Antimicrobial efficacy of chlorhexidine digluconate alone and in combination with eucalyptus oil, tea tree oil and thymol against planktonic and biofilm cultures of Staphylococcus epidermidis

Objectives Effective skin antisepsis and disinfection of medical devices are key factors in preventing many healthcare-acquired infections associated with skin microorganisms, particularly Staphylococcus epidermidis. The aim of this study was to investigate the antimicrobial efficacy of chlorhexidine digluconate (CHG), a widely used antiseptic in clinical practice, alone and in combination with tea tree oil (TTO), eucalyptus oil (EO) and thymol against planktonic and biofilm cultures of S. epidermidis. Methods Antimicrobial susceptibility assays against S. epidermidis in a suspension and in a biofilm mode of growth were performed with broth microdilution and ATP bioluminescence methods, respectively. Synergy of antimicrobial agents was evaluated with the chequerboard method. Results CHG exhibited antimicrobial activity against S. epidermidis in both suspension and biofilm (MIC 2–8 mg/L). Of the essential oils thymol exhibited the greatest antimicrobial efficacy (0.5–4 g/L) against S. epidermidis in suspension and biofilm followed by TTO (2–16 g/L) and EO (4–64 g/L). MICs of CHG and EO were reduced against S. epidermidis biofilm when in combination (MIC of 8 reduced to 0.25–1 mg/L and MIC of 32–64 reduced to 4 g/L for CHG and EO, respectively). Furthermore, the combination of EO with CHG demonstrated synergistic activity against S. epidermidis biofilm with a fractional inhibitory concentration index of <0.5. Conclusions The results from this study suggest that there may be a role for essential oils, in particular EO, for improved skin antisepsis when combined with CHG.

Expert systems for fault tree synthesis

Hazard and operability (HAZOP) studies on chemical process plants are very time consuming, and often tedious, tasks. The requirement for HAZOP studies is that a team of experts systematically analyse every conceivable process deviation, identifying possible causes and any hazards that may result. The systematic nature of the task, and the fact that some team members may be unoccupied for much of the time, can lead to tedium, which in turn may lead to serious errors or omissions. An aid to HAZOP are fault trees, which present the system failure logic graphically such that the study team can readily assimilate their findings. Fault trees are also useful to the identification of design weaknesses, and may additionally be used to estimate the likelihood of hazardous events occurring. The one drawback of fault trees is that they are difficult to generate by hand. This is because of the sheer size and complexity of modern process plants. The work in this thesis proposed a computer-based method to aid the development of fault trees for chemical process plants. The aim is to produce concise, structured fault trees that are easy for analysts to understand. Standard plant input-output equation models for major process units are modified such that they include ancillary units and pipework. This results in a reduction in the nodes required to represent a plant. Control loops and protective systems are modelled as operators which act on process variables. This modelling maintains the functionality of loops, making fault tree generation easier and improving the structure of the fault trees produced. A method, called event ordering, is proposed which allows the magnitude of deviations of controlled or measured variables to be defined in terms of the control loops and protective systems with which they are associated.

A method for automatically eliciting node weights in a hierarchical knowledge-based structure for reasoning with uncertainty

Hierarchical knowledge structures are frequently used within clinical decision support systems as part of the model for generating intelligent advice. The nodes in the hierarchy inevitably have varying influence on the decisionmaking processes, which needs to be reflected by parameters. If the model has been elicited from human experts, it is not feasible to ask them to estimate the parameters because there will be so many in even moderately-sized structures. This paper describes how the parameters could be obtained from data instead, using only a small number of cases. The original method [1] is applied to a particular web-based clinical decision support system called GRiST, which uses its hierarchical knowledge to quantify the risks associated with mental-health problems. The knowledge was elicited from multidisciplinary mental-health practitioners but the tree has several thousand nodes, all requiring an estimation of their relative influence on the assessment process. The method described in the paper shows how they can be obtained from about 200 cases instead. It greatly reduces the experts’ elicitation tasks and has the potential for being generalised to similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.

Stability analysis of tree structured decision functions

In multicriteria decision problems many values must be assigned, such as the importance of the different criteria and the values of the alternatives with respect to subjective criteria. Since these assignments are approximate, it is very important to analyze the sensitivity of results when small modifications of the assignments are made. When solving a multicriteria decision problem, it is desirable to choose a decision function that leads to a solution as stable as possible. We propose here a method based on genetic programming that produces better decision functions than the commonly used ones. The theoretical expectations are validated by case studies. © 2003 Elsevier B.V. All rights reserved.