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Hiepe P.,Friedrich - Schiller University of Jena | Herrmann K.-H.,Friedrich - Schiller University of Jena | Gullmar D.,Friedrich - Schiller University of Jena | Ros C.,Friedrich - Schiller University of Jena | And 5 more authors.
NMR in Biomedicine | Year: 2014

In the past, spin-echo (SE) echo planar imaging(EPI)-based diffusion tensor imaging (DTI) has been widely used to study the fiber structure of skeletal muscles in vivo. However, this sequence has several shortcomings when measuring restricted diffusion in small animals, such as its sensitivity to susceptibility-related distortions and a relatively short applicable diffusion time. To address these limitations, in the current work, a stimulated echo acquisition mode (STEAM) MRI technique, in combination with fast low-angle shot (FLASH) readout (turbo-STEAM MRI), was implemented and adjusted for DTI in skeletal muscles. Signal preparation using stimulated echoes enables longer effective diffusion times, and thus the detection of restricted diffusion within muscular tissue with intracellular distances up to 100μm. Furthermore, it has a reduced penalty for fast T2 muscle signal decay, but at the expense of 50% signal loss compared with a SE preparation. Turbo-STEAM MRI facilitates high-resolution DTI of skeletal muscle without introducing susceptibility-related distortions. To demonstrate its applicability, we carried out rabbit in vivo measurements on a human whole-body 3 T scanner. DTI parameters of the shank muscles were extracted, including the apparent diffusion coefficient, fractional anisotropy, eigenvalues and eigenvectors. Eigenvectors were used to calculate maps of structural parameters, such as the planar index and the polar coordinates θ and φ{symbol} of the largest eigenvector. These parameters were compared between three muscles. θ and φ{symbol} showed clear differences between the three muscles, reflecting different pennation angles of the underlying fiber structures. Fiber tractography was performed to visualize and analyze the architecture of skeletal pennate muscles. Optimization of tracking parameters and utilization of T2-weighted images for improved muscle boundary detection enabled the determination of additional parameters, such as the mean fiber length. The presented results support the applicability of turbo-STEAM MRI as a promising method for quantitative DTI analysis and fiber tractography in skeletal muscles. © 2013 John Wiley & Sons, Ltd.

Jayadeepa R.M.,Institute of Computational Biology | Sharma S.,Ganpat University
Current Computer-Aided Drug Design | Year: 2011

Taking into consideration the high importance of the drug target 5-α-reductase (5αR) in prostate cancer in this work we are going first to review previous works and discuss works related to the computer aided drug design of 5αR inhibitors. We report new results in the in silico screening of natural 5αR inhibitors. Traditionally, drugs were discovered by testing compounds synthesized in time consuming multi-step processes against a battery of in vivo biological screens. Promising compounds were then further studied in development, where their pharmacokinetic properties, metabolism and potential toxicity were investigated. Here we present a study on herbal lead compounds and their potential binding affinity to the effectors molecules of major disease like Prostate Cancer. Clinical studies demonstrate a positive correlation between the extent of 5αR type 2 (5αR2) and malignant progression of precancerous lesions in prostate. Therefore, identification of effective, well-tolerated 5αR inhibitors represents a rational chemo preventive strategy. This study has investigated the effects of naturally occurring non-protein compounds berberine and monocaffeyltartaric acid that inhibits 5αR type2. Our results reveal that these compounds use less energy to bind to 5αR and inhibit its activity. Their high ligand binding affinity to 5αR introduce the prospect for their use in chemopreventive applications; in addition they are freely available natural compounds that can be safely used to prevent prostate cancer. © 2011 Bentham Science Publishers.

Hahn K.,Institute of Computational Biology | Massopust P.R.,TU Munich | Prigarin S.,Russian Academy of Sciences
BMC Bioinformatics | Year: 2016

Background: Networks or graphs play an important role in the biological sciences. Protein interaction networks and metabolic networks support the understanding of basic cellular mechanisms. In the human brain, networks of functional or structural connectivity model the information-flow between cortex regions. In this context, measures of network properties are needed. We propose a new measure, Ndim, estimating the complexity of arbitrary networks. This measure is based on a fractal dimension, which is similar to recently introduced box-covering dimensions. However, box-covering dimensions are only applicable to fractal networks. The construction of these network-dimensions relies on concepts proposed to measure fractality or complexity of irregular sets in n Results: The network measure Ndim grows with the proliferation of increasing network connectivity and is essentially determined by the cardinality of a maximum k-clique, where k is the characteristic path length of the network. Numerical applications to lattice-graphs and to fractal and non-fractal graph models, together with formal proofs show, that Ndim estimates a dimension of complexity for arbitrary graphs. Box-covering dimensions for fractal graphs rely on a linear log-log plot of minimum numbers of covering subgraph boxes versus the box sizes. We demonstrate the affinity between Ndim and the fractal box-covering dimensions but also that Ndim extends the concept of a fractal dimension to networks with non-linear log-log plots. Comparisons of Ndim with topological measures of complexity (cost and efficiency) show that Ndim has larger informative power. Three different methods to apply Ndim to weighted networks are finally presented and exemplified by comparisons of functional brain connectivity of healthy and depressed subjects. Conclusion: We introduce a new measure of complexity for networks. We show that Ndim has the properties of a dimension and overcomes several limitations of presently used topological and fractal complexity-measures. It allows the comparison of the complexity of networks of different type, e.g., between fractal graphs characterized by hub repulsion and small world graphs with strong hub attraction. The large informative power and a convenient computational CPU-time for moderately sized networks may make Ndim a valuable tool for the analysis of biological networks. © 2016 Hahn et al.

Jayadeepa R.M.,Institute of Computational Biology | Ray A.,Padmashree Dr. D.Y. Patil University | Naik D.,Padmashree Dr. D.Y. Patil University | Sanyal D.N.,Padmashree Dr. D.Y. Patil University | Shah D.,Padmashree Dr. D.Y. Patil University
Current Drug Metabolism | Year: 2014

Plants and their natural components sophisticated with the cornerstone of traditional conventional medicinal system throughout the globe for many years and extend to furnish mankind with latest remedies. Natural Products act as lead molecules for the synthesis of various potent drugs. In the current research a study is conducted on herbal small molecule and their potential binding chemical affinity to the effect or molecules of major diseases such as pancreatic cancer. Clinical studies demonstrate correlation between Cyclin- Dependent Kinase 4 (CDK4) and malignant progression of Pancreatic Cancer. Using Bioruby Gem's we were able to analyze better characteristics of the target protein. VegaZZ and NAMD were used to minimize the energy of the target protein. Therefore identification of effective, well- tolerated targets was analyzed. Further the target protein was subjected to docking with the anti cancer inhibitors which represents a rational chemo preventive strategy using AutoDock Vina. Later using the dock score top ranked phytochemicals were analyzed for Toxicity Analysis. Using the BioRuby gem we were able to measure the distance between the amino acid. Various R scripting libraries were used to hunt the best leads, as in this case the phytochemicals. Phytochemicals such as Wedelolactones and Catechin were analyzed computationally. This study has presented the various effects of naturally occurring anti pancreatic cancer compounds Catechin, Wedelolactones that inhibits Cyclin Dependent Kinase 4. The study results reveal that compounds use less binding energy to CDK4 and inhibit its activity. Future investigation of other various wet lab studies such as cell line studies will confirm results of these two herbal chemical formulations potential ones for treating Pancreatic Cancer. © 2014 Bentham Science Publishers.

Today, cell biology no longer focuses only on static states, but rather seeks to understand the dynamic development of cells. One example for this is the formation of various types of blood cells, such as red blood cells or endothelial cells from their precursors, the blood stem cells. To understand how this process is genetically controlled, scientists analyze which genes are expressed by means of transcriptome analysis. "To me, it's still amazing that we are now even able to determine the transcriptome of single cells," said lead author Laleh Haghverdi, "especially when one realizes that a typical cell contains only a few picograms of RNA." The availability of these data is now beginning to revolutionize many fields of research, but new statistical methods are required to interpret these correctly. "For example, all cells of a sample never start their development synchronously, and their development takes different lengths of time. Therefore, we are always dealing with a dynamic mixture," added Haghverdi, doctoral student at the Institute of Computational Biology (ICB) at Helmholtz Zentrum München. "It is immensely difficult to construct multiple steps of a process from this, especially since the cells are only available for one measurement." Welcome to the era of pseudotime To decrypt developmental processes from the measurement of a single time point, quasi a snapshot measurement, the researchers led by ICB Director Prof. Dr. Dr. Fabian Theis developed an algorithm called diffusion pseudotime to interpret single cell sequencing data. This algorithm orders cells on a virtual timeline – the pseudotime – along which they show continuous changes in the transcriptome. Thus, it can be reconstructed which genes are expressed sequentially. By means of this method, researchers can graphically display the branching lineages of the developmental paths of different cell types. "For example, we can show how a relatively uniform cluster of blood stem cells develops into different cell types," said study leader Theis. "While some become red blood cells, others differentiate into endothelial cells. We can trace these fates based on the transcriptome data of the single cells." In addition, the scientists obtain information about which gene switches underlie the developments. The relatively diffuse mixture of cells which were found to be at different stages of their development can be disentangled on the computer and, after the analysis, provides a clear picture of the ongoing individual steps. However, this is only the beginning for the researchers because the processes of blood formation are relatively well understood. They served only as a test object to determine how well the method works. "In the future we want to focus on processes that have remained elusive until now or which may not have been discovered at all," said Theis.

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