Investigation of Plantago ovata Husk as Pharmaceutical Excipient for Solid Dosage Form (Orodispersible Tablets).

The objective of the study was to investigate the suitability of the Plantago ovata (PO) husk as a pharmaceutical excipient. Various phytoconstituents of the husk were determined according to the standard test procedures. The Plantago ovata husk was evaluated for various pharmaceutical parameters related to flow, swelling index, and compressibility index. Orodispersible tablets (ODTs) were prepared, containing different concentrations (2.5, 3, 5, 7.5, 10, and 15% w/w) of the Plantago ovata husk. Before compression, all the formulations were evaluated for their flow. Compressed ODTs were evaluated for physical characteristics (physical appearance, weight and weight variation, thickness, and moisture content), mechanical strength (crushing strength, specific crushing strength, tensile strength, and friability), disintegration behavior (disintegration time and oral disintegration time), drug content, and in vitro drug release. Phytochemical evaluation of the Plantago ovata husk confirmed the presence of various phytoconstituents like alkaloids, tannins, glycosides, saponins, flavonoids, and phenols. SEM photograph of the Plantago ovata husk showed that it has a fibrous structure, with a porous and rough surface. The Plantago ovata husk had a high swelling index (380%) which decreased by pulverization (310%). Precompression evaluation of the powder blend for all the formulations of ODTs showed good flow properties, indicating that the Plantago ovata husk improved the rheological characteristics of the powder blend. Compressed ODTs had good mechanical strength, and their friability was within the official limits (<1%). Best disintegration was observed with formulation F-6 containing 10% w/w of the Plantago ovata husk. It is concluded that the Plantago ovata husk can be used as a disintegrant in the formulation of ODTs.

Computer Based Melanocytic and Nevus Image Enhancement and Segmentation.

Digital dermoscopy aids dermatologists in monitoring potentially cancerous skin lesions. Melanoma is the 5th common form of skin cancer that is rare but the most dangerous. Melanoma is curable if it is detected at an early stage. Automated segmentation of cancerous lesion from normal skin is the most critical yet tricky part in computerized lesion detection and classification. The effectiveness and accuracy of lesion classification are critically dependent on the quality of lesion segmentation. In this paper, we have proposed a novel approach that can automatically preprocess the image and then segment the lesion. The system filters unwanted artifacts including hairs, gel, bubbles, and specular reflection. A novel approach is presented using the concept of wavelets for detection and inpainting the hairs present in the cancer images. The contrast of lesion with the skin is enhanced using adaptive sigmoidal function that takes care of the localized intensity distribution within a given lesion's images. We then present a segmentation approach to precisely segment the lesion from the background. The proposed approach is tested on the European database of dermoscopic images. Results are compared with the competitors to demonstrate the superiority of the suggested approach.

Glaucoma detection using novel optic disc localization, hybrid feature set and classification techniques.

Glaucoma is a chronic and irreversible neuro-degenerative disease in which the neuro-retinal nerve that connects the eye to the brain (optic nerve) is progressively damaged and patients suffer from vision loss and blindness. The timely detection and treatment of glaucoma is very crucial to save patient's vision. Computer aided diagnostic systems are used for automated detection of glaucoma that calculate cup to disc ratio from colored retinal images. In this article, we present a novel method for early and accurate detection of glaucoma. The proposed system consists of preprocessing, optic disc segmentation, extraction of features from optic disc region of interest and classification for detection of glaucoma. The main novelty of the proposed method lies in the formation of a feature vector which consists of spatial and spectral features along with cup to disc ratio, rim to disc ratio and modeling of a novel mediods based classier for accurate detection of glaucoma. The performance of the proposed system is tested using publicly available fundus image databases along with one locally gathered database. Experimental results using a variety of publicly available and local databases demonstrate the superiority of the proposed approach as compared to the competitors.