Assessment of perinatal outcome after sustained tocolysis in early labour (APOSTEL-II trial)

Contains fulltext : 80242.pdf (publisher's version ) (Open Access)BACKGROUND: Preterm labour is the main cause of perinatal morbidity and mortality in the Western world. At present, there is evidence that tocolysis for 48 hours is useful in women with threatened preterm labour at least before 32 weeks. This allows transfer of the patient to a perinatal centre, and maximizes the effect of corticosteroids for improved neonatal survival. It is questionable whether treatment with tocolytics should be maintained after 48 hours. METHODS/DESIGN: The APOSTEL II trial is a multicentre placebo-controlled study. Pregnant women admitted for threatened preterm labour who have been treated with 48 hours corticosteroids and tocolysis will be eligible to participate in the trial between 26+0 and 32+2 weeks gestational age. They will be randomly allocated to nifedipine (intervention) or placebo (control) for twelve days or until delivery, whatever comes first.Primary outcome is a composite of perinatal death, and severe neonatal morbidity up to evaluation at 6 months after birth. Secondary outcomes are gestational age at delivery, number of days in neonatal intensive care and total days of the first 6 months out of hospital. In addition a cost-effectiveness analysis will be performed. Analysis will be by intention to treat. The power calculation is based on an expected 11% difference in adverse neonatal outcome. This implies that 406 women have to be randomised (two sided test, beta 0.2 at alpha 0.05). DISCUSSION: This trial will provide evidence as to whether maintenance tocolysis reduces severe perinatal morbidity and mortality in women with threatened preterm labour before 32 weeks. TRIAL REGISTRATION: Clinical trial registration: http://www.trialregister.nl, NTR 1336, date of registration: June 3rd 2008

Consensus guidelines for the use and interpretation of angiogenesis assays

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference

An optimal estimation method for nonlinear models of mechanical systems

This paper presents an optimal estimation method for nonlinear mechanical systems. The a priori knowledge of the system in the form of a nonlinear model structure is taken as a starting point. The method determines estimates of the parameters and estimates of the positions, velocities, accelerations, and inputs of the system. The optimal estimation method is applied to an experimental mechanical system. The unknown parameters in this system relate to inertia, friction and elastic deformation. It is shown that the optimal estimation method on the basis of a relatively simple model structure can lead to a useful description of the syste

The use of nonlinear filtering for a mechanical characterization of biological tissues

Problems with regard to the mechanical characterization of biological tissues demand new methods to determine constitutive equations. Inhomogeneous properties, anisotropy and very complex time-dependent behavior forces investigators to develop numerical procedures to solve the field equations and limit the use of classical mechanical tests. A new approach based on the measurement of strain distributions and nonlinear filtering techniques from system dynamics is suggeste

Grey-box modeling of friction:an experimental case-study

\u3cp\u3eGrey-box modeling covers the domain where we want to use a balanced amount of first principles and empiricism. The two generic grey-box models presented, i.e., a Neural Network model and a Polytopic model are capable of identifying friction characteristics that are left unexplained by first principles modeling. In an experimental case study, both grey-box models are applied to identify a rotating arm subjected to friction. An augmented state extended Kalman filter is used iteratively and off-line for the estimation of unknown parameters. For the studied example and defined black-box topologies, little difference is observed between the two models.\u3c/p\u3