Rozprawy doktorskie: „Medicine – Mathematical models”

1

Campanelli, Mark Benjamin. "Multicellular mathematical models of somitogenesis". Thesis, Montana State University, 2009. http://etd.lib.montana.edu/etd/2009/campanelli/CampanelliM0809.pdf.

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Somitogenesis is an important pattern formation process in the developmental biology of vertebrates. The phenomenon has received wide attention from experimental, theoretical, and computational biologists. Numerous mathematical models of the process have been proposed, with the clock and wavefront mechanism rising to prominence over the last ten years. This work presents two multicellular mathematical models of somitogenesis. The first is a phenomenological phase oscillator model that reproduces both the clock and wavefront aspects of somitogenesis, but lacks a biological basis. The second is a biologically informed delay differential equation model of the clock-wave that is produced by coordinated oscillatory gene expression across many cells. Careful and efficient model construction, parameter estimation, and model validation identify important nonlinear mechanisms in the genetic control circuit of the somitogenesis clock. In particular, a graded control protein combined with differential decay of clock protein monomers and dimers is found to be a key mechanism for slowing oscillations and generating experimentally observed waves of gene expression. This represents a mode of combinatorial control that has not been previously examined in somitogenesis, and warrants further experimental and theoretical investigation.

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2

Stekel, Dov Joseph. "Mathematical models of immune system and virus dynamics". Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364143.

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CLOUGH, ANNE VIRGINIA. "A MATHEMATICAL MODEL OF SINGLE-PHOTON EMISSION COMPUTED TOMOGRAPHY (RADON TRANSFORM, COMPTON SCATTER, ATTENUATION, NUCLEAR MEDICINE)". Diss., The University of Arizona, 1986. http://hdl.handle.net/10150/188142.

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Single-photon emission computed tomography (SPECT) is a nuclear-medicine imaging technique that has been shown to provide clinically useful images of radionuclide distributions within the body. The problem of quantitative determination of tomographic activity images from a projection data set leads to a mathematical inverse problem which is formulated as an integral equation. The solution of this problem then depends on an accurate mathematical model as well as a reliable and efficient inversion algorithm. The effects of attenuation and Compton scatter within the body have been incorporated into the model in the hopes of providing a more physically realistic mathematical model. The attenuated Radon transform is the mathematical basis of SPECT. In this work, the case of constant attenuation is reviewed and a new proof of the Tretiak-Metz algorithm is presented. A space-domain version of the inverse attenuated Radon transform is derived. A special case of this transform that is applicable when the object is rotationally symmetric, the attenuated Abel transform is derived, and its inverse is found. A numerical algorithm for the implementation of the inverse attenuated Radon transform with constant attenuation is described and computer simulations are performed to demonstrate the results of the inversion procedure. With the use of the single-scatter approximation and an energy-windowed detector, the effects of Compton scatter are incorporated into the model. The data is then taken to be the sum of primary photons and single-scattered photons. The scattered photons are modeled by a scatter operator acting on the original activity distribution within the object where the operator consists of convolution with a given analytic kernel followed by a boundary cut-off operation. A solution is given by first applying the inverse attenuated Radon transform to the data set. This leads to a Fredholm integral equation to which a Neumann series solution is constructed. Again simulations are performed to validate the accuracy of the assumptions within the model as well as to numerically demonstrate the reconstruction procedure.

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Orme, Michelle Elaine. "The vascularization of solid tumours : mathematical models of tumour angiogenesis and vascular tumour growth". Thesis, University of Bath, 1996. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362238.

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Knight, Peter Robin. "Artificial intelligence and mathematical models for intelligent management of aircraft data". Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/355717/.

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Increasingly, large volumes of aircraft data are being recorded in an effort to adapt aircraft maintenance procedures from being time-based towards condition-based techniques. This study uses techniques of artificial intelligence and develops mathematical models to analyse this data to enable improvements to be made in aircraft management, affordability, availability, airworthiness and performance. In addition, it highlights the need to assess the integrity of data before further analysis and presents the benefits of fusing all relevant data sources together. The research effort consists of three separate investigations that were undertaken and brought together in order to provide a unified set of methods aimed at providing a safe, reliable, effective and efficient overall procedure. The three investigations are: 1. The management of helicopter Health Usage Monitoring System (HUMS) Condition Indicators (CIs) and their analysis, using a number of techniques, including adaptive thresholds and clustering. These techniques were applied to millions of CI values from Chinook HUMS data. 2. The identification of fixed-wing turbojet engine performance degradation, using anomaly detection techniques, applied to thousands of in-service engine runs from Tornado aircraft. 3. The creation of models to identify unusual aircraft behaviour, such as uncommanded flight control movements. Two Chinook helicopter systems were modelled and the models were applied to over seven hundred in-service flights. In each case, the existing techniques were directed toward a condition-based maintenance approach, giving improved detection and earlier warning of faults.

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Powell, Megan Olivia. "Mathematical Models of the Activated Immune System During HIV Infection". University of Toledo / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1301415627.

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Cargill, Ellen Bernadette. "A mathematical liver model and its application to system optimization and texture analysis". Diss., The University of Arizona, 1989. http://hdl.handle.net/10150/184936.

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This dissertation presents realistic mathematical models of normal and diseased livers and a nuclear medicine camera. The mathematical model of a normal liver is developed by creating a data set of points on the surface of the liver and fitting it to a truncated set of spherical harmonics. We model the depth-dependent MTF of a scintillation camera taking into account the effects of Compton scatter, linear attenuation, intrinsic detector resolution, collimator resolution, and Poisson noise. The differential diagnosis on a liver scan includes normal, focal disease, and diffuse disease. Object classes of normal livers are created by randomly perturbing the spherical harmonic coefficients. Object classes of livers with focal disease are created by introducing cold ellipsoids within the liver volume. Cirrhotic livers are created by modelling the gross morphological changes, heterogenous uptake, and decreased overall uptake. Simulated nuclear medicine images are made by projecting livers through nuclear imaging systems. The combination of object classes of simulated livers and models of different imaging systems is applied to imaging-system design optimization in a psycho-physical study. Human observer performance on simulated liver images made on nine different systems is compared to the Hotelling trace criterion (HTC). The system with the best observer performance is judged to be the best system. The correlation between the human performance metric dₐ and the HTC for this study was 0.829, suggesting that the HTC may have value as a predictor of observer performance. Texture in a liver scan is related to the three-dimensional distribution of functional acini, which changes with disease. One measure of texture is the fractal dimension, related to the Fourier power spectrum. We measured the average radial power spectra of 70 liver scans. All of these scans yield straight lines when plotted on a log-log scale, a characteristic of fractal objects. The slope of the line is related to the fractal dimension of the acini. The slopes are significantly higher for normal than abnormal livers (t = 4.04, df = 29, p = 0.005). On 32 liver scans with confirmed diagnoses, receiver operating characteristics (ROC) analysis was performed using power spectral slope as a feature. Analysis of the ROC curve yielded an area under the curve of 85, suggesting that power spectral slope may be a useful classifier of disease.

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8

Easton, Jonathan. "Mathematical models of health focusing on diabetes : delay differential equations and data mining". Thesis, Northumbria University, 2015. http://nrl.northumbria.ac.uk/23582/.

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Mathematical models have been applied to biology and health to gain a better understanding of physiological systems and disease, as well as to improve levels of treatment and care for certain conditions. This thesis will focus on two different methodologies to investigate models of health, namely delay differential equations andBayesian based data mining. The first approach uses delay differential equations to model the glucose-insulin regulation system. Many models exist in this area, typically including four exponential functions, and take a number of different forms. The model used here is a system of two delay differential equations with two time delays. The one delay form of this model has previously been widely studied, but less is known about the two delay system from an analytical view point. This work improves upon the existing models by incorporating Hill functions instead of exponential functions. The new model presented is studied for its appropriateness and robustness to changing parameters such as glucose infusion rate and insulin degradation. A local and global stability of the two-delay system is presented both in general terms and explicitly using Lyapunov functionals and linear matrix inequalities. The second method employs data mining techniques including a robust and transparent naïve Bayes classifier for classification and prediction of aspects of health. A study into prediction of post-stroke mortality is made on a data set of stroke patients. Interesting results are obtained for the classification of naturally arising mortality periods and an investigation into the role of age as a risk factor for post-stroke mortality. A wide range of risk factors are then investigated for significance which are used to build new predictive models. These two approaches have the joint aim of improving the understanding of aspects of health through mathematical modelling techniques. A new model of the glucose-insulin regulatory system is developed and for the first time an analysis of the global stability of the two-delay model by use of a Lyapunov functional is provided. The second approach sees typical and robust data mining techniques used to analyse medical data. New models for stroke mortality and prediction of diabetes and obesity are created, which review risk factors and also illustrate the benefit of data mining techniques for analysing medical data.

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9

Vogel, Vance T. "Determining personnel accession requirements for Medical Service Corps Health Care Administrators using a steady state analysis /". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Mar%5FVogel.pdf.

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Thesis (M.S. in Business Administration)--Naval Postgraduate School, March 2006.
Thesis Advisor(s): Anke Richter, Kathryn M. Kocher. Includes bibliographical references (p. 113-114 ). Also available online.

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10

Al-otoum, Mohammed Fawzi. "Evaluation of bootstrapping as a validation technique for population pharmacokinetic models". Scholarly Commons, 2004. https://scholarlycommons.pacific.edu/uop_etds/590.

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It has been recommended by the FDA and others that the population pharmacokinetic models (PPKM) need to be validated. This is particularly true when the model plays a key role in the construction of dosing strategies. It was the objective of the current study to evaluate the ability of bootstrapping to identify PPKMs that were estimated from data without influence observations versus PPKMs from data containing influence observations. The evaluation was performed in four phases. In phase I, ten no-influence index datasets and ten influence index datasets were created. A model parameter (theta !) was estimated for the index datasets. It was found that influence observations caused an over-estimation of the model parameter. In phase II, 200 bootstrap datasets were resampled with replacement from each of the twenty index datasets ( 4000 datasets total). In phase III, the bootstrapping validation method was executed using NONMEM for model estimation and the resulting statistics were used to detect models developed from influence data. The metrics of choice were mean absolute prediction error (MAPE) and mean squared prediction error (MSPE). In phase IV, the impact of achieving a global minimum in the NONMEM program on the non-parametric bootstrap validation process was investigated. This study showed that the current and widely followed procedure for application of the bootstrap for PPK model validation has significant deficiencies. The achievement of a global minimum in the NONMEM program proved to be an important and pivotal factor when applying bootstrapping to the PPKM validation process. Therefore, we concluded that each bootstrap dataset should be evaluated with several model control streams. Further, the suggested value for an acceptable difference between the NONMEM minimum objective function values for a global and a near global minimum should be 2.5 units.

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11

Simoni, Giulia. "Modeling Startegies for Computational Systems Biology". Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/254361.

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Mathematical models and their associated computer simulations are nowadays widely used in several research fields, such as natural sciences, engineering, as well as social sciences. In the context of systems biology, they provide a rigorous way to investigate how complex regulatory pathways are connected and how the disruption of these processes may contribute to the develop- ment of a disease, ultimately investigating the suitability of specific molecules as novel therapeutic targets. In the last decade, the launching of the precision medicine initiative has motivated the necessity to define innovative computational techniques that could be used for customizing therapies. In this context, the combination of mathematical models and computer strategies is an essential tool for biologists, which can analyze complex system pathways, as well as for the pharmaceutical industry, which is involved in promoting programs for drug discovery. In this dissertation, we explore different modeling techniques that are used for the simulation and the analysis of complex biological systems. We analyze the state of the art for simulation algorithms both in the stochastic and in the deterministic frameworks. The same dichotomy has been studied in the context of sensitivity analysis, identifying the main pros and cons of the two approaches. Moreover, we studied the quantitative system pharmacology (QSP) modeling approach that elucidates the mechanism of action of a drug on the biological processes underlying a disease. Specifically, we present the definition, calibration and validation of a QSP model describing Gaucher disease type 1 (GD1), one of the most common lysosome storage rare disorders. All of these techniques are finally combined to define a novel computational pipeline for patient stratification. Our approach uses modeling techniques, such as model simulations, sensitivity analysis and QSP modeling, in combination with experimental data to identify the key mechanisms responsible for the stratification. The pipeline has been applied to three test cases in different biological contexts: a whole-body model of dyslipidemia, the QSP model of GD1 and a QSP model of cardiac electrophysiology. In these test cases, the pipeline proved to be accurate and robust, allowing the interpretation of the mechanistic differences underlying the phenotype classification.

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12

YORIYAZ, HELIO. "Desenvolvimento de uma metodologia computacional para calculos em dosimetria interna". reponame:Repositório Institucional do IPEN, 2000. http://repositorio.ipen.br:8080/xmlui/handle/123456789/10791.

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Made available in DSpace on 2014-10-09T12:44:03Z (GMT). No. of bitstreams: 0
Made available in DSpace on 2014-10-09T14:06:55Z (GMT). No. of bitstreams: 1 06878.pdf: 8623929 bytes, checksum: 9403b721e2a38e59f0a65c626040f7f0 (MD5)
Tese (Doutoramento)
IPEN/T
Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP

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13

Pozdin, Maksym O. "Automated Extraction of Subdural Grid Electrodes from Post-Implant MRI Scans for Epilepsy Surgery". Thesis, Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4979.

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The objective of the current research was to develop an automated algorithm with no or little user assistance for extraction of Subdural Grid Electrodes (SGE) from post-implant MRI scans for epilepsy surgery. The algorithm utilizes the knowledge about the artifacts created by Subdural Electrodes (SE) in MRI scans. Also the algorithm does not only extract individual electrodes, but it also extracts them as a SGE structures. Information about the number and type of implanted electrodes is recorded during the surgery [1]. This information is used to reduce the search space and produce better results. Currently, the extraction of SGE from post-implant MRI scans is performed manually by a technologist [1, 2, 3]. It is a time-consuming process, requiring on average a few hours, depending on the number of implanted SE. In addition, the process does not conserve the geometry of the structures, since electrodes are identified individually. Usually SGE extraction is complicated by nearby artifacts, making manual extraction a non-trivial task that requires a good visualization of 3D space and orientation of SGE in it. Currently, most of the technologists use 2D slice viewers for extraction of SGE from 3D MRI scans. There is no commercial software to perform the automated extraction task. The only algorithm suggested in the literature is [4]. The goal of the proposed algorithm is to improve the performance of the algorithm in [4]. As a goal, the proposed algorithm performs extraction of SGE not only for individual electrodes, but by applying geometric constraints on SGE.

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Majeke, Lunga. "Preliminary investigation into estimating eye disease incidence rate from age specific prevalence data". Thesis, University of Fort Hare, 2011. http://hdl.handle.net/10353/464.

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This study presents the methodology for estimating the incidence rate from the age specific prevalence data of three different eye diseases. We consider both situations where the mortality may differ from one person to another, with and without the disease. The method used was developed by Marvin J. Podgor for estimating incidence rate from prevalence data. It delves into the application of logistic regression to obtain the smoothed prevalence rates that helps in obtaining incidence rate. The study concluded that the use of logistic regression can produce a meaningful model, and the incidence rates of these diseases were not affected by the assumption of differential mortality.

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Mannan, Haider Rashid. "Development and use of a Monte Carlo-Markov cycle tree model for coronary heart disease incidence-mortality and health service usage with explicit recognition of coronary artery revascularization procedures (CARPs)". University of Western Australia. School of Population Health, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0101.

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[Truncated abstract] The main objective of this study was to develop and validate a demographic/epidemiologic Markov model for population modelling/forecasting of CARPs as well as CHD deaths and incidence in Western Australia using population, linked hospital morbidity and mortality data for WA over the period 1980 to 2000. A key feature of the model was the ability to count events as individuals moved from one state to another and an important aspect of model development and implementation was the method for estimation of model transition probabilities from available population data. The model was validated through comparison of model predictions with actual event numbers and through demonstration of its use in producing forecasts under standard extrapolation methods for transition probabilities as well as improving the forecasts by taking into account various possible changes to the management of CHD via surgical treatment changes. The final major objective was to demonstrate the use of model for performing sensitivity analysis of some scenarios. In particular, to explore the possible impact on future numbers of CARPs due to improvements in surgical procedures, particularly the introduction of drug eluting stents, and to explore the possible impact of change in trend of CHD incidence as might be caused by the obesity epidemic. ... When the effectiveness of PCI due to introduction of DES was increased by reducing Pr(CABG given PCI) and Pr(a repeat PCI), there was a small decline in the requirements for PCIs and the effect seemed to have a lag. Finally, in addition to these changes when other changes were incorporated which captured that a PCI was used more than a CABG due to a change in health policy after the introduction of DES, there was a small increase in the requirements for PCIs with a lag in the effect. Four incidence scenarios were developed for assessing the effect of change in secular trends of CHD incidence as might be caused by the obesity epidemic in such a way that they gradually represented an increasing effect of obesity epidemic (assuming that other risk factors changed favourably) on CHD incidence. The strategy adopted for developing the scenarios was that based on past trends the most dominant component of CHD incidence was first gradually altered and finally the remaining components were altered. iv The results showed that if the most dominant component of CHD incidence, eg, Pr(CHD - no history of CHD) levelled off and the trends in all other transition probabilities continued into future, then the projected numbers of CABGs and PCIs for 2001-2005 were insensitive to these changes. Even increasing this probability by as much as 20 percent did not alter the results much. These results implied that the short-term effect on projected numbers of CARPs caused by an increase in the most dominant component of CHD incidence, possibly due to an ?obesity epidemic, is small. In the final incidence scenario, two of the remaining CHD incidence components-Pr(CABG - no history of CHD) and Pr(CHD death - no CHD and no history of CHD) were projected to level off over 2001-2005 because these probabilities were declining over the baseline period of 1998-2000. The projected numbers of CABGs were more sensitive (compared to the previous scenarios) to these changes but PCIs were not.

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Mukhtar, Abdulaziz Y. A. "Mathematical modeling of population dynamics of HIV with antiretroviral treatment and herbal medicine". Thesis, University of Western Cape, 2014. http://hdl.handle.net/11394/3351.

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>Magister Scientiae - MSc
Herbal medicines have been an important part of health and wellness for hundreds of years. Recently the World Health Organization estimated that 80% of people worldwide rely on herbal medicines. Herbs contain many substances that are good for protecting the body and are therefore used in the treatment of various illnesses. Along with traditional medicines, herbs are often used in the treatment of chronic diseases such as rheumatism, migraine, chronic fatigue, asthma, eczema, and irritable bowel syndrome, among others. Herbal medicines are also applied in certain traditional communities as treatment against infectious diseases such as flu, malaria, measles, and even human immunodeficiency virus HIV-infection. Approximately 34 million people are currently infected with the human immunodeficiency virus (HIV) and 2.5 million newly infected. Therefore, HIV has become one of the major public health problems worldwide. It is important to understand the impact of herbal medicines used on HIV/AIDS. Mathematical models enable us to make predictions about the qualitative behaviour of disease outbreaks and evaluation of the impact of prevention or intervention strategies. In this dissertation we explore mathematical models for studying the effect of usage of herbal medicines on HIV. In particular we analyze a mathematical model for population dynamics of HIV/AIDS. The latter will include the impact of herbal medicines and traditional healing methods. The HIV model exhibits two steady states; a trivial steady state (HIV-infection free population) and a non-trivial steady state (persistence of HIV infection). We investigate the local asymptotic stability of the deterministic epidemic model and similar properties in terms of the basic reproduction number. Furthermore, we investigate for optimal control strategies. We study a stochastic version of the deterministic model by introducing white noise and show that this model has a unique global positive solution. We also study computationally the stochastic stability of the white noise perturbation model. Finally, qualitative results are illustrated by means of numerical simulations. Some articles from the literature that feature prominently in this dissertation are [14] of Cai et al, [10] of Bhunu et al., [86] of Van den Driessche and Watmough, [64] of Naresh et al., Through the study in this dissertation, we have prepared a research paper [1], jointly with the supervisors to be submitted for publication in an accredited journal. The author of this dissertation also contributed to the research paper [2], which close to completion. 1. Abdulaziz Y.A. Mukhtar, Peter J. Witbooi and Gail D. Hughes. A mathematical model for population dynamics of HIV with ARV and herbal medicine. 2. P.J. Witbooi, T. Seatlhodi, A.Y.A. Mukhtar, E. Mwambene. Mathematical modeling of HIV/AIDS with recruitment of infecteds.

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17

Johnson, Kevin Robert. "In Vivo Coronary Wall Shear Stress Determination Using CT, MRI, and Computational Fluid Dynamics". Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14482.

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Wall shear stress (WSS) has long been identified as a factor in the development of atherosclerotic lesions. Autopsy studies have revealed a strong tendency for lesion development at arterial branch sites and along the inner walls of curvature areas that, in theory, should experience low WSS. Calculations of coronary artery WSS have typically been based upon average models of coronary artery geometry with average flow conditions and then compared to average lesion distributions. With all the averaging involved, a more detailed knowledge of the correlation between WSS and atherosclerotic lesion development might be obscured. Recent advancements in hemodynamic modeling now enable the calculation of WSS in individual subjects. An image-based approach for patient-specific calculation of in vivo WSS using computational fluid dynamics (CFD) would allow a more direct study of this correlation. New state-of-the-art technologies in multi-detector computed tomography (CT) and 3.0 Tesla magnetic resonance imaging (MRI) offer potential improvements for the measurement of coronary artery geometry and blood flow. The overall objective of this research was to evaluate the quantitative accuracy of multi-detector CT and 3.0 Tesla MRI and incorporate those imaging modalities into a patient-specific CFD model of coronary artery WSS. Using a series of vessel motion phantoms, it has been shown that 64-detector CT can provide accurate measurements of coronary artery geometry for heart rates below 70 beats per minute. A flow phantom was used to validate the use of navigator-echo gated, phase contrast MRI at 3.0 Tesla to measure velocity of coronary blood flow. Patient-specific, time-resolved CFD models of coronary WSS were created for two subjects. Furthermore, it was determined that population-average velocity curves or steady state velocities can predict locations of high or low WSS with high degrees of accuracy compared to the use of patient-specific blood flow velocity measurements as CFD boundary conditions. This work is significant because it constitutes the first technique to non-invasively calculate in vivo coronary artery WSS using image-based, patient-specific modeling.

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Harbord, Ruth. "Time-varying brain connectivity with multiregression dynamic models". Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/101426/.

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Functional magnetic resonance imaging (fMRI) is a non-invasive method for studying the human brain that is now widely used to study functional connectivity. Functional connectivity concerns how brain regions interact and how these interactions change over time, between subjects and in different experimental contexts and can provide deep insights into the underlying brain function. Multiregression Dynamic Models (MDMs) are dynamic Bayesian networks that describe contemporaneous, causal relationships between time series. They may therefore be applied to fMRI data to infer functional brain networks. This work focuses on the MDM Directed Graph Model (MDM-DGM) search algorithm for network discovery. The Log Predictive Likelihood (model evidence) factors by subject and by node, allowing a fast, parallelised model search. The estimated networks are directed and may contain the bidirectional edges and cycles that may be thought of as being representative of the true, reciprocal nature of brain connectivity. In Chapter 2, we use two datasets with 15 brain regions to demonstrate that the MDM-DGM can infer networks that are physiologically-interpretable. The estimated MDM-DGM networks are similar to networks estimated with the widely-used partial correlation method but advantageously also provide directional information. As the size of the model space prohibits an exhaustive search over networks with more than 20 nodes, in Chapter 3 we propose and evaluate stepwise model selection algorithms that reduce the number of models scored while optimising the networks. We show that computation time may be dramatically reduced for only a small trade-off in accuracy. In Chapter 4, we use non-local priors to derive new, closed-form expressions for the model evidence with a penalty on weaker, potentially spurious, edges. While the application of non-local priors poses a number of challenges, we argue that it has the potential to provide a flexible Bayesian framework to improve the robustness of the MDM-DGM networks.

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Williams, John. "A mathematical model of the dynamics of hepatitis B virus transmission in the UK under the influence of different vaccination control strategies". Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298721.

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Chin, Sze Vone. "Structural identifiability and indistinguishability analyses of glucose-insulin models". Thesis, University of Warwick, 2011. http://wrap.warwick.ac.uk/38108/.

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In this thesis, the structural identifiability analyses of established and novel glucose-insulin models was performed, to determine whether the models are globally structurally identifiable with the observations available. Structural identifiability analysis is an essential step in the modelling process and a key prerequisite to experimental design and parameter estimation. Analyses were performed assuming observations of both glucose and insulin concentrations on two versions of the well-cited Minimal Model (MM), the Original Minimal Model (OMM) and Extended Minimal Model (EMM) for the modelling of the responses to an Intravenous Glucose Tolerance Test (IVGTT); a Euglycemic Hyperinsulinemic Clamp model and two novel modified versions of the MM, a Closed-Loop Minimal Model (CLMM) and a Double-Pole in Closed-Loop Minimal Model (DPCLMM), when the models describe a complete course of glucose-insulin dynamics during an IVGTT. The CLMM proved to be unidentifiable so a reparameterisation procedure was performed on this model, yielding a globally structurally identifiable reparameterised model. Parameter estimation using these models was also performed for sets of IVGTT and glucose clamp data. The results of the parameter estimation demonstrated that global structural identifiability does not as always guarantee numerical identifiability, or vice versa. A structural indistinguishability analysis was also performed to compare the MM and the CLMM, given the same observations, where it was shown that both models are distinguishable over both pre- and post- insulin switching phases. This is the first time that all such analyses have been performed on these specific model structures. The generic and numerical results obtained demonstrate issues that may arise in practice when attempting to calculate insulin sensitivity when using such models.

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Ford, Ashley P. "Epidemic models and MCMC inference". Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/66495/.

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Statistical inference and model choice for partially observed epidemics provide a variety of challenges both practical and theoretical. This thesis studies some related aspects of models for epidemics and their inference. The use of the matrix exponential to facilitate exact calculations in the General Stochastic Epidemic (GSE) is demonstrated, most usefully in providing the exact marginal likelihood when infection times are unobserved. The bipartite graph epidemic is defined and shown to be a flexible framework which encompasses many existing models. It also provides a way in which a deeper understanding of the relation between existing models could be obtained. The Indian buffet epidemic is introduced as a non-parametric approach to modelling unknown heterogeneous contact structures in epidemics. Inference for the Indian buffet epidemic is a challenging problem, some progress has been made. However the algorithms which have been studied do not yet scale to the size of problem where significant differences from the GSE are apparent. Evidence confirming and demonstrating the importance of understanding the tail behaviour of proposals in importance sampling is presented. The adverse impact of heavy tailed proposals on the Grouped Independence Metropolis-Hastings (GIMH) and Monte Carlo within Metropolis (MCWM) algorithms is demonstrated. A new algorithm, the Kernel Metropolis Hastings (KMH), is proposed as an approximate algorithm for low dimensional marginal inference in situations where the GIMH algorithm fails because of sticking. The KMH is demonstrated on a challenging 2-d problem.

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Lima, Ernesto Augusto Bueno da Fonseca. "Phase-field models of tumor growth with angiogenesis". Laboratório Nacional de Computação Científica, 2014. https://tede.lncc.br/handle/tede/180.

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Conselho Nacional de Desenvolvimento Cientifico e Tecnologico
The development of predictive computational models of tumor initiation, growth, and decline is faced with many formidable challenges. Phenomenological models which attempt to capture the complex interactions of multiple tissue and cellular species must cope with moving interfaces of heterogeneous media and the huge uncertainties of the parameters and their evolution. They must be able to deliver predictions consistent with events that take place at cellular scales, and they must faithfully depict biological mechanisms and events that are known to be associated with various forms of cancer. In the present work, some models for the tumor behavior are presented which fall within the framework of phase-field (or diffuse-interface) models suggested by continuum mixture theory. This framework provides for the simultaneous treatment of interactions of multiple evolving species, such as tumor cells, necrotic cell cores, nutrients, and other cellular and tissue types that exist and interact in living tissue. In the present work, a hybrid phase field ten-species vascular model for the tumor growth is developed, which couples the tumor growth with sprouting through angiogenesis. The model is able to represent the branching of new vessels through coupling a discrete model for which the angiogenesis is started upon pre-defined conditions on the nutrient deprivation in the continuum model. Such conditions are represented by hypoxic cells that release tumor growth factors that ultimately trigger vascular growth. We discuss the numerical approximation of the model using mixed finite elements. We also consider an avascular stochastic six-species tumor growth model derived directly from the hybrid ten-species model. The stochasticity comes from modeling uncertainties in the parameters of the model. We perform a sensitivity analysis to identify the more relevant parameters on the tumor mass growth. The stochastic model is then developed taking into account the uncertainty of the most influential parameter. The numerical approximation of the model using Stochastic Collocation method to treat uncertainties in the nonlinear system is presented. The results of numerous numerical experiments are also presented and discussed.
Modelos matematicos e computacionais sao utilizados na compreensao de fenomenos complexos, sendo aplicados em diversas areas como engenharia, fisica e biologia. Na Medicina tem um importante papel na simulacao do tratamento e evolucao de algumas doencas, entre elas o cancer. O desenvolvimento de modelos computacionais para o crescimento tumoral se depara com desafios formidaveis. Modelos fenomenologicos que tentam capturar as complexas interacoes de multiplos tecidos e especies celulares devem lidar com interfaces em meios heterogeneos e as enormes incertezas dos parametros e suas evolucoes. Eles devem ser capazes de proporcionar predicoes consistentes com eventos que ocorrem em escalas celulares, e devem representar fielmente os mecanismos biologicos associados ao cancer. No presente trabalho, sao apresentados alguns modelos para o crescimento tumoral. Esses modelos inserem-se no ambito de modelos de campo de fase (ou interface difusiva) sugeridos pela teoria mistura. Esta metodologia preve o tratamento simultaneo de interacoes entre multiplos constituintes, como as celulas tumorais, celulas necroticas, nutrientes e outros tipos celulares e teciduais que existem e interagem em tecidos vivos. Neste trabalho, um modelo hibrido de campo de fases, de dez constituintes e desenvolvido para o crescimento tumoral vascular, que acopla o crescimento de tumores com crescimento de novos vasos sanguineos atraves da angiogenese. O modelo é capaz de representar a ramificacao de novos vasos atraves do acoplamento de um modelo discreto, no qual a angiogenese é iniciada mediante condicoes pre-definidas, relacionadas a privacao de nutrientes no modelo macroscopico. Tais condicoes sao representadas por celulas hipoxicas que liberam quimicos reponsaveis por induzir a angiogenese tumoral. A aproximacao numerica do modelo usando elementos finitos mistos é discutida. Considera-se tambem um modelo estocastico avascular de seis constituintes para o crescimento tumoral, derivado diretamente do modelo hibrido de dez constituintes. A estocasticidade vem de incertezas na modelagem dos parametros do modelo. Realiza-se uma analise de sensibilidade para identificar os parametros mais relevantes sobre o crescimento da massa tumoral. O modelo estocastico é entao desenvolvido tendo em conta a incerteza no parametro mais influente. A aproximacao numerica do modelo usando o metodo estocastico de Colocacao para tratar incertezas no sistema nao-linear é apresentada. Os resultados de varios experimentos numericos tambem sao apresentados e discutidos.

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23

Griffin, Adam. "Quasi-stationary distributions for evolving epidemic models : simulation and characterisation". Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/89671/.

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This thesis develops probabilistic models for the spread of infectious diseases in which individuals experience a period of transient immunity after recovering from infection. Quasi-stationary distributions (QSDs) and limiting conditional distributions (LCDs) are used to describe the temporary equilibrium that is reached between an initial exponential growth phase and the epidemic dying out. This thesis includes results characterising QSDs corresponding to existing birth-death processes and epidemic models and to new processes such as the Evolving Strain SIRS model which we define to describe the progression of a disease undergoing antigenic drift, such as seasonal influenza. Existence and uniqueness results are proven for specific LCDs. Results regarding marginals of special cases of these processes are proven, including the preservation of x-invariance for Q as discussed in Pollett [1988]. Many of the models considered in this thesis are multidimensional, which makes explicit calculation of QSDs extremely challenging. To combat this, specialised techniques for simulating QSDs are developed to illustrate and explore these distributions. These novel methods, involving variants on SMC samplers, are shown to facilitate the simulation of QSDs for discrete-valued stochastic processes, particularly reducible processes. A formal proof of convergence of the SMC sampler is provided for some simple examples. The simulation methods are then used to characterise the properties of QSDs and LCDs related to endemic epidemic models with evolving strains under an equivalence relation. These QSDs are used to define a reproduction number similar to Ro when the process starts from quasi-stationarity. The epidemic models with evolving strains are shown to have the standard SIR and SIRS epidemic models arising as limiting processes as evolution at each infection becomes certain.

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24

Barons, Martine J. "What is the added value of using non-linear models to explore complex healthcare datasets?" Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/58401/.

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Health care is a complex system and it is therefore expected to behave in a non-linear manner. It is important for the delivery of health interventions to patients that the best possible analysis of available data is undertaken. Many of the conventional models used for health care data are linear. This research compares the performance of linear models with non-linear models for two health care data sets of complex interventions. Logistic regression, latent class analysis and a classification artificial neural network were each used to model outcomes for patients using data from a randomised controlled trial of a cognitive behavioural complex intervention for non-specific low back pain. A Cox proportional hazards model and an artificial neural network were used to model survival and the hazards for different sub-groups of patients using an observational study of a cardiovascular rehabilitation complex intervention. The artificial neural network and an ordinary logistic regression were more accurate in classifying patient recovery from back pain than a logistic regression on latent class membership. The most sensitive models were the artificial neural network and the latent class logistic regression. The best overall performance was the artificial neural network, providing both sensitivity and accuracy. Survival was modelled equally well by the Cox model and the artificial neural network, when compared to the empirical Kaplan-Meier survival curve. Long term survival for the cardiovascular patients was strongly associated with secondary prevention medications, and fitness was also important. Moreover, improvement in fitness during the rehabilitation period to a fairly modest 'high fitness' category was as advantageous for long-term survival as having achieved that same level of fitness by the beginning of the rehabilitation period. Having adjusted for fitness, BMI was not a predictor of long term survival after a cardiac event or procedure. The Cox proportional hazards model was constrained by its assumptions to produce hazard trajectories proportional to the baseline hazard. The artificial neural network model produced hazard trajectories that vary, giving rise to hypotheses about how the predictors of survival interact in their influence on the hazard. The artificial neural network, an exemplar non-linear model, has been shown to match or exceed the capability of conventional models in the analysis of complex health care data sets.

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25

Charoenphon, Sutthirut. "Green's Functions of Discrete Fractional Calculus Boundary Value Problems and an Application of Discrete Fractional Calculus to a Pharmacokinetic Model". TopSCHOLAR®, 2014. http://digitalcommons.wku.edu/theses/1327.

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Fractional calculus has been used as a research tool in the fields of pharmacology, biology, chemistry, and other areas [3]. The main purpose of this thesis is to calculate Green's functions of fractional difference equations, and to model problems in pharmacokinetics. We claim that the discrete fractional calculus yields the best prediction performance compared to the continuous fractional calculus in the application of a one-compartmental model of drug concentration. In Chapter 1, the Gamma function and its properties are discussed to establish a theoretical basis. Additionally, the basics of discrete fractional calculus are discussed using particular examples for further calculations. In Chapter 2, we use these basic results in the analysis of a linear fractional difference equation. Existence of solutions to this difference equation is then established for both initial conditions (IVP) and two-point boundary conditions (BVP). In Chapter 3, Green's functions are introduced and discussed, along with examples. Instead of using Cauchy functions, the technique of finding Green's functions by a traditional method is demonstrated and used throughout this chapter. The solutions of the BVP play an important role in analysis and construction of the Green's functions. Then, Green's functions for the discrete calculus case are calculated using particular problems, such as boundary value problems, discrete boundary value problems (DBVP) and fractional boundary value problems (FBVP). Finally, we demonstrate how the Green's functions of the FBVP generalize the existence results of the Green's functions of DVBP. In Chapter 4, different compartmental pharmacokinetic models are discussed. This thesis limits discussion to the one-compartmental model. The Mathematica FindFit command and the statistical computational techniques of mean square error (MSE) and cross-validation are discussed. Each of the four models (continuous, continuous fractional, discrete and discrete fractional) is used to compute the MSE numerically with the given data of drug concentration. Then, the best fit and the best model are obtained by inspection of the resulting MSE. In the last Chapter, the results are summarized, conclusions are drawn, and directions for future work are stated.

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Ganjgahi, Habib. "Computationally efficient mixed effect model for genetic analysis of high dimensional neuroimaging data". Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/91328/.

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A new research direction in the neuroimaging discipline, so called imaging genetic, has emerged recently concerns describing individual differences in imaging phenotypes using genetic and environmental factors. The large number of voxel- and vertex-wise measurements in imaging genetics studies present a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software cannot perform essential genetic analyses including heritability and association estimations and testings, and yet standard quantitative genetics tools cannot provide essential neuroimaging inferences, like family-wise error corrected voxel- wise or cluster-wise P-values. Moreover, available genetic tools rely on P-values that can be inaccurate with usual parametric inference methods. In this thesis computationally efficient linear mixed effect model for voxel-wise genetic analyses of high-dimensional imaging phenotypes are developed. Specifically, fast estimation and inference procedures for heritability and association analyses are introduced using orthogonal transformations that dramatically simplify the likelihood and restricted likelihood functions of mixed effect model. We review the family of score, likelihood ratio and Wald tests and propose novel inference methods for fixed and random effect terms in the mixed effect models. To address problems with inaccuracies with the standard results used to find P-values, we propose different permutation schemes to allow semi-parametric inference (parametric likelihood-based estimation, non-parametric sampling distribution). In total, we evaluate different significance tests for heritability and association, with either asymptotic parametric or permutation-based P-value computations. We identify a number of tests that are both computationally efficient and powerful, making them ideal candidates for heritability and genome-wide association studies in the massive data setting.

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Ortega, Neli Regina de Siqueira. "Aplicação da teoria de conjuntos Fuzzy a problemas da biomedicina". Universidade de São Paulo, 2001. http://www.teses.usp.br/teses/disponiveis/43/43134/tde-04122013-133237/.

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Sistemas biológicos, médicos e epidêmicos apresentam vários tipos de incertezas inerentes aos seus processos. Muitas dessas incertezas tem sido tratadas de forma eficiente com modelos estatísticos e Bayesianos. Todavia, essas áreas ainda carecem de estruturas matemáticas que possibilitem o tratamento das incertezas não-estatísticas típicas de alguns desses sistemas. Além disso, a utilização de termos linguísticos para expressar uantitativamente as variáveis é muito comum em algumas dessas áreas. Assim, devido às suas características, a lógica fuzzy se apresenta como uma teoria adequada para tratar alguns desses problemas. O objetivo dessa tese foi desenvolver aplicações da teoria de conjuntos fuzzy a problemas da biomedicina. O nosso desafio foi propor caminhos, buscar maneiras, de realizar uma junção efetiva dessa teoria com as ´áreas citadas, principalmente a epidemiologia. Foram elaborados oito trabalhos, onde vários aspectos dessa teoria foram abordados, tais como: modelos linguísticos fuzzy estáticos e dinâmicos, processos de decisão fuzzy, probabilidade de eventos fuzzy, relações fuzzy e a utilização do princípio de extensão na construção de regras fuzzy. Concluímos que a teoria de conjuntos fuzzy pode auxiliar no tratamento de muitos problemas de cunho epidemiológico, bem como sistemas diagnóstico. Mostramos também que ela pode trabalhar de forma efetiva em processos de decisão de Saúde Pública. Alguns sistemas podem, potencialmente, ajudar os médicos no diagnóstico e prognóstico de doenças, principalmente na ausência de especialistas. Os modelos linguísticos estáticos funcionaram muito bem, entretanto, dificuldades quanto aos modelos dinâmicos precisam ainda ser superadas. Discutimos também o papel do especialista na elaboração de modelos fuzzy em epidemiologia e propomos um método para elaboração de modelos menos dependentes. Todos os trabalhos apresentaram bons resultados, estimulando a continuidade das pesquisas nessa área.
Biological, medical and epidemic systems present several types of inherent uncertainties to its processes. Many of these uncertainties have been treated in an efficient way with statistical and Bayesian models. Though, these areas still lack of mathematical structures that make possible the treatment of the non-statistical uncertainties typical of some of these systems. Besides, the use of linguistic terms to express quantitatively the variables is very common in these areas. So, due to its features, the fuzzy logic comes as an appropriate theory to treat some of these problems. The aim of this thesis was to develop applications of the fuzzy logic theory to problems of biomedicine. Our challenge was to propose paths, to look for ways, of accomplishing an effective junction off this theory with the mentioned areas, mainly with epidemiology. Eight works were elaborated, where several aspects of this theory were approached, such as: static and dynamic fuzzy linguistic models, fuzzy decision making, probability of fuzzy events, fuzzy relations and the use of extension principle in the construction of fuzzy rules. We conclude that the fuzzy logic theory can aid in the treatment of many epidemiological problems, as well as diagnostic systems. We also show that it can work effectively in decision making processes of Public Health. Some systems can, pottentially, help the physicians in the diagnosis and prognostic of diseases, mainly in the specialists absence. The static linguistic models worked well, however, difficulties concerning the dynamic models still need to be overcome. We also discuss the specialists role in the elaboration of fuzzy models in epidemiology and propose a method for elaboration of less dependent models. All the works presented good results, stimulating the continuity of the researches in this area.

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28

Wang, Lipeng. "THE ROLE OF ABCG2 IN DRUG ACTIVE TRANSPORT IN MILK". UKnowledge, 2010. http://uknowledge.uky.edu/gradschool_diss/59.

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Drug active transport into milk is a major concern for breastfeeding mothers and healthcare providers. Studies from the literature indicated that breast cancer resistance protein (ABCG2) plays an important role in drug transfer into milk. There has been limited study on stereoselective interactions with ABCG2. A mechanistic analysis of flux across cell monolayer model is a critical first step toward extrapolating in vitro results for predicting in vivo disposition (including distribution into milk), drug disposition or drug-drug interactions. The objectives of this thesis were (1) to establish a “Chemical knockout model” in rat for studying drug accumulation into milk, (2) to investigate the impact of stereoselective interaction between ABCG2/Abcg2 and pantoprazole on drug transport in milk, (3) to understand in vitro monolayer flux model using experimental data and a mechanistic mathematical model. Quantitive PCR, Western blotting and immunohistochemistry results indicated that Abcg2 was up-regulated during lactation and localized on apical side of epithelial cells in mammary gland. In vitro and in vivo experiments confirmed that Abcg2 is responsible for nitrofurantoin active transport in rat milk and GF120918 was established as a chemical knockout model. Abcg2 interacts stereoselectively with pantoprazole isomers. A significant different apical flux between two pantoprazole isomers was observed in Abcg2-MDCKII cell line. The milk to serum (M/S) ratio of (-)-pantoprazole was almost 3 times as that of (+)-pantoprazole in lactating rats. Administration GF120918 decreased M/S of (-)-pantoprazole (p<0.001) but not (+)-pantoprazole (p>0.05). A stably transfected ABCG2/Abcg2 overexpressing MDCKII cell line was successfully created and used to explore the theoretical relationships in a monolayer flux model. Based on the profiles of pantoprazole isomer transport, a simple three compartment model for drug transfer into breast milk incorporating the permeability-surface area products for passive diffusion (PSD), paracellular flux (PSPC) and apically efflux ABCG2 (PSA,E) transfection was developed. The mathematical model was developed to more fully understand the interplay of paracellular, passive diffusion, active transport, and flux kinetic parameters (Km, Vmax, IC50 and Ki). This model provided useful insights into the meaning and limitation of parameters obtained from monolayer flux.

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Rocha, Heber Lima da. "Modelagem híbrida multiescala para o crescimento tumoral". Laboratório Nacional de Computação Científica, 2016. https://tede.lncc.br/handle/tede/236.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Cancer is a huge world health problem, what is expanding researches on a wide variety of subjects associated with its onset, evolution and treatment. In this work, we perfom a detailed study on the tumor growth mechanisms in order to build a model to describe the evolution of tumors at different scales. We develop a multiscale hybrid model for the avascular tumor growth which integrates phenomena that occur at two scales, the cellular and tissue scales. The cellular scale is described through an agent based model, allowing to deal with each cell individually and to describe the cell behavior in the microenvironment. We represent the nutrient transport in the microenvironment at the tissue scale through a reaction-diffusion partial differential equation. The oxygen is considered the only source of nutrients and its uptake rate plays the role of the bridge between scales. The model encompasses tumor and normal cells, but the latter are kept in homeostasis. Phenotypic states differentiate tumor cells (quiescent, proliferative, apoptotic, hypoxic and necrotic), which may change in accordance with microenvironment conditions. The tumor growth dynamics is ruled by phenotypic transitions, which are mainly deterministic. However, the transitions from quiescent to proliferative and apoptotic states are stochastic. Each cell movement is driven by the force balance among cells, according to Newton's second law. By including normal cells, the tumor growth strongly depends on the mechanical interactions in the microenvironment. To describe these effects, we develop a model to represent the compressive stress accumulation within the growing tumor, which acts by inhibiting further cell proliferation. Computational simulations are conducted to demonstrate that the developed model can adequately describe the complex mechanisms of tumor dynamics, including growth arrest in avascular tumors.
O câncer é um enorme problema de saúde global, o que vem impulsionando pesquisas nas mais diversas áreas associadas ao seu surgimento, evolução e terapias. Neste trabalho realizamos um estudo minucioso acerca do crescimento tumoral a fim de construir um modelo que descreve o crescimento tumoral em diversas escalas. Desenvolvemos um modelo multiescala híbrido para o crescimento tumoral avascular que integra fenômenos que ocorrem em duas escalas, uma escala a nível celular e outra a nível de tecido. A escala celular é descrita através de um modelo baseado em agentes, que possibilita tratar cada célula individualmente e descrever seu comportamento no microambiente. Na escala do tecido representamos a dispersão de nutrientes no meio através de uma equação diferencial parcial de reação-difusão. Consideramos o oxigênio como a única fonte de nutrientes e seu consumo é o mecanismo através do qual o acoplamento entre as escalas é realizado. Consideramos que cada célula no modelo pode ser tumoral ou normal, sendo as células normais mantidas em homeostase. As células tumorais são diferenciadas pelos estados fenotípicos (quiescente, proliferativa, apoptótica, hipóxica e necrótica), que podem ser alterados em função das condições do meio. A dinâmica do crescimento tumoral é regida pelas transições entre estados fenotípicos, as quais, em sua maioria, são consideradas eventos determinísticos. Entretanto, as transições do estado quiescente para o proliferativo e para o apoptótico são assumidas como estocásticas. O movimento de cada célula no meio é determinado por um balanço de forças atuantes nas células, de acordo com a segunda lei de Newton. Com a inclusão de células normais, o crescimento do tumor é fortemente influenciado pelas interações mecânicas no microambiente. Para descrever estes efeitos, desenvolvemos um modelo para representar o acúmulo das tensões de compressão no interior do tumor à medida que o tumor cresce, o qual atua inibindo a probabilidade de proliferação das células tumorais. As simulações realizadas demonstram que o modelo desenvolvido consegue representar qualitativamente a dinâmica de tumores em um microambiente genérico e a estagnação do crescimento típica de tumores avasculares.

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30

Resende, Anna Claudia Mello de. "Sensitivity analysis as a tool for tumor growth modeling". Laboratório Nacional de Computação Científica, 2016. https://tede.lncc.br/handle/tede/237.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Mathematical and computational modeling of tumor growth have become valuable tools for learning and understanding various aspects of tumor onset and development. They can also help to generate new hypotheses for experimental testing and to verify the efficiency or optimize clinical therapies. From the computational point of view, a huge challenge is to deal with highly nonlinear and multi-components mathematical models that aim to display multiple types of biological interactions across different biological, temporal and physical scales. Computational and numerical difficulties usually appear. Also, nonlinear interactions may give rise to interesting and unexpected dynamics which make it difficult to anticipate a model's outcome. Here we make a step towards developing a model-building framework to improve the understanding of the model itself and the key issues to drive model modifications and simplifications. We develop a family of deterministic tumor growth models based on a mathematical model built in the literature, which is a continuous model of seven coupled nonlinear partial differential equations that can capture both avascular and vascular phases of the disease. Although simple, this model provides considerable insight about important mechanisms related to tumor progression, as angiogenesis, for example, which is the fundamental strategy tumors acquire to keep and improve growth. Its main assumptions and mathematical formulation are discussed in details, and we propose some modifications to fix ambiguities in the original model. The extension to multidimensional problems is considered, for which we develop reliable approximate finite element solution. We propose in this work a simple framework to build a hierarchical family of tumor growth models by selecting a subset of the most important parameters of our base model with respect to the evolution of the tumor volume. The importance of each parameter is identified through two model-free sensitivity analysis techniques, the construction of scatterplots and the elementary effects, due to their simplicity and low computational costs. This model framework encompasses the essential hypotheses and the limited set of important parameters acquired from the sensitivity analysis. In this way, we are able to create a family of models described by at least the same essential conditions and parameters but with different complexities regarding the number of parameters used. Numerical experiments are conducted to provide a comprehensive understanding of the hierarchical developed family of tumor growth models. Finally, we emphasize that the modeling framework in this manner provides a powerful way for studying a model itself or either its simplification or extension. The framework can also be tailored to form the basis for future models, incorporating new processes and phenomena.
A modelagem matemática e computacional do crescimento tumoral tornou-se uma ferramenta importante para o aprendizado e entendimento de vários aspectos relacionados ao surgimento e desenvolvimento de tumores. Esta ferramenta é também capaz de ajudar a construir novas hipóteses para testes experimentais, verificar a eficiência e até mesmo otimizar terapias. Do ponto de vista computacional, um grande desafio consiste em resolver e entender modelos matemáticos não-lineares com múltiplos componentes que objetivam representar interações que ocorrem em diferentes escalas biológicas, de tempo e espaço. Dificuldades numéricas e computacionais ocorrem frequentemente. Interações não-lineares dão também origem a dinâmicas interessantes e até mesmo inesperadas, dificultando antecipar os resultados de muitos modelos. Neste trabalho, dá-se um passo na direção do desenvolvimento de uma abordagem para a construção de modelos que permite tornar mais claro o entendimento destes e de seus aspectos-chave, auxiliando modificações e simplificações para tornar o processo de modelagem mais simples. Desenvolvemos uma família de modelos determinísticos para o crescimento tumoral baseada em um modelo não-linear e contínuo apresentado na literatura que contém sete equações diferenciais parciais acopladas, capaz de capturar as fases avascular e vascular da doença. Embora simples, este modelo proporciona o entendimento dos importantes mecanismos relacionados à progressão de tumores, como a angiogênese, por exemplo, que é a estratégia utilizada por um tumor para manter e impulsionar seu crescimento. As principais hipóteses e formulação matemática deste modelo-base são discutidas em detalhes, e algumas modificações são propostas para corrigir ambiguidades presentes no modelo original. A extensão para problemas multidimensionais é considerada, para a qual desenvolvemos uma solução robusta de elementos finitos. Neste trabalho, propomos uma abordagem simples para a construção de uma família hierárquica de modelos de crescimento tumoral através da seleção do conjunto de parâmetros mais importantes de um modelo-base com respeito à evolução do volume tumoral. A importância de cada parâmetro é identificada através de duas técnicas de análise de sensibilidade consideradas simples, de baixo custo computacional e independentes do modelo utilizado: a construção de scatterplots e efeitos elementares. A abordagem de modelagem inclui as hipóteses essenciais e um conjunto limitado de parâmetros identificados como importantes na análise de sensibilidade. Deste modo, é possível criar uma família de modelos descrita no mínimo pelas mesmas condições essenciais e parâmetros importantes, mas com diferentes complexidades com relação ao número de parâmetros utilizados na modelagem. Experimentos numéricos são realizados para promover o entendimento sobre a família hierárquica de modelos desenvolvida. Finalmente, enfatizamos que a abordagem de modelagem desenvolvida desta maneira proporciona um potencial mecanismo para estudar um modelo e suas simplificações e extensões. Esta abordagem pode ser ajustada para formar a base para modelos futuros, incorporando novos processos e fenômenos.

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31

Oyero, Oyebola E. "Comparison of Two Methods for Developing Aggregate Population-Based Models". Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etd/3139.

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Aggregate models incorporate the variation between individual parameters of individualbased models to construct a population-based model. This thesis focuses on the comparison of two different methods for creating these population-based models. The first method, the individual parameter distribution technique (IPD) focuses on the similarities and variation of parameters in an individual-based model as calculated using individual data sets [4]. The second method we consider is the nonlinear mixed effect method (NLME), which is primarily used in modeling repeated measurement data. In the NLME approach, both the fixed effects and random effects of the parameter values are estimated in the model by assuming a normal distribution for the parameter values across individuals[2]. Both methods were implemented on a one-compartment pharmacokinetic concentration model. Using the variation in parameters estimated using the two different approaches, a population model was generated and then compared to the dynamics seen in the individual data sets. We compare three features of the concentration data to the simulated population models. The values for all three features were captured by both methods; however, the biggest difference observed is 2 that there is a longer tail in the distribution for the population model developed using NLME than observed in the dynamics in the original data.

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32

Deschamps, Thomas. "Extraction de Courbes et Surfaces par Methodes de Chemins Minimaux et Ensembles de Niveaux. Applications en Imagerie Medicale 3D". Phd thesis, Université Paris Dauphine - Paris IX, 2001. http://tel.archives-ouvertes.fr/tel-00003335.

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Dans cette these nous nous interessons a l'utilisation des méthodes de chemins minimaux et des méthodes de contours actifs par Ensembles de Niveaux, pour l'extraction de courbes et de surfaces dans des images medicales 3D. Dans un premier temps, nous nous sommes attaches a proposer un éventail varié de techniques d'extraction de chemins minimaux dans des images 2D et 3D, basees sur la résolution de l'équation Eikonal par l'algorithme du Fast Marching. Nous avons montre des resultats de ces techniques appliquees a des problèmes d'imagerie médicale concrets, notamment en construction de trajectoires 3D pour l'endoscopie virtuelle, et en segmentation interactive, avec possibilité d'apprentissage. Dans un deuxieme temps, nous nous sommes interessés a l'extraction de surfaces. Nous avons developpé un algorithme rapide de pré-segmentation, sur la base du formalisme des chemins minimaux. Nous avons étudié en détail la mise en place d'une collaboration entre cette méthode et celle des Ensembles de Niveaux, dont un des avantages communs est de ne pas avoir d'a priori sur la topologie de l'objet a segmenter. Cette méthode collaborative a ensuite ete testée sur des problèmes de segmentation et de visualisation de pathologies telles que les anevrismes cerebraux et les polypes du colon. Dans un troisième temps nous avons fusionné les résultats des deux premières parties pour obtenir l'extraction de surfaces, et des squelettes d'objets anatomiques tubulaires. Les squelettes des surfaces fournissent des trajectoires que nous utilisons pour déplacer des cameras virtuelles, et nous servent a definir les sections des objets lorsque nous voulons mesurer l'étendue d'une pathologie. La dernière partie regroupe des applications de ces méthodes a l'extraction de structures arborescentes. Nous étudions le cas des arbres vasculaires dans des images médicales 3D de produit de contraste, ainsi que le problème plus difficile de l'extraction de l'arbre bronchique sur des images scanners des poumons.

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33

Padmanabhan, Kiran. "Modified alginates as a matrix for gene transfection in a HeLa cell model". Scholarly Commons, 2000. https://scholarlycommons.pacific.edu/uop_etds/547.

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The development of safe, efficient and well-characterized vector systems is one of the greatest limiting factors in gene therapy and transfection methodology. A number of synthetic vectors such as cationic polymers and liposomes have been developed for introducing 'therapeutic' genetic material into the cell. These approaches have the distinct advantage over viral vectors in being relatively non-immunogenic and noninfective. Though the efficiency of this procedure is poor compared to viruses, developing an effective and safe delivery system is a continuing challenge in pharmaceutical research and development. Five novel polymer matrices were developed by chemically modifying alginic acid. Alginic acid is a linear co-polymer consisting of ~-(1-4)-D-mannuronic acid and a- (1-4)-L-guluronic acid. The carbodiimide coupling method was used to couple aromatic amines to the carboxylic acid functional groups on the alginate backbone, thus giving it a cationic character. Diaminoacridine hydrochloride, thionin, basic fuchsin, acridine yellow and diaminomethyl triazine were the aromatic amines used. The polymers were then tested for their efficiency in transfecting the pSV -P-Galactosidase control vector into HeLa cells. Expression of the reporter protein, P-Galactosidase, was quantified using a colorimetric assay. Compared to the controls, the modified compounds showed moderate to significant improvements in transfection capability, with one exception. The matrix prepared by modifying alginate with diaminoacridine hydrochloride proved lethal to the growth of cells in culture. The use of the alginate basic fuchsin matrix resultes in a significant increase in transfection compared to the controls. The polymers prepared using thionin and acridine yellow showed moderate improvements in transfection, when compared to controls. The complex formation between the plasmid and the alginate-basic fuchsin polymer matrix was evaluated using Transmission Electron Microscopy (TEM). Although there was a significant improvement in transfection with basic fuchsin-alginate polymer, the levels of protein expression appeared to be low in this system. Nevertheless, these results demonstrate the potential of modified alginates as vectors for gene delivery.

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34

Sun, Jingjing. "Exploring the effect of alpha2 receptor on brain 5-HT via a mechanism-based pharmacodynamic model". Scholarly Commons, 2012. https://scholarlycommons.pacific.edu/uop_etds/154.

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Purpose: 5-hydroxytryptamine (5-HT) is an important neurotransmitter in depression. It is believed that α 1 and α 2 adrenoceptors mediate the 5-HT level in the brain. The mechanism is complex and not well explored. Especially in different combination treatments, the receptor systems may show varied modulation capability. Additionally, some research has suggested that α 2 heteroceptors may contribute to the time delay problem in dual depression treatment which is thought as the time needed for certain inhibition receptor to get desensitized. We hypothesized that the α 2 adrenoceptors had inhibition effect on 5-HT level in dorsal raphé nucleus (DRN), Prefrontal cortex (PFC) and Hippocampus (HP) with the dual reuptake inhibition. The present study was undertaken to explore the effect of BRL44408 (α 2 receptor antagonist) on 5-HT level in rat PFC, DRN and HP under dual antidepressant with blocking the α 1 receptor. Method: Serotonin reuptake inhibitor and norepinephrine reuptake inhibitor were used to mimic the dual reuptake inhibition antidepressant. To differentiate the α 2 adrenoceptors effect from al adrenoceptors effect, prazosin, an antagonist of α 1 adrenoceptors, was added to block α 1 adrenoceptors. Using the microdialysis method, the drug combination was examined in HP area and then DRN area to explore the drug effect on time course of 5-HT release in DRN and PFC. Based on the experiment results from DRN and PFC, a mechanism-based pharmacodynamic model was developed. Result: BRL44408 increased the serotonin (5-HT) level in rat PFC, DRN and HP to different degrees with the dual reuptake inhibition (p < 0.05). The overall model reasonably captured the time course of 5-HT in both DRN and PFC with different dose schemes of BRL44408. The model predicted EC50 of BRL44408 (0.0075 µM) for the α 2 heteroreceptor which control PFC 5-HT is close to the reported value of BRL44408 for α 2 adrenorceptor (0.008 µM). However, the model predicted EC50 of BRL44408 on the α 2 heteroreceptor which control DRN 5-HT need to be explained. Simulation result from this model suggested varied modulation capability of α 2 adrenoceptors on the 5-HT in DRN and the 5-HT in PFC. Conclusion: α 2 heteroceptor play a role in regulation 5-HT level under dual reuptake inhibition. Further exploration may bring a potential target for depression treatment. The mechanism model was developed to characterize and better understand the neurotransmitter mechanisms, providing estimations of various parameters of the disease related receptor system.

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35

Séqueira, Jean. "Modélisation interactive d'objets de forme complexe à partir de données hétérogènes : application à la représentation géométrique des organes du corps humain". Besançon, 1987. http://www.theses.fr/1987BESA2028.

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36

Jackson, Arthur Rhydon. "Predicting Flavonoid UGT Regioselectivity with Graphical Residue Models and Machine Learning". Digital Commons @ East Tennessee State University, 2009. https://dc.etsu.edu/etd/1820.

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Machine learning is applied to a challenging and biologically significant protein classification problem: the prediction of flavonoid UGT acceptor regioselectivity from primary protein sequence. Novel indices characterizing graphical models of protein residues are introduced. The indices are compared with existing amino acid indices and found to cluster residues appropriately. A variety of models employing the indices are then investigated by examining their performance when analyzed using nearest neighbor, support vector machine, and Bayesian neural network classifiers. Improvements over nearest neighbor classifications relying on standard alignment similarity scores are reported.

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37

Akcin, Haci Mustafa. "Direct adjustment method on Aalen's additive hazards model for competing risks data". unrestricted, 2008. http://etd.gsu.edu/theses/available/etd-04182008-095207/.

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Thesis (M.S.)--Georgia State University, 2008.
Title from file title page. Xu Zhang, committee chair; Yichuan Zhao, Jiawei Liu, Yu-Sheng Hsu, committee members. Electronic text (51 p.) : digital, PDF file. Description based on contents viewed July 15, 2008. Includes bibliographical references (p. 50-51).

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38

Vangala, Swathi. "Human Cytochrome P450 3A4 Over-Expressing IEC-18 and MDCK Cell Lines as an In-Vitro Model to Assess Gut Permeability and the Enzyme Metabolism". Scholarly Commons, 2013. https://scholarlycommons.pacific.edu/uop_etds/273.

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Purpose. The fate of an orally administered drug is dependent on many parameters before it can reach the systemic circulation, including drug absorption and first-pass metabolism in the gut and the liver. Mammalian cells lines such as MDCK and Caco-2 are commonly employed to assess drug permeability but they lack or have low expression level of drug metabolism enzyme expression such as CYP3A4, which contributes to significant first-pass gut and liver metabolism for many drugs. Consequently, these cell lines are not sufficient to integrate metabolism when assessing drug absorption. Here, we tested MDCK and IEC-18 cells transiently over-expressing CYP3A4 as models that can simultaneously assay a compound's permeability and metabolism potential in a single experiment. Method. A recombinant adenovirus carrying the hCYP3A4 cDNA was constructed according to Stratagene's AdEasy XL Adenoviral system. This adenovirus was used to transiently transfect hCYP3A4 into MDCK and IEC-18 cells. Western blot was performed to assess the level of hCYP3A4 expression in the wild type and CYP3A4 over-expressing IEC-18 and MDCK cells. In situ metabolism and transport studies were performed with wild-types and IEC-18-3A4 or MDCK-3A4 cells. Results. The amount of CYP3A4 present in MDCK-3A4 cells was 250 times to that of wild type cells which 1/4th the amount present in human liver microsomes. The amount of CYP3A4 present in IEC-18-3A4 cells was 150 times to that of wild type cells which 1/6th the amount present in human liver microsomes. In metabolism studies, there was higher formation of metabolites in cells transfected with hCYP3A4 compared to controls. In addition, apical to basal transport studies of several drugs in IEC-18-3A4 and MDCK-3A4 showed increased appearance of metabolites compared to the wild-type cells. Conclusions. This model may be a useful to assess the extent of drug absorption into systemic circulation after oral administration.

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39

Padmanabhan, Kiran. "Modified alginates as a matrix for gene transfection in a HeLa cell model : a thesis". Scholarly Commons, 2001. https://scholarlycommons.pacific.edu/uop_etds/547.

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The development of safe, efficient and well-characterized vector systems is one of the greatest limiting factors in gene therapy and transfection methodology. A number of synthetic vectors such as cationic polymers and liposomes have been developed for introducing 'therapeutic' genetic material into the cell. These approaches have the distinct advantage over viral vectors in being relatively non-immunogenic and noninfective. Though the efficiency of this procedure is poor compared to viruses, developing an effective and safe delivery system is a continuing challenge in pharmaceutical research and development. Five novel polymer matrices were developed by chemically modifying alginic acid. Alginic acid is a linear co-polymer consisting of ~-(1-4)-D-mannuronic acid and a- (1-4)-L-guluronic acid. The carbodiimide coupling method was used to couple aromatic amines to the carboxylic acid functional groups on the alginate backbone, thus giving it a cationic character. Diaminoacridine hydrochloride, thionin, basic fuchsin, acridine yellow and diaminomethyl triazine were the aromatic amines used. The polymers were then tested for their efficiency in transfecting the pSV -P-Galactosidase control vector into HeLa cells. Expression of the reporter protein, P-Galactosidase, was quantified using a colorimetric assay. Compared to the controls, the modified compounds showed moderate to significant improvements in transfection capability, with one exception. The matrix prepared by modifying alginate with diaminoacridine hydrochloride proved lethal to the growth of cells in culture. The use of the alginate basic fuchsin matrix resultes in a significant increase in transfection compared to the controls. The polymers prepared using thionin and acridine yellow showed moderate improvements in transfection, when compared to controls. The complex formation between the plasmid and the alginate-basic fuchsin polymer matrix was evaluated using Transmission Electron Microscopy (TEM). Although there was a significant improvement in transfection with basic fuchsin-alginate polymer, the levels of protein expression appeared to be low in this system. Nevertheless, these results demonstrate the potential of modified alginates as vectors for gene delivery.

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40

Santos, Fabiano de Sant'ana dos. "Incidência de infecções virais das vias aeríferas superiores em crianças e seu estudo por meio de um modelo matemático". Faculdade de Medicina de São José do Rio Preto, 2009. http://bdtd.famerp.br/handle/tede/58.

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Made available in DSpace on 2016-01-26T12:51:21Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-03-31
Acute respiratory infections, especially upper respiratory tract infections (URTI), are the most frequent causes of infantile morbidity in the world. Day-care facilities are closed, with great circulation of people and infectious agents as well, being therefore prone to the spreading of viral respiratory infections. Mathematical epidemic models are quantitative analysis methods that might be used for understanding and predicting the transmission dynamics of infectious diseases. Objective: Verify the monthly incidence of URTI, of 8 respiratory viruses, and to simulate a mathematical model, evaluating its qualitative and quantitative behavior regarding true data from URTI in school of infantile education in integral period children. Casuistic and Methods: From July 2003 to July 2004, all children (173) in the school of infantile education in integral period were followed from 1.6 to 12 months. Them presenting signs of respiratory infections were examined and their nasopharyngeal aspirate specimen was collected, in a total of 255 analyses. Soon after, specific multiplex trial of reverse transcription, followed by the polymerase chain reaction (multiplex RT-PCR), was accomplished for identification of the 8 viruses related to respiratory infections. Results and Conclusions: The average incidence of URTI was 2.33 episodes per child-year. URTI was observed throughout the year of study, especially in the fall and winter, lowering during spring and presenting few cases in summer. Rhinovirus presented the greatest incidence, being observed throughout the period of study. Influenza B, respiratory syncytial virus (RSV), and metapneumovirus presented lower incidence, especially during fall and winter. URTI caused by other analyzed viruses - influenza A, parainfluenza 1, 2, and 3 were rare. The evaluation of the mathematical model through simulations has provided promising results, as it was possible to get true data reproduction. The model is promising. Having its suppositions adequate, it might be useful for understanding the dynamics and spreading of diseases, planning and evaluating prevention and immunization strategies in epidemics.
As infecções respiratórias agudas, em especial as infecções das vias aeríferas superiores (IVAS), são as causas mais freqüentes de morbidade infantil no mundo. As creches são ambientes fechados, onde há grande circulação de pessoas e também de agentes infecciosos, sendo então favoráveis à disseminação de infecções respiratórias virais. Os modelos epidemiológicos matemáticos são métodos de análise quantitativos e podem ser usados para compreensão e predição da dinâmica de transmissão de uma doença infecciosa. Objetivo: Verificar a incidência mensal de IVAS, de 8 vírus respiratórios, e simular um modelo matemático, avaliando seu comportamento qualitativo e quantitativo em relação aos dados reais de IVAS nas crianças da Escola de Educação Infantil em período integral. Casuística e Método: Todas as crianças (173) que freqüentaram a escola no período de julho de 2003 a julho de 2004 foram acompanhadas por 1,6 a 12 meses. Elas apresentaram sinais de IVAS foram examinadas e tiveram coletado espécime de aspirado de nasofaringe, perfazendo um total de 255 análises. Em seguida, foi realizado ensaio específico multiplex de transcrição reversa seguida da reação em cadeia de polimerase (multiplex RT-PCR) para identificação dos 8 vírus relacionados às IVAS. Resultados e Conclusões: A incidência média de IVAS foi de 2,33 episódios por criança-ano. As IVAS incidiram durante todo o período do estudo, principalmente no outono e inverno, decaindo na primavera e com poucos casos no verão. O rinovírus teve maior incidência tendo sido observado em todos os períodos em que ocorreram episódios de IVAS. Influenza B, vírus sincicial respiratório (VSR) e metapneumovírus ocorreram com menor incidência, principalmente no outono e inverno. IVAS causadas pelos outros vírus analisados influenza A, parainfluenza 1, 2 e 3 foram raras. A avaliação do modelo matemático, por meio de simulações, forneceu resultados animadores, visto que se conseguiu a reprodução dos dados reais. O modelo é promissor. Com a adequação das suas suposições, pode ser útil para a compreensão das dinâmicas de disseminação de doenças, planejamento e avaliação de estratégias de prevenção e de imunização em epidemias.

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Olofsson, Nils. "Kidney Dynamic Model Enrichment". Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242315.

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This thesis explores and explains a method using discrete curvature as a feature to find regions of vertices that can be classified as being likely to indicate the presence of an underlying tumor on a kidney surface mesh. Vertices are tagged based on curvature type and mathematical morphology is used to form regions on the mesh. The size and location of the tumor is approximated by fitting a sphere to this region. The method is intended to be employed in noninvasive radiotherapy with a dynamic soft tissue model. It could also provide an alternative to volumetric methods used to segment tumors. A validation is made using the images from which the kidney mesh was constructed, the tumor is visible as a comparison to the method result. The dynamic kidney model is validated using the Hausdorff distance and it is explained how this can be computed in an effective way using bounding volume hierarchies. Both the tumor finding method and the dynamic model show promising results since they lie within the limit used by practitioners during therapy.

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42

Fraga, Keith Jeffrey. "Explorations into protein structure with the knob-socket model". Scholarly Commons, 2016. https://scholarlycommons.pacific.edu/uop_etds/264.

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Protein sequences contain the information in order for a protein to fold to a unique compact, three-dimensional native structure. The forces that drive protein structures to form compact folds are largely dominated by burial of hydrophobic amino acids, which results in non-specific packing of amino acid side-chains. The knob-socket model attempts to organize side-chain packing into tetrahedral packing motifs. This tetrahedral motif is characterized with a three residues on the same secondary structure forming the base of the tetrahedron packing with a side-chain from a separate secondary structure. The base of the motif is termed the socket, and the other side-chain is called the knob. Here, we focus on extending the knob-socket model to understand tertiary and quaternary structure. First, single knobs sometimes pack into more than one socket in real structures. We focus on understanding the topology and amino acid preferences of these tertiary packing surfaces. The main results from the study of tertiary packing surfaces is that they have a preferred handedness, some interactions are ancillary to the packing interaction, there are specific amino preferences for specific positions in packing surfaces, and there is no relationship between side-chain rotamer of the knob packing into the tertiary packing surface. Next, we examine the application of the knob-socket to irregular and mixed packing in protein structure. The main conclusions from these efforts show canonical packing modes between secondary structures and highlight the important of coil secondary structure in providing many of the knobs for packing. Third, we investigate protein quaternary structure with a clique analysis of side-chain interactions. We identify a possible pseudo knob-socket interaction, and compare knob-socket interactions between tertiary and quaternary structure. Lastly, we discuss the workflow used in CASP12 to predict side-chain contacts and atomic coordinates of proteins.

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43

Zhou, Zhu. "Exploring the effects of 5-HT2A and AMPA receptors on brain 5-HT via a mechanism-based pharmacodynamic model". Scholarly Commons, 2014. https://scholarlycommons.pacific.edu/uop_etds/143.

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Depression is a common mood disorder. Although major ethical challenges make it nearly impossible to invasively and directly measure serotonin (5-hydroxytryptamine, 5-HT) levels in human brains, neuroimaging technologies have shown macroscopic structural and functional abnormalities in the prefrontal cortex (PFC) of depressed patients. The monoamine hypothesis of depression is based on the neurotransmitter imbalance, such as deceased serotonin brain levels are implicated in the cause of depression. Research has focused on the control mechanisms involved in the dorsal raphé nucleus (DRN) which is the serotonergic control center located in the midbrain. We hypothesized that activation 5-HT 2A receptor in PFC would increase serotonin levels by an AMPA-dependent mechanism in both DRN and PFC. Enhancement of the 5-HT in DRN may inhibit 5-HT level in PFC by 5-HT 1A receptor. This becomes the full feedback loop system. While 5-HT levels in the PFC have been well studied, pathway that modulate this DRN pool through upstream cascade interactions leading to a downstream feedback loop have been difficult to elucidate. Developing a mechanism-based pharmacokinetics (PK) and pharmacodynamics (PD) model to quantitatively describe the effect of 5-HT 2A receptors regulation to serotonin in the DRN and PFC would help us to better understand the complex brain. 5-HT 2A receptor agonist and AMPA receptor agonist and antagonist were used to activate or block the related receptor. Male Wistar rats underwent neurosurgery for implantation of microdialysis (MD) probes. Three to five rats were randomly assigned to experimental arms. Using the MD method, the drug combination was examined to explore the drug effect on time course of 5-HT release in DRN and PFC. Based on the experiment results, a mechanism-based PD model was developed. Phoenix WinNonlin ® and Berkeley Madonna™ were used for model estimation, external validation with secondary data set, and simulation. The result supports the possibility of a 5-HT 2A /AMPA feedback control circuit that originates in the PFC and modulates DRN and PFC 5-HT levels through feedback coupling of 5-HT. The time-course profiles of 5-HT in both DRN and PFC was well modeled and model parameters were estimated with good precision (CV% ranged from 1.37% to 35.03%). The mechanism model was developed to characterize and better understand the neurotransmitter mechanisms, providing estimations of various parameters of the disease related receptor system.

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44

Zhao, Xiaoning. "Synthesis and applications of functional magnetic polymer beads; synthesis and mass spectrometry analysis of model peptides". Scholarly Commons, 2012. https://scholarlycommons.pacific.edu/uop_etds/156.

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The first part of the thesis describes the synthesis and application of functional magnetic polymer beads. The traditional suspension polymerization approach was used to synthesize polystyrene-iron oxide (Fe 3 O 4 ) based magnetic beads. The beads were coupled to different surface functional groups. The Fe 3 O 4 particles were encapsulated into a polystyrene shell. The surface functional groups were generated by graft-polymerization with functional monomers. The average size of the beads was in the range of 100-500 μm. Chemical tests showed that the beads were stable in strong acid, strong base and polar solvent. The beads had a fast response to an external magnetic field. A self-emulsion-polymerization approach was developed to synthesize smaller magnetic beads with the - OH groups on the surface. A modified approach based on traditional suspension-polymerization was developed to synthesize acid-durable beads with more Fe 3 O 4 encapsulated inside the beads. A novel emulsion-suspension polymerization method was successfully developed to synthesize much smaller magnetic beads ( A new peptide synthesis approach was developed using functional magnetic beads as the resin for solid phase synthesis. In this application, synthesized magnetic beads were further modified by a two-step reaction. The amino group was anchored onto the surface of these beads, followed by coupling with the Rink amide linker. The resulting beads were used as the resin to synthesize several model peptides. The peptides were successfully synthesized, and the sequences were confirmed by mass spectrometry analysis. The yields of the peptides were comparable to those obtained from commercial Rink amide resin. The second part of the thesis describes the synthesis and mass spectrometry analysis of two series of model peptides. One series has the linear (non-cyclic) structure, A n K, KA n , P n K, and AcA n K. The other series contains cyclic peptides, c-Ac-DAKAK and c-Ac-DADapAK. All peptides were synthesized using solid phase peptide synthesis. The relative proton affinities of the model peptides were measured using the collision induced dissociation experiments using a triple quadrupole mass spectrometer. It was found that the effective proton affinity of a cyclic peptide was significantly reduced compared to a linear analogue. The reduced proton affinity implies an increased lipophilicity of the peptide.

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45

Dong, Jin. "First-principle based pharmacokinetic modeling". Scholarly Commons, 2016. https://scholarlycommons.pacific.edu/uop_etds/128.

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Predicting drug concentrations in the blood and at the site of action is the hottest topic in pharmacokinetics (PK). In vitro-in vivo extrapolation (IVIVE) and physiological based pharmacokinetics (PBPK) models are two major PK prediction strategies. However, both IVIVE and PBPK models are considered as immature methodologies due to their poor predictability. The goal of the research is to investigate the discrepancies within IVIVE and PBPK predictions according to first-principles: convection, diffusion, metabolism, and carrier-mediated transport. In Chapter 2, non-permeability limited hepatic elimination under perfusion steady state is examined. The well-stirred model is re-derived from the convection-dispersion-elimination equation when both dispersion and concentration gradient are ignored and re-named as the zero-gradient model. Pang and Rowland’s lidocaine data are re-analyzed. Their data analysis was based on an unfair comparison of the zero-gradient and parallel- tube models at two different efficiency number ranges. The interference of sensitivity greatly biased the comparison. I also show that both theoretical discussions and experimental results indicate that apparent intrinsic clearance and intrinsic clearance could be affected by blood flow and protein binding. In Chapter 3, I discuss permeability limited hepatic elimination under perfusion steady state. Permeability limited elimination is classified to diffusion dominated, carrier-mediated transport mediated, and mixed effects based on drug passage mechanisms. Each of these three drug passage classes is sub-divided to sink condition and finite volume condition based on the boundary conditions of drug passage. In Chapter 4, the discrepancies within IVIVE for both non-permeability limited and permeability limited drugs are explored. The deficiencies in assay design and data analysis of common in vitro metabolism assays are investigated. The scaling/converting equations for both non-permeability limited and permeability limited drugs are derived. In Chapter 5, I focus on transient PK models. Numerical analysis using finite difference and finite volume methods are introduced into the derivation and discussion of transient PBPK models. In addition, the use of partition coefficient in the non-eliminating tissue/organ models is discussed.

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Chada, Kinnera. "COMPUTATIONAL ANALYSES OF THE UPTAKE AND DISTRIBUTION OF CARBON MONOXIDE (CO) IN HUMAN SUBJECTS". UKnowledge, 2011. http://uknowledge.uky.edu/gradschool_diss/224.

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Carbon monoxide (CO) is an odorless, colorless, tasteless gas that binds to hemoglobin with high affinity. This property underlies the use of low doses of CO to determine hemoglobin mass (MHb) in the fields of clinical and sports medicine. However, hemoglobin bound to CO is unable to transport oxygen and exposure to high CO concentrations is a significant environmental and occupational health concern. These contrasting aspects of CO—clinically useful in low doses but potentially lethal in higher doses—mandates a need for a quantitative understanding of the temporal profiles of the uptake and distribution of CO in the human body. In this dissertation I have (i) used a mathematical model to analyze CO-rebreathing techniques used to estimate total hemoglobin mass and proposed a CO-rebreathing procedure to estimate hemoglobin mass with low errors, (ii) enhanced and validated a multicompartment model to estimate O2, CO and CO2 tensions, bicarbonate levels, pH levels, blood carboxyhemoglobin (HbCO) levels, and carboxymyoglobin (MbCO) levels in all the vascular (arterial, mixed venous and vascular subcompartments of the tissues) and tissue (brain, heart and skeletal muscle) compartments of the model in normoxia, hypoxia, CO hypoxia, hyperoxia, isocapnic hyperoxia and hyperbaric oxygen, and (iii) used this developed mathematical model to propose a treatment to improve O2 delivery and CO removal by comparing O2 and CO levels during different treatment protocols administered for otherwise-healthy CO-poisoned subjects.

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47

Zhang, Changfeng. "Investigation of the endoplsmic reticulum calcium stores for their potential roles in neuroprotection using the NG115-401L neuronal cell line model". Scholarly Commons, 2014. https://scholarlycommons.pacific.edu/uop_etds/142.

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There is significant interest in the field of neuroscience to gain a better understanding of how neurons die in neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. We have used the neuronal cell line NG115-401L with unique calcium signaling characteristics to test the hypothesis that improving calcium loading into the endoplasmic reticulum (ER) to increase ER calcium levels acts as a possible neuroprotective response. We approached this problem using both pharmacological and genetic approaches targeting the central mediator of calcium uptake in the ER localized sarco/endoplasmic reticulum Ca 2+ ATPase (SERCA) enzyme. The pharmacological studies involved use of the ginger root compound 6-gingerol, which to date is the best documented agent for activating SERCA enzymes in heart and skeletal muscle. However, in our experiments, gingerol did not appear to activate NG115-401L SERCA pumps; indeed, the compound produced a response more like that of a SERCA inhibitor inducing a rapid ER calcium depletion. In addition, gingerol stimulated robust calcium influx responses, an unexpected result given the NG115-401L neural cell line is uniquely deficient in calcium influx pathways. Our genetic approach involved expressing the stromal interaction molecule 1 (STIM1) protein in the NG115-401L cell, which is also an ER localized protein that serves as a pivotal calcium influx channel regulator. NG115-40lL neurons present a native deficiency of STIM1 expression in a background phenotype with well characterized perturbations in ER calcium regulation and control of calcium influx pathways. Thus, STIM1 may be predicted to increase ER calcium levels, conferring protection against neuron cell death due to ER calcium store defects. STIM1 expression reconstituted the corrupted calcium influx pathway in NG115-401L neurons, which conferred neuroprotective responses to ER calcium perturbation, mitochondrial oxidative stress and subsequent cell death. Our results argue for unique and undiscovered regulatory effects of gingerol on the ER calcium circulation system, and suggest that the expression of STIM1 in these neurons protects against ER stress and oxidative stress via reconstruction of cellular calcium homeostasis.

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48

Laghetto, Beatriz Krabbe. "Um modelo matemático para estimar o risco de desenvolver câncer de pulmão por meio de sistemas fuzzy". Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/8040.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
The main aims of this work are to study the ways to model mathematically uncertainties using Fuzzy Sets Theory, and then propose a mathematical model to estimate the risk of an individual developing lung cancer through a system based rule fuzzy. Therefore, we consider risk factors, namely smoking, pollution, history of lung disease, family history and contact with chemical agents such as system input variables fuzzy. Lung cancer is a disease that has no symptoms in its early stages, making the diagnosis more difficult to be done and, therefore, most discovered when the cancer is already advanced. This type of cancer is highly lethal and frequent in the population, an increase of 2 % per year in its worldwide incidence.
Os principais objetivos desse trabalho são estudar as formas de modelar matematicamente certas incertezas por meio da Teoria dos Conjuntos Fuzzy e, em seguida, propor um modelo matemático para estimar o risco de um indivíduo desenvolver câncer de pulmão por meio um sistema baseado em regras fuzzy. Para isso, consideramos fatores de risco, tais como, tabagismo, poluição, histórico de doenças pulmonares, histórico familiar e contato com agentes químicos como variáveis de entrada do sistema fuzzy. O câncer de pulmão é uma doença que não apresenta sintomas em suas fases iniciais, tornando o diagnóstico mais difícil de ser feito e, por isso, a maioria descobre quando o câncer já está avançado. Esse tipo de câncer é altamente letal e frequente na população, apresentando aumento de 2% ao ano na sua incidência mundial.

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49

Pax, Benjamin M. "Prediction of Bronchopulmonary Dysplasia by a Priori and Longitudinal Risk Factors in Extremely Premature Infants". Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1522686042230784.

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Su, Dan. "Rational design, characterization and in vivo studies of antibody mimics against HER2". Scholarly Commons, 2015. https://scholarlycommons.pacific.edu/uop_etds/133.

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Human Epidermal Growth Factor Receptor 2 (HER2) is a cell surface receptor tyrosine kinase and plays a role in the signal pathways leading to cell proliferation and differentiation. Overexpression of HER2 is found in various cancers including breast, ovarian, gastric, colon, and non-small-cell lung cancers, which makes it an attractive target for cancer therapy. Specific antibodies, peptides and small molecules are developed by scientists to bind with HER2 as therapeutical agents, dimerization inhibitors and biological makers. Among these molecules, antibodies showed excellent binding affinity and specificity toward HER2. However, uses of antibodies are limited by their high cost of production, long development time, limited ability to penetrate tumor tissue and immunogenicity. Many of these limitations are due to the high molecular weight of antibodies. Compared to antibodies, peptides and small molecule that selectively recognize HER2 have advantages in solubility, permeability and immunogenicity. So far, the design of all peptides and small molecules for binding with HER2 either utilize phage display technique or rely on computational screen of large library of millions of small molecules. These approaches all suffer from the drawbacks of tedious, labor intensive, and time consuming as well as uncertainty of outcome. In this study, it was hypothesized that a novel approach based on molecular interactions of HER2-Pertuzumab complex and Knob-Socket model can be developed to design antibody mimics for targeting HER2. All designed antibody mimics were simulated and docked with HER2 using Molecular Operating Environment (MOE) software to estimate binding energy and analyze the detail interaction map. A series of mimics were then synthesized and characterized. HER2 positive breast cancer cells MDA-MB-361 and ZR-75-1 were used in confocal microscopic and flow cytometric studies to evaluate the binding specificity of all antibody mimics to HER2 in vitro, while human embryonic kidney cell (HEK293) was used as control. After incubation with antibody mimics, high fluorescence intensities were observed on MDA-MB-361 and ZR-75-1 cells, while only background fluorescence were observed on HEK293 cells. Surface plasma resonance (SPR) studies showed that all antibody mimics bind to HER2 protein with KD value in range of 55.4 nM- 525.5 nM. Western blot technique was used to evaluate inhibition capability of antibody mimics on phosphorylation of HER2 downstream signaling Akt and MAPK pathways that were crucial for cell differentiation and survival. When treated with antibody mimics at 10µM for 24 h, more than 85% phosphorylation of Akt pathway was inhibited while phosphorylation of MAPK pathway was not affected. This finding proved that antibody mimics could bind to HER2 extracellular domain and selectively inhibit the dimerization between HER2 and HER3 to block phosphorylation of Akt pathway in a similar way as Pertuzumab. In addition, in vivo studies on tumor bearing nude mice were carried out to investigate the distribution and binding specificity of antibody mimics towards HER2 positive tumor after injecting through vein tail. Signal intensity ratio (SIR) of tumor to muscle revealed about 10-fold increase in tumor retention of HER2-PEP11 compared to the Cy7.5 carboxylic acid and Cy7.5-HER2-PEP22, which confirmed excellent in vivo binding specificity of antibody mimic HER2-PEP11 to HER2 positive tumor. In conclusion, this study demonstrated that a rational design of antibody mimics with both binding specificity and affinity towards HER2 based on the molecular interaction between Pertuzumab and HER2 and Knob-Socket model is feasible.

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