通过分数框架对肿瘤-免疫相互作用动态进行稳健研究,促进公共健康

Meshal Shutaywi, Zahir Shah, Rashid Jan
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摘要

很明显,肿瘤是一种危险的组织肿块,它是通过细胞不受控制的分裂形成的,用异常组织取代健康组织。人体免疫系统由相互关联的特殊细胞、组织和器官组成,用于保护人体免受微生物、外来疾病、感染和毒素的侵害。因此,对肿瘤-免疫细胞交叉的概念和理解很有价值。本文通过数学框架阐述了肿瘤-免疫细胞相互作用的自然过程。然后通过涉及非局部和非奇异核的分数微积分算子来表示肿瘤-免疫相互作用的复杂动态。为研究拟议系统,介绍了非整数导数的定义和基本性质。此外,还介绍了一种新的动力学数值方案,显示了肿瘤免疫系统的混沌和振荡。在不同输入因素的影响下,所提出的肿瘤-免疫相互作用动力学得到了强调。我们的研究结果不仅有助于深入理解输入参数与肿瘤动力学之间复杂的相互作用,还找出了对动力学有重大影响的关键因素。这些结果对于完善和优化所提出的系统,以提高其预测准确性和肿瘤行为建模的有效性至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A robust study of the dynamics of tumor–immune interaction for public health via fractional framework

It is evident that a tumor is a dangerous lump of tissue developed through the uncontrollable division of cells, replacing healthy tissue with abnormal tissue. It is cancerous and spreads through the lymphatic system or blood within the body of a host individual while the human immune system, consisting of interrelated special cells, tissues, and organs, is employed for the protection of the body from microorganisms, foreign diseases, infections, and toxins. Thus, the conceptualization and understanding of the intersections of tumor–immune cells are valuable. In this article, the natural process of tumor–immune cell interactions is formulated through a mathematical framework. The intricate dynamics of tumor–immune interactions are then represented by means of operators of fractional calculus involving nonlocal and nonsingular kernels. The definitions and basic properties of non-integer derivatives are introduced for the investigation of the proposed system. In addition, a new numerical scheme is introduced for the dynamics, showing the chaos and oscillation of the tumor–immune system. The proposed dynamics of tumor–immune interaction are highlighted with the effect of different input factors. Our findings not only contribute to a thorough comprehension of the complex interactions between input parameters and tumor dynamics, but critical factors that have a major impact on the dynamics are also identified. These outcomes are pivotal for refining and optimizing the proposed system to enhance its predictive accuracy and efficacy in modeling tumor behavior.

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