A diabetic retinopathy detection method using an improved pillar K-means algorithm.

The paper presents a new approach for medical image segmentation. Exudates are a visible sign of diabetic retinopathy that is the major reason of vision loss in patients with diabetes. If the exudates extend into the macular area, blindness may occur. Automated detection of exudates will assist ophthalmologists in early diagnosis. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies K-means clustering to the image segmentation after getting optimized by Pillar algorithm; pillars are constructed in such a way that they can withstand the pressure. Improved pillar algorithm can optimize the K-means clustering for image segmentation in aspects of precision and computation time. This evaluates the proposed approach for image segmentation by comparing with Kmeans and Fuzzy C-means in a medical image. Using this method, identification of dark spot in the retina becomes easier and the proposed algorithm is applied on diabetic retinal images of all stages to identify hard and soft exudates, where the existing pillar K-means is more appropriate for brain MRI images. This proposed system help the doctors to identify the problem in the early stage and can suggest a better drug for preventing further retinal damage.

Computational investigation of pkcß inhibitors for the treatment of diabetic retinopathy.

Diabetic Retinopathy (DR) is one of the attenuating complications of diabetes mellitus. The key gene responsible for causing diabetic retinopathy is protein kinase C beta (PKCß). Protein kinase C is a family of protein kinase enzymes which are involved in controlling the function of other proteins through phosphorylation mechanism and plays a crucial role in signal transduction mechanisms. Among all the PKC isoenzymes, PKCß could be a significant isoenzyme involved in vascular dysfunction during hyperglycemia. Studies show that oral administration of PKCß inhibitor Ruboxistaurin (LY333531), decreases vessel permeability and improves retinal condition. Thus compounds that decrease the PKCß activation would be helpful in the treatment of diabetic retinopathy. The compounds similar to Ruboxistaurin are taken from Super Target database and docking analysis was performed. Maleimide derivative 3 showed highest binding affinities compared to Ruboxistaurin and so we advise that compound may be utilized in the treatment of diabetic retinopathy.

Bioinformatics analysis of diabetic retinopathy using functional protein sequences.

Diabetic retinopathy is the leading cause of blindness among patients with diabetes mellitus. We evaluated the role of several proteins that are likely to be involved in diabetic retinopathy by employing multiple sequence alignment using ClustalW tool and constructed a phylogram tree using functional protein sequences extracted from NCBI. Phylogram was constructed using Neighbor-Joining Algorithm in bioinformatics approach. It was observed that aldose reductase and nitric oxide synthase are closely associated with diabetic retinopathy. It is likely that vascular endothelial growth factor, pro-inflammatory cytokines, advanced glycation end products, and adhesion molecules that also play a role in diabetic retinopathy may do so by modulating the activities of aldose reductase and nitric oxide synthase. These results imply that methods designed to normalize aldose reductase and nitric oxide synthase activities could be of significant benefit in the prevention and treatment of diabetic retinopathy.

Alzheimer's disease and type 2 diabetes mellitus: the cholinesterase connection?

Alzheimer's disease and type 2 diabetes mellitus tend to occur together. We sought to identify protein(s) common to both conditions that could suggest a possible unifying pathogenic role. Using human neuronal butyrylcholinesterase (AAH08396.1) as the reference protein we used BLAST Tool for protein to protein comparison in humans. We found three groups of sequences among a series of 12, with an E-value between 0-12, common to both Alzheimer's disease and diabetes: butyrylcholinesterase precursor K allele (NP_000046.1), acetylcholinesterase isoform E4-E6 precursor (NP_000656.1), and apoptosis-related acetylcholinesterase (1B41|A). Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimer's disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.