Ontologies, Mental Disorders and Prototypes

As it emerged from philosophical analyses and cognitive research, most concepts exhibit typicality effects, and resist to the efforts of defining them in terms of necessary and sufficient conditions. This holds also in the case of many medical concepts. This is a problem for the design of computer science ontologies, since knowledge representation formalisms commonly adopted in this field do not allow for the representation of concepts in terms of typical traits. However, the need of representing concepts in terms of typical traits concerns almost every domain of real world knowledge, including medical domains. In particular, in this article we take into account the domain of mental disorders, starting from the DSM-5 descriptions of some specific mental disorders. On this respect, we favor a hybrid approach to the representation of psychiatric concepts, in which ontology oriented formalisms are combined to a geometric representation of knowledge based on conceptual spaces

Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks

Exact calculation of electronic properties of molecules is a fundamental step for intelligent and rational compounds and materials design. The intrinsically graph-like and non-vectorial nature of molecular data generates a unique and challenging machine learning problem. In this paper we embrace a learning from scratch approach where the quantum mechanical electronic properties of molecules are predicted directly from the raw molecular geometry, similar to some recent works. But, unlike these previous endeavors, our study suggests a benefit from combining molecular geometry embedded in the Coulomb matrix with the atomic composition of molecules. Using the new combined features in a Bayesian regularized neural networks, our results improve well-known results from the literature on the QM7 dataset from a mean absolute error of 3.51 kcal/mol down to 3.0 kcal/mol.Comment: Under review ICANN 201

Extramedullary disease in multiple myeloma: a systematic literature review

Extramedullary involvement (or extramedullary disease, EMD) represents an aggressive form of multiple myeloma (MM), characterized by the ability of a clone and/or subclone to thrive and grow independent of the bone marrow microenvironment. Several different definitions of EMD have been used in the published literature. We advocate that true EMD is restricted to soft-tissue plasmacytomas that arise due to hematogenous spread and have no contact with bony structures. Typical sites of EMD vary according to the phase of MM. At diagnosis, EMD is typically found in skin and soft tissues; at relapse, typical sites involved include liver, kidneys, lymph nodes, central nervous system (CNS), breast, pleura, and pericardium. The reported incidence of EMD varies considerably, and differences in diagnostic approach between studies are likely to contribute to this variability. In patients with newly diagnosed MM, the reported incidence ranges from 0.5% to 4.8%, while in relapsed/refractory MM the reported incidence is 3.4 to 14%. Available data demonstrate that the prognosis is poor, and considerably worse than for MM without soft-tissue plasmacytomas. Among patients with plasmacytomas, those with EMD have poorer outcomes than those with paraskeletal involvement. CNS involvement is rare, but prognosis is even more dismal than for EMD in other locations, particularly if there is leptomeningeal involvement. Available data on treatment outcomes for EMD are derived almost entirely from retrospective studies. Some agents and combinations have shown a degree of efficacy but, as would be expected, this is less than in MM patients with no extramedullary involvement. The paucity of prospective studies makes it difficult to justify strong recommendations for any treatment approach. Prospective data from patients with clearly defined EMD are important for the optimal evaluation of treatment outcomes

XM02 is superior to placebo and equivalent to Neupogen™ in reducing the duration of severe neutropenia and the incidence of febrile neutropenia in cycle 1 in breast cancer patients receiving docetaxel/doxorubicin chemotherapy

Abstract Background Recombinant granulocyte colony-stimulating factors (G-CSFs) such as Filgrastim are used to treat chemotherapy-induced neutropenia. We investigated a new G-CSF, XM02, and compared it to Neupogen™ after myelotoxic chemotherapy in breast cancer (BC) patients. Methods A total of 348 patients with BC receiving docetaxel/doxorubicin chemotherapy were randomised to treatment with daily injections (subcutaneous 5 μg/kg/day) for at least 5 days and a maximum of 14 days in each cycle of XM02 (n = 140), Neupogen™ (n = 136) or placebo (n = 72). The primary endpoint was the duration of severe neutropenia (DSN) in cycle 1. Results The mean DSN in cycle 1 was 1.1, 1.1, and 3.9 days in the XM02, Neupogen™, and placebo group, respectively. Superiority of XM02 over placebo and equivalence of XM02 with Neupogen™ could be demonstrated. Toxicities were similar between XM02 and Neupogen™. Conclusion XM02 was superior to placebo and equivalent to Neupogen™ in reducing DSN after myelotoxic chemotherapy. Trial Registration Current Controlled Trials ISRCTN02270769</p

Epidemic space

The aim of this article is to highlight the importance of 'spatiality' in understanding the materialization of risk society and cultivation of risk sensibilities. More specifically it provides a cultural analysis of pathogen virulence (as a social phenomenon) by means of tracing and mapping the spatial flows that operate in the uncharted zones between the microphysics of infection and the macrophysics of epidemics. It will be argued that epidemic space consists of three types of forces: the vector, the index and the vortex. It will draw on Latour's Actor Network Theory to argue that epidemic space is geared towards instability when the vortex (of expanding associations and concerns) displaces the index (of finding a single cause)

Who Believes in the Giant Skeleton Myth? An Examination of Individual Difference Correlates

This study examined individual difference correlates of belief in a narrative about the discovery of giant skeletal remains that contravenes mainstream scientific explanations. A total of 364 participants from Central Europe completed a survey that asked them to rate their agreement with a short excerpt describing the giant skeleton myth. Participants also completed measures of the Big Five personality factors, New Age orientation, anti-scientific attitudes, superstitious beliefs, and religiosity. Results showed that women, as compared with men, and respondents with lower educational qualifications were significantly more likely to believe in the giant skeleton myth, although effect sizes were small. Correlational analysis showed that stronger belief in the giant skeleton myth was significantly associated with greater anti-scientific attitudes, stronger New Age orientation, greater religiosity, stronger superstitious beliefs, lower Openness to Experience scores, and higher Neuroticism scores. However, a multiple regression showed that the only significant predictors of belief in myth were Openness, New Age orientation, and anti-scientific attitudes. These results are discussed in relation to the potential negative consequences of belief in myths