Clinical correlation between disease progression and central vein sign in pediatric onset multiple sclerosis: A binational study.

BACKGROUND: The central vein sign (CVS) has been proposed as a novel MRI biomarker to improve diagnosis of pediatric-onset MS (POMS). However, the role of CVS in POMS progression has yet to be discovered. OBJECTIVES: To investigate the appearance of CVS and its correlation with POMS disease progression. METHODS: One hundred fifty-six POMS from two MS centers in Israel and Czech Republic MS centers were followed for five years. Patient assessment was performed by the Expanded Disability Status Scale (EDSS) and Annual Relapse Rate (ARR). Patients in whom at least 40 % of brain MRI lesions had CVS ("rule of 40") were determined as CVS-positive. RESULTS: The total group of POMS consisted of 96 CVS-negative (61.5 %), aged 14.6 ± 1.9 years, EDSS 2.0, 75 % Interquartile Range (IQR) 1.0-3.0, disease duration (DD) 6.28 ± 0.38 years, and 60 CVS-positive (38.5 %), aged 15.1 ± 0.3 years, EDSS 2.0, IQR 1.5-3.0, DD 5.62 ± 0.13 years, were analyzed. After a three and five-year follow-up, the CVS-positive patients had higher EDSS scores than those who were CVS-negative, 2.0, IQR 1.0-2.5, vs 1.0, IQR 1.0-2.0, (p = 0.009) and 2.0, IQR 1.0-3.25 vs 1.0, IQR 1.0-2.0, (p = 0.0003), respectively. Patients with CVS-positive POMS were characterized by a significantly higher ARR (0.78 ± 0.08 vs 0.57 ± 0.04, p = 0.002). These results were confirmed in subgroups of Disease Modifying Treatments (DMT) untreated and treated patients. CONCLUSION: CVS-positive POMS is characterized by higher disability progression than CVS-negative, indicating the importance of CVS in disease pathogenesis.

Normalization of Large-Scale Transcriptome Data Using Heuristic Methods.

In this study, we introduce an artificial intelligent method for addressing the batch effect of a transcriptome data. The method has several clear advantages in comparison with the alternative methods presently in use. Batch effect refers to the discrepancy in gene expression data series, measured under different conditions. While the data from the same batch (measurements performed under the same conditions) are compatible, combining various batches into 1 data set is problematic because of incompatible measurements. Therefore, it is necessary to perform correction of the combined data (normalization), before performing biological analysis. There are numerous methods attempting to correct data set for batch effect. These methods rely on various assumptions regarding the distribution of the measurements. Forcing the data elements into pre-supposed distribution can severely distort biological signals, thus leading to incorrect results and conclusions. As the discrepancy between the assumptions regarding the data distribution and the actual distribution is wider, the biases introduced by such "correction methods" are greater. We introduce a heuristic method to reduce batch effect. The method does not rely on any assumptions regarding the distribution and the behavior of data elements. Hence, it does not introduce any new biases in the process of correcting the batch effect. It strictly maintains the integrity of measurements within the original batches.