Mycoplasmal lipid-associated membrane proteins and Mycoplasma arthritidis mitogen recognition by serum antibodies from patients with rheumatoid arthritis

Mycoplasmal lipid-associated membrane proteins (LAMPs) and Mycoplasma arthritidis mitogen (MAM superantigen) are potent stimulators of the immune system. The objective of this work was to detect antibodies to MAM and LAMPs of Mycoplasma hominis and M. fermentans in the sera of patients affected by rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) to identify mycoplasmal products that can be involved in the etiopathogenesis of these autoimmune diseases. Serum samples from female RA and SLE patients and controls, recombinant MAM, and LAMPs of M. hominis PG21 and M. fermentans PG18 were used in Western blot assays. A similar frequency of sera from patients and controls reactive to MAM was detected. A larger number of M. hominis and M. fermentans LAMPs were recognized by sera from RA patients than controls, but no differences were detected between sera from SLE patients and controls. Among the LAMPs recognized by IgG antibodies from RA patients, proteins of molecular masses in a range of < 49 and a parts per thousand yen20 KDa (M. hominis) and < 102 and a parts per thousand yen58 KDa (M. fermentans) were the most reactive. These preliminary results demonstrate the strong reactivity of antibodies of RA patients with some M. hominis and M. fermentans LAMPs. These LAMPs could be investigated as mycoplasmal antigens that can take part in the induction or amplification of human autoimmune responses.

3D face computational photography using PCA spaces

In this paper, we present a 3D face photography system based on a facial expression training dataset, composed of both facial range images (3D geometry) and facial texture (2D photography). The proposed system allows one to obtain a 3D geometry representation of a given face provided as a 2D photography, which undergoes a series of transformations through the texture and geometry spaces estimated. In the training phase of the system, the facial landmarks are obtained by an active shape model (ASM) extracted from the 2D gray-level photography. Principal components analysis (PCA) is then used to represent the face dataset, thus defining an orthonormal basis of texture and another of geometry. In the reconstruction phase, an input is given by a face image to which the ASM is matched. The extracted facial landmarks and the face image are fed to the PCA basis transform, and a 3D version of the 2D input image is built. Experimental tests using a new dataset of 70 facial expressions belonging to ten subjects as training set show rapid reconstructed 3D faces which maintain spatial coherence similar to the human perception, thus corroborating the efficiency and the applicability of the proposed system.