The absence of biological markers allowing for the assessment of the evolution and prognosis of glioblastoma (GBM) is a major impediment to the clinical management of GBM patients. The observed variability in patients' treatment responses and in outcomes implies biological heterogeneity and the existence of unidentified patient categories. Here, we define for the first time three GBM patient categories with distinct and clinically predictive three-dimensional nuclear-telomeric architecture defined by telomere number, size, and frequency of telomeric aggregates. GBM patient samples were examined by three-dimensional fluorescent in situ hybridization of telomeres using two independent three-dimensional telomere-measurement tools (TeloView program [P(1)] and SpotScan system [P(2)]). These measurements identified three patients categories (categories 1-3), displaying significant differences in telomere numbers/nucleus (P(1) = .0275; P(2)
A methodology is described for associating local invariant signature functions to smooth planar curves in order to enable their translation, rotation, and scale-invariant recognition from arbitrarily clipped portions. The suggested framework incorporates previous approaches, based on locating inflections, curvature extrema, breakpoints, and other singular points on planar object boundaries, and provides a systematic way of deriving novel invariant signature functions based on curvature or cumulative turn angle of curves. These new signatures allow the specification of arbitrarily dense feature points on smooth curves, whose locations are invariant under similarity transformations. The results are useful for detecting and recognizing partially occluded planar objects, a key task in low-level robot vision.
We have investigated the use of spectral imaging for multi‐color analysis of permanent cytochemical dyes and enzyme precipitates on cytopathological specimens. Spectral imaging is based on Fourier‐transform spectroscopy and digital imaging. A pixel‐by‐pixel spectrum‐based color classification is presented of single‐, double‐, and triple‐color in situ hybridization for centromeric probes in T24 bladder cancer cells, and immunocytochemical staining of nuclear antigens Ki‐67 and TP53 in paraffin‐embedded cervical brush material (AgarCyto). The results demonstrate that spectral imaging unambiguously identifies three chromogenic dyes in a single bright‐field microscopic specimen. Serial microscopic fields from the same specimen can be analyzed using a spectral reference library. We conclude that spectral imaging of multi‐color chromogenic dyes is a reliable and robust method for pixel color recognition and classification. Our data further indicate that the use of spectral imaging (a) may increase the number of parameters studied simultaneously in pathological diagnosis, (b) may provide quantitative data (such as positive labeling indices) more accurately, and (c) may solve segmentation problems currently faced in automated screening of cell‐ and tissue specimens. Figures on http://www.esacp.org/acp/2001/22‐3/macville.htm.
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