Enhanced chlorhexidine skin penetration with eucalyptus oil

Background Chlorhexidine digluconate (CHG) is a widely used skin antiseptic, however it poorly penetrates the skin, limiting its efficacy against microorganisms residing beneath the surface layers of skin. The aim of the current study was to improve the delivery of chlorhexidine digluconate (CHG) when used as a skin antiseptic. Method Chlorhexidine was applied to the surface of donor skin and its penetration and retention under different conditions was evaluated. Skin penetration studies were performed on full-thickness donor human skin using a Franz diffusion cell system. Skin was exposed to 2% (w/v) CHG in various concentrations of eucalyptus oil (EO) and 70% (v/v) isopropyl alcohol (IPA). The concentration of CHG (µg/mg of skin) was determined to a skin depth of 1500 µm by high performance liquid chromatography (HPLC). Results The 2% (w/v) CHG penetration into the lower layers of skin was significantly enhanced in the presence of EO. Ten percent (v/v) EO in combination with 2% (w/v) CHG in 70% (v/v) IPA significantly increased the amount of CHG which penetrated into the skin within 2 min. Conclusion The delivery of CHG into the epidermis and dermis can be enhanced by combination with EO, which in turn may improve biocide.

The use of spot simulation imagery for forestry mapping and management

SPOT simulation imagery was acquired for a test site in the Forest of Dean in Gloucestershire, U.K. This data was qualitatively and quantitatively evaluated for its potential application in forest resource mapping and management. A variety of techniques are described for enhancing the image with the aim of providing species level discrimination within the forest. Visual interpretation of the imagery was more successful than automated classification. The heterogeneity within the forest classes, and in particular between the forest and urban class, resulted in poor discrimination using traditional `per-pixel' automated methods of classification. Different means of assessing classification accuracy are proposed. Two techniques for measuring textural variation were investigated in an attempt to improve classification accuracy. The first of these, a sequential segmentation method, was found to be beneficial. The second, a parallel segmentation method, resulted in little improvement though this may be related to a combination of resolution in size of the texture extraction area. The effect on classification accuracy of combining the SPOT simulation imagery with other data types is investigated. A grid cell encoding technique was selected as most appropriate for storing digitised topographic (elevation, slope) and ground truth data. Topographic data were shown to improve species-level classification, though with sixteen classes overall accuracies were consistently below 50%. Neither sub-division into age groups or the incorporation of principal components and a band ratio significantly improved classification accuracy. It is concluded that SPOT imagery will not permit species level classification within forested areas as diverse as the Forest of Dean. The imagery will be most useful as part of a multi-stage sampling scheme. The use of texture analysis is highly recommended for extracting maximum information content from the data. Incorporation of the imagery into a GIS will both aid discrimination and provide a useful management tool.