Intracerebroventricular antisense knockdown of Gα<Subscript>i2</Subscript> results in ciliary stasis and ventricular dilatation in the rat

Background  

In the CNS, the heterotrimeric G protein Gα_i2 is a minor Gα subunit with restricted localization in the ventricular regions including the ependymal cilia. The localization of Gα_i2 is conserved in cilia of different tissues, suggesting a particular role in ciliary function. Although studies with Gα_i2-knockout mice have provided information on the role of this Gα subunit in peripheral tissues, its role in the CNS is largely unknown. We used intracerebroventricular (icv) antisense administration to clarify the physiological role of Gα_i2 in the ventricular system.     

Improving the computational efficiency in a global formulation (GLIDE) for interactive multiobjective optimization

In this paper, we present a new general formulation for multiobjective optimization that can accommodate several interactive methods of different types (regarding various types of preference information required from the decision maker). This formulation provides a comfortable implementation framework for a general interactive system and allows the decision maker to conveniently apply several interactive methods in one solution process. In other words, the decision maker can at each iteration of the solution process choose how to give preference information to direct the interactive solution process, and the formulation enables changing the type of preferences, that is, the method used, whenever desired. The first general formulation, GLIDE, included eight interactive methods utilizing four types of preferences. Here we present an improved version where we pay special attention to the computational efficiency (especially significant for large and complex problems), by eliminating some constraints and parameters of the original formulation. To be more specific, we propose two new formulations, depending on whether the multiobjective optimization problem to be considered is differentiable or not. Some computational tests are reported showing improvements in all cases. The generality of the new improved formulations is supported by the fact that they can accommodate six interactive methods more, that is, a total of fourteen interactive methods, just by adjusting parameter values.     

A study of high-level managerial decision processes, with implications for MCDM research

We study six real-world major strategic decisions and discuss the role that analytic Multiple Criteria Decision Making (MCDM) models could play in helping decision makers structure and solve such problems. We have interviewed successful and well-educated managers who had access to quantitative decision models, but did not use them as part of their decision process. Our approach is a clinical one that takes a close look at the decision processes. We believe that the normative MCDM framework is oversimplified and does not always fit well with complex, real-world organizational decision processes. This may be one reason why decision tools are not used more widely for solving high-level decision problems. We believe that it would be worthwhile to revise some of the MCDM mainstream postulates and practices to make existing models and tools more suitable for practical purposes. The MCDM mainstream research has until today focused on the choice among alternatives. One should realize that MCDM models could also be used in creating alternatives, in assessing the importance of criteria, in providing the decision makers with “post-commitment support”, and as part of a devil's advocate approach.     

NAUTILUS method: An interactive technique in multiobjective optimization based on the nadir point

Most interactive methods developed for solving multiobjective optimization problems sequentially generate Pareto optimal or nondominated vectors and the decision maker must always allow impairment in at least one objective function to get a new solution. The NAUTILUS method proposed is based on the assumptions that past experiences affect decision makers’ hopes and that people do not react symmetrically to gains and losses. Therefore, some decision makers may prefer to start from the worst possible objective values and to improve every objective step by step according to their preferences. In NAUTILUS, starting from the nadir point, a solution is obtained at each iteration which dominates the previous one. Although only the last solution will be Pareto optimal, the decision maker never looses sight of the Pareto optimal set, and the search is oriented so that (s)he progressively focusses on the preferred part of the Pareto optimal set. Each new solution is obtained by minimizing an achievement scalarizing function including preferences about desired improvements in objective function values. NAUTILUS is specially suitable for avoiding undesired anchoring effects, for example in negotiation support problems, or just as a means of finding an initial Pareto optimal solution for any interactive procedure. An illustrative example demonstrates how this new method iterates.     

Verbal ordinal classification with multicriteria decision aiding

Professionals in neuropsychology usually perform diagnoses of patients’ behaviour in a verbal rather than in a numerical form. This fact generates interest in decision support systems that process verbal data. It also motivates us to develop methods for the classification of such data. In this paper, we describe ways of aiding classification of a discrete set of objects, evaluated on set of criteria that may have verbal estimations, into ordered decision classes. In some situations, there is no explicit additional information available, while in others it is possible to order the criteria lexicographically. We consider both of these cases. The proposed Dichotomic Classification (DC) method is based on the principles of Verbal Decision Analysis (VDA). Verbal Decision Analysis methods are especially helpful when verbal data, in criteria values, are to be handled. When compared to the previously developed Verbal Decision Analysis classification methods, Dichotomic Classification method performs better on the same data sets and is able to cope with larger sizes of the object sets to be classified. We present an interactive classification procedure, estimate the effectiveness and computational complexity of the new method and compare it to one of the previously developed Verbal Decision Analysis methods. The developed and studied methods are implemented in the framework of a decision support system, and the results of testing on artificial sets of data are reported.     

Nanoformulation and Evaluation of Oral Berberine-Loaded Liposomes

Berberine (BBR) is a poorly water-soluble quaternary isoquinoline alkaloid of plant origin with potential uses in the drug therapy of hypercholesterolemia. To tackle the limitations associated with the oral therapeutic use of BBR (such as a first-pass metabolism and poor absorption), BBR-loaded liposomes were fabricated by ethanol-injection and thin-film hydration methods. The size and size distribution, polydispersity index (PDI), solid-state properties, entrapment efficiency (EE) and in vitro drug release of liposomes were investigated. The BBR-loaded liposomes prepared by ethanol-injection and thin-film hydration methods presented an average liposome size ranging from 50 nm to 244 nm and from 111 nm to 449 nm, respectively. The PDI values for the liposomes were less than 0.3, suggesting a narrow size distribution. The EE of liposomes ranged from 56% to 92%. Poorly water-soluble BBR was found to accumulate in the bi-layered phospholipid membrane of the liposomes prepared by the thin-film hydration method. The BBR-loaded liposomes generated by both nanofabrication methods presented extended drug release behavior in vitro. In conclusion, both ethanol-injection and thin-film hydration nanofabrication methods are feasible for generating BBR-loaded oral liposomes with a uniform size, high EE and modified drug release behavior in vitro.     

Deep Membrane Proteome Profiling Reveals Overexpression of Prostate-Specific Membrane Antigen (PSMA) in High-Risk Human Paraganglioma and Pheochromocytoma,Suggesting New Theranostic Opportunity

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors arising from chromaffin cells of adrenal medulla or sympathetic or parasympathetic paraganglia, respectively. To identify new therapeutic targets, we performed a detailed membrane-focused proteomic analysis of five human paraganglioma (PGL) samples. Using the Pitchfork strategy, which combines specific enrichments of glycopeptides, hydrophobic transmembrane segments, and non-glycosylated extra-membrane peptides, we identified over 1800 integral membrane proteins (IMPs). We found 45 “tumor enriched” proteins, i.e., proteins identified in all five PGLs but not found in control chromaffin tissue. Among them, 18 IMPs were predicted to be localized on the cell surface, a preferred drug targeting site, including prostate-specific membrane antigen (PSMA), a well-established target for nuclear imaging and therapy of advanced prostate cancer. Using specific antibodies, we verified PSMA expression in 22 well-characterized human PPGL samples. Compared to control chromaffin tissue, PSMA was markedly overexpressed in high-risk PPGLs belonging to the established Cluster 1, which is characterized by worse clinical outcomes, pseudohypoxia, multiplicity, recurrence, and metastasis, specifically including SDHB, VHL, and EPAS1 mutations. Using immunohistochemistry, we localized PSMA expression to tumor vasculature. Our study provides the first direct evidence of PSMA overexpression in PPGLs which could translate to therapeutic and diagnostic applications of anti-PSMA radio-conjugates in high-risk PPGLs.     

Some Theory and an Approach to Solving Sequential Multiple-Criteria Decision Problems

This paper examines a sequential multiple-criteria decision problem. The problem arises when a decision-maker is unable to consider all possible decision alternatives simultaneously. The decision-maker evaluates only a subset of all decision alternatives, from which he chooses the most preferred solution. Obviously, this solution is not necessarily ‘globally’ best. An interesting question is: how good is the most preferred solution and what are the chances of finding a better solution by considering additional alternatives? A unified approach to solving this problem based on probability theory is presented and illustrated with numerical examples.     

Finite Element Approximation of Vector-Valued Hemivariational Problems

In this paper we develop a finite element approximationfor vector-valuedhemivariational inequalities.This class of hemivariational problems wasintroducedin [12],[13]. We study two differentproblems: unconstrained oneand constrained one witha nonempty, closed, convex constraint set K.We shall show firstly that the discrete problemsare solvable by usingconsequences of Kakutanifixed point theorem and secondly that the solutionsof the discrete problemsare close on subsequences to the continuous ones.