Effects of gender and age on paediatric headache.

The aim of this study was to evaluate the impact of gender and age on headache characteristics and disability. Headache characteristics were assessed at an initial visit to a paediatric specialty care centre and five follow-up visits. A total number of 4121 patients were evaluated. Fifty-eight per cent of the sample was female. Boys were younger at their first headache and initial visit. They more frequently described headache pain as squeezing and location as top of the head. Girls reported more frequent and longer headaches. Girls more often described headache pain as sharp and location as back of the head. Age accounted for more variance than gender in headache severity, duration, frequency and disability. Gender differences exist in headache characteristics. Age is also an important factor in the variability in characteristics and disability. Longitudinal studies are needed to describe further the natural history of headaches in childhood and compare outcome between genders.

Assessing quality and normalization of microarrays: case studies using neurological genomic data.

BACKGROUND: Genomic analysis using microarray tools has the potential benefit of enhancing our understanding of neurological diseases. The analysis of these data is complex due to the large amount of data generated. Many tools have been developed to assist with this, but standard methods of analysis of these tools have not been established. OBJECTIVE: This study analyzed the sensitivity and specificity of different analytical methods for gene identification and presents a standardized approach. METHODS: Affymetrix HG-U133 plus 2.0 microarray datasets from two neurological diseases - chronic migraine and new-onset epilepsy - were used as source data and methods of analysis for normalization of data and identification of gene changes were compared. Housekeeping genes were used to identify non-specific changes and gender related genes were used to identify specific changes. RESULTS: Initial normalization of data revealed that 5-10% of the microarray were potential outliers due to technical errors. Two separate methods of analysis (dChip and Bioconductor) identified the same microarray chips as outliers. For specificity and sensitivity testing, performing a per-gene normalization was found to be inferior to standard preprocessing procedures using robust multichip average analysis. CONCLUSIONS: Technical variation in microarray preprocessing may account for chip-to-chip and batch-to-batch variations and outliers need to be removed prior to analysis. Specificity and sensitivity of the final results are best achieved following this identification and removal with standard genomic analysis techniques. Future tools may benefit from the use of standard tools of measurement.