Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).

Epothilone analogues with benzimidazole and quinoline side chains: chemical synthesis, antiproliferative activity, and interactions with tubulin

A series of epothilone B and D analogues bearing isomeric quinoline or functionalized benzimidazole side chains has been prepared by chemical synthesis in a highly convergent manner. All analogues have been found to interact with the tubulin/microtubule system and to inhibit human cancer cell proliferation in vitro, albeit with different potencies (IC(50) values between 1 and 150 nM). The affinity of quinoline-based epothilone B and D analogues for stabilized microtubules clearly depends on the position of the N-atom in the quinoline system, while the induction of tubulin polymerization in vitro appears to be less sensitive to N-positioning. The potent inhibition of human cancer cell growth by epothilone analogues bearing functionalized benzimidazole side chains suggests that these systems might be conjugated with tumor-targeting moieties to form tumor-targeted prodrugs.