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At least 19 recordsLinked to original sources

PubMed2002

Complexities in ETS-domain transcription factor function and regulation: lessons from the TCF (ternary complex factor) subfamily. The Colworth Medal Lecture.

The ETS-domain transcription factor family can be divided into a series of subfamilies. Elk-1 represents the founding member of the ternary complex factor (TCF) subfamily. By focusing on the TCF subfamily, we can demonstrate the complexities that exist in the function and regulation of ETS-domain transcription factors. This article focuses on Elk-1 in detail and summarizes the functions of other TCFs. The key themes covered include the domain structure of the TCFs, the mechanisms of complex formation with serum response factor, regulation of TCFs by mitogen-activated protein kinase cascades, and transcriptional regulatory properties of the TCFs. Finally, the emerging role of the TCFs in vivo is discussed. A picture is developing indicating that, while these proteins exhibit significant sequence and functional conservation, key differences in their structure and regulation are being identified which may relate to unique functions of these proteins in vivo.

Amino Acid Sequence↗

PubMed2004

Modelling land use change with generalized linear models--a multi-model analysis of change between 1860 and 2000 in Gallatin Valley, Montana.

This paper develops an approach to modelling land use change that links model selection and multi-model inference with empirical models and GIS. Land use change is frequently studied, and understanding gained, through a process of modelling that is an empirical analysis of documented changes in land cover or land use patterns. The approach here is based on analysis and comparison of multiple models of land use patterns using model selection and multi-model inference. The approach is illustrated with a case study of rural housing as it has developed for part of Gallatin County, Montana, USA. A GIS contains the location of rural housing on a yearly basis from 1860 to 2000. The database also documents a variety of environmental and socio-economic conditions. A general model of settlement development describes the evolution of drivers of land use change and their impacts in the region. This model is used to develop a series of different models reflecting drivers of change at different periods in the history of the study area. These period specific models represent a series of multiple working hypotheses describing (a) the effects of spatial variables as a representation of social, economic and environmental drivers of land use change, and (b) temporal changes in the effects of the spatial variables as the drivers of change evolve over time. Logistic regression is used to calibrate and interpret these models and the models are then compared and evaluated with model selection techniques. Results show that different models are 'best' for the different periods. The different models for different periods demonstrate that models are not invariant over time which presents challenges for validation and testing of empirical models. The research demonstrates (i) model selection as a mechanism for rating among many plausible models that describe land cover or land use patterns, (ii) inference from a set of models rather than from a single model, (iii) that models can be developed based on hypothesised relationships based on consideration of underlying and proximate causes of change, and (iv) that models are not invariant over time.

Agriculture↗

PubMed2006

Model-free model elimination: a new step in the model-free dynamic analysis of NMR relaxation data.

Model-free analysis is a technique commonly used within the field of NMR spectroscopy to extract atomic resolution, interpretable dynamic information on multiple timescales from the R1, R2, and steady state NOE. Model-free approaches employ two disparate areas of data analysis, the discipline of mathematical optimisation, specifically the minimisation of a chi2 function, and the statistical field of model selection. By searching through a large number of model-free minimisations, which were setup using synthetic relaxation data whereby the true underlying dynamics is known, certain model-free models have been identified to, at times, fail. This has been characterised as either the internal correlation times, tau(e), tau(f), or tau(s), or the global correlation time parameter, local tau(m), heading towards infinity, the result being that the final parameter values are far from the true values. In a number of cases the minimised chi2 value of the failed model is significantly lower than that of all other models and, hence, will be the model which is chosen by model selection techniques. If these models are not removed prior to model selection the final model-free results could be far from the truth. By implementing a series of empirical rules involving inequalities these models can be specifically isolated and removed. Model-free analysis should therefore consist of three distinct steps: model-free minimisation, model-free model elimination, and finally model-free model selection. Failure has also been identified to affect the individual Monte Carlo simulations used within error analysis. Each simulation involves an independent randomised relaxation data set and model-free minimisation, thus simulations suffer from exactly the same types of failure as model-free models. Therefore, to prevent these outliers from causing a significant overestimation of the errors the failed Monte Carlo simulations need to be culled prior to calculating the parameter standard deviations.

Magnetic Resonance Spectroscopy↗

PubMed1987

Tracer disposition kinetics in the determination of local cerebral blood flow by a venous equilibrium model, tube model, and distributed model.

Tracer distribution kinetics in the determination of local cerebral blood flow (LCBF) were examined by using three models, i.e., venous equilibrium, tube, and distributed models. The technique most commonly used for measuring LCBF is the tissue uptake method, which was first developed and applied by Kety (1951). The measurement of LCBF with the 14C-iodoantipyrine (IAP) method is calculated by using an equation derived by Kety based on the Fick's principle and a two-compartment model of blood-tissue exchange and tissue concentration at a single data point (Sakurada et al., 1978). The procedure, in which the tissue is to be in equilibrium with venous blood, will be referred to as the tissue equilibration model. In this article, effects of the concentration gradient of tracer along the length of the capillary (tube model) and the transverse heterogeneity in the capillary transit time (distributed model) on the determination of LCBF were theoretically analyzed for the tissue sampling method. Similarities and differences among these models are explored. The rank order of the LCBF calculated by using arterial blood concentration time courses and the tissue concentration of tracer based on each model were tube model (model II) less than distributed model (model III) less than venous equilibrium model (model I). Data on 14C-IAP kinetics reported by Ohno et al. (1979) were employed. The LCBFs calculated based on model I were 45-260% larger than those in models II or III. To discriminate among three models, we propose to examine the effect of altering the venous infusion time of tracer on the apparent tissue-to-blood concentration ratio (lambda app). A range of the ratio of the predicted lambda app in models II or III to that in model I was from 0.6 to 1.3. In the future, there may be a need to determine which model should be used to calculate the LCBF based on this discriminator and to develop another discriminator by using multiple data points based on positron emission tomography.

Animals↗

PubMed2003

A "FRankenstein's monster" approach to comparative modeling: merging the finest fragments of Fold-Recognition models and iterative model refinement aided by 3D structure evaluation.

We applied a new multi-step protocol to predict the structures of all targets during CASP5, regardless of their potential category. 1) We used diverse fold-recognition (FR) methods to generate initial target-template alignments, which were converted into preliminary full-atom models by comparative modeling. All preliminary models were evaluated (scored) by VERIFY3D to identify well- and poorly-folded fragments. 2) Preliminary models with similar 3D folds were superimposed, poorly-scoring regions were deleted and the "average model" structure was created by merging the remaining segments. All template structures reported by FR were superimposed and a composite multiple-structure template was created from the most conserved fragments. 3). The average model was superimposed onto the composite template and the structure-based target-template alignment was inferred. This alignment was used to build a new (intermediate) comparative model of the target, again scored with VERIFY3D. 4) For all poorly scoring regions series of alternative alignments were generated by progressively shifting the "unfit" sequence fragment in either direction. Here, we considered additional information, such as secondary structure, placement of insertions and deletions in loops, conservation of putative catalytic residues, and the necessity to obtain a compact, well-folded structure. For all alternative alignments, new models were built and evaluated. 5) All models were superimposed and the "FRankenstein's monster" (FR, fold recognition) model was built from best-scoring segments. The final model was obtained after limited energy minimization to remove steric clashes between sidechains from different fragments. The novelty of this approach is in the focus on "vertical" recombination of structure fragments, typical for the ab initio field, rather than "horizontal" sequence alignment typical for comparative modeling. We tested the usefulness of the "FRankenstein" approach for non-expert predictors: only the leader of our team had considerable experience in protein modeling - he registered as a separate group (020) and submitted models built only by himself. At the onset of CASP5, the other five members of the team (students) had very little or no experience with modeling. They followed the same protocol in a deliberately naïve way. In the fourth step they used solely the VERIFY3D criterion to compare their models and the leader's model (the latter regarded only as one of the many alternatives) and generated the hybrid or selected only one model for submission (group 517). In order to compare our protocol with the traditional "one target-one template-one alignment" approach, we submitted (as a separate group 242) models selected from those automatically generated by all CAFASP servers (i.e. obtained without any human intervention). Here, we compare the results obtained by the three "groups", describe successes and failures of the "FRankenstein" approach and discuss future developments of comparative modeling. The automatic version of our multi-step protocol is being developed as a meta-server; the prototype is freely available at http://genesilico.pl/meta/.

Algorithms↗

PubMed2003

Model uncertainty and choices made by modelers: lessons learned from the International Atomic Energy Agency model intercomparisons.

The treatment of uncertainties associated with modeling and risk assessment has recently attracted significant attention. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte Carlo modeling are often recommended. However, the issue of model uncertainty is still rarely addressed in practical applications of risk assessment. The use of several alternative models to derive a range of model outputs or risks is one of a few available techniques. This article addresses the often-overlooked issue of what we call "modeler uncertainty," i.e., difference in problem formulation, model implementation, and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit Working Group, which was created under the International Atomic Energy Agency (IAEA) BIOMASS program (BIOsphere Modeling and ASSessment). Model-model and model-data intercomparisons reviewed in this study were conducted by the working group for a total of three different scenarios. The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that parameter uncertainty (as evaluated by a probabilistic Monte Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in risk characterization.

Journal Article↗

PubMed2003

Complexity in multimedia mass balance models: when are simple models adequate and when are more complex models necessary?

Three environmental multimedia models of varying degrees of complexity are compared to assess when simple models are adequate and when more complex models are advantageous. The simplest model, the level II (L-II) model, assumes all environmental media are at chemical equilibrium, whereas the more complex models treat chemical disequilibrium between well-mixed media (standard level IV [L-IV] model) or the major media are subdivided into separate layers to simulate heterogeneity (high-resolution level IV [HR-IV] model). The three models are compared for their performance in predicting steady-state, regional concentrations; dynamic, local-scale concentrations; and chemical persistence in the environment. The results indicate that the L-IV model often provides adequate regional simulations when chemical emission occurs to air or water. This model also is useful for assessing chemical persistence in both steady-state and dynamic scenarios. More complex models, such as the HR-IV model, are suggested for local-scale, dynamic simulations or when the chemical emission occurs to soil because they better characterize rates of intramedia transport, which can greatly affect the model predictions. The simplest L-II model predicts environmental concentrations that can differ significantly from those of more complex models, but it is useful for establishing partitioning tendencies and for ranking chemicals for their relative persistence in steady-state situations.

Air Movements↗

PubMed1998

Physiologically relevant one-compartment pharmacokinetic models for skin. 2. Comparison of models when combined with a systemic pharmacokinetic model.

Transport of chemicals through skin is best modeled as passive diffusion through a membrane, but mathematical solutions for realistic conditions are cumbersome. Compartment models, representing skin as a stirred tank, are mathematically simpler but less physiologically relevant. In a previous paper, several different compartment models were developed assuming constant blood and vehicle concentrations. Here, five skin models (four of the previously described compartment models and one membrane model) are combined with a one-compartment systemic pharmacokinetic (PK) model to examine the effects of changing vehicle and blood concentrations and to clarify how differences between skin models affect the predicted systemic response. The skin-PK models were solved with the same input parameters (i.e., permeability coefficients, partition coefficients, skin thickness, and cutaneous blood flow rates) and compared for five different exposure scenarios. Because the models have different underlying assumptions, they do predict different results. For many exposure situations compartment models give acceptable results, with the most pronounced differences from the membrane model during short exposure times. Generally, the compartment model that most closely represents the membrane model was developed by forcing it to match the membrane model for conditions similar to those of the given exposure scenario.

Models, Biological↗

PubMed2003

Comparative protein structure modeling by iterative alignment, model building and model assessment.

Comparative or homology protein structure modeling is severely limited by errors in the alignment of a modeled sequence with related proteins of known three-dimensional structure. To ameliorate this problem, we have developed an automated method that optimizes both the alignment and the model implied by it. This task is achieved by a genetic algorithm protocol that starts with a set of initial alignments and then iterates through re-alignment, model building and model assessment to optimize a model assessment score. During this iterative process: (i) new alignments are constructed by application of a number of operators, such as alignment mutations and cross-overs; (ii) comparative models corresponding to these alignments are built by satisfaction of spatial restraints, as implemented in our program MODELLER; (iii) the models are assessed by a variety of criteria, partly depending on an atomic statistical potential. When testing the procedure on a very difficult set of 19 modeling targets sharing only 4-27% sequence identity with their template structures, the average final alignment accuracy increased from 37 to 45% relative to the initial alignment (the alignment accuracy was measured as the percentage of positions in the tested alignment that were identical to the reference structure-based alignment). Correspondingly, the average model accuracy increased from 43 to 54% (the model accuracy was measured as the percentage of the C(alpha) atoms of the model that were within 5 A of the corresponding C(alpha) atoms in the superposed native structure). The present method also compares favorably with two of the most successful previously described methods, PSI-BLAST and SAM. The accuracy of the final models would be increased further if a better method for ranking of the models were available.

Algorithms↗

PubMed2003

[New models in the model (Managerial challenges in the Hungarian managed care model)].

The Hungarian managed care model integrates the different levels of health care services. Its goal is a more efficient resource allocation within the health care system, quality improvement of services, developing incentives and financing methods. The basic concept of the model is to introduce the benefits of the American managed care and the British fundholding system. The managed care model brought a new approach in the Hungarian health care system and meets a lot of expectation in both professional and political context. The model has certain problems, but these are handled at macro level. Without an integrated approach of macro, meso and micro levels is hardly believable the efficient and effective functioning of the model. Such macro level problems are the cost-efficiency versus high quality health services, financing incentives, and risk taking. At meso and micro level we are facing with questionable efficiency and effectiveness of the MCOs other health care organisations, which are based on strong bureaucratic paradigms and are convicted to a long lasting crisis in their changing environment. The integrated use of Mintzberg's management models (machine model, network model, performance-control model, virtual government model and normative-control model) adapted by us for the Hungarian managed care model could resolve certain problems or make them at least more solvable. In the changing environment of the health cares organisations the top management has to frame the change-scenario, to initiate, realise and sustain organisational changes. Achieving this, the proposed management models are a useful support. Their practical application could contribute to the efficient and effective functioning of the Hungarian managed care model at macro, meso and micro levels, as well.

Health Care Reform↗

PubMed1984

Models for leprosy. An appraisal of graphic representations of the "spectrum" concept as models and a suggestion for a catastrophe theory model for leprosy.

Graphic representations of the spectrum concept of leprosy are examined in some detail as models for this disease. This reveals that this concept is somewhat inadequate and that the spectrum metaphor may itself be inappropriate because, by its very linearity of logic, it may not be able to depict the nonlinear behavior of leprosy properly. The assumptions underlying this concept and their logical consequences, brought out by the graphic representations, include an invariable relation between CMI and BI, identity of one type of leprosy with one specific level of CMI, a fixed sequence of types, and the consequent impossibility of skipping the sequence. However, our experience with leprosy does not bear out these assumptions. Further, development and progress of leprosy from a normal (nonleprous) state cannot be represented in these models. A search for alternative conceptual models therefore appears reasonable and even necessary. The catastrophe theory (a branch of topology in mathematics) describes a number of models for explaining how continuous causes could produce sudden or discontinuous changes. Of the various catastrophe theory models available, the relatively simple "cusp" model appears capable of application to leprosy. This model, as applied here, requires two control factors (identified tentatively as the amount of dead bacilli and the amount of living bacilli or their indicators) and one pattern of behavior, identified as progress towards limited or extensive disease. This model suggests under what conditions leprosy will change from one type to another and whether that will happen gradually or suddenly. It also suggests that for certain values of control factors the disease may manifest in one of two forms of borderline leprosy, and that lesions very similar to start with can progress to quite different states under similar conditions of change. The behavior of leprosy agrees more or less with that suggested by this model. The cusp model thus seems to: a) provide an insight into the behavior of leprosy, enabling us to understand the dynamics of the disease; b) explain some of its intriguing manifestations; c) ask meaningful questions; and d) plan new therapeutic approaches. Although this is a highly speculative and probably too simple a model, this attempt shows that it is possible to view leprosy outside the framework of the concepts of spectrum scale and polar types of leprosy, the conceptual models which dominate all of our current thinking about the disease.

Humans↗

PubMed2003

A new model-based RSA method validated using CAD models and models from reversed engineering.

Roentgen stereophotogrammetric analysis (RSA) was developed to measure micromotion of an orthopaedic implant with respect to its surrounding bone. A disadvantage of conventional RSA is that it requires the implant to be marked with tantalum beads. This disadvantage can potentially be resolved with model-based RSA, whereby a 3D model of the implant is used for matching with the actual images and the assessment of position and rotation of the implant. In this study, a model-based RSA algorithm is presented and validated in phantom experiments. To investigate the influence of the accuracy of the implant models that were used for model-based RSA, we studied both computer aided design (CAD) models as well as models obtained by means of reversed engineering (RE) of the actual implant. The results demonstrate that the RE models provide more accurate results than the CAD models. If these RE models are derived from the very same implant, it is possible to achieve a maximum standard deviation of the error in the migration calculation of 0.06 mm for translations in x- and y-direction and 0.14 mm for the out of plane z-direction, respectively. For rotations about the y-axis, the standard deviation was about 0.1 degrees and for rotations about the x- and z-axis 0.05 degrees. Studies with clinical RSA-radiographs must prove that these results can also be reached in a clinical setting, making model-based RSA a possible alternative for marker-based RSA.

Algorithms↗

PubMed2001

Theoretical kinetic studies of models for binding myosin subfragment-1 to regulated actin: Hill model versus Geeves model.

It was previously shown that a one-dimensional Ising model could successfully simulate the equilibrium binding of myosin S1 to regulated actin filaments (T. L. Hill, E. Eisenberg and L. Greene, Proc. Natl. Acad. Sci. U.S.A. 77:3186-3190, 1980). However, the time course of myosin S1 binding to regulated actin was thought to be incompatible with this model, and a three-state model was subsequently developed (D. F. McKillop and M. A. Geeves, Biophys. J. 65:693-701, 1993). A quantitative analysis of the predicted time course of myosin S1 binding to regulated actin, however, was never done for either model. Here we present the procedure for the theoretical evaluation of the time course of myosin S1 binding for both models and then show that 1) the Hill model can predict the "lag" in the binding of myosin S1 to regulated actin that is observed in the absence of Ca++ when S1 is in excess of actin, and 2) both models generate very similar families of binding curves when [S1]/[actin] is varied. This result shows that, just based on the equilibrium and pre-steady-state kinetic binding data alone, it is not possible to differentiate between the two models. Thus, the model of Hill et al. cannot be ruled out on the basis of existing pre-steady-state and equilibrium binding data. Physical mechanisms underlying the generation of the lag in the Hill model are discussed.

Actins↗

PubMed2003

On the undecidability among kinetic models: from model selection to model averaging.

This article deals with the problem of model selection for the mathematical description of tracer kinetics in nuclear medicine. It stems from the consideration of some specific data sets where different models have similar performances. In these situations, it is shown that considerate averaging of a parameter's estimates over the entire model set is better than obtaining the estimates from one model only. Furthermore, it is also shown that the procedure of averaging over a small number of "good" models reduces the "generalization error," the error introduced when the model selected over a particular data set is applied to different conditions, such as subject populations with altered physiologic parameters, modified acquisition protocols, and different signal-to-noise ratios. The method of averaging over the entire model set uses Akaike coefficients as measures of an individual model's likelihood. To facilitate the understanding of these statistical tools, the authors provide an introduction to model selection criteria and a short technical treatment of Akaike's information-theoretic approach. The new method is illustrated and epitomized by a case example on the modeling of [11C]flumazenil kinetics in the brain, containing both real and simulated data.

Carbon Radioisotopes↗

PubMed1990

Uncertainties in pharmacokinetic modeling for perchloroethylene. I. Comparison of model structure, parameters, and predictions for low-dose metabolism rates for models derived by different authors.

In recent years physiologically based pharmacokinetic models have come to play an increasingly important role in risk assessment for carcinogens. The hope is that they can help open the black box between external exposure and carcinogenic effects to experimental observations, and improve both high-dose to low-dose and interspecies projections of risk. However, to date, there have been only relatively preliminary efforts to assess the uncertainties in current modeling results. In this paper we compare the physiologically based pharmacokinetic models (and model predictions of risk-related overall metabolism) that have been produced by seven different sets of authors for perchloroethylene (tetrachloroethylene). The most striking conclusion from the data is that most of the differences in risk-related model predictions are attributable to the choice of the data sets used for calibrating the metabolic parameters. Second, it is clear that the bottom-line differences among the model predictions are appreciable. Overall, the ratios of low-dose human to bioassay rodent metabolism spanned a 30-fold range for the six available human/rat comparisons, and the seven predicted ratios of low-dose human to bioassay mouse metabolism spanned a 13-fold range. (The greater range for the rat/human comparison is attributable to a structural assumption by one author group of competing linear and saturable pathways, and their conclusion that the dangerous saturable pathway constitutes a minor fraction of metabolism in rats.) It is clear that there are a number of opportunities for modelers to make different choices of model structure, interpretive assumptions, and calibrating data in the process of constructing pharmacokinetic models for use in estimating "delivered" or "biologically effective" dose for carcinogenesis risk assessments. We believe that in presenting the results of such modeling studies, it is important for researchers to explore the results of alternative, reasonably likely approaches for interpreting the available data--and either show that any conclusions they make are relatively insensitive to particular interpretive choices, or to acknowledge the differences in conclusions that would result from plausible alternative views of the world.

Animals↗

PubMedDate not supplied

[Development of an acute myocardial infarction risk adjustment model for Spain: comparison to the Charlson model and the ICES model. Application for gauging care results].

BACKGROUND: Acute myocardial infarction has a major bearing on morbimortality in developed countries. This study is aimed at developing a risk adjustment model for assessing the results of managing this disease and comparing how this management is carried out with other models. METHODS: A risk adjustment model is developed for acute myocardial infarction by means of logistic regression with the information from an administrative database including various Spanish hospitals, taking the intrahospital mortality rate as the response variable and variables inherent to the patient proper as predictive variables. The predictability thereof is compared to the Charlson Model and the ICES model. These models are applied for assessing the intrahospital mortality rate. RESULTS: The age (OR: 1.07), the anterolateral location (OR: 2.32) and inferoposterior location (OR: 1.91), cardiogenic shock (OR: 39.99), arrhythmia (OR: 94.43), cerebrovascular disease (OR: 2.15) and kidney failure (OR: 1.87) are shown to be related to a higher risk of death. The model developed provides a better predictability (-2LL: 2240) than the Charlson model (-2LL: 3073) and the ICES model (-2LL: 2366). There are no significant differences in the risk-adjusted death rate for the 23 hospitals studied, nor any differences between public and private hospitals nor the care volume or technological level. Coronariography (RMAR: 0.66) and percutaneous coronary interventions (RMAR: 0.69) are procedures having a protective effect. CONCLUSIONS: The model developed may be a useful tool for assessing the hospital care provided for myocardial infarction.

Female↗

PubMed2002

Monte Carlo model of nonlinear chromatography: correspondence between the microscopic stochastic model and the macroscopic Thomas kinetic model.

The Monte Carlo model of chromatography is a description of the chromatographic process from a molecular (microscopic) point of view and it is intrinsically based on the stochastic theory of chromatography originally proposed by Giddings and Eyring. The program was previously validated at infinite dilution (i.e., in linear conditions) by some of the authors of the present paper. In this work, it has been further validated under nonlinear conditions. The correspondence between the Monte Carlo model and the well-known Thomas kinetic model (macroscopic model), for which closed-form solutions are available, is demonstrated by comparing Monte Carlo simulations, performed at different loading factors, with the numerical solutions of the Thomas model calculated under the same conditions. In all the cases investigated, the agreement between Monte Carlo simulations and Thomas model results is very satisfactory. Additionally, the exact correspondence between the Thomas kinetic model and Giddings model, when near-infinite dilution conditions are approached, has been demonstrated by calculating the limit of the Thomas model when the loading factor goes to zero. The model was also validated under limit conditions, corresponding to cases of very slow adsorption-desorption kinetics or very short columns. Different hypotheses about the statistical distributions of the random variables "residence time spent by the molecule in mobile and stationary phase' are investigated with the aim to explain their effect on the peak shape and on the efficiency of the separation.

Journal Article↗

PubMed2000

Time course of superoxide anion radicals generation in two ischaemia-reperfusion models: comparison between hindlimb replant model and isolated rectus femoris muscle model.

We examined differences in superoxide radical (O(2)(-)) production between a hindlimb replant model and isolated rectus femoris muscle model using rabbits. Using Japanese white rabbit hindlimbs, we made limb replant models (LR group: n = 10) and isolated rectus femoris muscle models (IM group: n = 8) by temporary 4-h clampings of femoral vessels and nutrient vessels, respectively. For sham control subjects, the same surgical procedures with no clampings in both models were done (Control-LR: n = 7, Control-IM: n = 6). The measurement of O(2)(-)was performed by our chemiluminescence (CLN) method. In both models, reperfused blood samples were collected before reperfusion, and 5, 15, 30 and 60 min after reperfusion, and CLN was measured. We defined the superoxide index (SI) in each individual specimen as post-reperfusion CLN/pre-reperfusion CLN. The time course of SI in the Control-LR group was significantly higher than that of SI in the Control-IM group (P< 0.05). There was no significant difference between the time course of SI in the LR group and that of SI in the IM group. Both the limb replant model and isolated muscle model are applicable for the evaluation of radical formation in the reperfused stage though there are potential differences between the two models caused by the surgical procedures themselves.

Animals↗