Virulenzfaktoren von Aggregatibacter actinomycetemcomitans und Klinik der Parodontitis

Das parodontopathogene Bakterium Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans) exprimiert zahlreiche Virulenzfaktoren. In dieser Studie wurden die Gene für die Virulenzfaktoren Leukotoxin (LtxA), Cytolethal Distending Toxin (CDT) und Fimbriae-assoziiertes Protein (Flp1) in 99 A. actinomycetemcomitans-Isolaten aus der Plaque von Parodontitispatienten aus vier deutschen Universitätskliniken untersucht. Die Proben wurden serotypisiert. Die Entnahme erfolgte mit sterilen Papierspitzen aus der jeweils tiefsten Tasche jedes Quadranten. Es wurden von den Patienten Sondierungstiefe (PD) und Attachmentlevel (AL) an sechs Stellen pro Zahn gemessen und ebenfalls die Tiefen an den vier Entnahmestellen notiert. Außerdem wurden ethnische Herkunft der Eltern, Geschlecht und Raucherstatus erfragt. Lediglich zwei A. actinomycetemcomitans-Isolate aus Frankfurt/Main wiesen das ltx-Gen mit Deletion auf. Diese zeigten signifikant höhere PD an den vier Entnahmestellen. Die übrigen 97 Proben hatten das ltx-Gen ohne Deletion in der DNA-Promotorregion ihrer A. actinomycetemcomitans-Stämme. Probanden mit Genlokus für das cdtB-Gen, mit drei cdt-Genen oder insgesamt fünf Genen für Virulenzfaktoren litten signifikant häufiger an aggressiver Parodontitis. A. actinomycetemcomitans-Isolate mit cdtA-Gen, cdtB-Gen, cdtCGen, drei cdt-Gene oder flp-1-Gen wiesen signifikant häufiger Serotyp b oder c auf. Probanden ohne cdtC-Gen oder flp-1-Gen in der DNA ihrer isolierten A. actinomycetemcomitans-Stämme zeigten am häufigsten Serotyp e. Probanden mit Genlokus für das cdtB-Gen oder drei cdt-Gene in den isolierten A. actinomycetemcomitans-Proben oder mit aggressiver Parodontitis stammten signifikant häufiger aus dem Ausland. Es wurde kein signifikanter Zusammenhang zwischen Vorkommen der Gene für Virulenzfaktoren und PD bzw. AL im gesamten Gebiss gefunden

Evaluation of Cluster Analysis Methods

Cluster analysis includes a range of methods and practices that are used primarily for classification of objects. It takes an important role in many areas. Since the resulting distribution of objects into clusters may vary depending on the selected methods and specifications, it is appropriate to assess the results obtained. This paper proposes new ways of evaluating these results in a situation where objects are characterized by qualitative variables or by variables of different types. These coefficients can be used either to compare different methods (in terms of better outcomes) or for finding of the optimal number of clusters. All of them are based on the detection of variability which is also used for measuring of dissimilarity of objects and clusters. The newly proposed evaluation methods are applied to real data sets (of different sizes, with different number of variables, including variables of different types) and the behavior of these coefficients in different conditions is being examined. These data sets have known as well as unknown classification of objects into clusters. The best coefficient for evaluating clustering results with different types of variables can be considered, based on the analysis carried out, the modified coefficient of CHF. Local maximum value according to which the results of the clustering are evaluated, almost always exists. The analysis has proven that in most cases this value meets the expected results of the well-known classification of objects into clusters. The existence of local extremes of the other coefficients depends on specific data sets and is not always feasible

Cluster analysis of households characterized by categorical indicators

In the paper we deal with evaluation of the results of cluster analysis which is applied to data files in which objects are characterized qualitative variables. We describe methods of clustering, determination of optimal cluster numbers, and evaluation of obtained clusters implemented in the procedure for two-step cluster analysis in the SPSS statistical software package. These techniques are applied to the selected household indicators gathered in the SILC (Statistics on Income and Living Conditions) survey in the Czech Republic in 2008. We clustered households characterized by the indicators expressing if a household owns a computer and a car as an example. We discuss the problem of determination of optimal cluster numbers by the approach based on information criteria (we use the Bayesian information criterion) and determine number of clusters by means of the silhouette coefficient. Then we describe four obtained clusters on the basis of indicators of working activity, degree of education and degree of urbanization. Moreover, we extended characterizing variables to the recoded indicators expressing how the household goes well with its income. On the basis of this example we illustrate investigation of variable importance. In this case we describe obtained three clusters by three variables used in the analysis. In conclusion we mention some other approaches to evaluation of clustering objects characterized by categorical variables. They consist in both coefficients based on multivariate analysis of variance with using specialized variability measure for nominal and ordinal data, and modification of some other coefficients for qualitative data. The problem of mixed type variables is also mentioned