The CNR preclinical database for knowledge management in spinal cord injury research (Abstr. SfN Meeting 2013, San Diego)

Poster presentation (#148.22) at the Society for Neuroscience annual meeting in San Diego (2013, Nov 9-13).


Research in the field of central nervous system (CNS) trauma is advancing fast and yields over 8,000 new publications per year growing at an exponential rate during the past 40 years, accumulating in a total number of approximately 140,000 PubMed-listed papers today. Thus a comprehensive overview of the world-wide published research efforts in traumatic brain (TBI about 100,000 papers) and spinal cord injury (SCI about 40,000 papers) is no longer feasible by the individual scientist, but needs assistance from information technology.
At the Center for Neuronal Regeneration, CNR e.V. (, a pilot version of such a preclinical database for SCI has been developed. The structure and main features of the CNR database were recently outlined and discussed at the 1st CNR Expert Conference on “Scientific Data Mining For Medical Knowledge Management In Translational Neuroscience“ in Düsseldorf (
The database collects information about the animal and injury models, experimental designs, behavioral and non-functional (molecular/cellular/histological) outcomes of therapies and allows dynamic filtering and clustering of the data.
Recent advances to perform objective grading of preclinical SCI therapies as published by Kwon et al. [A grading system to evaluate objectively the strength of pre-clinical data of acute neuroprotective therapies for clinical translation in spinal cord injury. 2011. J Neurotrauma 28:1525-43] were also implemented into the database to measure the level of evidence for a preclinical therapy. The CNR preclinical database is constructed as a dynamic tool which allows the users to adapt functions according to their needs, e.g., by use of flexible grading and filtering parameters in the software. The current pilot version of the CNR preclinical SCI database contains a subset of 143 manually curated published full text literature data from peer-reviewed journals. Filtering, clustering, visualization and analysis tools are presented and will be demonstrated on-site. For automatic data collection, a preclinical SCI ontology is currently developed.
The goal of the CNR is to provide a unique decision-making database support for the objective selection of the most promising innovative preclinical SCI therapies to accelerate their translation into clinical trials, reduce the chance of failure and save financial and personnel resources.
Supported by Alliance of Hope Foundation, Medical Faculty of the Heinrich Heine University Düsseldorf and the Office of Business Development of the City of Düsseldorf.