Efficacy and safety of ivacaftor in patients with cystic fibrosis who have an Arg117His-CFTR mutation: a double-blind, randomised controlled trial

BACKGROUND: Ivacaftor has been previously assessed in patients with cystic fibrosis with Gly551Asp-CFTR or other gating mutations. We assessed ivacaftor in patients with Arg117His-CFTR, a residual function mutation.

METHODS: We did a 24-week, placebo-controlled, double-blind, randomised clinical trial, which enrolled 69 patients with cystic fibrosis aged 6 years and older with Arg117His-CFTR and percentage of predicted forced expiratory volume in 1 s (% predicted FEV1) of at least 40. We randomly assigned eligible patients (1:1) to receive placebo or ivacaftor 150 mg every 12 h for 24 weeks. Randomisation was stratified by age (6-11, 12-17, and ≥18 years) and % predicted FEV1 (<70, ≥70 to ≤90, and >90). The primary outcome was the absolute change from baseline in % predicted FEV1 through week 24. Secondary outcomes included safety and changes in sweat chloride concentrations and Cystic Fibrosis Questionnaire-Revised (CFQ-R) respiratory domain scores. An open-label extension enrolled 65 of the patients after washout; after 12 weeks, we did an interim analysis.

FINDINGS: After 24 weeks, the treatment difference in mean absolute change in % predicted FEV1 between ivacaftor (n=34) and placebo (n=35) was 2·1 percentage points (95% CI -1·13 to 5·35; p=0·20). Ivacaftor treatment resulted in significant treatment differences in sweat chloride (-24·0 mmol/L, 95% CI -28·01 to -19·93; p<0·0001) and CFQ-R respiratory domain (8·4, 2·17 to 14·61; p=0·009). In prespecified subgroup analyses, % predicted FEV1 significantly improved with ivacaftor in patients aged 18 years or older (treatment difference vs placebo: 5·0 percentage points, 95% CI 1·15 to 8·78; p=0·01), but not in patients aged 6-11 years (-6·3 percentage points, -11·96 to -0·71; p=0·03). In the extension study, both placebo-to-ivacaftor and ivacaftor-to-ivacaftor groups showed % predicted FEV1 improvement (absolute change from post-washout baseline at week 12: placebo-to-ivacaftor, 5·0 percentage points [p=0·0005]; ivacaftor-to-ivacaftor, 6·0 percentage points [p=0·006]). We did not identify any new safety concerns. The studies are registered with ClinicalTrials.gov (the randomised, placebo-controlled study, number NCT01614457; the open-label extension study, number NCT01707290).

INTERPRETATION: Although this study did not show a significant improvement in % predicted FEV1, ivacaftor did significantly improve sweat chloride and CFQ-R respiratory domain scores and lung function in adult patients with Arg117His-CFTR, indicating that ivacaftor might benefit patients with Arg117His-CFTR who have established disease.

Adaptive data echo cancellation using cost function adaptation

For a digital echo canceller it is desirable to reduce the adaptation time, during which the transmission of useful data is not possible. LMS is a non-optimal algorithm in this case as the signals involved are statistically non-Gaussian. Walach and Widrow (IEEE Trans. Inform. Theory 30 (2) (March 1984) 275-283) investigated the use of a power of 4, while other research established algorithms with arbitrary integer (Pei and Tseng, IEEE J. Selected Areas Commun. 12(9)(December 1994) 1540-1547) or non-quadratic power (Shah and Cowan, IEE.Proc.-Vis. Image Signal Process. 142 (3) (June 1995) 187-191). This paper suggests that continuous and automatic, adaptation of the error exponent gives a more satisfactory result. The family of cost function adaptation (CFA) stochastic gradient algorithm proposed allows an increase in convergence rate and, an improvement of residual error. As special case the staircase CFA algorithm is first presented, then the smooth CFA is developed. Details of implementations are also discussed. Results of simulation are provided to show the properties of the proposed family of algorithms. (C) 2000 Elsevier Science B.V. All rights reserved.

Quantum annealing of an ising spin-glass by Green's function Monte Carlo

We present an implementation of quantum annealing (QA) via lattice Green's function Monte Carlo (GFMC), focusing on its application to the Ising spin glass in transverse field. In particular, we study whether or not such a method is more effective than the path-integral Monte Carlo- (PIMC) based QA, as well as classical simulated annealing (CA), previously tested on the same optimization problem. We identify the issue of importance sampling, i.e., the necessity of possessing reasonably good (variational) trial wave functions, as the key point of the algorithm. We performed GFMC-QA runs using such a Boltzmann-type trial wave function, finding results for the residual energies that are qualitatively similar to those of CA (but at a much larger computational cost), and definitely worse than PIMC-QA. We conclude that, at present, without a serious effort in constructing reliable importance sampling variational wave functions for a quantum glass, GFMC-QA is not a true competitor of PIMC-QA.