Computational learning of the conditional phase-type (C-Ph) distribution: Learning C-Ph distributions

This paper presents a new algorithm for learning the structure of a special type of Bayesian network. The conditional phase-type (C-Ph) distribution is a Bayesian network that models the probabilistic causal relationships between a skewed continuous variable, modelled by the Coxian phase-type distribution, a special type of Markov model, and a set of interacting discrete variables. The algorithm takes a dataset as input and produces the structure, parameters and graphical representations of the fit of the C-Ph distribution as output.The algorithm, which uses a greedy-search technique and has been implemented in MATLAB, is evaluated using a simulated data set consisting of 20,000 cases. The results show that the original C-Ph distribution is recaptured and the fit of the network to the data is discussed.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

Pharmacological Activity of Ibuprofen Release from Measoporous Silica

Novel drug delivery systems (DDS) to improve the pharmacokinetic profile of hydrophobic drugs following oral administration are an area of keen interest in drug research. An ideal DDS should not adversely affect drug activity, be capable of delivering a therapeutic dose of drug, and allow homogenous drug loading and drug release. Mesoporous silica has been proposed for this application, with ibuprofen employed as the model drug. It was hypothesised that mesoporous silica MCM-41 is capable of delivering a pharmacologically therapeutic dose of ibuprofen. Ibuprofen-loaded MCM-41 can be prepared reproducibly at a drug to carrier ratio of 30% (wt/wt). The release profile was seen to be 90% within 2 h. Initial assessment of COX-1 inhibitory activity suggests the absence of adverse effects attributable to drug-carrier interaction. The results of this study provide further evidence in support of the proposed use of mesoporous silica in drug delivery.

The Conditional Phase-type distribution: Graphical Models for Continuous Non-Normal Data:The Conditional Phase-type (C-Ph) distribution:

Conditional Gaussian (CG) distributions allow the inclusion of both discrete and continuous variables in a model assuming that the continuous variable is normally distributed. However, the CG distributions have proved to be unsuitable for survival data which tends to be highly skewed. A new method of analysis is required to take into account continuous variables which are not normally distributed. The aim of this paper is to introduce the more appropriate conditional phase-type (C-Ph) distribution for representing a continuous non-normal variable while also incorporating the causal information in the form of a Bayesian network.

A modified SILCS contraceptive diaphragm for long-term controlled release of the HIV microbicide dapivirine

Background: There is considerable interest in developing new multipurpose prevention technologies to address women's reproductive health needs. This study describes an innovative barrier contraceptive device--based on the SILCS diaphragm--that also provides long-term controlled release of the lead candidate anti-HIV microbicide dapivirine.

Study design: Diaphragm devices comprising various dapivirine-loaded polymer spring cores overmolded with a nonmedicated silicone elastomer sheath were fabricated by injection molding processes. In vitro release testing, thermal analysis and mechanical characterization were performed on the devices.

Results: A diaphragm device containing a polyoxymethylene spring core loaded with 10% w/w dapivirine provided continuous and controlled release of dapivirine over a 6-month period, with a mean in vitro daily release rate of 174 mcg/day. The mechanical properties of the new diaphragm were closely matched to the SILCS diaphragm.

Conclusions: The study demonstrates proof of concept for a dapivirine-releasing diaphragm with daily release quantities potentially capable of preventing HIV transmission. In discontinuous clinical use, release of dapivirine may be readily extended over 1 or more years. © 2013 Elsevier Inc. All rights reserved.