Double-blind, placebo-controlled, randomized study of eicosapentaenoic acid diester in patients with cancer cachexia

Purpose: Eicosapentaenoic acid (EPA) has been proposed to have specific anticachectic effects. This trial compared EPA diethyl ester with placebo in cachectic cancer patients for effects on weight and lean body mass. Patients and Methods: Five hundred eighteen weight-losing patients with advanced gastrointestinal or lung cancer were studied in a multicenter, double-blind, placebo controlled trial. Patients were randomly assigned to receive a novel preparation of pure EPA at a dose of 2 g or 4 g daily or placebo (2g EPA, n = 175; 4 g EPA, n = 172; placebo, n = 171). Patients were assessed at 4 weeks and 8 weeks. Results: The groups were well balanced at baseline. Mean weight loss at baseline was 18% (n = 518). Over the 8-week treatment period, both intention-to-treat analysis and per protocol analysis revealed no statistically significant improvements in survival, weight, or other nutritional variables. There was, however, a trend in favor of EPA with analysis of the primary end point, weight, at 8 weeks showing a borderline, nonsignificant treatment effect (P = .066). Relative to placebo, mean weight increased by 1.2 kg with 2 g EPA (95% CI, 0 kg to 2.3 kg) and by 0.3 kg with 4g EPA (-0.9 kg to 1.5 kg). Conclusion: The results indicate no statistically significant benefit from single agent EPA in the treatment of cancer cachexia. Future studies should concentrate on other agents or combination regimens. © 2006 by American Society of Clinical Oncology.

Spectrum redistribution for cognitive radios using discriminatory spectrum double auction

With the reformation of spectrum policy and the development of cognitive radio, secondary users will be allowed to access spectrums licensed to primary users. Spectrum auctions can facilitate this secondary spectrum access in a market-driven way. To design an efficient auction framework, we first study the supply and demand pressures and the competitive equilibrium of the secondary spectrum market, considering the spectrum reusability. In well-designed auctions, competition among participants should lead to the competitive equilibrium according to the traditional economic point of view. Then, a discriminatory price spectrum double auction framework is proposed for this market. In this framework, rational participants compete with each other by using bidding prices, and their profits are guaranteed to be non-negative. A near-optimal heuristic algorithm is also proposed to solve the auction clearing problem of the proposed framework efficiently. Experimental results verify the efficiency of the proposed auction clearing algorithm and demonstrate that competition among secondary users and primary users can lead to the competitive equilibrium during auction iterations using the proposed auction framework. Copyright © 2011 John Wiley & Sons, Ltd.

Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

Motivation: In any macromolecular polyprotic system - for example protein, DNA or RNA - the isoelectric point - commonly referred to as the pI - can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge - and thus the electrophoretic mobility - of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods. Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction. Contact: yperez@ebi.ac.uk Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.