Intracavitary therapy of neoplastic effusions with cytokines: comparison among interferon alpha, beta and interleukin-2

Preliminary studies have suggested that the intracavitary administration of cytokines may represent a new effective palliative therapy of malignant effusions. To define further the therapeutic role of cytokines in the treatment of neoplastic fluid accumulation, 70 cancer patients with pleural, pericardial or peritoneal cytologically proven neoplastic effusions were randomized to receive intracavitary cycles with interleukin-2 (IL-2; 6 x 10(6) IU), interferon (IFN alpha; 2 x 10(7) U) or IFN beta (6 x 10(6) U) every week for 2 or 3 weeks. A clinical control of fluid accumulation was obtained in 39/70 (56%) patients. In patients with mesothelioma, the response rate was significantly higher with IL-2 than with IFN alpha or -beta, while there was no difference in patients with tumors other than mesothelioma. Moreover, the duration of the period during which drainage was not required was significantly longer in patients treated with Il-2 than in the other groups. Toxicity was low in all patients. According to preliminary data, this study demonstrates that intracavitary administration of cytokines, including IL-2, IFN alpha and -beta, is a new well-tolerated palliative therapy for malignant effusions, with an efficacy substantially comparable to that described with the most commonly used treatments with tetracyclines or cytostatic agents.     

Nociceptin receptor binding in mouse forebrain membranes: thermodynamic characteristics and structure activity relationships

The present study describes the labelling of the nociceptin (NC) receptor, ORL1, in mouse forebrain membranes with a new ligand partially protected from metabolic degradation at the C-terminal; the ligand, [3H]-NC-NH2, has a specific activity of 24.5 Ci mmol(-1). Saturation experiments revealed a single class of binding sites with a KD value of 0.55 nM and Bmax of 94 fmol mg(-1) of protein. Non specific binding was 30% of total binding. Kinetic binding studies yielded the following rate constants: Kobs = 0.104 min(-1); K1 =0.034 min(-1): T1/2=20 min; K(+1)=0.07 min nM(-1). Thermodynamic analyses indicated that [3H]-NC-NH2 binding to the mouse ORL1 is totally entropy driven, similar to what has been observed for the labelled agonists to the opioid receptors OP1(delta), OP2(kappa) and OP3(mu). Receptor affinities of several NC fragments and analogues, including the newly discovered ORL-1 receptor antagonist [Phe1psi(CH2-NH)Gly2]NC(1-13)-NH2([F/G]NC(1-13)-NH2), were also evaluated in displacement experiments. The competition curves for these compounds were found to be parallel to that of NC and the following order of potency was determined for NC fragments: NC-OH = NC-NH2-NC(1-13)-NH2 > > NC(1-12)-NH2 > NC(1-13)-OH > > NC(1-11)-NH2, and for NC and NC(1-13)-NH2 analogues: [Tyr1]NC-NH2 > or = [Leu1]NC(1-13)-NH2 > or = [Tyr1]NC(1-13)-NH2 > or = [F/G]NC(1-13)-NH2 > > [Phe3]NC(1-13)-NH2 > [DF/G]NC(1-13)-NH2. Standard opioid receptor ligands (either agonists or antagonists) were unable to displace [3H]-NC-NH2 binding when applied at concentrations up to 10 microM indicating that this new radioligand interacts with a non opioid site, probably the ORL1 receptor.     

Hopelessness, depression, and suicidal intent among psychiatrically disturbed inpatient children.

The Hoplessness Scale for Children was developed and administered to 66 8–13 yr old children along with the Children's Depression Inventory, Bellevue Index of Depression, Depression Symptom Checklist, and the Self-Esteem Inventory. As predicted, Ss who scored high on the Hopelessness Scale showed significantly more severe depression and lower self-esteem than those who scored low on the scale. Ss who evinced suicidal attempt or ideation, independently assessed at intake diagnosis, showed greater hopelessness than Ss with no such intent. Suicidal intent was more consistently correlated with hopelessness than with depression, a finding parallel to results obtained with adults. Overall, findings suggest that negative expectations toward oneself and the future can be assessed in children and are related both to depression and suicidal intent. (26 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Semiotics of virtual reality as a communication process

ABSTRACT

The experience of immersive virtual reality (VR) can be considered as a communication process between human beings, mediated by computer systems, which uses visualisation and other sensory stimulation. In this paper, we analyse how VR characteristics can be explored using semiotic theory and, with methods of generative semiotics, we explore aspects of narrative and interaction in VR. We propose a semiotic analysis of VR communication focusing on syntax, semantics and pragmatics and considering also some principles of generative semiotics. The syntactic level is analysed as determined by the characteristics of the visual communication adopted. The semantic of VR is related to the functional model chosen to realise the virtual system. The pragmatic of VR is based on the human–computer interaction that changes the user's role. We explore how these aspects can be characterised in the context of VR communication design and what principles can be adopted for a VR application, and we present an analysis and a classification of the iconic signs that are being used in VR. Moreover, we present a framework that can be used to classify and describe different kinds of virtual reality systems and to better understand communication in VR, and we use it to classify eight popular systems for e-learning and collaboration.     

Make new friends or keep the old: Gender and personality differences in social networking use

The present study examined the influence of gender and personality on individuals’ use of online social networking websites such as Facebook and MySpace. Participants were 238 undergraduate students who reported being members of Facebook, MySpace, or both. Based on prior research examining online behavior, we expected that gender and scores on the Big Five personality scale would moderate online social networking behavior. The results supported our predictions. Specifically, men reported using social networking sites for forming new relationships while women reported using them more for relationship maintenance. Furthermore, women low in agreeableness reported using instant messaging features of social networking sites more often than women high in agreeableness, whereas men low in openness reported playing more games on social networking sites compared to men high in openness. Overall, these results indicate the importance of examining individual differences in online behavior.     

Preconditioned Bayesian Regression for Stochastic Chemical Kinetics

We develop a preconditioned Bayesian regression method that enables sparse polynomial chaos representations of noisy outputs for stochastic chemical systems with uncertain reaction rates. The approach is based on the definition of an appropriate multiscale transformation of the state variables coupled with a Bayesian regression formalism. This enables efficient and robust recovery of both the transient dynamics and the corresponding noise levels. Implementation of the present approach is illustrated through applications to a stochastic Michaelis–Menten dynamics and a higher dimensional example involving a genetic positive feedback loop. In all cases, a stochastic simulation algorithm (SSA) is used to compute the system dynamics. Numerical experiments show that Bayesian preconditioning algorithms can simultaneously accommodate large noise levels and large variability with uncertain parameters, and that robust estimates can be obtained with a small number of SSA realizations.     

A Granular Computing approach to the design of optimized graph classification systems

Research on Graph-based pattern recognition and Soft Computing systems has attracted many scientists and engineers in several different contexts. This fact is motivated by the reason that graphs are general structures able to encode both topological and semantic information in data. While the data modeling properties of graphs are of indisputable power, there are still different concerns about the best way to compute similarity functions in an effective and efficient manner. To this end, suited transformation procedures are usually conceived to address the well-known Inexact Graph Matching problem in an explicit embedding space. In this paper, we propose two graph embedding algorithms based on the Granular Computing paradigm, which are engineered as key procedures of a general-purpose graph classification system. Tests have been conducted on benchmarking datasets relying on both synthetic and real-world data, achieving competitive results in terms of test set classification accuracy.     

Query-driven soft traceability links for models

Model repositories play a central role in the model driven development of complex software-intensive systems by offering means to persist and manipulate models obtained from heterogeneous languages and tools. Complex models can be assembled by interconnecting model fragments by hard links, i.e., regular references, where the target end points to external resources using storage-specific identifiers. This approach, in certain application scenarios, may prove to be a too rigid and error prone way of interlinking models. As a flexible alternative, we propose to combine derived features with advanced incremental model queries as means for soft interlinking of model elements residing in different model resources. These soft links can be calculated on-demand with graceful handling for temporarily unresolved references. In the background, the links are maintained efficiently and flexibly by using incremental model query evaluation. The approach is applicable to modeling environments or even property graphs for representing query results as first-class relations, which also allows the chaining of soft links that is useful for modular applications. The approach is evaluated using the Eclipse Modeling Framework (EMF) and EMF-IncQuery in two complex industrial case studies. The first case study is motivated by a knowledge management project from the financial domain, involving a complex interlinked structure of concept and business process models. The second case study is set in the avionics domain with strict traceability requirements enforced by certification standards (DO-178b). It consists of multiple domain models describing the allocation scenario of software functions to hardware components.