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Register a new userGeneralized calculus in radiobiology: Physical implications
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Non-extensive statistical physics has allowed to generalize mathematical functions such as exponential and logarithms. The same framework is used to generalize sum and product so that the operations allow a more fluid way to work with mathematical expressions emerging from non-additive formulation of statistical physics. In this work we employ the generalization of the exponential, logarithm and product to obtain a formula for the survival fraction corresponding to the application of several radiation doses on a living tissue. Also we provide experimental recommendations to determine the universal characteristics of living tissues in interaction with radiation. These results have a potential application in radiobiology and radiation oncology.
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The comprehension of tumor growth is a intriguing subject for scientists. New researches has been constantly required to better understand the complexity of this phenomenon. In this paper, we pursue a physical description that account for some experimental facts involving avascular tumor growth. We have proposed an explanation of some phenomenological (macroscopic) aspects of tumor, as the spatial form and the way it growths, from a individual-level (microscopic) formulation. The model proposed here is based on a simple principle: competitive interaction between the cells dependent on their mutual distances. As a result, we reproduce many empirical evidences observed in real tumors, as exponential growth in their early stages followed by a power law growth. The model also reproduces the fractal space distribution of tumor cells and the universal behavior presented in animals and tumor growth, conform reported by West, Guiot {it et. al.}cite{West2001,Guiot2003}. The results suggest that the universal similarity between tumor and animal growth comes from the fact that both are described by the same growth equation - the Bertalanffy-Richards model - even they does not necessarily share the same biophysical properties.
Complex spatiotemporal patterns of action potential duration have been shown to occur in many mammalian hearts due to a period-doubling bifurcation that develops with increasing frequency of stimulation. Here, through high-resolution optical mapping and numerical simulations, we quantify voltage length scales in canine ventricles via spatiotemporal correlation analysis as a function of stimulation frequency and during fibrillation. We show that i) length scales can vary from 40 to 20 cm during one to one responses, ii) a critical decay length for the onset of the period-doubling bifurcation is present and decreases to less than 3 cm before the transition to fibrillation occurs, iii) fibrillation is characterized by a decay length of about 1 cm. On this evidence, we provide a novel theoretical description of cardiac decay lengths introducing an experimental-based conduction velocity dispersion relation that fits the measured wavelengths with a fractional diffusion exponent of 1.5. We show that an accurate phenomenological mathematical model of the cardiac action potential, fine-tuned upon classical restitution protocols, can provide the correct decay lengths during periodic stimulations but that a domain size scaling via the fractional diffusion exponent of 1.5 is necessary to reproduce experimental fibrillation dynamics. Our study supports the need of generalized reaction-diffusion approaches in characterizing the multiscale features of action potential propagation in cardiac tissue. We propose such an approach as the underlying common basis of synchronization in excitable biological media.
Osteocytes and their cell processes reside in a large, interconnected network of voids pervading the mineralized bone matrix of most vertebrates. This osteocyte lacuno-canalicular network (OLCN) is believed to play important roles in mechanosensing, mineral homeostasis, and for the mechanical properties of bone. While the extracellular matrix structure of bone is extensively studied on ultrastructural and macroscopic scales, there is a lack of quantitative knowledge on how the cellular network is organized. Using a recently introduced imaging and quantification approach, we analyze the OLCN in different bone types from mouse and sheep that exhibit different degrees of structural organization not only of the cell network but also of the fibrous matrix deposited by the cells. We define a number of robust, quantitative measures that are derived from the theory of complex networks. These measures enable us to gain insights into how efficient the network is organized with regard to intercellular transport and communication. Our analysis shows that the cell network in regularly organized, slow-growing bone tissue from sheep is less connected, but more efficiently organized compared to irregular and fast-growing bone tissue from mice. On the level of statistical topological properties (edges per node, edge length and degree distribution), both network types are indistinguishable, highlighting that despite pronounced differences at the tissue level, the topological architecture of the osteocyte canalicular network at the subcellular level may be independent of species and bone type. Our results suggest a universal mechanism underlying the self-organization of individual cells into a large, interconnected network during bone formation and mineralization.
Severe Acute Respiratory Syndrome-CoronaVirus 2 (SARS-CoV2) caused the ongoing pandemic. This pandemic devastated the world by killing more than a million people, as of October 2020. It is imperative to understand the transmission dynamics of SARS-CoV2 so that novel and interdisciplinary prevention, diagnostic, and therapeutic techniques could be developed. In this work, we model and analyze the transmission of SARS-CoV2 through the human respiratory tract from a molecular communication perspective. We consider that virus diffusion occurs in the mucus layer so that the shape of the tract does not have a significant effect on the transmission. Hence, this model reduces the inherent complexity of the human respiratory system. We further provide the impulse response of SARS-CoV2-ACE2 receptor binding event to determine the proportion of the virus population reaching different regions of the respiratory tract. Our findings confirm the results in the experimental literature on higher mucus flow rate causing virus migration to the lower respiratory tract. These results are especially important to understand the effect of SARS-CoV2 on the different human populations at different ages who have different mucus flow rates and ACE2 receptor concentrations in the different regions of the respiratory tract.
Ion-induced cell killing has been reported to depend on the irradiation dose but also on the projectile parameters. In this paper we focus on two approaches developed and extensively used to predict cell survival in response to ion irradiation: the Local Effect Model and the Katz Model. These models are based on a track-structure description summarized in the concept of radial dose. This latter is sensitive to ion characteristics parameters and gives to both models the ability to predict some important radiobiological features for ion irradiations. Radial dose is however an average quantity, which does not include stochastic effects. These radiation-intrinsic effects are investigated by means of a Monte-Carlo simulation of dose deposition. We show that both models are not fully consistent with the nanometric and microscopic dose deposition statistics.
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