Informatics Publishing Limited

A. Sreedevi , K. Bharathi , K. V. S. R. G. Prasad

Abstract

Objective: To study the nephroprotector activity of the bioflavonoid, rutin (50 and 100 mg/kg, p.o.) on cisplatininduced nephrotoxicity (6mg/kg, i.p.) in albino rats. Materials and Methods: Nephroprotector activity of rutin was assessed by estimating the levels of blood urea nitrogen, serum creatinine, serum total proteins, urinary protein, creatinine clearance, urine to serum creatinine ratio and lipid peroxidation in kidney. Results: Cisplatin elevated the serum markers level, increased the protein excretion in urine, reduced the creatinine clearance, urine to serum creatinine ratio and renal malondialdehyde level was increased. Animals which received rutin, in curative and prophylactic groups significantly reversed the effects induced by cisplatin. Conclusion: From the results it is revealed that rutin effectively reduced the cisplatin-induced renal toxicity in albino rats.

Keywords

Cisplatin, Nephrotoxicity, Rutin, Nephroprotector Activity

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Amrita Ray Chaudhury ¹, Kaveri K. Iychettira ¹, Ranjani Iyer ¹, A. Sreedevi ²

Affiliations
1 Electrical and Electronics Department of R. V. College of Engineering, Mysore Road, Bangalore-560059, IN
2 Department of Electrical and Electronics Engineering at RV College of Engineering, Mysore Road, Bangalore-560059, IN

Abstract

DNA microarray technology is a recently developed, rapidly evolving field which analyses cellular data at the genomic level. It is widely used in the analysis of gene expression levels using which gene sequencing and molecular structure can be studied with a high amount of accuracy and clarity. Image processing plays a critical role in the analysis of microarray. It is used to address feature extraction, gene clustering and data mining and thus aid the analysis of differentially expressed genes. Reliable and robust gridding is a critical step in microarray based studies. Automatic gridding techniques have been conventionally applied to suit rectangular spot arrangements. Some microarrays manufactured using recent technologies have a honeycomb or hexagonal arrangement of gene spots where they cannot be placed along definite rows and hence cannot be subjected to rectangular gridding directly. Our algorithm demonstrates a novel method to improve existing automatic gridding techniques such that the algorithm is applicable to both types of spot arrangements. It operates independent of manual intervention, is simple and has the ability to identify every spot individually. Further, the algorithm performs complete analysis of microarray images. It performs spot location, segmentation, intensity extraction and calculates the gene expression for each spot after normalization using Lowess and Quantile techniques.

Keywords

Gene Expression, Gridding, Image Processing, Microarray, Normalization.

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Himajit Aithal ¹, A. Anil Kumar ¹, A. Sreedevi ², D. S. Jangamashetti ³

Affiliations
1 Electrical and Electronics Department, R.V. College of Engineering, Bangalore, IN
2 Department of Electrical and Electronics, R.V. College of Engineering, Bangalore, IN
3 Department of Electrical and Electronics, Basaveshwara Engineering College, Bagalkot, IN

Abstract

Microarray image technology is a powerful tool for monitoring thousands of genes simultaneously. The size of microarray images is very large; hence efficient compression routines that take advantage of the way in which spots are represented in a microarray image are required. In this paper we introduce an image compression method that aims to minimize the number of pixels to be stored, to represent a spot. The compressed data is then used to reconstruct the microarray. The reconstructed array is then compared with the original image to determine the deviation between the two. The results of the implementation of this method are compared with other image compression methods and higher compression ratio is obtained. The reconstructed image has low ischolar_main mean square error value which can be further reduced by subtracting the background.

Keywords

Centre Detection, Edge Detection, Image Compression, Microarray, Root Mean Square Error, Spot.

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