An Immunohistochemical Study of the Distribution of the Measles Virus Receptors, CD46 and SLAM, In Normal Human Tissues and Subacute Sclerosing Panencephalitis

We have compared the expression of the known measles virus (MV) receptors, membrane cofactor protein (CD46) and the signaling lymphocyte-activation molecule (SLAM), using immunohistochemistry, in a range of normal peripheral tissues (known to be infected by MV) as well as in normal and subacute sclerosing panencephalitis (SSPE) brain. To increase our understanding of how these receptors could be utilized by wild-type or vaccine strains in vivo, the results have been considered with regard to the known route of infection and systemic spread of MV. Strong staining for CD46 was observed in endothelial cells lining blood vessels and in epithelial cells and tissue macrophages in a wide range of peripheral tissues, as well as in Langerhans' and squamous cells in the skin. In lymphoid tissues and blood, subsets of cells were positive for SLAM, in comparison to CD46, which stained all nucleated cell types. Strong CD46 staining was observed on cerebral endothelium throughout the brain and also on ependymal cells lining the ventricles and choroid plexus. Comparatively weaker CD46 staining was observed on subsets of neurons and oligodendrocytes. In SSPE brain sections, the areas distant from lesion sites and negative for MV by immunocytochemistry showed the same distribution for CD46 as in normal brain. However, cells in lesions, positive for MV, were negative for CD46. Normal brain showed no staining for SLAM, and in SSPE brain only subsets of leukocytes in inflammatory infiltrates were positive. None of the cell types most commonly infected by MV show detectable expression of SLAM, whereas CD46 is much more widely expressed and could fulfill a receptor function for some wild-type strains. In the case of wild-type stains, which are unable to use CD46, a further as yet unknown receptor(s) would be necessary to fully explain the pathology of MV infection.

Synthetic peptides interacting with the 67-kd laminin receptor can reduce retinal ischemia and inhibit hypoxia-induced retinal neovascularization.

The high-affinity 67-kd laminin receptor (67LR) is expressed by proliferating endothelial cells during retinal neovascularization. The role of 67LR has been further examined experimentally by administration of selective 67LR agonists and antagonists in a murine model of proliferative retinopathy. These synthetic 67LR ligands have been previously shown to stimulate or inhibit endothelial cell motility in vitro without any direct effect on proliferation. In the present study, a fluorescently labeled 67LR antagonist (EGF33–42) was injected intraperitoneally into mice and its distribution in the retina was assessed by confocal scanning laser microscopy. Within 2 hours this peptide was localized to the retinal vasculature, including preretinal neovascular complexes, and a significant amount had crossed the blood retinal barrier. For up to 24 hours postinjection, the peptide was still present in the retinal vascular walls and, to a lesser extent, in the neural retina. Non-labeled EGF33–42 significantly inhibited pre-retinal neovascularization in comparison to controls treated with phosphate-buffered saline or scrambled peptide (P <0.0001). The agonist peptide (Lamß1925–933) also significantly inhibited proliferative retinopathy; however, it caused a concomitant reduction in retinal ischemia in this model by promoting significant revascularization of the central retina (P <0.001). Thus, 67LR appears to be an important target receptor for the modulation of retinal neovascularization. Agonism of this receptor may be valuable in reducing the hypoxia-stimulated release of angiogenic growth factors which drives retinal angiogenesis.

Apoptosis and cell-cycle regulatory proteins in colorectal carcinoma: relationship to tumour stage and patient survival

The quantitative assessment of apoptotic index (AI) and mitotic index (MI) and the immunoreactivity of p53, bcl-2, p21, and mdm2 were examined in tumour and adjacent normal tissue samples from 30 patients with colonic and 22 with rectal adenocarcinoma. Individual features and combined profiles were correlated with clinicopathological parameters and patient survival data to assess their prognostic value. Increased AI was significantly associated with increased bcl-2 expression (p

The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia

Quantitative examination of prostate histology offers clues in the diagnostic classification of lesions and in the prediction of response to treatment and prognosis. To facilitate the collection of quantitative data, the development of machine vision systems is necessary. This study explored the use of imaging for identifying tissue abnormalities in prostate histology. Medium-power histological scenes were recorded from whole-mount radical prostatectomy sections at × 40 objective magnification and assessed by a pathologist as exhibiting stroma, normal tissue (nonneoplastic epithelial component), or prostatic carcinoma (PCa). A machine vision system was developed that divided the scenes into subregions of 100 × 100 pixels and subjected each to image-processing techniques. Analysis of morphological characteristics allowed the identification of normal tissue. Analysis of image texture demonstrated that Haralick feature 4 was the most suitable for discriminating stroma from PCa. Using these morphological and texture measurements, it was possible to define a classification scheme for each subregion. The machine vision system is designed to integrate these classification rules and generate digital maps of tissue composition from the classification of subregions; 79.3% of subregions were correctly classified. Established classification rates have demonstrated the validity of the methodology on small scenes; a logical extension was to apply the methodology to whole slide images via scanning technology. The machine vision system is capable of classifying these images. The machine vision system developed in this project facilitates the exploration of morphological and texture characteristics in quantifying tissue composition. It also illustrates the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision.