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At least 127 records · Page 7Linked to original sources
PubMed1998

Determinants of carcinogen-induced malignant conversion in rat brain: proliferative properties of neural precursor cell subpopulations analyzed by multi-parameter flow cytometry.

The induction of neuroectodermal tumors in BDIX rats by N-ethyl-N-nitrosourea (EtNU) is a model system for the analysis of transformation risk as a function of target cell properties. The yield of neural tumors induced by EtNU varies with the developmental window chosen for the carcinogen pulse; i.e. with the relative proportions of different neural precursor cells exposed to EtNU at distinct developmental stages. Different subsets of fetal brain cells have been characterized previously with respect to their relative risk of malignant transformation using monoclonal antibodies. As DNA replication of target cells is considered to be a prerequisite for malignant conversion, we analyzed the cell cycle distributions, using flow-cytometry, of 4 subsets of neural precursor cells considered to be at high or low risk, respectively, of malignant conversion by EtNU in vivo. Cell populations associated with an elevated risk of transformation exhibited higher proportions of cells in S-phase. One of the 2 putative low-risk populations exhibited a significantly lower fraction of S-phase cells, while the value of the second one exceeded those obtained for the 2 high-risk subpopulations. Therefore, a higher than average fraction of cells in S-phase appears to be positively correlated with the cellular risk of malignant transformation by EtNU, but does not represent a dominant risk determinant per se.

Animals↗
PubMed1998

On the use of neural network techniques to analyze sleep EEG data. Third communication: robustification of the classificator by applying an algorithm obtained from 9 different networks.

This is the third communication on the use of neural network techniques to classify sleep stages. In our first communication we presented the algorithms and the selection of the feature space and its reduction by using evolutionary and genetic procedures. In our second communication we trained the evolutionary optimized networks on the basis of multiple subject data in context with some smoothing algorithms in analogy of Rechtschaffen and Kales (RK). In this third communication we could demonstrate that the robustness concerning individual specific features of automatically generated sleep profiles could be reasonably improved by an additional modification of the procedure used by SASCIA (Sleep Analysis System to Challenge Innovative Artificial Networks). The outputs of nine different networks that were created by the data of 9 different subjects were used simultaneously for classification. The medians of the values obtained in each output measure were selected for the allocation to a sleep stage. The fitness criteria of 16 automatically generated sleep profiles showed reasonable concordance with the expert profile. Even though in single cases the concordance between conventional RK classifications and automatically generated profiles were a few percentages lower, the average correct classification of the 12 classified subjects improved substantially, thus proving that the classifier is more robust against individuum-specific variability. Despite the fact that the expert generally employs three channels (EEG, EMG and EOG), at least to build up sleep profiles, the SASCIA system was able to produce profiles on the basis of only one EEG channel with 80% concordance and a correlation coefficient of 0.86. The feature selections were performed by genetic algorithms and the topologies of the networks were optimized by evolutionary algorithms. This algorithm will now be used for larger sample forward classification.

Algorithms↗
PubMed2006

Increased polysialic acid neural cell adhesion molecule expression in human hippocampus of heroin addicts.

Chronic exposure to heroin is known to cause cognitive deficits. However, little is known about the underlying molecular mechanisms. It has been suggested that opiate-induced neurotoxicity as well as impaired plasticity and regeneration may be relevant. One of the target regions where regeneration still can be observed in the adult brain is the hippocampus. Since polysialic acid neural cell adhesion molecule is regarded as one of the key players involved in plasticity and regeneration of neural tissue, we analyzed polysialic acid neural cell adhesion molecule expression in the fascia dentate hilus of the human hippocampus of 29 lethally intoxicated heroin addicts and matched controls. Immunohistochemistry with an antibody directed against polysialic acid neural cell adhesion molecule revealed its expression in differently sized cells which could be identified as neurons and glial cells. We observed an increase in the percentage of polysialic acid neural cell adhesion molecule positive neurons in hippocampal hilus of heroin addicts compared with controls (P = 0.001).Interestingly, we also observed polysialic acid neural cell adhesion molecule expression in glial cells as evidenced by double immunofluorescence with glial fibrillary acidic protein and polysialic acid neural cell adhesion molecule using confocal laser scanning microscopy. The fraction of polysialic acid neural cell adhesion molecule positive glial cells was also higher in heroin addicts compared with controls (P = 0.009). In addition, within the group of addicts morphine blood concentrations showed a positive correlation with the percentage of polysialic acid neural cell adhesion molecule positive neurons (P = 0.04; r = 0.547). In conclusion, we observed an increase in polysialic acid neural cell adhesion molecule positive neurons and glial cells in hippocampi of heroin addicts. This might reflect an attempt to repair cell damage due to heroin exposure.

Adolescent↗