The role of T helper 17 (Th17) and T regulatory cells (Treg) in the pathogenesis of Graves' disease (GD) remains uncertain. The influence of methimazole (MMI) on the human immune system is still poorly understood. The aim of the present research was to assess changes in the frequencies of peripheral blood Th17 and Treg cells during GD treatment in the group of teenagers. The frequencies of Th17 and Treg were measured by flow cytometry in 60 adolescents at the time of GD diagnosis and after achieving MMI-induced euthyreosis. The control group consisted of 20 healthy volunteers. Lower percentages and absolute counts of Treg cells were found in the study group before the treatment in comparison with healthy controls (p = 0.032 and p = 0.006, respectively). Treatment with MMI caused an increase in the percentages and absolute counts of Treg lymphocytes (p = 0.037 and p = 0.007). After the treatment, no clinically significant differences in Treg cells between GD patients and controls were found. Higher absolute counts of Th17 lymphocytes were found in hyperthyroid adolescents before the treatment initiation and after achieving euthyreosis than in healthy individuals (p = 0.0001 and p = 0.047). Treatment with MMI caused a significant decrease in the percentages and absolute counts of Th17 lymphocytes (p = 0.047 and p = 0.043). The present study demonstrates that both Th17 and Treg cells might play a role in the pathogenesis of GD. Increased percentage of Treg after MMI therapy seems a predictor of response to anti-hypertensive treatment as it is associated with the normalization of thyroid hormone levels.

A complete gradient clustering algorithm formed with kernel estimators: The aim of this paper is to provide a gradient clustering algorithm in its complete form, suitable for direct use without requiring a deeper statistical knowledge. The values of all parameters are effectively calculated using optimizing procedures. Moreover, an illustrative analysis of the meaning of particular parameters is shown, followed by the effects resulting from possible modifications with respect to their primarily assigned optimal values. The proposed algorithm does not demand strict assumptions regarding the desired number of clusters, which allows the obtained number to be better suited to a real data structure. Moreover, a feature specific to it is the possibility to influence the proportion between the number of clusters in areas where data elements are dense as opposed to their sparse regions. Finally, the algorithm—by the detection of oneelement clusters—allows identifying atypical elements, which enables their elimination or possible designation to bigger clusters, thus increasing the homogeneity of the data set.

The aim of this paper is to present a Complete Gradient Clustering Algorithm, its applicational aspects and properties, as well as to illustrate them with specific practical problems from the subject of bioinformatics (the categorization of grains for seed production), management (the design of a marketing support strategy for a mobile phone operator) and engineering (the synthesis of a fuzzy controller). The main property of the Complete Gradient Clustering Algorithm is that it does not require strict assumptions regarding the desired number of clusters, which allows to better suit its obtained number to a real data structure. In the basic version it is possible to provide a complete set of procedures for defining the values of all functions and parameters relying on the optimization criterions. It is also possible to point out parameters, the potential change which implies influence on the size of the number of clusters (while still not giving an exact number) and the proportion between their numbers in dense and sparse areas of data elements. Moreover, the Complete Gradient Clustering Algorithm can be used to identify and possibly eliminate atypical elements (outliers). These properties proved to be very useful in the presented applications and may also be functional in many other practical problems.

Regarding wireless sensor network parameter estimation of the propagation model is a most important issue. Variations of the received signal strength indicator (RSSI) parameter are a fundamental problem of a system based on signal strength. In the present paper, we propose an algorithm based on Bayesian filtering techniques for estimating the path-loss exponent of the log-normal shadowing propagation model for outdoor RSSI measurements. Furthermore, in a series of experiments, we will demonstrate the usefulness of the particle filter for estimating the RSSI data. The stability of this algorithm and the differences in determined path-loss exponent for both method were also analysed. The proposed method of dynamic estimation results in significant improvements of the accuracy of RSSI values when compared with the experimental measurements. It should be emphasised that the path-loss exponent mainly depends on the RSSI data. Our results also indicate that increasing the number of inserted particles does not significantly raise the quality of the estimated parameters.