Cell cycle reactivation of cochlear progenitor cells in neonatal FUCCI mice by a GSK3 small molecule inhibitor.

Due to the lack of regenerative capacity of the mammalian auditory epithelium, sensory hair cell loss results in permanent hearing deficit. Nevertheless, a population of tissue resident stem/progenitor cells has been recently described. Identification of methods to trigger their activity could lead to exploitation of their potential therapeutically. Here we validate the use of transgenic mice reporting cell cycle progression (FUCCI), and stemness (Lgr5-GFP), as a valuable tool to identify regulators of cell cycle re-entry of supporting cells within the auditory epithelium. The small molecule compound CHIR99021 was used to inhibit GSK3 activity. This led to a significant increase in the fraction of proliferating sphere-forming cells, labeled by the FUCCI markers and in the percentage of Lgr5-GFP + cells, as well as a selective increase in the fraction of S-G2-M cells in the Lgr5 + population. Using whole mount cultures of the organ of Corti we detected a statistically significant increment in the fraction of proliferating Sox2 supporting cells after CHIR99021 treatment, but only rarely appearance of novel MyoVIIa +/Edu + hair cells. In conclusion, these tools provide a robust mean to identify novel regulators of auditory organ regeneration and to clarify the contribution of stem cell activity.

Complexity of biomedical data models in cardiology: the Intranet-based AF registry.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with major complications. Ongoing research is focused on new pacing devices for alternative treatment of this disease. The objective of an AF registry is to store prospectively all relevant data covering clinical information, quality of life and device parameters and by this means provide a platform for long-term follow-up. For statistical analysis, categorical and numerical items are required, thus a high-granular data structure must be defined and implemented in the clinical setting. Facing the limits of formalization, we developed an XML-based documentation scheme consisting of 619 items in eight tables and implemented it with state-of-the-art Intranet technology. At present detailed information on 88 patients is recorded. The pacing device generates per patient and follow-up visit a file consisting of approximately 400-500 parameters provided on a floppy disk, which are transferred by means of a specific interface into the database. Success factors for integration of a complex research database into the routine workflow of a busy university hospital are interfaces between data sources to enable non-redundant data entry, intensive fine tuning by iterative software engineering and benefit for the clinical users in form of clinical reports and patient-specific summaries. Data quality must be assured by plausibility checks. To get an overview of this complex dataset we developed a dedicated visualization tool. Due to the high number of items a large patient collective must be recruited for statistical evaluation. Interinstitutional cooperation is required for a consensus on common minimal documentation schemes to enable pooling of data.

XML-based visual data mining in medicine.

Medical databases in general are characterized by a high degree of complexity in terms of quantity of items, number of parameter values and data types (free text, categorical, numerical and other). Substantial domain knowledge is required for adequate formalization of medical entities. In this context we developed medical database plot (mdplot), a data mining tool to visualize both structure and quality of data in medical databases to identify items suitable for evaluation. Data models are provided in XML format. Missing data is identified to enable targeted efforts to improve data quality prior to analysis. Database items are classified as 1:1- related to the patient (i.e. variables are collected once per patient) and 1:n related. mdplot provides a list of all classes contained in a database, the number of records each and a condensed bar chart for semi-quantitative description of completeness according to four types of items: categorical, numerical, text and other. All items in a category are grouped from left to right, the height of each bar represents the proportion of non-missing values with respect to the total number of records in the class; thus the amount of content in a specific class is visualized. By selection of a specific class, a detailed description of it is provided including mean completeness in each item category as well as number of values per item. The new methodology was applied to a cardiological research database consisting of 619 items on 88 patients.