scholarly journals Personalized Virus Load Curves of SARS-CoV-2 Infection

2021 ◽  
Author(s):  
Thomas Hillen ◽  
Carlos Contreras ◽  
Jay M. Newby
Keyword(s):  
Influenza A ◽  
Patient Specific ◽  
Model Parameters ◽  
Physiological Effects ◽  
Specific Level ◽  
Macaque Monkeys ◽  
Virus Load ◽  
Load Function ◽  
Load Analysis ◽  
Intuitive Model

AbstractWe introduce an explicit function that describes virus-load curves on a patient-specific level. This function is based on simple and intuitive model parameters. It allows virus load analysis without solving a full virus load dynamic model. We validate our model on data from influenza A as well as SARS-CoV-2 infection data for Macaque monkeys and humans. Further, we compare the virus load function to an established target model of virus dynamics, which shows an excellent fit. Our virus-load function offers a new way to analyse patient virus load data, and it can be used as input to higher level models for the physiological effects of a virus infection, for models of tissue damage, and to estimate patient risks.

scholarly journals Personalized Virus Load Curves of SARS-CoV-2 Infection

2021 ◽  
Author(s):  
Thomas Hillen ◽  
Carlos Contreras ◽  
Jay M. Newby
Keyword(s):  
Influenza A ◽  
Patient Specific ◽  
Model Parameters ◽  
Physiological Effects ◽  
Specific Level ◽  
Macaque Monkeys ◽  
Virus Load ◽  
Load Function ◽  
Load Analysis ◽  
Intuitive Model

AbstractWe introduce an explicit function that describes virus-load curves on a patient-specific level. This function is based on simple and intuitive model parameters. It allows virus load analysis without solving a full virus load dynamic model. We validate our model on data from influenza A as well as SARS-CoV-2 infection data for Macaque monkeys and humans. Further, we compare the virus load function to an established target model of virus dynamics, which shows an excellent fit. Our virus-load function offers a new way to analyse patient virus load data, and it can be used as input to higher level models for the physiological effects of a virus infection, for models of tissue damage, and to estimate patient risks.

scholarly journals Personalized Virus Load Curves for Acute Viral Infections

Viruses ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1815
Author(s):  
Carlos Contreras ◽  
Jay M. Newby ◽  
Thomas Hillen
Keyword(s):  
Influenza A ◽  
Viral Infections ◽  
Patient Specific ◽  
Model Parameters ◽  
Specific Level ◽  
Large Variability ◽  
Virus Load ◽  
Target Model ◽  
Load Function ◽  
Specific Virus

We introduce an explicit function that describes virus-load curves on a patient-specific level. This function is based on simple and intuitive model parameters. It allows virus load analysis of acute viral infections without solving a full virus load dynamic model. We validate our model on data from mice influenza A, human rhinovirus data, human influenza A data, and monkey and human SARS-CoV-2 data. We find wide distributions for the model parameters, reflecting large variability in the disease outcomes between individuals. Further, we compare the virus load function to an established target model of virus dynamics, and we provide a new way to estimate the exponential growth rates of the corresponding infection phases. The virus load function, the target model, and the exponential approximations show excellent fits for the data considered. Our virus-load function offers a new way to analyze patient-specific virus load data, and it can be used as input for higher level models for the physiological effects of a virus infection, for models of tissue damage, and to estimate patient risks.

scholarly journals A Standard Virus Load Function

2020 ◽  
Author(s):  
Thomas Hillen
Keyword(s):  
Virus Infection ◽  
Dynamic Model ◽  
Tissue Damage ◽  
Influenza A ◽  
Treatment Strategies ◽  
Virus Dynamics ◽  
Physiological Effects ◽  
Macaque Monkeys ◽  
Virus Load ◽  
Load Function

AbstractThe idea is to design a simple function that can describe typical virus-load curves without solving a full virus load dynamic model. We present such a standard virus load function and validate it on data from influenza A as well as SARS-CoV-2 infection data for Macaque monkeys and humans. Further, we compare the virus load function to an established target model of virus dynamics as presented by A. Smith in [1]. This virus-load function can be used as input to higher level models for the physiological effects of a virus infection, for models for tissue damage, and to develop treatment strategies.

scholarly journals A flexible framework for sequential estimation of model parameters in computational hemodynamics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher J. Arthurs ◽  
Nan Xiao ◽  
Philippe Moireau ◽  
Tobias Schaeffter ◽  
C. Alberto Figueroa
Keyword(s):  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Sequential Estimation ◽  
Wall Material ◽  
Patient Specific ◽  
Model Parameters ◽  
Computational Framework ◽  
Lumped Parameter ◽  
Estimation Of Model Parameters

AbstractA major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.

scholarly journals A nonlinear model for the characterization and optimization of athletic training and performance

2017 ◽  
Vol 9 (1) ◽  
pp. 82-93 ◽  
Author(s):  
James D. Turner ◽  
Michael J. Mazzoleni ◽  
Jared A. Little ◽  
Dane Sequeira ◽  
Brian P. Mann
Keyword(s):  
Athletic Training ◽  
Nonlinear Model ◽  
Linear Models ◽  
Performance Data ◽  
Step Response ◽  
Performance Goals ◽  
Model Parameters ◽  
Physiological Effects ◽  
And Performance ◽  
Training Protocols

Summary Study aim: Mathematical models of the relationship between training and performance facilitate the design of training protocols to achieve performance goals. However, current linear models do not account for nonlinear physiological effects such as saturation and over-training. This severely limits their practical applicability, especially for optimizing training strategies. This study describes, analyzes, and applies a new nonlinear model to account for these physiological effects. Material and methods: This study considers the equilibria and step response of the nonlinear differential equation model to show its characteristics and trends, optimizes training protocols using genetic algorithms to maximize performance by applying the model under various realistic constraints, and presents a case study fitting the model to human performance data. Results: The nonlinear model captures the saturation and over-training effects; produces realistic training protocols with training progression, a high-intensity phase, and a taper; and closely fits the experimental performance data. Fitting the model parameters to subsets of the data identifies which parameters have the largest variability but reveals that the performance predictions are relatively consistent. Conclusions: These findings provide a new mathematical foundation for modeling and optimizing athletic training routines subject to an individual’s personal physiology, constraints, and performance goals.

Individualized Transfer Functions for the Noninvasive Estimation of Central Pressure From Brachial Pressure Readings

2009 ◽  
Author(s):  
Ashis Mookerjee ◽  
Ahmed M. Al-Jumaily ◽  
Andrew Lowe
Keyword(s):  
Blood Pressure ◽  
Transfer Function ◽  
Brachial Artery ◽  
Transfer Functions ◽  
Patient Characteristics ◽  
Patient Specific ◽  
Model Parameters ◽  
Brachial Blood Pressure ◽  
Noninvasive Estimation ◽  
Artery Segment

A model-based investigation is carried out with the aim of developing an ab-initio methodology for the patient-specific estimation of central pressures from brachial blood pressure readings. The subclavian root-brachial artery segment is modeled as a 1-D tube with all model parameters linked to patient characteristics. A simulation is also run with typical physiological parameters, which gives a “first estimate” of the transfer function (TF). The TF derived using the patient characteristics is studied in detail to investigate the change in the arterial TF occurring with changes in patient characteristics. This TF is compared with the “first estimate” to evaluate the feasibility of using standard arterial properties.

scholarly journals Parameter subset reduction for patient-specific modelling of arrhythmogenic cardiomyopathy-related mutation carriers in the CircAdapt model

2020 ◽  
Vol 378 (2173) ◽  
pp. 20190347 ◽  
Author(s):  
Nick van Osta ◽  
Aurore Lyon ◽  
Feddo Kirkels ◽  
Tijmen Koopsen ◽  
Tim van Loon ◽  
...  
Keyword(s):  
Right Ventricular ◽  
Computational Models ◽  
Clinical Decision Making ◽  
Early Stage ◽  
Patient Specific ◽  
Model Parameters ◽  
Arrhythmogenic Cardiomyopathy ◽  
Tissue Properties ◽  
Myocardial Disease ◽  
Deformation Patterns

Arrhythmogenic cardiomyopathy (AC) is an inherited cardiac disease, clinically characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. Patient-specific computational models could help understand the disease progression and may help in clinical decision-making. We propose an inverse modelling approach using the CircAdapt model to estimate patient-specific regional abnormalities in tissue properties in AC subjects. However, the number of parameters ( n  = 110) and their complex interactions make personalized parameter estimation challenging. The goal of this study is to develop a framework for parameter reduction and estimation combining Morris screening, quasi-Monte Carlo (qMC) simulations and particle swarm optimization (PSO). This framework identifies the best subset of tissue properties based on clinical measurements allowing patient-specific identification of right ventricular tissue abnormalities. We applied this framework on 15 AC genotype-positive subjects with varying degrees of myocardial disease. Cohort studies have shown that atypical regional right ventricular (RV) deformation patterns reveal an early-stage AC disease. The CircAdapt model of cardiovascular mechanics and haemodynamics has already demonstrated its ability to capture typical deformation patterns of AC subjects. We, therefore, use clinically measured cardiac deformation patterns to estimate model parameters describing myocardial disease substrates underlying these AC-related RV deformation abnormalities. Morris screening reduced the subset to 48 parameters. qMC and PSO further reduced the subset to a final selection of 16 parameters, including regional tissue contractility, passive stiffness, activation delay and wall reference area. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

Fluid Tonicity Affects Peritoneal Characteristics Derived by 3-PORE Model

2019 ◽  
Vol 39 (3) ◽  
pp. 243-251 ◽  
Author(s):  
Joanna Stachowska-Pietka ◽  
Jan Poleszczuk ◽  
Josep Teixido-Planas ◽  
Josep Bonet-Sol ◽  
Maria I. Troya-Saborido ◽  
...  
Keyword(s):  
Model Fitting ◽  
Hydraulic Conductance ◽  
Membrane Properties ◽  
Patient Specific ◽  
Model Parameters ◽  
Peritoneal Membrane ◽  
Substantial Impact ◽  
Pore Model ◽  
Using Data ◽  
The Impact

Background It is typically assumed that within short time-frames, patient-specific peritoneal membrane characteristics are constant and do not depend on the initial fluid tonicity and dwell duration. The aim of this study was to check whether this assumption holds when membrane properties are estimated using the 3-pore model (3PM). Methods Thirty-two stable peritoneal dialysis (PD) patients underwent 3 8-hour peritoneal equilibration tests (PETs) with different glucose-based solutions (1.36%, 2.27%, and 3.86%). Temporary drainage was performed at 1 and 4 hours. Glucose, urea, creatinine, sodium, and phosphate concentrations were measured in dialysate and blood samples. Three-pore model parameters were estimated for each patient and each 8-hour PET separately. In addition, model parameters were estimated using data truncated to the initial 4 hours of peritoneal dwell. Results In all cases, model-estimated parameter values were within previously reported ranges. The peritoneal absorption (PA) and diffusive permeability for all solutes except sodium increased with fluid tonicity, with about 18% increase when switching from glucose 2.27% to 3.86%. Glucose peritoneal reflection coefficient and osmotic conductance (OsmCond), and fraction of hydraulic conductance for ultrasmall pores decreased with fluid tonicity (over 40% when switching from glucose 1.36%). Model fitting to the truncated 4-hour data resulted in little change in the parameters, except for PA, peritoneal hydraulic conductance, and OsmCond, for which higher values for the 4-hour dwell were found. Conclusion Initial fluid tonicity has a substantial impact on the 3PM-estimated characteristics of the peritoneal membrane, whereas the impact of dwell duration was relatively small and possibly influenced by the change in the patient's activity.

scholarly journals Human stem cell-based models for studying autism spectrum disorder-related neuronal dysfunction

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Arquimedes Cheffer ◽  
Lea Jessica Flitsch ◽  
Tamara Krutenko ◽  
Pascal Röderer ◽  
Liubov Sokhranyaeva ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Stem Cells ◽  
Stem Cell ◽  
Pluripotent Stem Cells ◽  
Disease Modeling ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Patient Specific ◽  
Neuronal Dysfunction ◽  
Specific Level

AbstractThe controlled differentiation of pluripotent stem cells (PSCs) into neurons and glia offers a unique opportunity to study early stages of human central nervous system development under controlled conditions in vitro. With the advent of cell reprogramming and the possibility to generate induced pluripotent stem cells (iPSCs) from any individual in a scalable manner, these studies can be extended to a disease- and patient-specific level. Autism spectrum disorder (ASD) is considered a neurodevelopmental disorder, with substantial evidence pointing to early alterations in neurogenesis and network formation as key pathogenic drivers. For that reason, ASD represents an ideal candidate for stem cell-based disease modeling. Here, we provide a concise review on recent advances in the field of human iPSC-based modeling of syndromic and non-syndromic forms of ASD, with a particular focus on studies addressing neuronal dysfunction and altered connectivity. We further discuss recent efforts to translate stem cell-based disease modeling to 3D via brain organoid and cell transplantation approaches, which enable the investigation of disease mechanisms in a tissue-like context. Finally, we describe advanced tools facilitating the assessment of altered neuronal function, comment on the relevance of iPSC-based models for the assessment of pharmaceutical therapies and outline potential future routes in stem cell-based ASD research.

scholarly journals Global Stability of Pneumococcal Pneumonia with Awareness and Saturated Treatment

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fulgensia Kamugisha Mbabazi ◽  
J. Y. T. Mugisha ◽  
Mark Kimathi
Keyword(s):  
Sensitivity Analysis ◽  
Antibiotic Resistance ◽  
Steady State ◽  
Global Stability ◽  
Influenza A ◽  
Pneumococcal Pneumonia ◽  
Effective Rate ◽  
Model Parameters ◽  
Saturated Treatment ◽  
Disease Free

Pneumocccal pneumonia, a secondary bacterial infection that follows influenza A infection, is responsible for morbidity and mortality in children, elderly, and immunocomprised groups. A mathematical model to study the global stability of pneumococcal pneumonia with awareness and saturated treatment is presented. The basic reproduction number, R0, is computed using the next generation matrix method. The results show that if R0<1, the disease-free steady state is locally asymptotically stable; thus, pneumococcal pneumonia would be eradicated in the population. On the other hand, if R0>1 the endemic steady state is globally attractive; thus, the disease would persist in the population. The quadratic-linear and Goh–Voltera Lyapunov functionals approach are used to prove the global stabilities of the disease-free and endemic steady states, respectively. The sensitivity analysis of R0 on model parameters shows that, it is positively sensitive to the maximal effective rate before antibiotic resistance awareness, rate of relapse encountered in administering treatment, and loss of information by aware susceptible individuals. Contrarily, the sensitivity analysis of R0 on model parameters is negatively sensitive to recovery rate due to treatment and the rate at which unaware susceptible individuals become aware. The numerical analysis of the model shows that awareness about antibiotic resistance and treatment plays a significant role in the control of pneumococcal pneumonia.

Sign in / Sign up

Export Citation Format

Share Document