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111.
Copper sulfate can cause different pathologies in different organ systems during development. We determined the effects of toxic levels of copper sulfate on brain development in term Hubbard broiler chicks using stereological and biochemical analyses. Hubbard broiler chicken eggs were divided into three groups: controls with no treatment, sham-treated animals and an experimental group. On day 1, 0.1 ml saline was injected into the air chambers of the sham and experimental groups. The experimental group received also 50 μg copper sulfate. At term (day 21), all chick brains were removed and their volumes were determined using the Cavalieri volume estimation. Parallel biochemical analyses were carried out for glutathione and malondialdehyde levels in the brain tissues as indicators of oxidative damage. With copper treatment, the mean brain volume (8079 μm3) was significantly decreased compared to both the control (10075 μm3) and sham (9547 μm3) groups. Copper treatment (143.8 nmol/g tissue) showed significantly decreased malondialdehyde levels compared to the control (293.6 nmol/g tissue) and sham groups (268.8 nmol/g tissue). Copper treatment (404.5 nmol/g tissue) showed significantly increased malondialdehyde levels compared to the control (158.6 nmol/g tissue) and sham (142.8 nmol/g tissue) groups. The morphological and biochemical parameters we measured demonstrated that in term Hubbard broiler chicks, toxic levels of copper sulfate cause developmental and oxidative brain damage. 相似文献
112.
Timm Konold Yoon Hee Lee Michael J Stack Claire Horrocks Robert B Green Melanie Chaplin Marion M Simmons Steve AC Hawkins Richard Lockey John Spiropoulos John W Wilesmith Gerald AH Wells 《BMC veterinary research》2006,2(1):1-20
Background
Given the theoretical proposal that bovine spongiform encephalopathy (BSE) could have originated from sheep scrapie, this study investigated the pathogenicity for cattle, by intracerebral (i.c.) inoculation, of two pools of scrapie agents sourced in Great Britain before and during the BSE epidemic. Two groups of ten cattle were each inoculated with pools of brain material from sheep scrapie cases collected prior to 1975 and after 1990. Control groups comprised five cattle inoculated with sheep brain free from scrapie, five cattle inoculated with saline, and for comparison with BSE, naturally infected cattle and cattle i.c. inoculated with BSE brainstem homogenate from a parallel study. Phenotypic characterisation of the disease forms transmitted to cattle was conducted by morphological, immunohistochemical, biochemical and biological methods.Results
Disease occurred in 16 cattle, nine inoculated with the pre-1975 inoculum and seven inoculated with the post-1990 inoculum, with four cattle still alive at 83 months post challenge (as at June 2006). The different inocula produced predominantly two different disease phenotypes as determined by histopathological, immunohistochemical and Western immunoblotting methods and biological characterisation on transmission to mice, neither of which was identical to BSE. Whilst the disease presentation was uniform in all scrapie-affected cattle of the pre-1975 group, the post-1990 inoculum produced a more variable disease, with two animals sharing immunohistochemical and molecular profile characteristics with animals in the pre-1975 group.Conclusion
The study has demonstrated that cattle inoculated with different pooled scrapie sources can develop different prion disease phenotypes, which were not consistent with the phenotype of BSE of cattle and whose isolates did not have the strain typing characteristics of the BSE agent on transmission to mice. 相似文献113.
Background
In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. 相似文献114.