Platelets modulate gastric ulcer healing: Role of endostatin and vascular endothelial growth factor release

Bleeding and delayed healing of ulcers are well recognized clinical problems associated with the use of aspirin and other nonsteroidal antiinflammatory drugs, which have been attributed to their antiaggregatory effects on platelets. We hypothesized that antiplatelet drugs might interfere with gastric ulcer healing by suppressing the release of growth factors, such as vascular endothelial growth factor (VEGF), from platelets. Gastric ulcers were induced in rats by serosal application of acetic acid. Daily oral treatment with vehicle, aspirin, or ticlopidine (an ADP receptor antagonist) was started 3 days later and continued for 1 week. Ulcer induction resulted in a significant increase in serum levels of VEGF and a significant decrease in serum levels of endostatin (an antiangiogenic factor). Although both aspirin and ticlopidine markedly suppressed platelet aggregation, only ticlopidine impaired gastric ulcer healing and angiogenesis as well as reversing the ulcer-associated changes in serum levels of VEGF and endostatin. The effects of ticlopidine on ulcer healing and angiogenesis were mimicked by immunodepletion of circulating platelets, and ticlopidine did not influence ulcer healing when given to thrombocytopenic rats. Incubation of human umbilical vein endothelial cells with serum from ticlopidine-treated rats significantly reduced proliferation and increased apoptosis, effects reversed by an antibody directed against endostatin. Ticlopidine treatment resulted in increased platelet endostatin content and release. These results demonstrate a previously unrecognized contribution of platelets to the regulation of gastric ulcer healing. Such effects likely are mediated through the release from platelets of endostatin and possibly VEGF. As shown with ticlopidine, drugs that influence gastric ulcer healing may do so in part through altering the ability of platelets to release growth factors.

Approximation algorithms

Increasing global competition, rapidly changing markets, and greater consumer awareness have altered the way in which corporations do business. To become more efficient, many industries have sought to model some operational aspects by gigantic optimization problems. It is not atypical to encounter models that capture 106 separate “yes” or “no” decisions to be made. Although one could, in principle, try all 2106 possible solutions to find the optimal one, such a method would be impractically slow. Unfortunately, for most of these models, no algorithms are known that find optimal solutions with reasonable computation times. Typically, industry must rely on solutions of unguaranteed quality that are constructed in an ad hoc manner. Fortunately, for some of these models there are good approximation algorithms: algorithms that produce solutions quickly that are provably close to optimal. Over the past 6 years, there has been a sequence of major breakthroughs in our understanding of the design of approximation algorithms and of limits to obtaining such performance guarantees; this area has been one of the most flourishing areas of discrete mathematics and theoretical computer science.