Preclinical tests for cerebral stroke

Stroke is the second single highest cause of death in Europe. The low reliability of animal models in replicating the human disease is one of the most serious problems in the field of medical and pharmaceutical research about stroke. The standard models for the study of ischemic stroke are often poorly predictive as they simulate only partially the human disease. This work aims at investigating animal models with diseases typically associated with the onset of stroke in human patients. We have designed and realised a knowledge base for collecting, elaborating, and extracting analytical results of genomic, pro-teomic, biochemical, morphological investigations from animal models of cerebral stroke. Data analysis techniques are tailored to make the data available for processing and correlation, in order to increase the predictive value of the preclinical data, to perform biosimulation studies, and to support both academic and industrial research in the area of cerebral stroke therapy. A first statistical analysis of the retrieved information leads to the validation of our animal models and suggests a predictive and translational value for parameters related to a specific model. In particular, concerning gene expression data, we have applied a data analysis pipeline that initially takes into account an initial set of 64,000 genes and brings down the focus on a few tens of them.