Mathematical Medicine and Biology-A Journal of the Ima最新文献

Excessive activation of the regulatory cytokine transforming growth factor $beta $ (TGF-$beta $) via contraction of airway smooth muscle (ASM) is associated with the development of asthma. In this study, we develop an ordinary differential equation model that describes the change in density of the key airway wall constituents, ASM and extracellular matrix (ECM), and their interplay with subcellular signalling pathways leading to the activation of TGF-$beta $. We identify bistable parameter regimes where there are two positive steady states, corresponding to either reduced or elevated TGF-$beta $ concentration, with the latter leading additionally to increased ASM and ECM density. We associate the former with a healthy homeostatic state and the latter with a diseased (asthmatic) state. We demonstrate that external stimuli, inducing TGF-$beta $ activation via ASM contraction (mimicking an asthmatic exacerbation), can perturb the system irreversibly from the healthy state to the diseased one. We show that the properties of the stimuli, such as their frequency or strength, and the clearance of surplus active TGF-$beta $, are important in determining the long-term dynamics and the development of disease. Finally, we demonstrate the utility of this model in investigating temporal responses to bronchial thermoplasty, a therapeutic intervention in which ASM is ablated by applying thermal energy to the airway wall. The model predicts the parameter-dependent threshold damage required to obtain irreversible reduction in ASM content, suggesting that certain asthma phenotypes are more likely to benefit from this intervention.

This paper focuses on tracing the connectivity of white matter fascicles in the brain. In particular, a generalized order algorithm based on mixture of non-central Wishart distribution model is proposed for this purpose. The proposed algorithm utilizes the generalization of integer order based approach with the mixture of non-central Wishart distribution model. Pseudo super anomalous behavior of water diffusion inside human brain is the prime motivation of the the present study. We have shown results on multiple synthetic simulations with fibers orientations in two and three directions in each voxel as well as experiments on real data. Synthetic simulations were performed with varying noise levels and diffusion weighting gradient i.e. $b-$values. The proposed model performed outstanding especially for distinguishing closely oriented fibers.

Inhibiting a signalling pathway concerns controlling the cellular processes of a cancer cell's viability, cell division and death. Assay protocols created to see if the molecular structures of the drugs being tested have the desired inhibition qualities often show great variability across experiments, and it is imperative to diminish the effects of such variability while inferences are drawn. In this paper, we propose the study of experimental data through the lenses of a mathematical model depicting the inhibition mechanism and the activation-inhibition dynamics. The method is exemplified through assay data obtained from an experimental study of the inhibition of the chemokine receptor 4 (CXCR4) and chemokine ligand 12 (CXCL12) signalling pathway of melanoma cells. The quantitative analysis is conducted as a two step process: (i) deriving theoretically from the model the cell viability as a function of time depending on several parameters; (ii) estimating the values of the parameters by using the experimental data. The cell viability is obtained as a function of concentration of the inhibitor and time, thus providing a comprehensive characterization of the potential therapeutic effect of the considered inhibitor, e.g. $IC_{50}$ can be computed for any time point.

Current understanding of arrhythmia mechanisms and design of anti-arrhythmic drug therapies hinges on the assumption that myocytes from the same region of a single heart have similar, if not identical, action potential waveforms and drug responses. On the contrary, recent experiments reveal significant heterogeneity in uncoupled healthy myocytes both from different hearts as well as from identical regions within a single heart. In this work, a methodology is developed for quantifying the individual electrophysiological properties of large numbers of uncoupled cardiomyocytes under ion channel block in terms of the parameters values of a conceptual fast-slow model of electrical excitability. The approach is applied to a population of nearly 500 rabbit ventricular myocytes for which action potential duration (APD) before and after the application of the drug nifedipine was experimentally measured (Lachaud et al., 2022, Cardiovasc. Res.). To this end, drug action is represented by a multiplicative factor to an effective ion conductance, a closed form asymptotic expression for APD is derived and inverted to determine model parameters as functions of APD and $varDelta $APD (drug-induced change in APD) for each myocyte. Two free protocol-related quantities are calibrated to experiment using an adaptive-domain procedure based on an original assumption of optimal excitability. The explicit APD expression and the resulting set of model parameter values allow (a) direct evaluation of conditions necessary to maintain fixed APD or $varDelta $APD, (b) predictions of the proportion of cells remaining excitable after drug application, (c) predictions of stimulus period dependency and (d) predictions of dose-response curves, the latter being in agreement with additional experimental data.

We present an individual-based model for the coevolutionary dynamics between CD8+ cytotoxic T lymphocytes (CTLs) and tumour cells. In this model, every cell is viewed as an individual agent whose phenotypic state is modelled by a discrete variable. For tumour cells, this variable represents a parameterization of the antigen expression profiles, while for CTLs it represents a parameterization of the target antigens of T-cell receptors (TCRs). We formally derive the deterministic continuum limit of this individual-based model, which comprises a non-local partial differential equation for the phenotype distribution of tumour cells coupled with an integro-differential equation for the phenotype distribution of CTLs. The biologically relevant homogeneous steady-state solutions of the continuum model equations are found. The linear-stability analysis of these steady-state solutions is then carried out in order to identify possible conditions on the model parameters that may lead to different outcomes of immune competition and to the emergence of patterns of phenotypic coevolution between tumour cells and CTLs. We report on computational results of the individual-based model, and show that there is a good agreement between them and analytical and numerical results of the continuum model. These results shed light on the way in which different parameters affect the coevolutionary dynamics between tumour cells and CTLs. Moreover, they support the idea that TCR-tumour antigen binding affinity may be a good intervention target for immunotherapy and offer a theoretical basis for the development of anti-cancer therapy aiming at engineering TCRs so as to shape their affinity for cancer targets.

The pandemic caused by SARS-CoV-2 is responsible for a terrible health devastation with profoundly harmful consequences for the economic, social and political activities of communities on a global scale. Extraordinary efforts have been made by the world scientific community, who, in solidarity, shared knowledge so that effective vaccines could be produced quickly. However, it is still important to study therapies that can reduce the risk, until group immunity is reached, which, globally, will take a time that is still difficult to predict. On the other hand, the immunity time guaranteed by already approved vaccines is still uncertain. The current study proposes a therapy whose foundation lies in the important role that innate immunity may have, by preventing the disease from progressing to the acute phase that may eventually lead to the patient's death. Our focus is on natural killer (NK) cells and their relevant role. NKs are considered the primary defence lymphocytes against virus-infected cells. They play a critical role in modulating the immune system. Preliminary studies in COVID-19 patients with severe disease suggest a reduction in the number and function of NK cells, resulting in decreased clearance of infected and activated cells and unchecked elevation of inflammation markers that damage tissue. SARS-CoV-2 infection distorts the immune response towards a highly inflammatory phenotype. Restoring the effector functions of NK cells has the potential to correct the delicate immune balance needed to effectively overcome SARS-CoV-2 infection.

In the mathematical epidemiology community, there is an increasing interest in shaping the complex interplay between human behaviour and disease spreading. We give a contribution in this direction by illustrating a method to derive behavioural change epidemic models from a stochastic particle description by the means of kinetic equations. We consider a susceptible-infected-removed-like model where contact rates depend on the behavioural patterns adopted across the population. The selection of the social behaviour happens during the interactions between individuals adopting alternative strategies and it is driven by an imitation game dynamics. Agents have a double microscopic state: a discrete label, which denotes the epidemiological compartment to which they belong, and the degree of flexibility of opinion, i.e. a measure of the personal attitude to change opinion and, hence, to switch between the alternative social contact patterns. We derive kinetic evolution equations for the distribution functions of the degree of flexibility of opinion of the individuals for each compartment, whence we obtain macroscopic equations for the densities and average flexibilities of opinion. After providing the basic properties of the macroscopic model, we numerically investigate it by focusing on the impact of the flexibility of opinion on the epidemic course and on the consequent behavioural responses.

Mal de Debarquement Syndrome (MdDS) is a puzzling central vestibular disorder characterized by a long-lasting perception of oscillatory postural instability that may occur after sea travels or flights. We have postulated that MdDS originates from the post-disembarking persistence of an adaptive internal oscillator consisting of a loop system, involving the right and left vestibular nuclei, and the Purkinje cells of the right and left flocculonodular cerebellar cortex, connected by GABAergic and glutamatergic fibers. We have formulated here a mathematical model of the vestibulo-cerebellar loop system and carried out a computational analysis based on a set of differential equations describing the interactions among the loop elements and containing Hill functions that model input-output firing rates relationships among neurons. The analysis indicates that the system acquires a spontaneous and permanent oscillatory behavior for a decrease of threshold and an increase of sensitivity in neuronal input-output responses. These results suggest a role for synaptic plasticity in MdDS pathophysiology, thus reinforcing our previous hypothesis that MdDS may be the result of excessive synaptic plasticity acting on the vestibulo-cerebellar network during its entraining to an oscillatory environment. Hence, our study points to neuroendocrine pathways that lead to increased synaptic response as possible new therapeutic targets for the clinical treatment of the disorder.

A traditional method of in vitro cell culture involves a monolayer of cells at the base of a petri dish filled with culture medium. While the primary role of the culture medium is to supply nutrients to the cells, drug or other solutes may be added, depending on the purpose of the experiment. Metabolism by cells of oxygen, nutrients and drug is typically governed by Michaelis-Menten (M-M) kinetics. In this paper, a mathematical model of solute transport with M-M kinetics is developed. Upon non-dimensionalization, the reaction/diffusion system is re-characterized in terms of Volterra integral equations, where a parameter $beta $, the ratio of the initial solute concentration to the M-M constant, proves important: $beta ll 1$ is relevant to drug metabolism for the liver, whereas $beta gg 1$ is more appropriate in the case of oxygen metabolism. Regular perturbation expansions for both cases are obtained. A small-time expansion and steady-state solution are also presented. All results are compared against the numerical solution of the Volterra integral equations, and excellent agreement is found. The utility of the model and analytical solutions are discussed in the context of assisting experimental researchers to better understand the environment within in vitro cell culture experiments.

In this article, we investigate the importance of demography and contact patterns in determining the spread of COVID-19 and to the effectiveness of social distancing policies. We investigate these questions proposing an augmented epidemiological model with an age-structured model, with the population divided into susceptible (S), exposed (E), asymptomatic infectious (A), hospitalized (H), symptomatic infectious (I) and recovered individuals (R), to simulate COVID-19 dissemination. The simulations were carried out using six combinations of four types of isolation policies (work restrictions, isolation of the elderly, community distancing and school closures) and four representative fictitious countries generated over alternative demographic transition stage patterns (aged developed, developed, developing and least developed countries). We concluded that the basic reproduction number depends on the age profile and the contact patterns. The aged developed country had the lowest basic reproduction number ($R0=1.74$) due to the low contact rate among individuals, followed by the least developed country ($R0=2.00$), the developing country ($R0=2.43$) and the developed country ($R0=2.64$). Because of these differences in the basic reproduction numbers, the same intervention policies had higher efficiencies in the aged and least developed countries. Of all intervention policies, the reduction in work contacts and community distancing were the ones that produced the highest decrease in the $R0$ value, prevalence, maximum hospitalization demand and fatality rate. The isolation of the elderly was more effective in the developed and aged developed countries. The school closure was the less effective intervention policy, though its effects were not negligible in the least developed and developing countries.