SEARCH · PubMed Health

Results for “Model”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3Linked to original sources

PubMed1988

X-ray diffraction studies of 14-filament models of deoxygenated sickle cell hemoglobin fibers. II. Models based on the deoxygenated sickle hemoglobin crystal structure.

The calculated transforms of a number of crystal-based models of the deoxygenated sickle cell hemoglobin fiber have been compared with X-ray diffraction data of 15 A (1 A = 0.1 nm) resolution. The fiber models consist of 14 single strands of sickle cell hemoglobin (HbS) molecules, which associate into seven protofilaments arranged similarly to those present in the crystal structure. Six of the protofilaments are arranged in three crystallographic until cells extending in the c-axis direction with the seventh protofilament positioned so as to provide an elliptical cross-section when the assemblage is viewed down the fiber axis. Models were generated by systematically and independently translating each of the model's three subcells in steps of 3.5 A along the fiber axis. The seventh protofilament was kept fixed as a point of reference. Each translation of a subcell corresponded to a different fiber model whose transform was then compared with observed data. In all, over 46,000 transforms were computed; of these, three models with minimal residuals were identified. The free energy of packing for all crystal-based models was evaluated to find configurations of protofilaments possessing minimal free energies. The results of the calculations support the subcell configurations of two of the three models with minimal residuals.

Hemoglobin, Sickle↗

PubMed2025

Refining sequence-to-activity models by increasing model resolution.

Decoding the cis-regulatory syntax that controls gene expression is essential for improving our understanding of cell differentiation and disease. To identify regulatory motifs and their regulatory syntax, deep learning based sequence-to-activity (S2A) models learn transcription factor binding motifs and their combinations from DNA sequence by modeling measured chromatin accessibility. Previously, we developed AI-TAC, a S2A model that predicts chromatin accessibility across various immune cell types in multi-task fashion, effectively decoding the regulatory syntax underlying immune cell differentiation. While ATAC-seq is commonly used to measure regional accessibility, it also provides high-resolution profiles, the distribution of Tn5 insertion sites, that offer additional insights into the precise location and strength of TF binding sites. Here we demonstrate that modeling ATAC-seq profiles alongside accessibility consistently improves predictions of differential chromatin accessibility across cell types. Moreover, we also find that multi-task learning across related immune cell types consistently outperforms single-task models. To understand what additional information bpAITAC learns from ATAC-seq profiles, we systematically compare sequence attributions from models trained with and without ATAC-seq profiles. We identify novel motifs with strong effect sizes that emerge only when profile data is included. Our findings suggest that modeling ATAC-seq at base-pair resolution enables the model to learn a more nuanced and sensitive representation of the cis-regulatory syntax driving immune cell-specific chromatin landscapes.

ATAC-seq↗

PubMed1985

The influence of muscle model complexity in musculoskeletal motion modeling.

A comparative study of four different muscle models in a musculoskeletal motion problem is made. The models vary in complexity from the simple input-output model to the more complex model of Hatze [1]. These models are used to solve a minimum time kicking problem using an optimal control algorithm. The results demonstrate the strong influence of the model choice on the various predicted kinematic and kinetic parameters in the problem. The study illustrates some of the advantages and disadvantages involved in trade-offs between model complexity and practicability in musculoskeletal motion studies. The results also illustrate the importance of appropriate detailed parameter estimation studies in the mathematical modeling of the musculoskeletal system.

Humans↗

PubMed1990

Energetics of the time-varying elastance model, a visco-elastic model, matches Mommaerts' unifying concept of the Fenn effect of muscle.

It is generally believed that the Fenn effect contradicts all visco-elastic models of muscle, including the new elastic body and the time-varying elastance models. Although it is clear that the new elastic body model can be discarded, the Fenn effect does not preclude the time-varying elastance model. Although no visco-elastic models can simulate the extra energy utilization for work above the level of the energy utilized for the maximal isometric contraction, the extra energy observed by Fenn is not generally observed, even in skeletal muscles. However, work-related extra energy utilization, above the isometric energy utilization at equivalent force (Mommaerts' unifying concept of the Fenn effect), is generally observed in both skeletal and cardiac muscles. This unifying concept of the Fenn effect in cardiac muscle can be simulated by a simple time-varying elastance model. This study demonstrates the essential difference in energetics between the new elastic body model and the time-varying elastance model.

Elasticity↗

PubMed1988

A dynamic life table model of Psorophora columbiae in the southern Louisiana rice agroecosystem with supporting hydrologic submodel. Part 1. Analysis of literature and model development.

During the past decade, the rice agroecosystem and its associated mosquitoes have been the subject of an extensive research effort directed toward the development and implementation of integrated pest management (IPM) strategies. The objective of this work was to synthesize the literature and unpublished data on the rice agroecosystem into a comprehensive simulation model of the key elements of the system known to influence the population dynamics of Psorophora columbiae. Subsequent companion papers will present a validation of these models, provide an in-depth analysis of the population dynamics of Ps. columbiae, and evaluate current and proposed IPM strategies for this mosquito. This paper describes the development of 2 models: WaterMod: Because spatial and temporal distributions of surface water and soil moisture play a decisive role in the dynamics of Ps. columbiae, an essentially hydrological simulator was developed. Its purpose is to provide environmental inputs for a second model (PcSim) which simulates the population dynamics of Ps. columbiae. WaterMod utilizes data on weather, agricultural practices, and soil characteristics for a particular region to generate a data set containing daily estimates of soil moisture and depth of water table for 12 representative areas comprising the rice agroecosystem. This model could be used to provide hydrologic inputs for additional simulation models of other riceland mosquito species. PcSim: This model simulates the population dynamics of Ps. columbiae by using the computer to maintain a daily accounting of the absolute number of mosquitoes within each daily age class for each life stage. The model creates estimates of the number of eggs, larvae, pupae, and adults for a representative l-ha area of a rice agroecosystem.

Actuarial Analysis↗

PubMed1989

Modelling sibship environment in the regressive logistic model for familial disease.

Recently analytical models for pedigree disease data have been developed that combine genetic and epidemiological modelling techniques. The regressive logistic model [Bonney, Biometrics 42: 611-625; 1986] relies on decomposing the likelihood of a pedigree into the product of conditional probabilities, one for each individual, by imposing a (natural) order on pedigree members. In addition to modelling measured epidemiological variables, vertical transmission, transmission of unmeasured ousiotypes (a special case being genotypes), and some modelling of sibship dependencies have been proposed. In this paper the model is extended to include an unmeasured sibship environment factor using a log-linear model for binary pedigree traits [Hopper et al., Genet Epidemiol 1: 183-188; 1984], which breaks the pedigree into conditionally independent groups. Statistical issues, such as designs for which these factors will be discernible and tests of fit, are discussed.

Environment↗

PubMed1992

Building population pharmacokinetic--pharmacodynamic models. I. Models for covariate effects.

One major task in clinical pharmacology is to determine the pharmacokinetic-pharmacodynamic (PK-PD) parameters of a drug in a patient population. NONMEM is a program commonly used to build population PK-PD models, that is, models that characterize the relationship between a patient's PK-PD parameters and other patient specific covariates such as the patient's (patho) physiological condition, concomitant drug therapy, etc. This paper extends a previously described approach to efficiently find the relationships between the PK-PD parameters and covariates. In a first step, individual estimates of the PK-PD parameters are obtained as empirical Bayes estimates, based on a prior NONMEN fit using no covariates. In a second step, the individual PK-PD parameter estimates are regressed on the covariates using a generalized additive model. In a third and final step, NONMEM is used to optimize and finalize the population model. Four real-data examples are used to demonstrate the effectiveness of the approach. The examples show that the generalized additive model for the individual parameter estimates is a good initial guess for the NONMEM population model. In all four examples, the approach successfully selects the most important covariates and their functional representation. The great advantage of this approach is speed. The time required to derive a population model is markedly reduced because the number of necessary NONMEM runs is reduced. Furthermore, the approach provides a nice graphical representation of the relationships between the PK-PD parameters and covariates.

Adult↗

PubMed1991

Domain of validity of classical models of leucine metabolism assessed by compartmental modeling.

Whole-body modeling of in vivo leucine (an essential amino acid) metabolism is fundamentally difficult due to the complexity of the system. This has favored the use of two simple kinetic models, the so-called primary and reciprocal pool models, to interpret tracer data, but their domain of validity is uncertain. We define here the error of these two approaches by using comprehensive compartmental models of leucine metabolism as true representations of the leucine system. Of particular interest is the comparison of the two simple models with an 11-compartment model characterized by a rich intracellular compartmentation that has recently been proposed as a sound physiological description of the system. Formulas are derived that define in structural terms the error of the primary and reciprocal pool models.

Humans↗

PubMed1992

Sexual mixing models: a comparison of analogue deterministic and stochastic models.

Models for sexual partner choice are discussed for the case of highly variable sexual activity in the population. It is demonstrated that the variances in the number of infected persons may be extremely large. For the random mixing model, higher order cumulants are also evaluated. On the basis of these results the applicability of deterministic models and models for expectations only are questioned. A general model is proposed for handling nonrandom, or correlated, mixing. The problem of inconsistency is overcome by considering the couples having sex as the natural unit in the model. In the case of s discrete homogeneous groups it is shown that only (s2) parameters defining the interaction between the groups can be chosen freely. Finally, the effect of correlation in partner choice is demonstrated by a bivariate lognormal model for partner choice.

Female↗

PubMed1989

Animal models of human immunodeficiency virus infection. Public Health Service Animal Models Committee.

The search for a model of HIV infection continues. While much of the initial work focussed on animal models of AIDS, more recent efforts have sought animal models of HIV infection in which one or more signs of AIDS may be reproduced. Most initial small animal modelling efforts were negative and many such efforts remain unpublished. In 1988, the Public Health Service (PHS) AIDS Animal Model Committee conducted a survey among PHS agencies to identify published and unpublished data on animal models of HIV. To date, the chimpanzee is the only animal to be reliably infected with HIV albeit without development of signs and symptoms normally associated with human AIDS. One recent study has shown the gibbon to be similarly susceptible to infection with HIV. Mice carrying a chimera of elements of the human immune system have been shown to support the growth of HIV and F1 progeny of transgenic mice containing intact copies of HIV proviral DNA, have developed a disease that resembles some aspects of human AIDS. Rabbits, baboons and rhesus monkeys have also been shown to be infected under certain conditions and/or with selected strains of HIV but again without the development of AIDS symptomatology. This report briefly summarizes published and available unpublished data on these efforts to develop an animal model of HIV infection.

Acquired Immunodeficiency Syndrome↗

PubMed2026

Causal circuit tracing reveals distinct computational architectures in single-cell foundation models: inhibitory dominance, biological coherence, and cross-model convergence.

MOTIVATION: Sparse autoencoders (SAEs) decompose foundation-model activations into interpretable features, but the model-internal causal interactions between those features (i.e. what ablating one feature does to the others, as distinct from the biological causal structure of the underlying cells)-and how those model-internal relationships relate to biological structure-are uncharacterized in single-cell foundation models. RESULTS: We introduce model-internal causal circuit tracing-zeroing one SAE feature at a source layer and measuring the resulting change in all downstream SAE features, for each of 120 source features-and apply it to Geneformer V2-316M and scGPT whole-human across four conditions (96 892 ablation-derived edges, 80 191 forward passes). On annotation-selected source features, edges share GO/KEGG/Reactome/STRING/TRRUST ontology terms at 50.9%-68.5%, a 2.9-6.2× enrichment over a configuration-preserving permutation null (P<.002); on 20 randomly sampled source features this attenuates to 21.5%-26.3%-still 2.5-3.1× above null-quantifying the annotation-selection contribution. Inhibitory dominance (fraction of ablation edges with d<0, i.e. source activation supports downstream target) is 65.5%-89.4%. scGPT produces larger raw per-edge effects (mean |d|=1.40 versus 1.05); after feature-share normalization, Geneformer is stronger (paired gene-pair ratio 0.64 on 33 301 shared pairs). Cross-model consensus yields 1142 architecture-invariant domain pairs (ordered pairs of GO biological-process categories "A→B" each connected by at least one ablation edge in both models; 10.6× enrichment over permutation null; P<.001). Circuit edge magnitude explains <1% of the variance in marginal driver-gene coexpression on the same cells (R2=0.010, n=31 176): the graph encodes structure beyond bivariate correlation. Against a matched-cell-type ENCODE ChIP-seq prior, circuit-predicted transcription factor (TF)→target pairs are enriched 2.06× (Fisher OR 5.84), markedly higher than 1.12× against TRRUST; direct ChIP-seq-supported target pairs show 10-30× larger CRISPRi sign-bias-corrected excess than indirect pairs. Gene-level CRISPRi validation on Replogle K562 and the noncancer RPE1 arm (and a true primary-T-cell control from Shifrut E, Carnevale J, Tobin V et al. Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell 2018; 175: 1958-71.e15) after sign-bias correction shows excess over baseline of +0.03 and +0.35 percentage points on K562 and RPE1, respectively (baseline already 52%-56% from sign marginals); effect-magnitude Spearman correlations ρ≈0. Bootstrap and per-cell-type stability (N∈{50,100,200}; B cell, CD4 + T, macrophage) give Pearson r≥0.97 on shared edges with 100% sign agreement; edge Jaccard grows monotonically with sample size. The circuit graph is therefore highly reproducible as an effect-size map, cell type specific in edge identity, consistent with coexpression encoding, and weakly but detectably enriched for ChIP-seq-supported direct regulatory edges. AVAILABILITY AND IMPLEMENTATION: https://github.com/Biodyn-AI/bio-sae-circuits (Python). Archival DOI: 10.5281/zenodo.19,633,166 (Zenodo).

Humans↗

PubMed1989

Human respiratory tract model for radiological protection: a revision of the ICRP Dosimetric Model for the Respiratory System.

In 1984, the International Commission on Radiological Protection (ICRP) appointed a task group of Committee 2 to review and revise, as necessary, the ICRP Dosimetric Model for the Respiratory System. The model was originally published in 1966, modified slightly in Publication No. 19, and again in Publication No. 30 (in 1979). The task group concluded that research during the past 20 y suggested certain deficiencies in the ICRP Dosimetric Model for the Respiratory System. Research has also provided sufficient information for a revision of the model. The task group's approach has been to review, in depth, morphology and physiology of the respiratory tract; deposition of inhaled particles in the respiratory tract; clearance of deposited materials; and the nature and specific sites of damage to the respiratory tract caused by inhaled radioactive substances. This review has led to a redefinition of the regions of the respiratory tract for dosimetric purposes. The redefinition has a morphologic and physiological basis and is consistent with observed deposition and clearance of particles and with resultant pathology. Regions, as revised, are the extrathoracic (E-T) region, comprising the nasal and oral regions, the pharynx, larynx, and upper part of the trachea; the fast-clearing thoracic region (T[f]), comprising the remainder of the trachea and bronchi; and the slow-clearing thoracic region (T[s]), comprising the bronchioles, alveoli, and thoracic lymph nodes. A task group report will include models for calculating radiation doses to these regions of the respiratory tract following inhalation of representative alpha-, beta-, and gamma-emitting particulate and gaseous radionuclides. The models may be implemented as a package of computer codes available to a wide range of users. This should facilitate application of the revised human respiratory tract model to worldwide radiation protection needs.

Aerosols↗

PubMed1992

A computer model to predict the effects of level of nutrition on composition of empty body gain in beef cattle: II. Evaluation of the model.

A computer model developed to predict composition of empty body gain of beef cattle fed at different levels of nutrition was evaluated with data from one unpublished and seven published experiments. These experiments used several breeds of beef cattle growing at rates that varied from negative to fast and various combinations of these growth rates. There was close agreement between observed and simulated absolute treatment means for fatness, except when animals were fed diets low in protein. In this case, experimental animals fed inadequate protein grew at slower rates and became fatter than contemporaries fed adequate protein, whereas the model predicted the opposite. Ability of the model to predict effects of nutrition not associated with changes in BW was evaluated using the proportion of residual variation in experimental fat percentage accounted for by the model, after fitting linear and quadratic terms for empty BW. The model accounted for 13.8 to 56.2% of the residual variation in observed fatness in four experiments in which significant differences in fatness were observed among nutritional treatments after accounting for differences in empty BW. Regression of observed fatness on model-simulated fatness resulted in regression coefficients that were positive and close to 1 in these four experiments. This suggests that the model can accurately predict some of the effects of nutrition on fatness that are not associated with changes in empty BW.

Adipose Tissue↗

PubMed1975

The simulated drinking gang: an experimental model for the study of a systems approach to alcoholism. I. Description of the model.

This two-part paper deals with an experimental model for studying interactional behavior from a systems point of view. The experimental model incorporates a research strategy utilizing both a token economy and the simulation of a naturally occurring clinical phenomenon, the alcoholic drinking gang. The model is primarily geared to do two things: a) study the changes in clinical behavior that occur when experimental conditions are manipulated to facilitate the formation of the behavioral "system" as opposed to experimental conditions which interfere with system formation; and b) build into the model tasks which produce measurable performance data as a method of monitoring behavior deemed essential for the successful development of an operational system. The experimental model involved admitting up to six alcoholic individuals to a research ward specifically designed for studies utilizing experimentally induced intoxication. Each study was divided into a 7-day predrinking period, a 10- to 14-day drinking period, during which time alcohol was available to research subjects, and a 5- to 7-day withdrawal period. A token economy was established which allowed for the following essential features to be present: a) a group of chronic alcoholic individuals who desire to go through a drinking experience together; b) the pooling of resources in order to purchase alcohol; c) rules established by the group for the sharing of whatever alcohol becomes available to the group; and d) the opportunity to earn money for the purchase of additional alcohol after the initial supply runs out, in order to keep the group drinking experience going. All "money", or tokens, could be earned only via successful performance at the Cooperative Task Device (CTD), a cooperative, two-person game, and tokens could be utilized to purchase three types of commodities from an automated dispensing machine, alcohol, cigarettes, and television time. Automated data recording provided detailed data about CTD performance and commodity-purchasing records. The discussion centers around difficulties inherent in a simulated model of natural behavior. It is pointed out that laboratory simulation vs. natural setting, as the preferred site for carrying on behavioral research, remains an active controversy, and the arguments on both sides are presented. Difficulties inherent in doing "systems" research are also discussed. A series of alternative hypotheses to explain behavior within the simulated drinking gang are listed, and the data that would be necessary in order to substantiate these alternative hypotheses are also listed. It is maintained that the simulated drinking gang is an advantageous experimental model in that it closely approximates the basic criteria of the naturally occurring phenomenon, while at the same time allowing subjects a range of behaviors, each of which substantiates a different hypothesis about the central organizing feature in alcoholic groups.

Alcohol Drinking↗

PubMed2026

Dual β-lactam therapy against high-risk Pseudomonas aeruginosa isolates: a dynamic in-vitro infection model study integrating population genomics with quantitative systems pharmacology modelling and simulations.

BACKGROUND: Pseudomonas aeruginosa has an extraordinary capacity for resistance emergence during treatment, even with newer antipseudomonals. There is a gap in understanding how resistance mechanisms affect the time-course of bacterial response to these newer agents. Traditional approaches for predicting pathogen response to an antibiotic do not apply to combination therapy. We aimed to develop a modelling framework to predict treatment response based on resistome information, using isolates of the worldwide-disseminated high-risk clone sequence type (ST) 235 and β-lactam antibiotics as the example. METHODS: In this hollow-fibre in-vitro infection study, we used three extensively drug-resistant ST235 clinical isolates from the national collection of the Clinical Microbiology Department of the Hospital Son Espases (Palma de Mallorca, Spain) that were hospital-acquired, were isolated following routine microbiological procedures from different patients between 2017 and 2022, were susceptible to ceftolozane-tazobactam, and had different levels of meropenem resistance. The selected isolates (ST235-05, ST235-09, and ST235-10) showed classical β-lactam resistance mechanisms pre-treatment. The isolates were investigated in 240-h dynamic hollow-fibre in-vitro infection models (HFIMs). The studies exposed the isolates to pharmacokinetic profiles of ceftolozane-tazobactam (simulating 1 g of ceftolozane and 0·5 g of tazobactam as a 3-h infusion every 8 h) and meropenem (simulating 6 g per day continuous infusion) as observed in hospitalised patients, as monotherapy and in combination. Treatment response was assessed through the quantification of the time-courses of viable total and resistant bacteria. Whole-genome sequencing identified the mechanisms of emerging resistance. A quantitative systems pharmacology (QSP) approach was used to model total and resistant bacterial counts and corresponding pharmacokinetic data from the HFIM. Monte Carlo simulations were used to predict treatment responses in 1000 virtual infected patients treated with ceftolozane-tazobactam and meropenem as monotherapies or in combination over 10 days. FINDINGS: In the HFIMs, each antibiotic alone amplified resistance by approximately 48 h for all isolates; that is, monotherapies resulted in a higher concentration of resistant bacteria compared with the control treatment at the respective time, except ceftolozane-tazobactam against ST235-10. Combination of ceftolozane-tazobactam and meropenem was synergistic (bacterial counts ≥2 log10 colony forming units [CFU] per mL lower than the best performing monotherapy and initial inoculum) against all isolates and suppressed resistance. Against ST235-10, ceftolozane-tazobactam monotherapy reduced counts to less than 1 log10 CFU per mL from 192 h onwards, whereas the combination reached less than 1 log10 CFU per mL by 24 h. Across strains, population genomics confirmed monotherapy failures were associated with emerging resistance mechanisms (ceftolozane-tazobactam: ampC Ω-loop mutations; meropenem: ftsl mutation). The developed QSP model incorporated baseline resistance mechanisms and those emerging in resistant mutant subpopulations. The model explained and predicted the monotherapy failures involving amplification of these subpopulations, and synergistic killing and resistance suppression by the combination. Simulations using the model predicted bacterial regrowth above the initial inoculum for more than 90% of patients after 0 to approximately 3 days for meropenem monotherapy across all strains and for ceftolozane-tazobactam monotherapy against ST235-05 and ST235-09. For ceftolozane-tazobactam monotherapy against ST235-10, regrowth was predicted for approximately 30% of patients. In contrast, the simulations predicted sustained bacterial killing of at least 2 log10 CFU per mL compared with the initial inoculum by the combination for more than 89% of patients across all strains. INTERPRETATION: To our knowledge, this model is the first to characterise and predict the time-course of responses of clinical isolates to antibiotics only by the resistance mechanisms present and their complex interplay, representing a step towards pathogen-specific, personalised medicine. FUNDING: Australian National Health and Medical Research Council.

Pseudomonas aeruginosa↗

PubMed1977

Quantitative structure--activity relationships. 7. The bilinear model, a new model for nonlinear dependence of biological activity on hydrophobic character.

The bilinear model, log 1/C =a log P-b log (betaP+1) +C, a new model for nonlinear dependence of biological activity on hydrophobic character, is applied to 57 data sets of biological activity values in homologous series. From a comparison of the statistical parameters and the residuals obtained with the bilinear model and the parabolic model, the superiority of the bilinear model for a precise quantitative description of both linear and nonlinear parts of sturcture-activity relationships can be derived; the bilinear model explains the particular effect that in homologous series the relationship between biological activity and hydrophobic character is strictly linear for the lower members, while for higher members this relationship is nonlinear.

Animals↗

PubMed1992

On models of quantitative genetic variability: a stabilizing selection-balance model.

A model of stabilizing selection on a multilocus character is proposed that allows the maintenance of stable allelic polymorphism and linkage disequilibrium. The model is a generalization of Lerner's model of homeostasis in which heterozygotes are less susceptible to environmental variation and hence are superior to homozygotes under phenotypic stabilizing selection. The analysis is carried out for weak selection with a quadratic-deviation model for the stabilizing selection. The stationary state is characterized by unequal allele frequencies, unequal proportions of complementary gametes, and a reduction of the genetic (and phenotypic) variance by the linkage disequilibrium. The model is compared with Mather's polygenic balance theory, with models that include mutation-selection balance, and others that have been proposed to study the role of linkage disequilibrium in quantitative inheritance.

Alleles↗

PubMed1992

A mathematical model for the quantification of mitral regurgitation. Experimental validation in the canine model using contrast echocardiography.

BACKGROUND: Because the clearance of contrast from the left atrium (LA) relative to the left ventricle (LV) depends on the degree of mitral regurgitation (MR), we hypothesized that a mathematical model can be developed that would provide a quantitative estimation of MR from the washout of contrast from these chambers. METHODS AND RESULTS: After mathematically developing the model, we performed experiments in two groups of dogs with the use of contrast echocardiography. Group 1 consisted of nine dogs in which different degrees of MR were produced by creating ischemic LV dysfunction. Contrast was injected into the LV, and MR was graded visually on a scale of from 0 to 4+. Videointensity plots generated from the LA and LV were provided to the model. There was excellent correlation between visual assessment of MR and model-derived regurgitant fraction in the 33 stages: y = 0.16x + 0.002 (r = 0.97, p less than 0.001, SEE = 0.06). To obtain a more quantitative validation, we placed electromagnetic flow probes on the aorta and just cephalad to the mitral annulus in six dogs (group 2) during cardiopulmonary bypass. Different degrees of MR were produced by chordal traction and/or myocardial ischemia. Regurgitant fraction was calculated at each stage from the flow probe and videointensity data. There was excellent correlation between flow probe and model-derived regurgitant fraction (y = 0.90x + 0.03; r = 0.96, p less than 0.001, SEE = 0.06), and close interobserver and intraobserver correlations were noted using flow probe and contrast echocardiographic data. CONCLUSIONS: A mathematical model that uses the clearance of contrast from the LA relative to the LV can be used to accurately measure the severity of MR. These findings may have important practical implications for the quantification of MR.

Animals↗