Biomedical subjects

Mehul N Vora

Publications and source records attributed to Mehul N Vora.

2 recordsLinked to original sources

PubMed2005

Multiarray sensors with pattern recognition for the detection, classification, and differentiation of bacteria at subspecies and strain levels.

This work describes the integration of a fully autonomous electrochemical biosensor with pattern recognition techniques for the detection and classification of bacteria at subspecies and strain level. The system provides a continuous, real-time monitoring of bacteria activity upon exposure to antibiotics. The system utilizes 96-well-type electrodes array (DOX-dissolved oxygen sensor) with principal component analysis (PCA) for rapid and routine classification of different classes of bacteria and related strains. A representative sample of a section of the bacteria kingdom has been analyzed and classified using the proposed DOX-PCA system, including the following: Corynebacterium glutamicum, Micrococcus luteus, Staphylococcus epidermidis, Yersinia ruckeri, Escherichia adecarboxylata, Comamonas acidovorans, Alcaligenes odorans, Bacillus globigii, and three strains of Escherichia coli (K12, SM10, ATCC 25922). The new classification scheme is based on the hypothesis that, under identical experimental conditions, various bacteria consume oxygen at different rates and are affected in different ways by selected antibiotics. Thus, the response of the individual electrode in the array is indirectly altered, compared to that of cells growing on medium, by the addition of the antibiotic. By using three different antibiotics in separate wells, a unique fingerprint can be created for a specific bacterium. With the proposed DOX-PCA system, classification of bacteria was achieved at subspecies and strain level in real time. This study represents a basic research tool that may allow researchers to rapidly detect, quantify, and classify bacteria type at subspecies and strain levels.

Ampicillin↗

PubMed2005

Identification of Africanized honeybees.

Gas chromatography and pattern recognition methods were used to develop a potential method for differentiating European honeybees from Africanized honeybees. The test data consisted of 237 gas chromatograms of hydrocarbon extracts obtained from the wax glands, cuticle, and exocrine glands of European and Africanized honeybees. Each gas chromatogram contained 65 peaks corresponding to a set of standardized retention time windows. A genetic algorithm (GA) for pattern recognition was used to identify features in the gas chromatograms characteristic of the genotype. The pattern recognition GA searched for features in the chromatograms that optimized the separation of the European and Africanized honeybees in a plot of the two or three largest principal components of the data. Because the largest principal components capture the bulk of the variance in the data, the peaks identified by the pattern recognition GA primarily contained information about differences between gas chromatograms of European and Africanized honeybees. The principal component analysis routine embedded in the fitness function of the pattern recognition GA acted as an information filter, significantly reducing the size of the search space since it restricted the search to feature sets whose principal component plots showed clustering on the basis of the bees' genotype. In addition, the algorithm focused on those classes and/or samples that were difficult to classify as it trained using a form of boosting. Samples that consistently classify correctly are not as heavily weighted as samples that are difficult to classify. Over time, the algorithm learns its optimal parameters in a manner similar to a neural network. The pattern recognition GA integrates aspects of artificial intelligence and evolutionary computations to yield a "smart" one-pass procedure for feature selection and classification.

Animals↗