically clearer models have, however, used application domains of modest size. Data models in organizational settings are likely to be substantially larger than those used in these experiments. This research used an experiment to investigate whether the benefits of improved information retrieval performance associated with ontologically clearer models are robust as the size of the application domains increase. The experiment used an application domain of approximately twice the size as tested in prior experiments. The results indicate that, relative to the users of the parsimonious implementation, end users of the ontologically clearer implementation made significantly more semantic errors, took significantly more time to compose their queries, and were significantly less confident in the accuracy of their queries.