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PubMed2002

Models and modeling systems in Alzheimer disease drug discovery.

The rapid pace of neurobiology research has increased the prospects of developing drugs to prevent neurodegenerative disorders. Although the goal of delaying the onset of brain disorders may be within the grasp of modern medicine, there are several critical barriers to progress. Among these is the lack of appropriate models and modeling systems for specific neurodegenerative diseases. Traditionally, in drug discovery, testing, and development, a combination of models is used. These include in vitro, in vivo, transgenic, and other animal models. However, each of these models has limitations. In this article, the author advocates the use of "in silico" modeling systems, which could complement currently available models and enable investigators to simulate alternative strategies to modulate neural function in a dynamic interactive mode. Advances in computer technology, including increasing speed and memory, and ready access to parallel processing systems have made it easier for investigators to develop databases for computer abstractions of neural function and dysfunction and to begin to develop prototypes for use in complex systems modeling environments. Multimodeling systems have been widely used in other areas of science to study emergent behavior of complex systems, such as the impact of atmospheric changes on weather, flight patterns of birds in a flock, and the behavior of traders in a commodities market. Adoption of such approaches should increase understanding of the complexities of signal transduction pathways in neural networks and accelerate the drug discovery process.

Alzheimer Disease↗

PubMed1999

Models of intervention in menopause: proposal of a holistic or integral model.

OBJECTIVE: On the threshold of the third millennium, menopause has become a crucial social and health problem in developed countries. In the present work, we analyze possible healthcare strategies, describing their advantages and disadvantages within the context of different underlying conceptions of menopause. DESIGN: We have considered three theoretical frameworks or "menopause models": the biological model, the psychosocial model, and the holistic or integral model. Each model is examined under three subheadings: concept, contributions, and criticisms. RESULTS: The holistic model is proposed and defended as a valid option for the management of menopause. CONCLUSIONS: Adoption of the holistic model by health personnel may improve the attention given to menopausal women. As characteristics of such a model, the following are important: (1) The positive vision of the climacteric transition as an opportunity for personal growth, (2) the biopsychosocial perspective of the phenomena responsible for the different effects on the level of health and quality of life of women, (3) the development of health policies focused on health promotion, (4) the protagonism of women through strategies based on self-care and self-help groups, and (5) individualized use of hormone replacement therapy.

Aged↗

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↗

PubMed2003

Open capture-recapture models with heterogeneity: I. Cormack-Jolly-Seber model.

In open population capture-recapture studies, it is usually assumed that similar animals (e.g., of the same sex and age group) have similar survival rates and capture probabilities. These assumptions are generally perceived to be an oversimplification, and they can lead to incorrect model selection and biased parameter estimates. Allowing for individual variability in survival and capture probabilities among apparently similar animals is now becoming possible, due to advances in closed population models and improved computing power. This article presents a flexible framework of likelihood-based models which allow for individual heterogeneity in survival and capture rates. Heterogeneity is modeled using finite mixtures, which have enough flexibility of distribution shape to accommodate a wide variety of different patterns of individual variation. The models condition on the first capture of each animal, and include as a special case the Cormack-Jolly-Seber model. Model selection is done either using Akaike's information criterion or by likelihood ratio tests, making available checks of different influences on survival rates. Bias in parameter estimates is reduced by including individual heterogeneity. Model selection and bias reduction are important in population studies and for making informed management decisions.

Animals↗

PubMed2006

The principal components model: a model for advancing spirituality and spiritual care within nursing and health care practice.

AIM: The aim of this study was to generate a deeper understanding of the factors and forces that may inhibit or advance the concepts of spirituality and spiritual care within both nursing and health care. BACKGROUND: This manuscript presents a model that emerged from a qualitative study using grounded theory. Implementation and use of this model may assist all health care practitioners and organizations to advance the concepts of spirituality and spiritual care within their own sphere of practice. The model has been termed the principal components model because participants identified six components as being crucial to the advancement of spiritual health care. DESIGN: Grounded theory was used meaning that there was concurrent data collection and analysis. Theoretical sampling was used to develop the emerging theory. These processes, along with data analysis, open, axial and theoretical coding led to the identification of a core category and the construction of the principal components model. METHODS: Fifty-three participants (24 men and 29 women) were recruited and all consented to be interviewed. The sample included nurses (n=24), chaplains (n=7), a social worker (n=1), an occupational therapist (n=1), physiotherapists (n=2), patients (n=14) and the public (n=4). The investigation was conducted in three phases to substantiate the emerging theory and the development of the model. RESULTS: The principal components model contained six components: individuality, inclusivity, integrated, inter/intra-disciplinary, innate and institution. CONCLUSION: A great deal has been written on the concepts of spirituality and spiritual care. However, rhetoric alone will not remove some of the intrinsic and extrinsic barriers that are inhibiting the advancement of the spiritual dimension in terms of theory and practice. RELEVANCE TO CLINICAL PRACTICE: An awareness of and adherence to the principal components model may assist nurses and health care professionals to engage with and overcome some of the structural, organizational, political and social variables that are impacting upon spiritual care.

Adolescent↗

PubMed2005

Modeling animals' behavioral response by Markov chain models for capture-recapture experiments.

A bivariate Markov chain approach that includes both enduring (long-term) and ephemeral (short-term) behavioral effects in models for capture-recapture experiments is proposed. The capture history of each animal is modeled as a Markov chain with a bivariate state space with states determined by the capture status (capture/noncapture) and marking status (marked/unmarked). In this framework, a conditional-likelihood method is used to estimate the population size and the transition probabilities. The classical behavioral model that assumes only an enduring behavioral effect is included as a special case of the bivariate Markovian model. Another special case that assumes only an ephemeral behavioral effect reduces to a univariate Markov chain based on capture/noncapture status. The model with the ephemeral behavioral effect is extended to incorporate time effects; in this model, in contrast to extensions of the classical behavioral model, all parameters are identifiable. A data set is analyzed to illustrate the use of the Markovian models in interpreting animals' behavioral response. Simulation results are reported to examine the performance of the estimators.

Animals↗

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↗

PubMed1994

[Therapeutic study on biofilm of the urinary tract using a severely complicated bladder model (biofilm model of the urinary tract)--experimental study using an automatic simulator of urinary antimicrobial agent concentration, and clinical study].

For the purpose of conducting a therapeutic study on biofilm of the urinary tract, we devised a computer-controlled severely complicated bladder model (biofilm model of the urinary tract) enabling us to simulate the time-course of the concentration of antimicrobial agents in the urine. Using this model, we investigated clarithromycin (CAM), which has been reported to have anti-biofilm action, at concentrations close to its urinary levels at the time of clinical use in order to predict its effect on biofilm of the urinary tract. On the basis of those experimental results, we also conducted a clinical examination. The following results were obtained. 1. The action of ciprofloxacin (CPFX, MIC: 8 micrograms/ml) alone, which shows anti-P. aeruginosa activity, caused apparent elimination of P. aeruginosa from the model. However, regrowth of the microbes occurred when CPFX was removed from the bladder model. Moreover, the biofilm was not eliminated by the antimicrobial action of CPFX, and this was surmised to be the cause of the regrowth. 2. CAM (MIC: above 128 micrograms/ml), which has no anti-P. aeruginosa activity, was similarly tested as anti-biofilm agent when added alone to the biofilm model. The P. aeruginosa recovered to its initial concentration within 48 hours, but the biofilm disappeared due to the action of CAM. 3. The combined action of CPFX and CAM caused microbial elimination from the bladder model without microbial regrowth, even after these antimicrobial agents were removed from the bladder model. After the action of CPFX and CAM, the biofilm disappeared, and no microbial adherence was noted. 4. Measurement of time-course of the alginate content, which is the main component of P. aeruginosa biofilm, in the presence of CAM found that the alginate content decreased below the limit of detection after day 5. 5. The clinical study of complicated urinary tract infection revealed the microbial elimination rate and the efficacy rate to be higher in the combined CPFX-CAM administration group than in the CPFX-only administration group. 6. Based on the above results, we surmise that the combined use of an antimicrobial agent which is active against the causative microbe and anti-biofilm agent such as CAM will show some degree of efficacy in eliminating biofilm of the urinary tract.

Ciprofloxacin↗

PubMed2001

Pharmacokinetic and pharmacodynamic modeling of anidulafungin (LY303366): reappraisal of its efficacy in neutropenic animal models of opportunistic mycoses using optimal plasma sampling.

The compartmental pharmacokinetics of anidulafungin (VER-002; formerly LY303366) in plasma were characterized with normal rabbits, and the relationships between drug concentrations and antifungal efficacy were assessed in clinically applicable infection models in persistently neutropenic animals. At intravenous dosages ranging from 0.1 to 20 mg/kg of body weight, anidulafungin demonstrated linear plasma pharmacokinetics that fitted best to a three-compartment open pharmacokinetic model. Following administration over 7 days, the mean (+/- standard error of the mean) peak plasma concentration (C(max)) increased from 0.46 +/- 0.02 microg/ml at 0.1 mg/kg to 63.02 +/- 2.93 microg/ml at 20 mg/kg, and the mean area under the concentration-time curve from 0 h to infinity (AUC(0-infinity)) rose from 0.71 +/- 0.04 to 208.80 +/- 24.21 microg. h/ml. The mean apparent volume of distribution at steady state (V(ss)) ranged from 0.953 +/- 0.05 to 1.636 +/- 0.22 liter/kg (nonsignificant [NS]), and clearance ranged from 0.107 +/- 0.01 to 0.149 +/- 0.00 liter/kg/h (NS). Except for a significant prolongation of the terminal half-life and a trend toward an increased V(ss) at the higher end of the dosage range after multiple doses, no significant differences in pharmacokinetic parameters were noted in comparison to single-dose administration. Concentrations in tissue at trough after multiple dosing (0.1 to 10 mg/kg/day) were highest in lung and liver (0.85 +/- 0.16 to 32.64 +/- 2.03 and 0.32 +/- 0.05 to 43.76 +/- 1.62 microg/g, respectively), followed by spleen and kidney (0.24 +/- 0.65 to 21.74 +/- 1.86 and <0.20 to 16.92 +/- 0.56, respectively). Measurable concentrations in brain tissue were found at dosages of > or =0.5 mg/kg (0.24 +/- 0.02 to 3.90 +/- 0.25). Implementation of optimal plasma sampling in persistently neutropenic rabbit infection models of disseminated candidiasis and pulmonary aspergillosis based on the Bayesian approach and model parameters from normal animals as priors revealed a significantly slower clearance (P < 0.05 for all dosage groups) with a trend toward higher AUC(0-24) values, higher plasma concentrations at the end of the dosing interval, and a smaller volume of distribution (P < 0.05 to 0.193 for the various comparisons among dosage groups). Pharmacodynamic modeling using the residual fungal tissue burden in the main target sites as the primary endpoint and C(max), AUC(0-24), time during the dosing interval of 24 h with plasma drug concentration equaling or exceeding the MIC or the minimum fungicidal concentration for the isolate, and tissue concentrations as pharmacodynamic parameters showed predictable pharmacokinetic-pharmacodynamic relationships in experimental disseminated candidiasis that fitted well with an inhibitory sigmoid maximum effect pharmacodynamic model (r(2), 0.492 to 0.819). However, no concentration-effect relationships were observed in experimental pulmonary aspergillosis using the residual fungal burden in lung tissue and survival as parameters of antifungal efficacy. Implementation of optimal plasma sampling in discriminative animal models of invasive fungal infections and pharmacodynamic modeling is a novel approach that holds promise of improving and accelerating our understanding of the action of antifungal compounds in vivo.

Anidulafungin↗

PubMed2005

Analysis of the contribution of Salmonella pathogenicity islands 1 and 2 to enteric disease progression using a novel bovine ileal loop model and a murine model of infectious enterocolitis.

We have developed a novel ileal loop model for use in calves to analyze the contribution of Salmonella enterica serovar Typhimurium type III secretion systems to disease processes in vivo. Our model involves constructing ileal loops with end-to-end anastamoses to restore the patency of the small intestine, thereby allowing experimental animals to convalesce following surgery for the desired number of days. This model overcomes the time constraint imposed by ligated ileal loop models that have precluded investigation of Salmonella virulence factors during later stages of the infection process. Here, we have used this model to examine the enteric disease process at 24 h and 5 days following infection with wild-type Salmonella and mutants lacking the virulence-associated Salmonella pathogenicity island 1 (SPI-1) or SPI-2 type III secretion systems. We show that SPI-2 mutants are dramatically attenuated at 5 days following infection and report a new phenotype for SPI-1 mutants, which induce intestinal pathology in calves similar to wild-type Salmonella in the 5-day ileal loop model. Both of these temporal phenotypes for SPI-1 and SPI-2 mutants were corroborated in a second animal model of enteric disease using streptomycin-pretreated mice. These data delineate novel phenotypes for SPI-1 and SPI-2 mutants in the intestinal phase of bovine and murine salmonellosis and provide working models to further investigate the effector contribution to these pathologies.

Animals↗

PubMed2001

Gradient model versus mosaic model of the sinoatrial node.

BACKGROUND: A radical reinterpretation (mosaic model) of the makeup of the sinoatrial (SA) node has been proposed to explain the characteristic regional differences in electrical activity between the periphery and center of the SA node. According to the mosaic model, the differences result from a change in the mix of atrial cells and uniform SA node cells from periphery to center, whereas according to the alternative gradient model, there are no atrial cells within the functional SA node, and the differences result from a change in the intrinsic properties of SA node cells from periphery to center. METHODS AND RESULTS: A mosaic model of peripheral and central tissue has been constructed computationally by use of a coupled ordinary differential equation network (CODE) in a 2D lattice (20x20), with each node of the lattice designated randomly as an atrial cell or SA node cell (in correct proportions for periphery and center). The mosaic model fails to predict the characteristic differences in action potential rate and shape between the periphery and center, whereas the existing gradient model can do so. CONCLUSIONS: The mosaic model of the SA node is untenable, and the SA node is adequately described by the gradient model.

Animals↗

PubMed2004

Use of pharmacokinetic modeling to design studies for pathway-specific exposure model evaluation.

Validating an exposure pathway model is difficult because the biomarker, which is often used to evaluate the model prediction, is an integrated measure for exposures from all the exposure routes and pathways. The purpose of this article is to demonstrate a method to use pharmacokinetic (PK) modeling and computer simulation to guide the design of field studies to validate pathway models. The children's dietary intake model is discussed in detail as an example. Three important aspects are identified for a successful design to evaluate the children's dietary intake model: a) longitudinally designed study with significant changes in the exposure for the route/pathway of interest, b) short biologic half-life of the selected chemical, and c) surface loading of the selected chemical at sufficient levels. Using PK modeling to guide a study design allowed a path-specific exposure model to be evaluated using urinary metabolite biomarkers.

Biomarkers↗

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↗

PubMed1996

[Hierarchical model: a proposal for a model to be applied in the investigation of risk factors for severe diarrhea].

In epidemiological investigations of infant diseases, data are usually collected on a large set of variables. The associations between presumptive risk factors and the outcome is commonly evaluated through statistical modeling, but the model-building strategies are seldom described. In the project, data collected in a case-control study of risk factors for dehydrating diarrhea in infants have been used to present a hierarchized approach to the assessment of risk factors. The variables were grouped into a hierarchy of categories, ranging from distal determinants to proximate ones. These categories included, in this order, socioeconomic, environmental, reproductive maternal, nutritional and demographic sets. According to the univariate analyses all variables were associated with the outcome. As the purpose was to identify a parsimonious model to explain the data, in each set the confounders were selected through backward elimination, according to an alpha level of 0.10. The risk factors were evaluated through logistic regression after adjustment for confounders in the same set or in hierarchically superior sets. This approach allows researchers to quantify the contribution of each level of adjustment, to understand the model-building strategy as well as interpret the independent associations. The goodness-of-fit assessed at each set showed significant improvements in the model. The gamma coefficient of association was employed to quantify the proportion of cases and controls correctly identified by comparing the observed value with that predicted by the variables in the model. The final model resulted in a gamma of 0.74. The children's ages did not improve the prediction of cases and controls, but they have been kept in the model as they affect some exposures such as breastfeeding.

Case-Control Studies↗

PubMed2004

Comparison of hospital charge prediction models for colorectal cancer patients: neural network vs. decision tree models.

Analysis and prediction of the care charges related to colorectal cancer in Korea are important for the allocation of medical resources and the establishment of medical policies because the incidence and the hospital charges for colorectal cancer are rapidly increasing. But the previous studies based on statistical analysis to predict the hospital charges for patients did not show satisfactory results. Recently, data mining emerges as a new technique to extract knowledge from the huge and diverse medical data. Thus, we built models using data mining techniques to predict hospital charge for the patients. A total of 1,022 admission records with 154 variables of 492 patients were used to build prediction models who had been treated from 1999 to 2002 in the Kyung Hee University Hospital. We built an artificial neural network (ANN) model and a classification and regression tree (CART) model, and compared their prediction accuracy. Linear correlation coefficients were high in both models and the mean absolute errors were similar. But ANN models showed a better linear correlation than CART model (0.813 vs. 0.713 for the hospital charge paid by insurance and 0.746 vs. 0.720 for the hospital charge paid by patients). We suggest that ANN model has a better performance to predict charges of colorectal cancer patients.

Algorithms↗

PubMed2001

Graphical representation of a generalized linear model-based statistical test estimating the fit of the single-hit Poisson model to limiting dilution assays.

Standardized statistical and graphical methods for analysis of limiting dilution assays are highly desirable to enable investigators to compare and interpret results and conclusions with greater accuracy and precision. According to these requirements, we present in this work a powerful statistical slope test that estimates the fit of the single-hit Poisson model to limiting dilution experiments. This method is readily amenable to a graphical representation. This slope test is obtained by modeling limiting dilution data according to a linear log-log regression model, which is a generalized linear model specially designed for modeling binary data. The result of the statistical slope test can then be graphed to visualize whether the data are compatible or not with the single-hit Poisson model. We demonstrate this statistical test and its graphical representation by using two examples: a real limiting dilution experiment evaluating the growth frequency of IL-2-responsive tumor-infiltrating T cells in a malignant lymph node involved by a B cell non-Hodgkin's lymphoma, and a simulation of a limiting dilution assay corresponding to a theoretical non-single-hit Poisson model, suppressor two-target Poisson model.

Allergy and Immunology↗

PubMed2006

The tissue microarray object model: a data model for storage, analysis, and exchange of tissue microarray experimental data.

CONTEXT: Tissue microarray (TMA) is an array-based technology allowing the examination of hundreds of tissue samples on a single slide. To handle, exchange, and disseminate TMA data, we need standard representations of the methods used, of the data generated, and of the clinical and histopathologic information related to TMA data analysis. OBJECTIVE: To create a comprehensive data model with flexibility that supports diverse experimental designs and with expressivity and extensibility that enables an adequate and comprehensive description of new clinical and histopathologic data elements. DESIGN: We designed a tissue microarray object model (TMA-OM). Both the array information and the experimental procedure models are created by referring to the microarray gene expression object model, minimum information specification for in situ hybridization and immunohistochemistry experiments, and the TMA data exchange specifications. The clinical and histopathologic information model is created by using College of American Pathologists cancer protocols and National Cancer Institute common data elements. Microarray Gene Expression Data Ontology, the Unified Medical Language System, and the terms extracted from College of American Pathologists cancer protocols and NCI common data elements are used to create a controlled vocabulary for unambiguous annotation. RESULT: The TMA-OM consists of 111 classes in 17 packages to represent clinical and histopathologic information as well as experimental data for any type of cancer. We implemented a Web-based application for TMA-OM, supporting data export in XML format conforming to the TMA data exchange specifications or the document type definition derived from TMA-OM. CONCLUSIONS: The TMA-OM provides a comprehensive data model for storage, analysis, and exchange of TMA data and facilitates model-level integration of other biological models.

Gene Expression Profiling↗

PubMed1999

Down-regulation models and modeling of testosterone production induced by recombinant human choriogonadotropin.

Chorionic gonadotropin (CG) is a glycoprotein hormone, whose action is mediated by the luteinizing hormone/CG receptor. Testosterone concentrations from six pituitary-desensitized, healthy male volunteers were obtained after four different administrations of recombinant-human CG (rhCG). We present a modeling study to provide a possible explanation for the observations that increased exposure to rhCG induces higher and then lower testosterone concentrations and that marked rebound effects are observed at the end of repeated administration of rhCG. We used semimechanistic models (in which flexible functions represent unknown parts of the models) to identify the relationship of rhCG concentrations to the testosterone levels. Based on the results obtained with the semimechanistic models, different mechanistic down-regulation models were devised and tested. The final model uses a one-compartment model to describe the endogenous production rate of testosterone; rhCG affects the production rate with a mechanism consistent with a two-site binding site, with effect proportional to one-site bound concentration. The modeling results indicate that when rhCG concentration increases, the testosterone production rate increases to 45 times the baseline value. However, at an rhCG concentration of more than about 30 IU/liter, the production rate decreases. Simulations showed that both dose and dosing interval profoundly influence testosterone response to rhCG.

Adult↗