Mutheneni Srinivasa Rao

Synthesis and antimicrobial evaluation of some novel thiomorpholine derived 1,4-disubstituted 1,2,3-triazoles

A convenient synthesis of novel 1,4-disubstituted 1,2,3-triazoles ( 4a – j and 5a – j ) is reported via copper(I)-catalyzed one pot [3+2] cycloaddition of various alkyl halides, sodium azide with 4-(prop-2-yn-1-yl)thiomorpholine and 4-(prop-2-yn-1-yl)thiomorpholine 1,1-dioxide. All the synthesized compounds were investigated for their antimicrobial activity. Compounds 4a , 4b , 4c , 4g , 5a and 5j against Staphylococcus epidermidis , 4a, 5a and 5d against Pseudomonas aeruginosa, 4a , 4b and 4g against Klebsiella pneumoniae , 4b , 5a and 5d against S. aureus and 5b , 5e and 5j against Bacillus subtilis showed excellent antibacterial activity compared to the standard drugs penicillin and streptomycin. Compounds 4c, 4e , 4f , 4j , 5c , 5d , 5g and 5j registered moderate antifungal activity as compared with the standard drug amphotericin B.

Prioritization of malaria endemic zones using self-organizing maps in the Manipur state of India

Due to the availability of a huge amount of epidemiological and public health data that require analysis and interpretation by using appropriate mathematical tools to support the existing method to control the mosquito and mosquito-borne diseases in a more effective way, data-mining tools are used to make sense from the chaos. Using data-mining tools, one can develop predictive models, patterns, association rules, and clusters of diseases, which can help the decision-makers in controlling the diseases. This paper mainly focuses on the applications of data-mining tools that have been used for the first time to prioritize the malaria endemic regions in Manipur state by using Self Organizing Maps (SOM). The SOM results (in two-dimensional images called Kohonen maps) clearly show the visual classification of malaria endemic zones into high, medium and low in the different districts of Manipur, and will be discussed in the paper.

Applications of Self-Organising Map (SOM) for prioritisation of endemic zones of filariasis in Andhra Pradesh, India

Entomological and epidemiological data of Lymphatic Filariasis (LF) was collected from 120 villages of four districts of Andhra Pradesh, India. Self-Organising Maps (SOMs), data-mining techniques, was used to classify and prioritise the endemic zones of filariasis. The results show that, SOMs classified all the villages into three major clusters by considering the data of Microfilaria (MF) rate, infection, infectivity rate and Per Man Hour (PMH). By considering the patterns of cluster, appropriate decision can be drawn for each parameter that is responsible for disease transmission of filariasis. Hence, SOM will certainly be a suitable tool for management of filariasis. The detailed application of SOM is discussed in this paper.

Rapid identification of female Culexmosquito species using Expert System in the South East Asian region.

Rapid identification of mosquito (vector) species is critical for vector control and disease management. Pictorial keys of mosquito species are currently used for the identification of new mosquito species. However, this approach is not very effective. Here, we describe the use of an ID3 algorithm (part of artificial intelligence) for the rapid identification of the South East Asian female Culex mosquito species. Availability http://www.envisiict.org/

PREDICTION OF JAPANESE ENCEPHALITIS VECTORS IN KURNOOL DISTRICT OF ANDHRA PRADESH, INDIA BY USING BAYESIAN NETWORK

Japanese encephalitis (JE), a complex viral disease transmitted by mosquitoes. Determination of vector (mosquito) density is a prerequisite for devising effective control measures against this disease. Bayesian network is a widely used tool that has recently found application in the epidemiological surveillance studies. This article describes the application of Bayesian network tool to predict the Japanese encephalitis vector density using the longitudinal data collected from the Kurnool district of Andhra Pradesh, India, from 2001 to 2006. The entomological parameter from the study area indicates that various contributing factors are responsible for the prevalence of these vectors, making it difficult to estimate the importance of any particular parameter contributing to the increase of vector density. The application of this approach resulted in 73.12% to 95.12% accuracy compared to the test data with the corrected data.