2 resultados para Normal colonic mucosa
em WestminsterResearch - UK
Resumo:
Magnetic resonance imaging is a diagnostic tool used for detecting abnormal organs and tissues, often using Gd(III) complexes as contrast-enhancing agents. In this work, core–shell polymer fibers have been prepared using coaxial electrospinning, with the intent of delivering gadolinium (III) diethylenetriaminepentaacetate hydrate (Gd(DTPA)) selectively to the colon. The fibers comprise a poly(ethylene oxide) (PEO) core loaded with Gd(DTPA), and a Eudragit S100 shell. They are homogeneous, with distinct core–shell phases. The components in the fibers are dispersed in an amorphous fashion. The proton relaxivities of Gd(DTPA) are preserved after electrospinning. To permit easy visualization of the release of the active ingredient from the fibers, analogous materials are prepared loaded with the dye rhodamine B. Very little release is seen in a pH 1.0 buffer, while sustained release is seen at pH 7.4. The fibers thus have the potential to selectively deliver Gd(DTPA) to the colon. Mucoadhesion studies reveal there are strong adhesive forces between porcine colon mucosa and PEO from the core, and the dye-loaded fibers can be successfully used to image the porcine colon wall. The electrospun core–shell fibers prepared in this work can thus be developed as advanced functional materials for effective imaging of colonic abnormalities.
Resumo:
We present a method for recovering facial shape using an image of a face and a reference model. The zenith angle of the surface normal is recovered directly from the intensities of the image. The azimuth angle of the reference model is then combined with the calculated zenith angle in order to get a new field of surface normals. After integration of the needle map, the recovered surface has the effect of mapped facial features over the reference model. Experiments demonstrate that for the lambertian case, surface recovery is achieved with high accuracy. For non-Lambertian cases, experiments suggest potential for face recognition applications.