Endogenous and synthetic MMP inhibitors in CNS physiopathology.

Matrix metalloproteinases (MMPs, including the membrane-type MMPs (MT-MMPs)), a disintegrin and metalloproteinase (ADAM), and ADAM with thrombospondin motifs belong to the metzincins, a subclass of metalloproteinases that contain a Met residue and a Zn(2+) ion at the catalytic site necessary for enzymatic reaction. MMP proteolytic activity is mainly controlled by their natural tissue inhibitors of metalloproteinase (TIMP). A number of synthetic inhibitors have been developed to control deleterious MMP activity. The roles of MMPs and some of their ECM substrates in CNS physiology and pathology are covered by other chapters of the present volume and will thus not be addressed in depth. This chapter will focus (i) on the endogenous MMP inhibitors in the CNS, (ii) on MMP and TIMP regulations in three large classes of neuropathologic processes (inflammatory, neurodegenerative, and infectious), and (iii) on synthetic inhibitors of MMPs and the perspective of their use in different brain diseases.

Ruler Based Automatic C-Arm Image Stitching Without Overlapping Constraint

In this paper, we propose a new method for stitching multiple fluoroscopic images taken by a C-arm instrument. We employ an X-ray radiolucent ruler with numbered graduations while acquiring the images, and the image stitching is based on detecting and matching ruler parts in the images to the corresponding parts of a virtual ruler. To achieve this goal, we first detect the regular spaced graduations on the ruler and the numbers. After graduation labeling, for each image, we have the location and the associated number for every graduation on the ruler. Then, we initialize the panoramic X-ray image with the virtual ruler, and we “paste” each image by aligning the detected ruler part on the original image, to the corresponding part of the virtual ruler on the panoramic image. Our method is based on ruler matching but without the requirement of matching similar feature points in pairwise images, and thus, we do not necessarily require overlap between the images. We tested our method on eight different datasets of X-ray images, including long bones and a complete spine. Qualitative and quantitative experiments show that our method achieves good results.