PLANTAR FASCIITIS AND ACHILLES TENDINITIS AMONG 150 CASES OF SERONEGATIVE SPONDARTHRITIS

SUMMARY A painful heel syndrome (plantar fasciitis and/or Achilles tendinitis) was found in 33 among 150 patients suffering from a seronegative spondarthritis. The clinical and radiological manifestations of this syndrome were similar in the nosological entities included in the seronegative spondarthritis group. HLA-B27 antigen was found in 91% of the patients, radiological sacroiliitis in 64% and an asymmetric peripheral arthritis in all cases. By contrast, Achilles tendinitis was not encountered in 220 cases of rheumatoid arthritis; plantar fasciitis was exceptional in the same case

Auxiliary subunit regulation of high-voltage activated calcium channels expressed in mammalian cells

The effects of auxiliary calcium channel subunits on the expression and functional properties of high-voltage activated (HVA) calcium channels have been studied extensively in the Xenopus oocyte expression system, but are less completely characterized in a mammalian cellular environment. Here, we provide the first systematic analysis of the effects of calcium channel beta and alpha(2)-delta subunits on expression levels and biophysical properties of three different types (Ca(v)1.2, Ca(v)2.1 and Ca(v)2.3) of HVA calcium channels expressed in tsA-201 cells. Our data show that Ca(v)1.2 and Ca(v)2.3 channels yield significant barium current in the absence of any auxiliary subunits. Although calcium channel beta subunits were in principle capable of increasing whole cell conductance, this effect was dependent on the type of calcium channel alpha(1) subunit, and beta(3) subunits altogether failed to enhance current amplitude irrespective of channel subtype. Moreover, the alpha(2)-delta subunit alone is capable of increasing current amplitude of each channel type examined, and at least for members of the Ca(v)2 channel family, appears to act synergistically with beta subunits. In general agreement with previous studies, channel activation and inactivation gating was regulated both by beta and by alpha(2)-delta subunits. However, whereas pronounced regulation of inactivation characteristics was seen with the majority of the auxiliary subunits, effects on voltage dependence of activation were only small (< 5 mV). Overall, through a systematic approach, we have elucidated a previously underestimated role of the alpha(2)-delta(1) subunit with regard to current enhancement and kinetics. Moreover, the effects of each auxiliary subunit on whole cell conductance and channel gating appear to be specifically tailored to subsets of calcium channel subtypes

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data