Duchenne muscular dystrophy from brain to muscle: The role of brain dystrophin isoforms in motor functions

Brain function and its effect on motor performance in Duchenne muscular dystrophy (DMD) is an emerging concept. The present study explored how cumulative dystrophin isoform loss, age, and a corticosteroid treatment affect DMD motor outcomes. A total of 133 genetically confirmed DMD patients from Sri Lanka were divided into two groups based on whether their shorter dystrophin isoforms (Dp140, Dp116, and Dp71) were affected: Group 1, containing patients with Dp140, Dp116, and Dp71 affected (n = 98), and Group 2, containing unaffected patients (n = 35). A subset of 52 patients (Group 1, n = 38; Group 2, n = 14) was followed for up to three follow-ups performed in an average of 28-month intervals. The effect of the cumulative loss of shorter dystrophin isoforms on the natural history of DMD was analyzed. A total of 74/133 (56%) patients encountered developmental delays, with 66/74 (89%) being in Group 1 and 8/74 (11%) being in Group 2 (p \u3c 0.001). Motor developmental delays were predominant. The hip and knee muscular strength, according to the Medical Research Council (MRC) scale and the North Star Ambulatory Assessment (NSAA) activities, “standing on one leg R”, “standing on one leg L”, and “walk”, declined rapidly in Group 1 (p \u3c 0.001 In the follow-up analysis, Group 1 patients became wheelchair-bound at a younger age than those of Group 2 (p = 0.004). DMD motor dysfunction is linked to DMD mutations that affect shorter dystrophin isoforms. When stratifying individuals for clinical trials, considering the DMD mutation site and its impact on a shorter dystrophin isoform is crucial

Generating Productive Dialogue between Consulting Statisticians and their Clients in the Pharmaceutical and Medical Research Settings

Due to the ever-increasing complexity of scientific technologies and resulting data, consulting statisticians are becoming more involved in the design, conduct, and analysis of biomedical research. This requires extensive collaboration between the consulting statistician and nonstatisticians, such as researchers, clinicians, and corporate executives. Consequently, a successful consulting career is becoming ever more dependent on the statistician's ability to effectively communicate with nonstatisticians. This is especially true when more complex, nontraditional analytical methods are required. In this paper, we examine the collaboration between statisticians and nonstatisticians from three different professional perspectives. Integrating these perspectives, we discuss ways to help the consulting statistician generate productive dialogue with clients. Finally, we examine how universities can better prepare students for careers in statistical consulting by incorporating more communication-based elements into their curriculum and by offering students ample opportunities to collaborate with nonstatisticians. Overall, we designed this exercise to help the consulting statistician generate dialogue with clients that results in more productive collaborations and a more satisfying work experience

Types of the cerebral arterial circle (circle of Willis) in a Sri Lankan Population

<p>Abstract</p> <p>Background</p> <p>The variations of the circle of Willis (CW) are clinically important as patients with effective collateral circulations have a lower risk of transient ischemic attack and stroke than those with ineffective collaterals. The aim of the present cadaveric study was to investigate the anatomical variations of the CW and to compare the frequency of prevalence of the different variations with previous autopsy studies as variations in the anatomy of the CW as a whole have not been studied in the Indian subcontinent.</p> <p>Methods</p> <p>The external diameter of all the arteries forming the CW in 225 normal Sri Lankan adult cadaver brains was measured using a calibrated grid to determine the prevalence in the variation in CW. Chisquared tests and a correspondence analysis were performed to compare the relative frequencies of prevalence of anatomical variations in the CW across 6 studies of diverse ethnic populations.</p> <p>Results</p> <p>We report 15 types of variations of CW out of 22 types previously described and one additional type: hypoplastic precommunicating part of the anterior cerebral arteries (A1) and contralateral posterior communicating arteries (PcoA) 5(2%). Statistically significant differences (p < 0.0001) were found between most of the studies except for the Moroccan study. An especially notable difference was observed in the following 4 configurations: 1) hypoplastic precommunicating part of the posterior cerebral arteries (P1), and contralateral A1, 2) hypoplastic PcoA and contralateral P1, 3) hypoplastic PcoA, anterior communicating artery (AcoA) and contralateral P1, 4) bilateral hypoplastic P1s and AcoA in a Caucasian dominant study by Fisher versus the rest of the studies.</p> <p>Conclusion</p> <p>The present study reveals that there are significant variations in the CW among intra and inter ethnic groups (Caucasian, African and Asian: Iran and Sri Lanka dominant populations), and warrants further studies keeping the methods of measurements, data assessment, and the definitions of hypoplasia the same.</p

Analysis of data from viral DNA microchips

Viral DNA microchips, arrays of genes from a viral genome printed over a glass slide, are powerful tools for rapidly characterizing the expression pattern of these genes in the presence of infection. The chips are exposed to a solution of fluorescently labeled cDNAs prepared from either mock or true infected human fibroblast cells and the expression levels of the various genes are recorded with the objective of detecting which viral genes are expressed to a significantly higher degree when exposed to the true infection as compared to the mock infection. The data were initially examined visually via image plots and scatterplots. These reveal that analysis of such data presents many challenges due to, among other problems, high interchip and intrachip variability with low signal-to-noise ratio, differential intensity scales that need to be adjustd nonlinearly, nonGaussian data, data for a large number of genes with little replication, scratches and dark spots on the chips, outliers, and an inability to quantitate intensities below a detection limit or above a threshold. The first step of the analysis was to standardize the chips to a single intensity scale using a photograph analogy. Next, the average expression level of each gene was estimated using a highly resistant repeated median estimator to avoid being misled by aberrant values. Finally, a simulation based approach was used to make a distribution-free assessment of significance

Biostatistics Advance Access published June 14, 2007 Microarray learning with ABC

Standard clustering algorithms when applied to DNA microarray data often tend to produce erroneous clusters. A major contributor to this divergence is the feature characteristic of microarray data sets that the number of predictors (genes) in such data far exceeds the number of samples by many orders of magnitude, with only a small percentage of predictors being truly informative with regards to the clustering while the rest merely add noise. An additional complication is that the predictors exhibit an unknown complex correlational configuration embedded in a small subspace of the entire predictor space. Under these conditions, standard clustering algorithms fail to find the true clusters even when applied in tandem with some sort of gene filtering or dimension reduction to reduce the number of predictors. We propose, as an alternative, a novel method for unsupervised classification of DNA microarray data. The method, which is based on the idea of aggregating results obtained from an ensemble of randomly resampled data (where both samples and genes are resampled), introduces a way of tilting the procedure so that the ensemble includes minimal representation from less important areas of the gene predictor space. The method produces a measure of dissimilarity between each pair of samples that can be used in conjunction with (a) a method like Ward’s procedure to generate a cluster analysis and (b) multidimensional scaling to generate useful visualizations of the data. We call the dissimilarity measures ABC dissimilarities since they are obtained by aggregating bundles of clusters. An extensive comparison of several clustering methods using actual DNA microarray data convincingly demonstrates that classification using ABC dissimilarities offers significantly superior performance