Is FLT3 internal tandem duplication an unfavorable risk factor for high risk children with acute myeloid leukemia? : Polish experience

According to the AML-BFM 2004 Interim, a treatment protocol used in Poland since 2005, presence of FLT3 internal tandem duplication (FLT3/ITD) qualifies a patient with acute myeloid leukemia (AML) to a high-risk group (HRG). The present study was aimed to identify the prevalence of FLT3/ITD in children with AML in Poland and to evaluate its prognostic significance in the HRG patients. Out of 291 children with de novo AML treated in 14 Polish centers between January 2006 and December 2012, samples from 174 patients were available for FLT3/ITD analysis. Among study patients 108 children (61.7%) were qualified to HRG. Genomic DNA samples from bone marrow were tested for identification of FLT3/ITD mutation by PCR amplification of exon 14 and 15 of FLT3 gene. Clinical features and treatment outcome in patients with and without FLT3/ITD were analyzed in the study. The FLT3/ITD was found in 14 (12.9%) of 108 HRG children. There were no significant differences between children with and without FLT3/ITD in age and FAB distribution. The white blood cells count in peripheral blood at diagnosis was significantly higher (p <0.01) in the children with FLT3/ITD. Over 5-year overall survival rate for FLT3/ITD positive children was worse (42.4%) comparing to FLT3/ITD negative children (58.9%), but the statistical difference was not significant. However, over 5-year survivals free from treatment failures were similar. The FLT3/ITD rate (12.9%) observed in the study corresponded to the published data. There was no significant impact of FLT3/ITD mutation on survival rates, although further studies are needed on this subject

Semi-Empirical Topological Method for Prediction of the Relative Retention Time of Polychlorinated Biphenyl Congeners on 18 Different HR GC Columns

High resolution gas chromatographic relative retention time (HRGC-RRT) models were developed to predict relative retention times of the 209 individual polychlorinated biphenyls (PCBs) congeners. To estimate and predict the HRGC-RRT values of all PCBs on 18 different stationary phases, a multiple linear regression equation of the form RRT = ao + a1 (no. o-Cl) + a2 (no. m-Cl) + a3 (no. p-Cl) + a4 (VM or SM) was used. Molecular descriptors in the models included the number of ortho-, meta-, and para-chlorine substituents (no. o-Cl, m-Cl and p-Cl, respectively), the semi-empirically calculated molecular volume (VM), and the molecular surface area (SM). By means of the final variable selection method, four optimal semi-empirical descriptors were selected to develop a QSRR model for the prediction of RRT in PCBs with a correlation coefficient between 0.9272 and 0.9928 and a leave-one-out cross-validation correlation coefficient between 0.9230 and 0.9924 on each stationary phase. The root mean squares errors over different 18 stationary phases are within the range of 0.0108–0.0335. The accuracy of all the developed models were investigated using cross-validation leave-one-out (LOO), Y-randomization, external validation through an odd–even number and division of the entire data set into training and test sets

Diagnostic Value of Lysozyme Activity Estimation in the Feces of Infants with Acute Diarrhoea

Peer Reviewe

Prognozowanie kierunku zmiany indeksów giełdowych za pomocą klasyfikatora liniowego typu CPL

Stocks, indexes, commodities, and precious metals price prediction is a difficult task where many approaches are used: traditional technical analysis, econometric time series or modern data mining techniques. One particular data mining technique - linear classifier is described in this article. Prediction based on linear classifier is done using current market state, which can be described by various data sets (attributes, features). The simplest form of this model could use data from yesterday’s price movement. Advanced models are using more historical price movements. Very advanced models include various historical price movements for indexes from other countries and other instruments like currencies, commodities, etc. Using more features requires extended time to estimate model parameters.We build the linear classifier models by the minimisation of a convex and piecewise-linear function which is very efficient comparing to other functions. Computational costs for building the model are similar to linear programming. We also use feature selection method called RLS. Those techniques allow us to explore data with many features. Four scenarios are considered, in each scenario a different amount of market data is used to create a model. In the simplest scenario only one day’s change in price is taken, in the most complicated one 421 historical prices of 43 different instruments are taken. Best results were achieved by using middle range of 52 attributes. In this scenario, the model was right 53.19% times. Meaning the directions of daily change in S&P500 index (up or down) were predicted correctly. This doesn’t seem a lot, but if those predictions would have been used for investing, they could produce a total profit of 77% in the tested time period from November 2008 to March 2011 (2 years 4 months), or an average of 28% per year.Prognozowanie cen akcji i wartośsci indeksów giełdowych jest zadaniem trudnym, dla którego użzywanych jest wiele różnych podejść. Artykuł ten przedstawia wprowadzenie do pewnych standardowych technik. Przedstawiona została tradycyjna analiza techniczna, ekonometryczne modele szeregów czasowych oraz współczesne metody eksploracji danych. Jedna z metod eksploracji danych, klasyfikator liniowy został przedstawiony bardziej szczegółowo. Został on użyty w przeprowadzonym eksperymencie do prognozowania wartości indeksu giełdy amerykańskiej. Prognozowanie takie oparte jest o dane opisujące obecny stan giełdy. Stan giełdy można opisać różną ilością danych (atrybutów, cech). W najprostszym przypadku może to być tylko jednodniowa zmiana ceny prognozowanego indeksu. W bardziej rozbudowanym modelu można użyć wielu cen historycznych. W modelu jeszcze bardziej rozbudowanym można użyć danych z innych giełd, kursów walut, cen towarów jak np. ropa. Użycie dużej ilości danych wymaga dłuższego czasu obliczeń parametrów modelu. W prezentowanym podejściu klasyfikator liniowy budowany jest w oparciu o minimalizację wypukłej i odcinkowo-liniowej funkcji kryterialnej. Metoda ta jest bardzo wydajna o koszcie zbliżonym do programowania liniowego. Dodatkowo użyta została metoda selekcji cech RLS. Techniki te pozwoliły na efektywną eksplorację danych o wielu wymiarach. W artykule przedstawiono cztery scenariusze o różnej ilości danych opisujących giełdę. W najprostszym użyto tylko jednej danej, w najbardziej rozbudowanym 421 danych o 43 instrumentach finansowych. Najlepsze wyniki uzyskano dla pośredniego modelu o 52 cechach, w którym model przewidział prawidłowo 53.19% kierunków dziennych zmian indeksu S&P500. Otrzymany wynik nie wydaje się być wysoki, jednak gdyby inwestowano w indeks zgodnie z modelem zysk z takich inwestycji wyniósłby 77% w okresie od października 2008 do marca 2011, dając średnio 28% zysku rocznie