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Background:Standard therapy for acute promyelocytic leukaemia (APL) includes retinoic acid (all-trans retinoic acid (ATRA)), which promotes differentiation of promyelocytic blasts. Although co-administration of arsenic trioxide (ATO) with ATRA has emerged as an effective option to treat APL, the molecular basis of this effect remains unclear.Methods:Four leukaemia cancer human models (HL60, THP-1, NBR4 and NBR4-R2 cells) were treated either with ATO alone or ATO plus ATRA. Cancer cell survival was monitored by trypan blue exclusion and DEVDase activity assays. Gene and protein expression changes were assessed by RT-PCR and western blot.Results:ATO induced an antioxidant response characterised by Nrf2 nuclear translocation and enhanced transcription of downstream target genes (that is, HO-1, NQO1, GCLM, ferritin). In cells exposed to ATO plus ATRA, the Nrf2 nuclear translocation was prevented and cytotoxicity was enhanced. HO-1 overexpression reversed partially the cytotoxicity by ATRA-ATO in HL60 cells. The inhibitory effects of ATRA on ATO-mediated responses were not observed in either the ATRA-resistant NB4-R2 cells or in NB4 cells pre-incubated with the RARα antagonist Ro-41-52-53.Conclusions:The augmented cytotoxicity observed in leukaemia cells following combined ATO-ATRA treatment is likely due to inhibition of Nrf2 activity, thus explaining the efficacy of combined ATO-ATRA treatment in the APL therapy. [ABSTRACT FROM AUTHOR]

We consider data generating mechanisms which can be represented as mixtures of finitely many regression or autoregression models. We propose nonparametric estimators for the functions characterising the various mixture components based on a local quasi maximum likelihood approach and prove their consistency. We present an EM algorithm for calculating the estimates numerically which is mainly based on iteratively applying common local smoothers and discuss its convergence properties. [ABSTRACT FROM AUTHOR]

We have obtained the asymptotic bias and the limiting distribution for the Yule-Walker estimator of the autoregressive parameter under a considerably weaker assumption than that of independence in the noise sequence. Among other things, these suggest robustness of the classical results and throw some light on the use of simulations based on pseudorandom numbers in verifying these results. [ABSTRACT FROM AUTHOR]