Drug Hypersensitivity: How Drugs Stimulate T Cells via Pharmacological Interaction with Immune Receptors.

Small chemicals like drugs tend to bind to proteins via noncovalent bonds, e.g. hydrogen bonds, salt bridges or electrostatic interactions. Some chemicals interact with other molecules than the actual target ligand, representing so-called 'off-target' activities of drugs. Such interactions are a main cause of adverse side effects to drugs and are normally classified as predictable type A reactions. Detailed analysis of drug-induced immune reactions revealed that off-target activities also affect immune receptors, such as highly polymorphic human leukocyte antigens (HLA) or T cell receptors (TCR). Such drug interactions with immune receptors may lead to T cell stimulation, resulting in clinical symptoms of delayed-type hypersensitivity. They are assigned the 'pharmacological interaction with immune receptors' (p-i) concept. Analysis of p-i has revealed that drugs bind preferentially or exclusively to distinct HLA molecules (p-i HLA) or to distinct TCR (p-i TCR). P-i reactions differ from 'conventional' off-target drug reactions as the outcome is not due to the effect on the drug-modified cells themselves, but is the consequence of reactive T cells. Hence, the complex and diverse clinical manifestations of delayed-type hypersensitivity are caused by the functional heterogeneity of T cells. In the abacavir model of p-i HLA, the drug binding to HLA may result in alteration of the presenting peptides. More importantly, the drug binding to HLA generates a drug-modified HLA, which stimulates T cells directly, like an allo-HLA. In the sulfamethoxazole model of p-i TCR, responsive T cells likely require costimulation for full T cell activation. These findings may explain the similarity of delayed-type hypersensitivity reactions to graft-versus-host disease, and how systemic viral infections increase the risk of delayed-type hypersensitivity reactions.

Virtopsy - the concept of a centralized database in forensic medicine for analysis and comparison of radiological and autopsy data

Recent developments in clinical radiology have resulted in additional developments in the field of forensic radiology. After implementation of cross-sectional radiology and optical surface documentation in forensic medicine, difficulties in the validation and analysis of the acquired data was experienced. To address this problem and for the comparison of autopsy and radiological data a centralized database with internet technology for forensic cases was created. The main goals of the database are (1) creation of a digital and standardized documentation tool for forensic-radiological and pathological findings; (2) establishing a basis for validation of forensic cross-sectional radiology as a non-invasive examination method in forensic medicine that means comparing and evaluating the radiological and autopsy data and analyzing the accuracy of such data; and (3) providing a conduit for continuing research and education in forensic medicine. Considering the infrequent availability of CT or MRI for forensic institutions and the heterogeneous nature of case material in forensic medicine an evaluation of benefits and limitations of cross-sectional imaging concerning certain forensic features by a single institution may be of limited value. A centralized database permitting international forensic and cross disciplinary collaborations may provide important support for forensic-radiological casework and research.

A Concept of Semantics Extraction from Web Data by Induction of Fuzzy Ontologies

Traditionally, ontologies describe knowledge representation in a denotational, formalized, and deductive way. In addition, in this paper, we propose a semiotic, inductive, and approximate approach to ontology creation. We define a conceptual framework, a semantics extraction algorithm, and a first proof of concept applying the algorithm to a small set of Wikipedia documents. Intended as an extension to the prevailing top-down ontologies, we introduce an inductive fuzzy grassroots ontology, which organizes itself organically from existing natural language Web content. Using inductive and approximate reasoning to reflect the natural way in which knowledge is processed, the ontology’s bottom-up build process creates emergent semantics learned from the Web. By this means, the ontology acts as a hub for computing with words described in natural language. For Web users, the structural semantics are visualized as inductive fuzzy cognitive maps, allowing an initial form of intelligence amplification. Eventually, we present an implementation of our inductive fuzzy grassroots ontology Thus,this paper contributes an algorithm for the extraction of fuzzy grassroots ontologies from Web data by inductive fuzzy classification.