Background:The increasing incidence of Type 2 diabetes mellitus globally has collaterally increased the incidence of diabetes-associated complications such as neuropathy. Oxidative stress induced DNA damage is one of the mechanisms implicated in the pathogenesis of diabetic complications. Here we aimed to evaluate the extent of DNA damage in diabetes patients with and without clinical neuropathy using the Cytokinesis Block Micronucleus Cytome assay, in a group of South Indian population.Materials and Methods:The Cytokinesis Block Micronucleus Cytome assay was performed in lymphocyte cultures of 42 type 2 diabetes patients (22 with neuropathy and 20 without neuropathy) and 42 age and sex matched controls. Nuclear aberrations like Nuclear Buds, Nucleoplasmic Bridges and Micronuclei were analyzed.Results:The frequency of nuclear aberrations in diabetes patients with neuropathy was higher than compared to diabetes patients without neuropathy. The mean frequencies of nuclear aberrations per cell in diabetes patients with neuropathy and without neuropathy were 0.02 ± 0.02 and 0.01 ± 0.01, respectively. This was significantly higher than in the controls (0.002 ± 0.002) (P < 0.0001). An increasing trend of nuclear aberrations in correlation with the duration of diabetes was observed.Conclusion:This study highlights the use of the Cytokinesis Block Micronucleus Cytome assay as a potent tool for the identification of DNA damage, which may prove to be useful biomarker to assess the severity diabetes-associated complications such as neuropathy. Implementation of this technique at the clinical level would potentially enhance the quality of management of patients with diabetes and its complications like neuropathy.
A Probabilistic Neural Network (PNN) is a statistical algorithm and consists of a grouping of multi-class data. The conventional method of detection of DNA mutations by the human eye may not detect the minute variations in PCR-SSCP bands, which may lead to false positive or false negative results. The detection by photographic images may contain a blare (noise) caused during the time of photography; therefore, image processing techniques were used to reduce image noise. PCR-SSCP gels of T2DM patients (n = 100) and controls (n = 100) were initially photographed with equal ratio of pixels and later subjected to a two-stage analysis: feature extraction and PNN. The evaluation of the results was done by quality training and the accuracy was up to 95%, and the human eye analysis showed 80% mutation detection rate. This study proves to be very reliable and gives accurate and fast detection for mutation analysis in diabetes. This method could be extended for analysis in other human diseases.
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