PEPtalk2: results of a pilot randomised controlled trial to compare VZIG and aciclovir as postexposure prophylaxis (PEP) against chickenpox in children with cancer.

OBJECTIVE: To determine the likely rate of patient randomisation and to facilitate sample size calculation for a full-scale phase III trial of varicella zoster immunoglobulin (VZIG) and aciclovir as postexposure prophylaxis against chickenpox in children with cancer. DESIGN: Multicentre pilot randomised controlled trial of VZIG and oral aciclovir. SETTING: England, UK. PATIENTS: Children under 16 years of age with a diagnosis of cancer: currently or within 6 months of receiving cancer treatment and with negative varicella zoster virus (VZV) serostatus at diagnosis or within the last 3 months. INTERVENTIONS: Study participants who have a significant VZV exposure were randomised to receive PEP in the form of VZIG or aciclovir after the exposure. MAIN OUTCOME MEASURES: Number of patients registered and randomised within 12 months of the trial opening to recruitment and incidence of breakthrough varicella. RESULTS: The study opened in six sites over a 13-month period. 482 patients were screened for eligibility, 32 patients were registered and 3 patients were randomised following VZV exposure. All three were randomised to receive aciclovir and there were no cases of breakthrough varicella. CONCLUSIONS: Given the limited recruitment to the PEPtalk2 pilot, it is unlikely that the necessary sample size would be achievable using this strategy in a full-scale trial. The study identified factors that could be used to modify the design of a definitive trial but other options for defining the best means to protect such children against VZV should be explored. TRIAL REGISTRATION NUMBER: ISRCTN48257441, EudraCT number: 2013-001332-22, sponsor: University of Birmingham

Architecture of ensemble neural networks for risk analysis

Assembling of nemal networks refened to as "Ensemble nemal networks·· consist with many small "expei1 networks" that leam small parts of the complex problem. which are established by decomposing it into its sub leYels. Ensemble nemal network architecnue has been proposed to so lYe complex problems with large munbers of variables. In this paper. this architecture is used to analyze maintainability risks ofhigh-rise buildings. An ensemble neural network that consists with four expert networks to represent four building elements namely roof. fa<;:ade. basement and intemal areas is deYeloped to forecast the maintenance efficiency (ME) of buildings. The model is tested and the results showed good performance. The model is fmther validated using a real case study