Italian version of the Rasch-Built Overall Amyotrophic Lateral Sclerosis Disability Scale (ROADS): validation and longitudinal performance.

OBJECTIVE: To validate an Italian version of the Rasch-Built Overall ALS Disability Scale (ROADS) in a broad population of patients and assess its longitudinal performance over time. METHODS: 270 ALS patients referring to the Motor Neuron Disease Clinic of the University of Padova and Modena (Italy) accepted to compile the Italian version of the ROADS and results were correlated with the ALSFRSr and ALSAQ-40 scores, FVC values, and creatinine or albumin blood levels. To verify test-retest reliability, patients were asked to fill in a second copy of the scale within 5-7 days. Thirty-nine patients compiled a further copy of questionnaire during the follow up visit (after 133 days on average) which allowed us a longitudinal assessment of the scale. RESULTS: We found a good external construct validity between ROADS and either ALSFRS-R (correlation coefficient = 0.85) or ALSAQ-40 (correlation coefficient = - 0.84). Test-retest reliability was excellent with a concordance-correlation coefficient of 0.93. Yet, we observed a significant correlation between changes over time of the ROADS normalised sum score (- 2.18 point loss per month) and those of both the ALSFRS-R (positive correlation; Rho = 0.64, p ≤ 0.0001) or the ALSAQ-40 (negative correlation; Rho = - 0.60, p = 0.014). CONCLUSIONS: The Italian version of ROADS proved to be a reliable marker to monitor overall disability in ALS patients. Further studies are necessary to assess its longitudinal performance.

CorrelaGenes: a new tool for the interpretation of the human transcriptome.

BACKGROUND: The amount of gene expression data available in public repositories has grown exponentially in the last years, now requiring new data mining tools to transform them in information easily accessible to biologists. RESULTS: By exploiting expression data publicly available in the Gene Expression Omnibus (GEO) database, we developed a new bioinformatics tool aimed at the identification of genes whose expression appeared simultaneously altered in different experimental conditions, thus suggesting co-regulation or coordinated action in the same biological process. To accomplish this task, we used the 978 human GEO Curated DataSets and we manually performed the selection of 2,109 pair-wise comparisons based on their biological rationale. The lists of differentially expressed genes, obtained from the selected comparisons, were stored in a PostgreSQL database and used as data source for the CorrelaGenes tool. Our application uses a customized Association Rule Mining (ARM) algorithm to identify sets of genes showing expression profiles correlated with a gene of interest. The significance of the correlation is measured coupling the Lift, a well-known standard ARM index, and the χ(2) p value. The manually curated selection of the comparisons and the developed algorithm constitute a new approach in the field of gene expression profiling studies. Simulation performed on 100 randomly selected target genes allowed us to evaluate the efficiency of the procedure and to obtain preliminary data demonstrating the consistency of the results. CONCLUSIONS: The preliminary results of the simulation showed how CorrelaGenes could contribute to the characterization of molecular pathways and biological processes integrating data obtained from other applications and available in public repositories.