PubMed · 11987607
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W Biernat. 2001. [Congenital neoplasms].. https://pubmed.ncbi.nlm.nih.gov/11987607/
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Beta(ig)-h3 is a secretory protein that is induced by transforming growth factor (TGF)-beta. We have recently found that beta(ig)-h3 expression is induced in cultured astrocytes by TGF-beta1 and in rat cerebral cortex by stab wound. The purpose of this study was to examine the effect of the secreted beta(ig)-h3 on cell adhesion of astrocytes and the underlying mechanisms. When U87 human astrocytoma cells were seeded on dishes coated with recombinant beta(ig)-h3, cell adhesion was significantly enhanced. Blocking experiments using various antibodies to the integrin subunit suggested alpha6beta4 integrin could be involved in the beta(ig)-h3-mediated astrocyte cell adhesion. Cell adhesion to beta(ig)-h3 substrate was substantially blocked by preincubation with the inhibitor to the src kinase. When cells were plated on beta(ig)-h3-coated dishes, tyrosine phosphorylation of focal adhesion kinase was prominently increased within 20 min in a beta4 integrin-dependent manner. The results suggest that alpha6beta4 integrin-mediated interactions of astrocytes with beta(ig)-h3 transduce intracellular signals through the focal adhesion proteins, which may regulate certain aspects of astrocyte response to brain injury.
Magnetic resonance spectroscopy (MRS) provides a non-invasive measurement of the biochemistry of living tissue. However, signal variation due to tissue heterogeneity causes considerable mixing between different disease categories, making accurate class assignments difficult. This paper compares a systematic methodology for classifier design using multivariate bayesian variable selection (MBVS), with one based on feature extraction using independent component analysis (ICA). We illustrate the methodology and assess the classification performance using a data set comprising 41 magnetic resonance spectra acquired in vivo from two grades of brain tumour, namely low- and medium-grade astrocytic tumours, labelled astrocytomas (AST), and high-grade gliomas and glioblastomas labelled glioblastomas (GL). The aim of this study is threefold. First, to describe the application of the alternative methodologies to MRS, then to benchmark their classification performance, and finally to interpret the classification models in terms of biologically relevant signals derived from the spectra. The classification performance is assessed using the bootstrap method and by application to a test sample in a retrospective study.
Automated pattern recognition techniques are needed to help radiologists categorize MRS data of brain tumors according to histological type and grade. A major question is whether a computer program "trained" on spectra from one hospital will be able to classify those from another, particularly if the acquisition protocol is different. A subset of 144 histopathologically validated brain tumor spectra in the INTERPRET database, obtained from three of the collaborating centers, was grouped into meningiomas, low-grade astrocytomas, and "aggressive tumors" (glioblastomas and metastases). Spectra from two centers formed the training set (94 spectra) while the third acted as the test set (50 spectra). Linear discriminant analysis successfully classified 48/50 in the test set; the remaining two were atypical cases. When the training and test sets were combined, 133 of the 144 spectra were correctly classified using the leave-one-out procedure. These spectra had been obtained using different sequences (STEAM and PRESS), different echo times (20, 30, 31, and 32 ms), different repetition times (1600 and 2000 ms), and different manufacturers' instruments (GE and Philips). Pattern recognition algorithms are less sensitive to acquisition parameters than had been expected.