Anti-FGA monoclonal antibody, clone 2G8

Objective: Gap in knowledge on benefit/risk ratio of second generation antipsychotics (SGA) in the paediatric population represents a challenge for the scientific community. This study aims to analyse all suspected adverse drug reactions (ADRs) to SGA observed during the study period; compare the safety profiles of risperidone and aripiprazole; evaluate the effect of switching from risperidone to aripiprazole or to a first generation antipsychotic (FGA). Methods: Prospective analysis of spontaneously reported ADRs concerning 184 paediatric outpatients between 2012 and 2014.; clinical outcomes of drug switch were evaluated. Results: Out of the 184 patients, 130 experienced at least one ADR; ADRs were usually not serious and more frequently associated with aripiprazole. Switching to aripiprazole was associated with better results than switching to FGAs in the Clinical Global Impression scale-Efficacy (CGI-E) scores (p = 0.018), Disturbed behaviour checklist-parents (DBC-P) self-absorption subscale (p = 0.010); only a trend for difference between changing to aripiprazole vs FGAs in the DBC-P total score (p = 0.054) and social relating subscale (p = 0.053) was observed. Conclusions: SGAs safety data were consistent with the ones already known; however, there is still a need to improve the knowledge in pharmacovigilance field among clinicians. Switching to aripiprazole may be a valid alternative to risperidone.

In hybrid cloud environments, reasonable data placement strategies are critical to the efficient execution of scientific workflows. Due to various loads, bandwidth fluctuations, and network congestions between different data centers as well as the dynamics of hybrid cloud environments, the data transmission time is uncertain. Thus, it poses huge challenges to the efficient data placement for scientific workflows. However, most of the traditional solutions for data placement focus on deterministic cloud environments, which lead to the excessive data transmission time of scientific workflows. To address this problem, we propose an adaptive discrete particle swarm optimization algorithm based on the fuzzy theory and genetic algorithm operators (DPSO-FGA) to minimize the fuzzy data transmission time of scientific workflows. The DPSO-FGA can rationally place the scientific workflow data while meeting the requirements of data privacy and the capacity limitations of data centers. Simulation results show that the DPSO-FGA can effectively reduce the fuzzy data transmission time of scientific workflows in hybrid cloud environments.