3-Hydroxy-3-methylglutaryl coenzyme A reductase inhibitor Atorvastatin mediated effects depend on the activation status of target cells in PLP-EAE.

The effect of Atorvastatin on transcriptional activity in murine experimental autoimmune encephalomyelitis (EAE) induced by PLP peptide 139-151 was analyzed by DNA microarray technique in lymph nodes and spinal cord at onset (10 days), height (20 days) and first remission (30 days) of disease. Fourteen genes were selectively influenced by Atorvastatin in EAE mice. They are mainly related to immune cell functions and regulation of cell-to-cell interaction. Interestingly, seven genes were also differentially regulated in CFA-injected control mice. But qualitative and quantitative differences to EAE mice argue for a dependency of statin effects on the activation status of target cells. Differential regulation of the newly detected candidate genes of statin effects COX-1 and HSP-105 and the previously known statin-responsive genes ICAM-1 and CD86 was confirmed by Western blot and immunohistochemistry. Flow cytometric analysis of lymph node cells revealed that the effect of Atorvastatin treatment in non-immunized healthy animals resembled the effect of immunization with PLP peptide regarding changes of T helper cells, activated B cells and macrophages. In EAE mice, these effects were partially reversed by Atorvastatin treatment. Monitoring of expression of the newly identified candidate genes and patterns of lymphocyte subpopulations might predict the responsiveness of multiple sclerosis patients to statin treatment.

Peroxisome proliferator-activated receptor gamma overexpression inhibits pro-fibrogenic activities of immortalised rat pancreatic stellate cells.

Pancreatic stellate cells (PSCs) play a key role in the development of pancreatic fibrosis, a constant feature of chronic pancreatitis and pancreatic cancer. In response to pro-fibrogenic mediators, PSCs undergo an activation process that involves proliferation, enhanced production of extracellular matrix proteins and a phenotypic transition towards myofibroblasts. Ligands of the peroxisome proliferator-activated receptor gamma (PPARgamma), such as thiazolidinediones, are potent inhibitors of stellate cell activation and fibrogenesis in pancreas and liver. The effects of PPARgamma ligands, however, are at least in part mediated through PPARgamma-independent pathways. Here, we have chosen a different approach to study regulatory functions of PPARgamma in PSCs. Using immortalised rat PSCs, we have established a model of tetracycline (tet)-regulated PPARgamma overexpression. Induction of PPARgamma expression strongly inhibited proliferation and enhanced the rate of apoptotic cell death. Furthermore, PPARgamma-overexpressing cells synthesised less collagen than controls. To monitor effects of PPARgamma on PSC gene expression, we employed Affymetrix microarray technology. Using stringent selection criteria, we identified 21 up- and 19 down-regulated genes in PPARgamma-overexpressing cells. Most of the corresponding gene products are either involved in lipid metabolism, play a role in signal transduction, or are secreted molecules that regulate cell growth and differentiation. In conclusion, our data suggest an active role of PPARgamma in the induction of a quiescent PSC phenotype. PPARgamma-regulated genes in PSCs may serve as novel targets for the development of antifibrotic therapies.

Geomedical warning system against epidemics.

We describe aims, usage and evaluation of the computer-based early warning system telecommunication on medical events (TeCoMed), which achieves tracking down of temporal and spatial spread of epidemics for seasonal communicable diseases. It uses experiences from former waves of communicable diseases down to fine-grained local areas. Data is delivered by the biggest German insurance scheme. The results are presented by means of a commercial, geomedical information system. The evaluation of the system's performance concerning influenza shows that timely risk assessment and warnings are possible.

Representation of individual gene expression in completely pooled mRNA samples.

Designing microarray experiments, scientists are often confronted with the question of pooling due to financial constraints, but discussion of the validity of pooling tends toward a sub-pooling recommendation. Since complete pooling protocols can be considered part of sub-pooling designs, gene expression data from three complete pooling experiments were analyzed. Data from complete pooled versus individual mRNA samples of rat brain tissue were compared to answer the question whether the pooled sample represents individual samples in small-sized experiments. Our analytic approach provided clear results concerning the Affymetrix MAS 5.0 signal and detection call parameters. Despite a strong similarity of arrays within experimental groups, the individual signals were evidently not appropriately represented in the pooled sample, with slightly more than half of all the genes considered. Our analysis reveals problems in cases of small complete pooling designs with less than six subjects pooled.

A prognostic model for temporal courses that combines temporal abstraction and case-based reasoning.

Since clinical management of patients and clinical research are essentially time-oriented endeavours, reasoning about time has become a hot topic in medical informatics. Here we present a method for prognosis of temporal courses, which combines temporal abstractions with case-based reasoning. It is useful for application domains where neither well-known standards, nor known periodicity, nor a complete domain theory exist. We have used our method in two prognostic applications. The first one deals with prognosis of the kidney function for intensive care patients. The idea is to elicit impairments on time, especially to warn against threatening kidney failures. Our second application deals with a completely different domain, namely geographical medicine. Its intention is to compute early warnings against approaching infectious diseases, which are characterised by irregular cyclic occurrences. So far, we have applied our program on influenza and bronchitis. In this paper, we focus on influenza forecast and show first experimental results.

A prognostic model for temporal courses that combines temporal abstraction and case-based reasoning.

Since clinical management of patients and clinical research are essentially time-oriented endeavours, reasoning about time has recently become a hot topic in medical informatics. Here we present a method for prognosis of temporal courses, which combines temporal abstractions with Case-based Reasoning. We have applied our method in two programs. The first one deals with multiparametric time course prognosis of the kidney function. The intention of the second program is to compute early warnings against approaching infectious diseases like influenza.

Methods, architecture, evaluation and usability of a case-based antibiotics advisor.

In this paper, we discuss the usability of an antibiotics therapy adviser, with a broad, complex spectrum of functions which we have developed within the ICONS project. We present the architecture of the system, case-based reasoning methods used, steps and results of medical evaluations, which are concerning the quality of the recommended therapies, the user friendliness of the system and the interpretation of laboratory results. Furthermore, we discuss problems of transferability of such a system from one site to another as well as problems of local susceptibility patterns and individual dose regimens.

RESIS3D--online information about spatial and temporal spreading of resistant pathogens.

Considering resistances of supposed or identified pathogens is of vital importance in antibiotics therapy. Additionally local details play a very important role. So the resistance situation can be different on two wards of the same facility. Furthermore it is possible that there is spatial extension of resistances across the wards that the physicians suppose a future resistance development in a specific unit. The idea of RESIS3D is to visualize spatial and temporal development of resistant pathogens and to give the attending physician (as well as the laboratory) important information for using antibiotics. RESIS3D is an addition to our computer-based therapy adviser called ICONS for calculated antibiotics therapy in intensive care medicine.

Applying temporal abstraction and case-based reasoning to predict approaching influenza waves.

The goal of the TeCoMed project is to send early warnings against forthcoming waves or even epidemics of infectious diseases, especially of influenza, to interested practitioners, pharmacists etc. in the German federal state Mecklenburg-Western Pomerania. The forecast of these waves is based on written confirmations of unfitness for work of the main German health insurance company. Since influenza waves are difficult to predict because of their cyclic but not regular behaviour, statistical methods based on the computation of mean values are not helpful. Instead, we have developed a prognostic model that makes use of similar former courses. Our method combines Case-based Reasoning with Temporal Abstraction to decide whether early warning is appropriate.

A system architecture for genomic data analysis.

MOTIVATION: Most of diseases are caused by a set of gene defects, which occur in a complex association. The association scheme of expressed genes can be modelled by genetic networks. Genetic networks are efficiently facilities to understand the dynamic of pathogenic processes by modelling molecular reality of cell conditions. In this sense a genetic network consists of first, a set of genes of specified cells, tissues or species and second, causal relations between these genes determining the functional condition of the biological system, i. e. under disease. A relation between two genes will exist if they both are directly or indirectly associated with disease [8]. Our goal is to characterize diseases (especially autoimmune diseases like chronic pancreatitis CP, multiple sclerosis MS, rheumatoid arthritis RA) by genetic networks generated by a computer system. We want to introduce this practice as a bioinformatic approach for finding targets.