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Chronic obstructive pulmonary disease (COPD) is a major global health problem and is predicted to become the third most common cause of death by 2020. Apart from the important preventive steps of smoking cessation, there are no other specific treatments for COPD that are as effective in reversing the condition, and therefore there is a need to understand the pathophysiological mechanisms that could lead to new therapeutic strategies. The development of experimental models will help to dissect these mechanisms at the cellular and molecular level. COPD is a disease characterized by progressive airflow obstruction of the peripheral airways, associated with lung inflammation, emphysema and mucus hypersecretion. Different approaches to mimic COPD have been developed but are limited in comparison to models of allergic asthma. COPD models usually do not mimic the major features of human COPD and are commonly based on the induction of COPD-like lesions in the lungs and airways using noxious inhalants such as tobacco smoke, nitrogen dioxide, or sulfur dioxide. Depending on the duration and intensity of exposure, these noxious stimuli induce signs of chronic inflammation and airway remodelling. Emphysema can be achieved by combining such exposure with instillation of tissue-degrading enzymes. Other approaches are based on genetically-targeted mice which develop COPD-like lesions with emphysema, and such mice provide deep insights into pathophysiological mechanisms. Future approaches should aim to mimic irreversible airflow obstruction, associated with cough and sputum production, with the possibility of inducing exacerbations.
Two tetrahydroisoquinoline alkaloids were extracted from the alkaloid fraction of a methanol extract of the seeds of Calycotome Villosa Subsp. intermedia. Their structures were established as (R)-1-hydroxymethyl-7-8-dimethoxy-1,2,3,4-tetrahydro- isoquinoline (1) and (S)-7-hydroxymethyl-2-3-dimethoxy-7,8,9,10-tetrahydroisoquinoline chloride (2) by spectroscopic techniques and X-ray diffraction analysis.
We have used the SLIMCAT 3-D off-line chemical transport model (CTM) to quantify the Arctic chemical ozone loss in the year 2002/2003 and compare it with similar calculations for the winters 1999/2000 and 2003/2004. Recent changes to the CTM have improved the model's ability to reproduce polar chemical and dynamical processes. The updated CTM uses σ-θ as a vertical coordinate which allows it to extend down to the surface. The CTM has a detailed stratospheric chemistry scheme and now includes a simple NAT-based denitrification scheme in the stratosphere.
In the model runs presented here the model was forced by ECMWF ERA40 and operational analyses. The model used 24 levels extending from the surface to ~55 km and a horizontal resolution of either 7.5°×7.5° or 2.8°×2.8°. Two different radiation schemes, MIDRAD and the CCM scheme, were used to diagnose the vertical motion in the stratosphere. Based on tracer observations from balloons and aircraft, the more sophisticated CCM scheme gives a better representation of the vertical transport in this model which includes the troposphere. The higher resolution model generally produces larger chemical O3 depletion, which agrees better with observations.
The CTM results show that very early chemical ozone loss occurred in December 2002 due to extremely low temperatures and early chlorine activation in the lower stratosphere. Thus, chemical loss in this winter started earlier than in the other two winters studied here. In 2002/2003 the local polar ozone loss in the lower stratosphere was ~40% before the stratospheric final warming. Larger ozone loss occurred in the cold year 1999/2000 which had a persistently cold and stable vortex during most of the winter. For this winter the current model, at a resolution of 2.8°×2.8°, can reproduce the observed loss of over 70% locally. In the warm and more disturbed winter 2003/2004 the chemical O3 loss was generally much smaller, except above 620 K where large losses occurred due to a period of very low minimum temperatures at these altitudes.
Since the description of sepsis by Schottmüller in 1914, the amount on knowledge available on sepsis and its underlying pathophysiology has substantially increased. Epidemiologic examinations of abdominal septic shock patients show the potential for high risk posed by and the extensive therapy situation in the intensive care unit (ICU) (5). Unfortunately, until now it has not been possible to significantly reduce the mortality rate of septic shock, which is as high as 50-60% worldwide, although PROWESS' results (1) are encouraging. This paper summarizes the main results of the MEDAN project and their medical impacts. Several aspects are already published, see the references. The heterogeneity of patient groups and the variations in therapy strategies is seen as one of the main problems for sepsis trials. In the MEDAN multi-center study of 71 intensive care units in Germany, a group of 382 patients made up exclusively of abdominal septic shock patients who met the consensus criteria for septic shock (3) was analysed. For use within scores or stand-alone experiments variables are often studied as isolated variables, not as a multidimensional whole, e.g. a recent study takes a look at the role thrombocytes play (15). To avoid this limitation, our study compares several established scores (SOFA, APACHE II, SAPS II, MODS) by a multi-dimensional neuronal network analysis. For outcome prediction the data of 382 patients was analysed by using most of the commonly documented vital parameters and doses of medicine (metric variables). Data was collected in German hospitals from 1998 to 2001. The 382 handwritten patient records were transferred to an electronic database giving the amount of 2.5 million data entries. The metric data contained in the database is composed of daily measurements and doses of medicine. We used range and plausibility checks to allow no faulty data in the electronic database. 187 of the 382 patients are deceased (49 %).
Data driven automatic model selection and parameter adaptation – a case study for septic shock
(2004)
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. This paper propose as model selection criterion the least complex description of the observed data by the model, the minimum description length. For the small, but important example of inflammation modeling the performance of the approach is evaluated.
In bioinformatics, biochemical signal pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically obtaining the most appropriate model and learning its parameters is extremely interesting. One of the most often used approaches for model selection is to choose the least complex model which “fits the needs”. For noisy measurements, the model which has the smallest mean squared error of the observed data results in a model which fits too accurately to the data – it is overfitting. Such a model will perform good on the training data, but worse on unknown data. This paper propose as model selection criterion the least complex description of the observed data by the model, the minimum description length. For the small, but important example of inflammation modeling the performance of the approach is evaluated. Keywords: biochemical pathways, differential equations, septic shock, parameter estimation, overfitting, minimum description length.
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. In this paper, for the small, important example of inflammation modeling a network is constructed and different learning algorithms are proposed. It turned out that due to the nonlinear dynamics evolutionary approaches are necessary to fit the parameters for sparse, given data. Keywords: model parameter adaption, septic shock. coupled differential equations, genetic algorithm.
We report on the rapidity and centrality dependence of proton and antiproton transverse mass distributions from 197Au + 197Au collisions at sqrt[sNN ]=130 GeV as measured by the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). Our results are from the rapidity and transverse momentum range of |y| <0.5 and 0.35< pt <1.00 GeV/c . For both protons and antiprotons, transverse mass distributions become more convex from peripheral to central collisions demonstrating characteristics of collective expansion. The measured rapidity distributions and the mean transverse momenta versus rapidity are flat within |y| <0.5 . Comparisons of our data with results from model calculations indicate that in order to obtain a consistent picture of the proton (antiproton) yields and transverse mass distributions the possibility of prehadronic collective expansion may have to be taken into account.
We report results on rho (770)0--> pi + pi - production at midrapidity in p+p and peripheral Au+Au collisions at sqrt[sNN]=200 GeV. This is the first direct measurement of rho (770)0--> pi + pi - in heavy-ion collisions. The measured rho 0 peak in the invariant mass distribution is shifted by ~40 MeV/c2 in minimum bias p+p interactions and ~70 MeV/c2 in peripheral Au+Au collisions. The rho 0 mass shift is dependent on transverse momentum and multiplicity. The modification of the rho 0 meson mass, width, and shape due to phase space and dynamical effects are discussed.
The main results obtained within the energy scan program at the CERN SPS are presented. The anomalies in energy dependence of hadron production indicate that the onset of deconfinement phase transition is located at about 30 A GeV. For the first time we seem to have clear evidence for the existence of a deconfined state of matter in nature. PACS numbers: 24.85.+p