DAPSONE HYPERSENSITIVITY SYNDROME: A COMPLICATION OF DAPSONE THERAPY

Dapsone is chemically sulfonamide with its leprostatic mechanism used in the treatment of Hansen’s disease. It is one of the safest drugs in leprosy patients. Apart from its safety, it is associated with various adverse effects such as hemolytic anemia, allergic dermatitis, agranulocytosis, methaemoglobinemia, and dapsone hypersensitivity syndrome (DHS). DHS typically presents with fever, skin eruptions, Jaundice, and hepatomegaly (organ involvement). We present a case of 35-year-old female attended to Government General Hospital with complaints of fever, skin rashes, and yellowish discoloration of the eyes. She had past medication history of dapsone taken for paucibacillary leprosy for 4 weeks. Her symptoms appeared after a month and become intolerable to dapsone. Laboratory investigations revealed hepatomegaly, anemic with jaundice. Based on dermatological examination, her diagnosis was confirmed as DHS. The drug was stopped and the patient was treated with drugs for the symptomatic cure. She was recovered from her condition and the multibacillary leprosy multidrug treatment regimen was continued without dapsone

BRCA1 Mutation Leads to Deregulated Ubc9 Levels which Triggers Proliferation and Migration of Patient-Derived High Grade Serous Ovarian Cancer and Triple Negative Breast Cancer Cells

Women who carry a germline mutation in BRCA1 gene typically develop triple negative breast cancers (TNBC) and high grade serous ovarian cancers (HGSOC). Previously, we reported that wild type BRCA1 proteins, unlike the disease-associated mutant BRCA1 proteins to bind the sole sumo E2-conjugating enzyme Ubc9. In this study, we have used clinically relevant cell lines with known BRCA1 mutations and report the in-vivo association of BRCA1 and Ubc9 in normal mammary epithelial cells but not in BRCA1 mutant HGSOC and TNBC cells by immunofluorescence analysis. BRCA1-mutant HGSOC / TNBC cells and ovarian tumor tissues showed increased expression of Ubc9 compared to BRCA1 reconstituted HGSOC, normal mammary epithelial cells and matched normal ovarian tissues. Knockdown of Ubc9 expression resulted in decreased proliferation and migration of BRCA1 mutant TNBC and HGSOC cells. This is the first study demonstrating the functional link between BRCA1 mutation, high Ubc9 expression and increased migration of HGSOC and TNBC cells. High Ubc9 expression due to BRCA1 mutation may trigger an early growth and transformation advantage to normal breast and ovarian epithelial cells resulting in aggressive cancers. Future work will focus on studying whether Ubc9 expression could show a positive correlation with BRCA1 linked HGSOC and basal like TNBC phenotype

Selenium and Mercury in the Brazilian Amazon: Opposing Influences on Age-Related Cataracts

BACKGROUND: Age-related cataracts (ARCs) are an important cause of blindness in developing countries. Although antioxidants may be part of the body's defense to prevent ARC, environmental contaminants may contribute to cataractogenesis. In fish-eating populations of the lower Tapajos region, elevated exposure to mercury (Hg) has been reported, and blood levels of selenium (Se) range from normal to very high (> 1,000 mu g/L). OBJECTIVES: We examined ARCs in relation to these elements among adults (>= 40 years of age) from 12 riverside communities. METHODS: Participants (n = 211) provided blood samples and underwent an extensive ocular examination. Inductively coupled plasma mass spectrometry was used to assess Hg and Se in blood and plasma. RESULTS: One-third (n = 69; 32.7%) of the participants had ARC. Lower plasma Se (P-Se; < 25th percentile, 110 mu g/L) and higher blood Hg (B-Hg; >= 25th percentile, 25 mu g/L) were associated with a higher prevalence odds ratio (POR) of ARC [adjusted POR (95% confidence interval), 2.69 (1.11-6.56) and 4.45 (1.43-13.83), respectively]. Among participants with high P-Se, we observed a positive but nonsignificant association with high B-Hg exposure, whereas among those with low B-Hg, we observed no association for P-Se. However, compared with the optimum situation (high P-Se, low B-Hg), the POR for those with low P-Se and high B-Hg was 16.4 (3.0-87.9). This finding suggests a synergistic effect. CONCLUSION: Our results suggest that persons in this population with elevated Hg, the cataractogenic effects of Hg may be offset by Se. Because of the relatively small sample size and possible confounding by other dietary nutrients, additional studies with sufficient power to assess multiple nutrient and toxic interactions are required to confirm these findings.Canadian Institutes of Health Research (CIHR)Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Canadian Natural Sciences and Engineering Council (NSERC)International Development Research Centre (IDRC) - Canad

Walker-Independent Features for Gait Recognition from Motion Capture Data

MoCap-based human identification, as a pattern recognition discipline, can be optimized using a machine learning approach. Yet in some applications such as video surveillance new identities can appear on the fly and labeled data for all encountered people may not always be available. This work introduces the concept of learning walker-independent gait features directly from raw joint coordinates by a modification of the Fisher’s Linear Discriminant Analysis with Maximum Margin Criterion. Our new approach shows not only that these features can discriminate different people than who they are learned on, but also that the number of learning identities can be much smaller than the number of walkers encountered in the real operation

Systematic reconstruction of TRANSPATH data into Cell System Markup Language

<p>Abstract</p> <p>Background</p> <p>Many biological repositories store information based on experimental study of the biological processes within a cell, such as protein-protein interactions, metabolic pathways, signal transduction pathways, or regulations of transcription factors and miRNA. Unfortunately, it is difficult to directly use such information when generating simulation-based models. Thus, modeling rules for encoding biological knowledge into system-dynamics-oriented standardized formats would be very useful for fully understanding cellular dynamics at the system level.</p> <p>Results</p> <p>We selected the TRANSPATH database, a manually curated high-quality pathway database, which provides a plentiful source of cellular events in humans, mice, and rats, collected from over 31,500 publications. In this work, we have developed 16 modeling rules based on hybrid functional Petri net with extension (HFPNe), which is suitable for graphical representing and simulating biological processes. In the modeling rules, each Petri net element is incorporated with Cell System Ontology to enable semantic interoperability of models. As a formal ontology for biological pathway modeling with dynamics, CSO also defines biological terminology and corresponding icons. By combining HFPNe with the CSO features, it is possible to make TRANSPATH data to simulation-based and semantically valid models. The results are encoded into a biological pathway format, Cell System Markup Language (CSML), which eases the exchange and integration of biological data and models.</p> <p>Conclusion</p> <p>By using the 16 modeling rules, 97% of the reactions in TRANSPATH are converted into simulation-based models represented in CSML. This reconstruction demonstrates that it is possible to use our rules to generate quantitative models from static pathway descriptions.</p

Building nonparametric nn-body force fields using Gaussian process regression

Constructing a classical potential suited to simulate a given atomic system is a remarkably difficult task. This chapter presents a framework under which this problem can be tackled, based on the Bayesian construction of nonparametric force fields of a given order using Gaussian process (GP) priors. The formalism of GP regression is first reviewed, particularly in relation to its application in learning local atomic energies and forces. For accurate regression it is fundamental to incorporate prior knowledge into the GP kernel function. To this end, this chapter details how properties of smoothness, invariance and interaction order of a force field can be encoded into corresponding kernel properties. A range of kernels is then proposed, possessing all the required properties and an adjustable parameter $n$ governing the interaction order modelled. The order $n$ best suited to describe a given system can be found automatically within the Bayesian framework by maximisation of the marginal likelihood. The procedure is first tested on a toy model of known interaction and later applied to two real materials described at the DFT level of accuracy. The models automatically selected for the two materials were found to be in agreement with physical intuition. More in general, it was found that lower order (simpler) models should be chosen when the data are not sufficient to resolve more complex interactions. Low $n$ GPs can be further sped up by orders of magnitude by constructing the corresponding tabulated force field, here named "MFF".Comment: 31 pages, 11 figures, book chapte

Polycation-π Interactions Are a Driving Force for Molecular Recognition by an Intrinsically Disordered Oncoprotein Family

Molecular recognition by intrinsically disordered proteins (IDPs) commonly involves specific localized contacts and target-induced disorder to order transitions. However, some IDPs remain disordered in the bound state, a phenomenon coined "fuzziness", often characterized by IDP polyvalency, sequence-insensitivity and a dynamic ensemble of disordered bound-state conformations. Besides the above general features, specific biophysical models for fuzzy interactions are mostly lacking. The transcriptional activation domain of the Ewing's Sarcoma oncoprotein family (EAD) is an IDP that exhibits many features of fuzziness, with multiple EAD aromatic side chains driving molecular recognition. Considering the prevalent role of cation-π interactions at various protein-protein interfaces, we hypothesized that EAD-target binding involves polycation- π contacts between a disordered EAD and basic residues on the target. Herein we evaluated the polycation-π hypothesis via functional and theoretical interrogation of EAD variants. The experimental effects of a range of EAD sequence variations, including aromatic number, aromatic density and charge perturbations, all support the cation-π model. Moreover, the activity trends observed are well captured by a coarse-grained EAD chain model and a corresponding analytical model based on interaction between EAD aromatics and surface cations of a generic globular target. EAD-target binding, in the context of pathological Ewing's Sarcoma oncoproteins, is thus seen to be driven by a balance between EAD conformational entropy and favorable EAD-target cation-π contacts. Such a highly versatile mode of molecular recognition offers a general conceptual framework for promiscuous target recognition by polyvalent IDPs. © 2013 Song et al

Algorithms for effective querying of compound graph-based pathway databases

<p>Abstract</p> <p>Background</p> <p>Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools.</p> <p>Results</p> <p>Towards this goal, we developed a querying framework, along with a number of graph-theoretic algorithms from simple neighborhood queries to shortest paths to feedback loops, that is applicable to all sorts of graph-based pathway databases, from PPIs (protein-protein interactions) to metabolic and signaling pathways. The framework is unique in that it can account for compound or nested structures and ubiquitous entities present in the pathway data. In addition, the queries may be related to each other through "AND" and "OR" operators, and can be recursively organized into a tree, in which the result of one query might be a source and/or target for another, to form more complex queries. The algorithms were implemented within the querying component of a new version of the software tool P<smcaps>ATIKA</smcaps><it>web </it>(Pathway Analysis Tool for Integration and Knowledge Acquisition) and have proven useful for answering a number of biologically significant questions for large graph-based pathway databases.</p> <p>Conclusion</p> <p>The P<smcaps>ATIKA</smcaps> Project Web site is <url>http://www.patika.org</url>. P<smcaps>ATIKA</smcaps><it>web </it>version 2.1 is available at <url>http://web.patika.org</url>.</p