Recurrence of Dupuytren’s contracture: A consensus-based definition

Purpose: One of the major determinants of Dupyutren disease (DD) treatment efficacy is recurrence of the contracture. Unfortunately, lack of agreement in the literature on what constitutes recurrence makes it nearly impossible to compare the multiple treatments alternatives available today. The aim of this study is to bring an unbiased pool of experts to agree upon what would be considered a recurrence of DD after treatment; and from that consensus establish a much-needed definition for DD recurrence. Methods: To reach an expert consensus on the definition of recurrence we used the Delphi method and invited 43 Dupuytren’s research and treatment experts from 10 countries to participate by answering a series of questionnaire rounds. After each round the answers were analyzed and the experts received a feedback report with another questionnaire round to further hone in of the definition. We defined consensus when at least 70% of the experts agreed on a topic. Results: Twenty-one experts agreed to participate in this study. After four consensus rounds, we agreed that DD recurrence should be defined as “more than 20 degrees of contracture recurrence in any treated joint at one year post-treatment compared to six weeks post-treatment”. In addition, “recurrence should be reported individually for every treated joint” and afterwards measurements should be repeated and reported yearly. Conclusion: This study provides the most comprehensive to date definition of what should be considered recurrence of DD. These standardized criteria should allow us to better evaluate the many treatment alternatives

Changes in brain function during administration of venlafaxine or placebo to normal subjects

Previous research has demonstrated neurophysiologic effects of antidepressants in depressed subjects. We evaluated neurophysiologic effects of venlafaxine in normal subjects. Healthy adults (n=32) received a 1-week placebo lead-in followed by 4 weeks randomized double-blind treatment with venlafaxine IR 150 mg. (n = 17) or placebo (n = 15). Brain function was examined using quantitative electroencephalographic (QEEG) power and theta cordance. Normal subjects receiving venlafaxine showed a decrease in theta-band cordance in the midline-and-right-frontal (MRF) region at 48 hours and at 1 week after randomization. Decreases in relative power also were seen in the MRF region; there were no significant changes in absolute power. These changes were significantly different from those in subjects receiving placebo. Changes in MRF cordance accurately identified treatment condition at 48 hours in 81.3% of subjects, and relative power from this region identified 60.7% of subjects. In conclusion, cordance may detect the pharmacological effects of antidepressant medication in normal subjects. Future studies should examine other classes of medication, as well as antidepressants with other mechanisms of action, to determine if cordance detects antidepressant medication effects in general in normal subjects. </jats:p

A time-series analysis of blood-based biomarkers within a 25-year longitudinal dolphin cohort.

Causal interactions and correlations between clinically-relevant biomarkers are important to understand, both for informing potential medical interventions as well as predicting the likely health trajectory of any individual as they age. These interactions and correlations can be hard to establish in humans, due to the difficulties of routine sampling and controlling for individual differences (e.g., diet, socio-economic status, medication). Because bottlenose dolphins are long-lived mammals that exhibit several age-related phenomena similar to humans, we analyzed data from a well controlled 25-year longitudinal cohort of 144 dolphins. The data from this study has been reported on earlier, and consists of 44 clinically relevant biomarkers. This time-series data exhibits three starkly different influences: (A) directed interactions between biomarkers, (B) sources of biological variation that can either correlate or decorrelate different biomarkers, and (C) random observation-noise which combines measurement error and very rapid fluctuations in the dolphin's biomarkers. Importantly, the sources of biological variation (type-B) are large in magnitude, often comparable to the observation errors (type-C) and larger than the effect of the directed interactions (type-A). Attempting to recover the type-A interactions without accounting for the type-B and type-C variation can result in an abundance of false-positives and false-negatives. Using a generalized regression which fits the longitudinal data with a linear model accounting for all three influences, we demonstrate that the dolphins exhibit many significant directed interactions (type-A), as well as strong correlated variation (type-B), between several pairs of biomarkers. Moreover, many of these interactions are associated with advanced age, suggesting that these interactions can be monitored and/or targeted to predict and potentially affect aging

A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data

<div><p>A common goal in data-analysis is to sift through a large data-matrix and detect any significant submatrices (i.e., biclusters) that have a low numerical rank. We present a simple algorithm for tackling this biclustering problem. Our algorithm accumulates information about 2-by-2 submatrices (i.e., ‘loops’) within the data-matrix, and focuses on rows and columns of the data-matrix that participate in an abundance of low-rank loops. We demonstrate, through analysis and numerical-experiments, that this loop-counting method performs well in a variety of scenarios, outperforming simple spectral methods in many situations of interest. Another important feature of our method is that it can easily be modified to account for aspects of experimental design which commonly arise in practice. For example, our algorithm can be modified to correct for controls, categorical- and continuous-covariates, as well as sparsity within the data. We demonstrate these practical features with two examples; the first drawn from gene-expression analysis and the second drawn from a much larger genome-wide-association-study (GWAS).</p></div

Illustration of the GSE48091 gene-expression data-set used in Example-A (see main text).

<p>Each row corresponds to a patient, and each column to a ‘gene’ (i.e., gene-expression measurement): the color of each pixel codes for the intensity of a particular measurement of a particular patient (see colorbar to the bottom).<i>M</i>_<i>D</i> = 340 of these patients are cases, the other <i>M</i>_<i>X</i> = 166 are controls; we group the former into the case-matrix ‘<i>D</i>’, and the latter into the control-matrix ‘<i>X</i>’.</p

Illustration of the loops within a 3-dimensional array.

<p>We sketch the structure of a 3-dimensional data-array <i>D</i>, with <i>J</i> rows, <i>K</i> columns and <i>P</i> ‘layers’. Each entry <i>D</i>_{<i>j</i>,<i>k</i>,<i>l</i>} will lie in the cube shown. The loops within <i>D</i> can be divided into 3-categories: (a) iso-layer loops that stretch across 2 rows and 2 columns, (b) iso-column loops that stretch across 2 rows and 2 layers, and (c) iso-row loops that stretch across 2 columns and 2 layers. The row-score [<i>Z</i>_ROW]_<i>j</i> aggregates all the iso-column and iso-layer loops associated with row-<i>j</i>. The column-score [<i>Z</i>_COL]_<i>k</i> aggregates all the iso-row and iso-layer loops associated with column-<i>k</i>. The layer-score [<i>Z</i>_LYR]_<i>l</i> aggregates all the iso-row and iso-column loops associated with layer-<i>l</i>.</p

Contrasting a bicluster with controls.

<p>This shows the bicluster of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006105#pcbi.1006105.g005" target="_blank">Fig 5B</a> on top, and the rest of the controls on the bottom. The control-patients have been rearranged in order of their correlation with the co-expression pattern of the bicluster. Even though a few of the controls (i.e,. ∼ 3/166) exhibit a coexpression pattern comparable to that expressed by the bicluster, the vast majority do not.</p

A scatterplot of the data shown in Fig 10.

<p>Each row-trace shown on the left in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006105#pcbi.1006105.g010" target="_blank">Fig 10</a> is plotted as a single point in 2-dimensional space; the horizontal-axis corresponds to the maximum row-trace and the vertical-axis corresponds to the average row-trace (taken across the iterations). The original-data is indicated with a ‘⊗’, and each of the random shuffles with a colored ‘•’. The <i>p</i>-value for any point in this plane is equal to the fraction of label-shuffled-traces that have either an <i>x</i>-position larger than <i>x</i>_<i>w</i> or a <i>y</i>-position larger than <i>y</i>_<i>w</i>, where <i>x</i>_<i>w</i> and <i>y</i>_<i>w</i> are the <i>x</i>- and <i>y</i>-percentiles associated with the most extreme coordinate of (details given in section ) of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006105#pcbi.1006105.s006" target="_blank">S2 Text</a>. Each random shuffle is colored by its p-value determined by the label-shuffled-distribution. By comparing the original-trace with the shuffled-distribution we can read off a p-value for the original-data of ≲ 0.008.</p

Continuous–covariate-distribution for the bicluster shown in Example-B.

<p>As mentioned in the introduction, our algorithm proceeds iteratively, removing rows and columns from the case-matrix until there are none left. One of our goals is to ensure that, during this process, our algorithm focuses on biclusters which involve case-patients that are relatively well balanced in covariate-space. On the left we show a scatterplot illustrating the 2-dimensional distribution of covariate-components across the remaining <i>m</i> = 115 case-patients within the bicluster shown in Example-B (i.e., <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006105#pcbi.1006105.g007" target="_blank">Fig 7</a>). The horizontal and vertical lines in each subplot indicate the medians of the components of the covariate-distribution. On the right we show the same data again, except in contour form (note colorbar). The continuous-covariates remain relatively well-distributed even though relatively few case-patients are left (compare with <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006105#pcbi.1006105.g009" target="_blank">Fig 9</a>).</p

Row-traces for the bicluster shown in Example-A.

<p>This bicluster was found by running our algorithm on the data shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006105#pcbi.1006105.g004" target="_blank">Fig 4</a>. Because we corrected for controls, we compare our original-data to the distribution we obtain under the null-hypothesis H0 (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006105#sec011" target="_blank">Methods</a>). On the left we show the row-trace as a function of iteration for the original-data (red) as well as each of the 256 random shuffles (blue). On the right we replot this same trace data, showing the 5th, 50th and 95th percentile (across iterations) of the H0 distribution. Because we are not correcting for any covariates, the column-traces are identical to the row-traces.</p