Micro-spot with integrated pillars (MSIP) for detection of dengue virus NS1.

In this paper, we demonstrate an extremely efficient technique of diagnosing dengue virus non-structural protein (NS1) using Micro-Spot with Integrated Pillars (MSIP). Detection using MSIP is performed by employing fluorescence immunoassay specific to dengue virus NS1. MSIPs are chemically modified to ensure efficient covalent binding of antibodies on the micropillars, whereas the enormous increase in the surface area (available for the reaction) induced by the micropillars amplifies the apparent rate, which enhances the signal intensity. Therefore, the detection response of a MSIP, quantified by the intensity of the fluorescence signal, is found to be almost five times magnified than the response of a similar size micro-spot without micropillars. The response of the micropillars also depend on the pillar arrangement, since for identical concentration of dengue NS1 antigen, a stronger intensity signal is obtained for a hexagonal close packed array (staggered) pillar arrangement as compared to a square array arrangement.

Automated and unsupervised detection of malarial parasites in microscopic images.

BACKGROUND: Malaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis) and prone to human error (leading to erroneous diagnosis), even in experienced hands. The focus of this study is to develop a robust, unsupervised and sensitive malaria screening technique with low material cost and one that has an advantage over other techniques in that it minimizes human reliance and is, therefore, more consistent in applying diagnostic criteria. METHOD: A method based on digital image processing of Giemsa-stained thin smear image is developed to facilitate the diagnostic process. The diagnosis procedure is divided into two parts; enumeration and identification. The image-based method presented here is designed to automate the process of enumeration and identification; with the main advantage being its ability to carry out the diagnosis in an unsupervised manner and yet have high sensitivity and thus reducing cases of false negatives. RESULTS: The image based method is tested over more than 500 images from two independent laboratories. The aim is to distinguish between positive and negative cases of malaria using thin smear blood slide images. Due to the unsupervised nature of method it requires minimal human intervention thus speeding up the whole process of diagnosis. Overall sensitivity to capture cases of malaria is 100% and specificity ranges from 50-88% for all species of malaria parasites. CONCLUSION: Image based screening method will speed up the whole process of diagnosis and is more advantageous over laboratory procedures that are prone to errors and where pathological expertise is minimal. Further this method provides a consistent and robust way of generating the parasite clearance curves.