Examination of surface properties and in vitro biological performance of amorphous diamond-like carbon-coated polyurethane

Despite the emerging use of diamond-like carbon (DLC) as a coating for medical devices, few studies have examined the resistance of DLC coatings onto medical polymers to both microbial adherence and encrustation. In this study, amorphous DLC of a range of refractive indexes (1.7-1.9) and thicknesses (100-600 nm) was deposited onto polyurethane, a model polymer, and the resistance to microbial adherence (Escherichia coli; clinical isolate) and encrustation examined using in vitro models. In comparison to the native polymer, the advancing and receding contact angles of DLC-coated polyurethane were lower, indicating greater hydrophilic properties. No relationship was observed between refractive index, thickness, and advancing contact angle, as determined using multiple correlation analysis. The resistances of the various DLC-coated polyurethane films to encrustation and microbial adherence were significantly greater than that to polyurethane; however, there were individual differences between the resistances of the various DLC coatings. In general, increasing the refractive index of the coatings (100 nm thickness) decreased the resistance of the films to both hydroxyapatite and struvite encrustation and to microbial adherence. Films of lower thicknesses (100 and 200 nm; of defined refractive index, 1.8), exhibited the greatest resistance to encrustation and to microbial adherence. In conclusion, this study has uniquely illustrated both the microbial antiadherence properties and resistance to urinary encrustation of DLC-coated polyurethane. The resistances to encrustation and microbial adherence were substantial, and in light of this, it is suggested that DLC coatings of low thickness and refractive index show particular promise as coatings of polymeric medical devices. (c) 2006 Wiley Periodicals, Inc.

The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia

Quantitative examination of prostate histology offers clues in the diagnostic classification of lesions and in the prediction of response to treatment and prognosis. To facilitate the collection of quantitative data, the development of machine vision systems is necessary. This study explored the use of imaging for identifying tissue abnormalities in prostate histology. Medium-power histological scenes were recorded from whole-mount radical prostatectomy sections at × 40 objective magnification and assessed by a pathologist as exhibiting stroma, normal tissue (nonneoplastic epithelial component), or prostatic carcinoma (PCa). A machine vision system was developed that divided the scenes into subregions of 100 × 100 pixels and subjected each to image-processing techniques. Analysis of morphological characteristics allowed the identification of normal tissue. Analysis of image texture demonstrated that Haralick feature 4 was the most suitable for discriminating stroma from PCa. Using these morphological and texture measurements, it was possible to define a classification scheme for each subregion. The machine vision system is designed to integrate these classification rules and generate digital maps of tissue composition from the classification of subregions; 79.3% of subregions were correctly classified. Established classification rates have demonstrated the validity of the methodology on small scenes; a logical extension was to apply the methodology to whole slide images via scanning technology. The machine vision system is capable of classifying these images. The machine vision system developed in this project facilitates the exploration of morphological and texture characteristics in quantifying tissue composition. It also illustrates the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision.