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.

Agonistic approach of omega-3, omega-6 and its metabolites with BDNF: An in-silico study.

Brain derived neurotrophic factor (BDNF) is a member of neurotrophic family of growth factors, mainly found in the hippocampus and cerebral cortex of brain. Studies have shown that there is a link between BDNF and cognitive dysfunction, as well as there is a relationship between the PUFAs intake and their effect on BDNF production. Intake of PUFAs, mainly omega-3 and omega-6 has show increase in production of BDNF in brain. In our study we performed docking studies on PUFAs and their metabolites with BDNF using MVD (Molegro Virtual Docker), this has shown that the metabolites of the PUFAs mainly LXA_4, NPD1, HDHA have shown more binding affinity towards BDNF. These metabolites of PUFAs are responsible for modulation of BDNF activity.

Views and opinion on BDNF as a target for diabetic cognitive dysfunction.

Type 2 diabetes mellitus (T2DM) is a known cause of cognitive dysfunction and involves increased risk of dementia. Brain-derived neurotrophic factor (BDNF) is a member of neurotrophic family of nerve growth factors, a key protein in promoting memory, growth and survival of neurons. BDNF is recognized as a metabotrophic factor, a molecule that is involved in Alzheimer's disease (AD) as well as in other neurological disorders. It provides cellular and local regulatory mechanisms for mediating synaptic plasticity. Impaired BDNF signaling can compromise many aspects of brain functions. Studies investigating the relationship between diabetes and BDNF in adults demonstrate that BDNF levels are decreased in T2DM and are regulated in response to plasma levels of glucose. BDNF could serve as biomarker in predicting the development of obesity and T2DM. Thirty-two cavities were predicted to locate the active sites of BDNF for the ligands to bind. The shape of the site was identified by extracting the cavity volume surfaces enclosing regions with highest probability. Different ligands can be chosen for interaction of active sites of BDNF and can be targeted for drug discovery. This review focuses on computational exploitation selectively to deliver BDNF as a drug to appropriate hypothalamic neurons, which can serve as a novel approach in diabetic encephalopathy treatment.

A tool for the post data analysis of screened compounds derived from computer-aided docking scores.

UNLABELLED: A method is described for the analysis of the results obtained from the docking studies applied on a protein target and small molecules chemical compounds as ligands from various sources using different docking tools. We show the use of Dempster Shafer Theory (DST) to select the high ranking top compounds for further analysis and consideration. AVAILABILITY: Application is freely available at http://allamapparao.org/dst/

Advances in binding free energies calculations: QM/MM-based free energy perturbation method for drug design.

Multiple approaches have been devised and evaluated to computationally estimate binding free energies. Results using a recently developed Quantum Mechanics (QM)/Molecular Mechanics (MM) based Free Energy Perturbation (FEP) method suggest that this method has the potential to provide the most accurate estimation of binding affinities to date. The method treats ligands/inhibitors using QM while using MM for the rest of the system. The method has been applied and validated for a structurally diverse set of fructose 1,6- bisphosphatase (FBPase) inhibitors suggesting that the approach has the potential to be used as an integral part of drug discovery for both lead identification lead optimization, where there is a structure available. In addition, this QM/MM-based FEP method was shown to accurately replicate the anomalous hydration behavior exhibited by simple amines and amides suggesting that the method may also prove useful in predicting physical properties of molecules. While the method is about 5-fold more computationally demanding than conventional FEP, it has the potential to be less demanding on the end user since it avoids development of MM force field parameters for novel ligands and thereby eliminates this time-consuming step that often contributes significantly to the inaccuracy of binding affinity predictions using conventional FEP methods. The QM/MM-based FEP method has been extensively tested with respect to important considerations such as the length of the simulation required to obtain satisfactory convergence in the calculated relative solvation and binding free energies for both small and large structural changes between ligands. Future automation of the method and parallelization of the code is expected to enhance the speed and increase its use for drug design and lead optimization.

Exploring the role of BCHE in the onset of Diabetes, Obesity and Neurological Disorders.

Diabetes, Obesity and Neurological disturbances, most often show co-occurrence. There has been an extensive research in this domain, but the exact mechanism underlying the co-occurrence of the three conditions is still an enigma. The current paper is an approach to establish the role of Butyryl cholinesterase (BCHE) in Diabetes, Obesity and Neurological disorders by performing a comparative analysis with Neuroligin (NLGN2) a protein belonging to the same family. BCHE has its role in glucose regulation, Lipid metabolism and nerve signaling. Emphasis is laid on BCHE's diverse functions whose impediment affects the above mentioned metabolic pathways. Insilco techniques were employed to analyze the sequence, structural and functional similarities of the two proteins. A point mutation is focused which is common to both BCHE and Neuroligin. The mutation occurs at the homologous position in both the proteins making them deficient. This affects the three metabolic pathways leading to the respective disorders. The work describes the pathway that describes the role of BCHE in the onset of obesity mediated diabetes. The pathway further explains the association between Diabetes, Obesity and neurological disturbances.

Virtual screening of plant derived compounds for aldose reductase inhibition using molecular docking.

The role of the aldose reductase in type 2 diabetes is widely described. Therefore, it is of interest to identify plant derived compounds to inhibit its activity. We studied the protein-ligand interaction of 267 compounds from different parts of seven plants (Allium sativum, Coriandrum sativum, Dacus carota, Murrayyakoneigii, Eucalyptus, Calendula officinalis and Lycopersicon esculentum) with aldose reductase as the target protein. Molecular docking and re-scoring of top ten compounds (using GOLD, AutoDock Vina, eHiTS, PatchDock and MEDock) followed by rank-sum technique identified compound allium38 with high binding affinity for aldose reductase.

PDBToSDF: Create ligand structure files from PDB file.

Protein Data Bank (PDB) file contains atomic data for protein and ligand in protein-ligand complexes. Structure data file (SDF) contains data for atoms, bonds, connectivity and coordinates of molecule for ligands. We describe PDBToSDF as a tool to separate the ligand data from pdb file for the calculation of ligand properties like molecular weight, number of hydrogen bond acceptors, hydrogen bond receptors easily.

Sequence analysis and homology modeling Gallus gallus glutathione S-transferase (Q08392).

Glutathione S-transferases (GST) belong to the transferase group of enzymes; GST are a family of enzymes that catalyze the addition of glutathione to endogenous or xenobiotic, often toxic electrophilic chemicals, and a major group of detoxification enzymes. We used the homology modeling technique to construct the structure of Gallus gallus GST. The amino acid sequence identity between the target protein and sequence of template protein 1ML6 (Mus musculus) was 66.2%. Based on the template structure, the protein model was constructed by using the Homology program Modeller9v1, and briefly refined by energy minimization steps; it was validated by PROCHECK. In all, 94.4% of the amino acids were in allowed regions of Ramachandran plot, showing the accuracy of the model and good stereochemical quality. Our results correlated well with the experimental data reported earlier, which proved the quality of the model. This generated model can be further used for the design and development of more potent GST inhibitors.

Butyrylcholinesterase in metabolic syndrome.

Butyrylcholinesterase may have a role in a number of metabolic functions and could affect the expression of insulin resistance syndrome. We present our integrated work using clinical, biochemical and bioinformatic approaches to delineate the possible function of this enzyme. Initially, we constructed a phylogenic tree with nucleotides and amino acid sequences and showed the existence of similar sequences in bacteria, plants and in other animals. We also demonstrated a possible pathogenic role for BChE in the common existence of insulin resistance, type 2 diabetes and Alzheimer's disease by in silico method and followed it up with a diabetic mouse study where cognition was slowed along with changes in BChE levels. In the next group of in silico studies, we employed THEMATICS method to identify the amino acids at the active site and later performed docking studies with drugs. THEMATICS predicted two clusters of ionisable amino acid residues that are in proximity: one with two residues and another with 11 showed perturbation in the THEMATICS curves. Using ISIS/Draw 2.5SP4, ARGUSLAB 4.0.1 and HEX 5.1. software. 3-D ligands were docked with BChE motif (from PDB). We did not find any of the ligands studied with significant docking distance, indicating they did not have direct interaction with the active site. Subsequently we performed in silico studies to compare the secondary structure and domain of BChE. Protein-protein interaction showed the following intersections with BChE UBE21, CHAT, APOE, AATF, DF ALDH9A1, PDHX, PONI PSME3 and ATP6VOA2. The integrative physiological roles of proteins with poorly known functions can be approached by generating leads in silico, which can be studied in vivo, setting into movement an iterative process.

Built environment and diabetes.

Development of type 2 diabetes mellitus is influenced by built environment, which is, 'the environments that are modified by humans, including homes, schools, workplaces, highways, urban sprawls, accessibility to amenities, leisure, and pollution.' Built environment contributes to diabetes through access to physical activity and through stress, by affecting the sleep cycle. With globalization, there is a possibility that western environmental models may be replicated in developing countries such as India, where the underlying genetic predisposition makes them particularly susceptible to diabetes. Here we review published information on the relationship between built environment and diabetes, so that appropriate modifications can be incorporated to reduce the risk of developing diabetes mellitus.

Cluster analysis and phylogenetic relationship in biomarker identification of type 2 diabetes and nephropathy.

Cluster analysis of DNA microarray data that uses statistical algorithms to arrange the genes according to similarity in patterns of gene expression and the output displayed graphically is described in this article. Hierarchical clustering is a multivariate tool often used in phylogenetics, comparative genomics to relate the evolution of species. The patterns seen in microarray expression data can be interpreted as indications of the status of the genes responsible for nephropathy in peripheral blow cells of type 2 diabetes (T2DN). Out of 415 genes totally expressed in the 3 DNA chips it was concluded that only 116 genes expressed in T2DN and in that only 50 are functional genes. These 50 functional genes are responsible for diabetic nephropathy; of these 50, some of the genes which are more expressed and responsible are AGXT: Alanine-glyoxylate aminotransferase, RHOD: Ras homolog gene family, CAPN6: Calpain 6, EFNB2: Ephrin-B2, ANXA7: Annexin A7, PEG10: Paternally expressed 10, DPP4: Dipeptidyl-peptidase 4 (CD26, adenosine deaminase complexing protein 2), ENSA: Endosulfine alpha, IGFBP2: Insulin-like growth factor binding protein 2, 36kDa, CENPB: Centromere protein B, 80kDa, MLL3: Myeloid/lymphoid or mixed-lineage leukemia 3, BDNF: Brain-derived neurotrophic factor, EIF4A2: Eukaryotic translation initiation factor 4A, isoform 2, PPP2R1A: Protein phosphatase 2 (formerly 2A), regulatory subunit A, alpha isoform. Fifty genes and their nucleotide sequences are taken from NCBI and a phylogenetic tree is constructed using CLUSTAL W and the distances are closer to each other concluding that based on the sequence similarity and evolution the genes are expressed similarly. Literature survey is done for each gene in OMIM and the genes responsible for diabetic nephropathy are listed.

G-IMEx: A comprehensive software tool for detection of microsatellites from genome sequences.

Microsatellites are ubiquitous short tandem repeats found in all known genomes and are known to play a very important role in various studies and fields including DNA fingerprinting, paternity studies, evolutionary studies, virulence and adaptation of certain bacteria and viruses etc. Due to the sequencing of several genomes and the availability of enormous amounts of sequence data during the past few years, computational studies of microsatellites are of interest for many researchers. In this context, we developed a software tool called Imperfect Microsatellite Extractor (IMEx), to extract perfect, imperfect and compound microsatellites from genome sequences along with their complete statistics. Recently we developed a user-friendly graphical-interface using JAVA for IMEx to be used as a stand-alone software named G-IMEx. G-IMEx takes a nucleotide sequence as an input and the results are produced in both html and text formats. The Linux version of G-IMEx can be downloaded for free from http://www.cdfd.org.in/imex.

Techniques for integrating -omics data.

The challenge for -omics research is to tackle the problem of fragmentation of knowledge by integrating several sources of heterogeneous information into a coherent entity. It is widely recognized that successful data integration is one of the keys to improve productivity for stored data. Through proper data integration tools and algorithms, researchers may correlate relationships that enable them to make better and faster decisions. The need for data integration is essential for present -omics community, because -omics data is currently spread world wide in wide variety of formats. These formats can be integrated and migrated across platforms through different techniques and one of the important techniques often used is XML. XML is used to provide a document markup language that is easier to learn, retrieve, store and transmit. It is semantically richer than HTML. Here, we describe bio warehousing, database federation, controlled vocabularies and highlighting the XML application to store, migrate and validate -omics data.

Development and validation of LC-MS/MS method for the quantification of oxcarbazepine in human plasma using an experimental design.

A rapid tandem mass spectrometric (MS-MS) method for the quantification of Oxcarbazepine (OXB) in human plasma using imipramine as an internal standard (IS) has been developed and validated. Chromatographic separation was achieved isocratically on a C18 reversed-phase column within 3.0 min, using a mobile phase of acetonitrile-10 mM ammonium formate (90 : 10 v/v) at a flow rate of 0.3 ml/min. Quantitation was achieved using multiple reaction monitoring (MRM) scan at MRM transitions m/z 253>208 and m/z 281>86 for OXB and the IS respectively. Calibration curves were linear over the concentration range of 0.2-16 mug/ml (r>0.999) with a limit of quantification of 0.2 mug/ml. Analytical recoveries of OXB from spiked human plasma were in the range of 74.9 to 76.3%. Plackett-Burman design was applied for screening of chromatographic and mass spectrometric factors; factorial design was applied for optimization of essential factors for the robustness study. A linear model was postulated and a 2(3) full factorial design was employed to estimate the model coefficients for intermediate precision. More specifically, experimental design helps the researcher to verify if changes in factor values produce a statistically significant variation of the observed response. The strategy is most effective if statistical design is used in most or all stages of the screening and optimizing process for future method validation of pharmacokinetic and bioequivalence studies.

Bioinformatic analysis of functional proteins involved in obesity associated with diabetes.

The twin epidemic of diabetes and obesity pose daunting challenges worldwide. The dramatic rise in obesity-associated diabetes resulted in an alarming increase in the incidence and prevalence of obesity an important complication of diabetes. Differences among individuals in their susceptibility to both these conditions probably reflect their genetic constitutions. The dramatic improvements in genomic and bioinformatic resources are accelerating the pace of gene discovery. It is tempting to speculate the key susceptible genes/proteins that bridges diabetes mellitus and obesity. In this regard, we evaluated the role of several genes/proteins that are believed to be involved in the evolution of obesity associated diabetes 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 a bioinformatic tool. Our bioinformatic analysis reports resistin gene as ominous link with obesity associated diabetes. This bioinformatic study will be useful for future studies towards therapeutic inventions of obesity associated type 2 diabetes.

Proteomic analysis in diabetic cardiomyopathy using bioinformatics approach.

Diabetic cardiomyopathy is a distinct clinical entity that produces asymptomatic heart failure in diabetic patients without evidence of coronary artery disease and hypertension. Abnormalities in diabetic cardiomyopathy include: myocardial hypertrophy, impairment of contractile proteins, accumulation of extracellular matrix proteins, formation of advanced glycation end products, and decreased left ventricular compliance. These abnormalities lead to the most common clinical presentation of diabetic cardiomyopathy in the form of diastolic dysfunction.We evaluated the role of various proteins that are likely to be involved in diabetic cardiomyopathy by employing multiple sequence alignment using ClustalW tool and constructed a Phylogenetic tree using functional protein sequences extracted from NCBI. Phylogenetic tree was constructed using Neighbour-Joining Algorithm in bioinformatics approach. These results suggest a causal relationship between altered calcium homeostasis and diabetic cardiomyopathy that implies that efforts directed to normalize calcium homeostasis could form a novel therapeutic approach.

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.

Bioinformatics analysis of functional protein sequences reveals a role for brain-derived neurotrophic factor in obesity and type 2 diabetes mellitus.

Using bioinformatics techniques and sequence analyses algorithms, a comparative study between human and rodents revealed similarity in the behavior of genes involved in the control of energy homeostasis. Brain-derived neurotrophic factor (BDNF) modulates the secretion and actions of insulin, leptin, ghrelin, various neurotransmitters and peptides, and pro-inflammatory cytokines involved in energy homeostasis suggesting that it (BDNF) has a significant role in the pathobiology of obesity and type 2 diabetes mellitus. Based on these evidences, we propose that obesity and type 2 diabetes could be disorders of the brain and BDNF could serve as a biomarker in predicting their development. Hence, methods developed to selectively deliver BDNF to appropriate hypothalamic neurons may form a novel approach in their treatment.

Development and validation of high-throughput liquid chromatography-tandem mass spectrometric method for simultaneous quantification of loratadine and desloratadine in human plasma.

As a continuation of effort to improve our high flow on-line bioanalytical approach for high-throughput quantification of drugs and metabolites in plasma by high-throughput liquid chromatography tandem mass spectrometry (HTLC-MS/MS), we have developed a simple, sensitive and reliable method for simultaneous quantification of loratadine and desloratadine in human plasma. We have performed on-line coupling of extraction with Cyclone P 50 mm x 0.5 mm 50 microm HTLC column and chromatographic separation is performed with Zorbax XDB C18 50 mm x 2.1 mm 5 microm, followed by quantification with mass detector. The method is validated and showed good performances in terms of linearity, sensitivity, precision, accuracy and stability. A marked improvement in sample throughput efficiency is realized with this method and the proposed method will be useful for pharmacokinetic and/or bioequivalence studies.