Platinum drugs in the treatment of non-small-cell lung cancer

The use of chemotherapy is considered standard therapy in patients with locally advanced non-small-cell lung cancer that cannot be treated with radiotherapy and in those with metastatic non-small-cell lung cancer and good performance status. This approach is also accepted in patients with earlier stage disease, when combined with radiotherapy in those with non-resectable locally advanced disease, or in the preoperative setting. Randomised clinical studies and meta-analyses of the literature have confirmed the beneficial survival effect of platinum-based chemotherapy. Cisplatin and carboplatin have been successfully used with other drugs in a wide variety of well-established two-drug combinations while three-drug combinations are still under investigation. Cisplatin and carboplatin use is limited by toxicity and inherent resistance. These considerations have prompted research into new platinum agents, such as the trinuclear platinum agent BBR3464, the platinum complex ZD0473 and oxaliplatin. These compounds could be developed in combination with agents such as paclitaxel, gemcitabine or vinorelbine in patients with advanced and/or refractory solid tumours

Density Estimation Using Multiscale Local Polynomial Transforms

The estimation of a density function with an unknown number of singularities or discontinuities is a typical example of a multiscale problem, with data observed at nonequispaced locations. The data are analyzed through a multiscale local polynomial transform (MLPT), which can be seen as a slightly overcomplete, non-dyadic alternative for a wavelet transform, equipped with the benefits from a local polynomial smoothing procedure. In particular, the multiscale transform adopts a sequence of kernel bandwidths in the local polynomial smoothing as resolution level-dependent, user-controlled scales. The MLPT analysis leads to a reformulation of the problem as a variable selection in a sparse, high-dimensional regression model with exponentially distributed responses. The variable selection is realized by the optimization of the l1-regularized maximum likelihood, where the regularization parameter acts as a threshold. Fine-tuning of the threshold requires the optimization of an information criterion such as AIC. This paper develops discussions on results in[9].SCOPUS: cp.pinfo:eu-repo/semantics/publishe