62,040 research outputs found
Comparing Strategies to Prevent Stroke and Ischemic Heart Disease in the Tunisian Population: Markov Modeling Approach Using a Comprehensive Sensitivity Analysis Algorithm.
Background. Mathematical models offer the potential to analyze and compare the effectiveness of very different interventions to prevent future cardiovascular disease. We developed a comprehensive Markov model to assess the impact of three interventions to reduce ischemic heart diseases (IHD) and stroke deaths: (i) improved medical treatments in acute phase, (ii) secondary prevention by increasing the uptake of statins, (iii) primary prevention using health promotion to reduce dietary salt consumption. Methods. We developed and validated a Markov model for the Tunisian population aged 35–94 years old over a 20-year time horizon. We compared the impact of specific treatments for stroke, lifestyle, and primary prevention on both IHD and stroke deaths. We then undertook extensive sensitivity analyses using both a probabilistic multivariate approach and simple linear regression (metamodeling). Results. The model forecast a dramatic mortality rise, with 111,134 IHD and stroke deaths (95% CI 106567 to 115048) predicted in 2025 in Tunisia. The salt reduction offered the potentially most powerful preventive intervention that might reduce IHD and stroke deaths by 27% (−30240 [−30580 to −29900]) compared with 1% for medical strategies and 3% for secondary prevention. The metamodeling highlighted that the initial development of a minor stroke substantially increased the subsequent probability of a fatal stroke or IHD death. Conclusions. The primary prevention of cardiovascular disease via a reduction in dietary salt consumption appeared much more effective than secondary or tertiary prevention approaches. Our simple but comprehensive model offers a potentially attractive methodological approach that might now be extended and replicated in other contexts and populations
Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks
Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model
Lattice and Continuum Modelling of a Bioactive Porous Tissue Scaffold
A contemporary procedure to grow artificial tissue is to seed cells onto a
porous biomaterial scaffold and culture it within a perfusion bioreactor to
facilitate the transport of nutrients to growing cells. Typical models of cell
growth for tissue engineering applications make use of spatially homogeneous or
spatially continuous equations to model cell growth, flow of culture medium,
nutrient transport, and their interactions. The network structure of the
physical porous scaffold is often incorporated through parameters in these
models, either phenomenologically or through techniques like mathematical
homogenization. We derive a model on a square grid lattice to demonstrate the
importance of explicitly modelling the network structure of the porous
scaffold, and compare results from this model with those from a modified
continuum model from the literature. We capture two-way coupling between cell
growth and fluid flow by allowing cells to block pores, and by allowing the
shear stress of the fluid to affect cell growth and death. We explore a range
of parameters for both models, and demonstrate quantitative and qualitative
differences between predictions from each of these approaches, including
spatial pattern formation and local oscillations in cell density present only
in the lattice model. These differences suggest that for some parameter
regimes, corresponding to specific cell types and scaffold geometries, the
lattice model gives qualitatively different model predictions than typical
continuum models. Our results inform model selection for bioactive porous
tissue scaffolds, aiding in the development of successful tissue engineering
experiments and eventually clinically successful technologies.Comment: 38 pages, 16 figures. This version includes a much-expanded
introduction, and a new section on nonlinear diffusion in addition to polish
throughou
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
Addressing current challenges in cancer immunotherapy with mathematical and computational modeling
The goal of cancer immunotherapy is to boost a patient's immune response to a
tumor. Yet, the design of an effective immunotherapy is complicated by various
factors, including a potentially immunosuppressive tumor microenvironment,
immune-modulating effects of conventional treatments, and therapy-related
toxicities. These complexities can be incorporated into mathematical and
computational models of cancer immunotherapy that can then be used to aid in
rational therapy design. In this review, we survey modeling approaches under
the umbrella of the major challenges facing immunotherapy development, which
encompass tumor classification, optimal treatment scheduling, and combination
therapy design. Although overlapping, each challenge has presented unique
opportunities for modelers to make contributions using analytical and numerical
analysis of model outcomes, as well as optimization algorithms. We discuss
several examples of models that have grown in complexity as more biological
information has become available, showcasing how model development is a dynamic
process interlinked with the rapid advances in tumor-immune biology. We
conclude the review with recommendations for modelers both with respect to
methodology and biological direction that might help keep modelers at the
forefront of cancer immunotherapy development.Comment: Accepted for publication in the Journal of the Royal Society
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