Differential effects of alpha-helical and beta-hairpin antimicrobial peptides against Acanthamoeba castellanii

In this work we evaluated the ability of different types of antimicrobial peptides to promote permeabilization and growth inhibition of Acanthamoeba castellanii trophozoites, which cause eye keratitis. We used cationic alpha-helical peptides P5 and a beta-hairpin amphipathic molecule (gomesin), of the spider Acanthoscurria gomesiana haemocytes. A. castellanii permeabilization was obtained after 1 h incubation with micromolar concentrations of both types of peptides. While permeabilization induced by gomesin increased with longer incubations, P5 permeabilization did not increase with time and occurred at doses that are more toxic for SIRC cells, P5, however, at doses below the critical dose used to kill rabbit corneal cells was quite effective in promoting growth inhibition. Similarly, P5 was more effective when serine protease inhibitor was added simultaneously to the permeabilization assay. High performance chromatography followed by mass spectrometry analysis confirmed that, in contrast to gomesin, P5 is hydrolysed by A. castellanii culture supernatants. We conclude that the use of antimicrobial peptides to treat A. castellanii infections requires the search of more specific peptides that are resistant to proteolysis.

Objective Detection and Delineation of Oral Neoplasia Using Autofluorescence Imaging

Although the oral cavity is easily accessible to inspection, patients with oral cancer most often present at a late stage, leading to high morbidity and mortality. Autofluorescence imaging has emerged as a promising technology to aid clinicians in screening for oral neoplasia and as an aid to resection, but current approaches rely on subjective interpretation. We present a new method to objectively delineate neoplastic oral mucosa using autofluorescence imaging. Autofluorescence images were obtained from 56 patients with oral lesions and 11 normal volunteers. From these images, 276 measurements from 159 unique regions of interest (ROI) sites corresponding to normal and confirmed neoplastic areas were identified. Data from ROIs in the first 46 subjects were used to develop a simple classification algorithm based on the ratio of red-to-green fluorescence; performance of this algorithm was then validated using data from the ROIs in the last 21 subjects. This algorithm was applied to patient images to create visual disease probability maps across the field of view. Histologic sections of resected tissue were used to validate the disease probability maps. The best discrimination between neoplastic and nonneoplastic areas was obtained at 405 nm excitation; normal tissue could be discriminated from dysplasia and invasive cancer with a 95.9% sensitivity and 96.2% specificity in the training set, and with a 100% sensitivity and 91.4% specificity in the validation set. Disease probability maps qualitatively agreed with both clinical impression and histology. Autofluorescence imaging coupled with objective image analysis provided a sensitive and noninvasive tool for the detection of oral neoplasia.