Mathematical Modeling and Growth Model Analysis for Preventing the Cancer Cell Development

Dimitrios Boucharas, Chryssa Anastasiadou, S. Karkabounas, E. Antonopoulou, G. Manis
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Abstract

Cancer, one of the leading causes of morbidity across the globe, accounts for more than ten million deaths in 2020. The tremendous effort employed by the scientific community improves the efficiency of chemotherapy treatments, while the work in preventing cancer is comparably limited. This study attempts to mathematically model the cancer cell growth. Cancer was chemically induced to Naval Medical Research Institute inbred mice utilizing a fully carcinogenic agent. Specific organic compounds from the polyamine and thiol families were mixed with the agent to observe if the former can cease or delay the oncogenesis incidence by neutralizing the carcinogenic agent. As a result, a series of records containing the tumor size and the corresponding examination date was accumulated. A plethora of complex mathematical functions was recruited to evaluate the constructed curve in terms of the best fit to the series of data points. The developed models were explored based on their ability to predict future values, while the importance of the model parameters was exploited in a devised classification problem. The results presented herein are encouraging and can potentially expand the scope of this research into other research areas such as the development of effective nutritional supplements able to inhibit carcinogenesis,
预防癌细胞发展的数学建模与生长模型分析
癌症是全球发病的主要原因之一,2020年造成1000多万人死亡。科学界付出的巨大努力提高了化疗的效率,而在预防癌症方面的工作却相对有限。这项研究试图建立癌细胞生长的数学模型。利用一种完全致癌的药剂,化学诱导海军医学研究所近交小鼠患癌。将多胺和硫醇家族的特定有机化合物与致癌物混合,观察前者是否能通过中和致癌物来停止或延缓肿瘤的发生。因此,积累了一系列包含肿瘤大小和相应检查日期的记录。采用了大量复杂的数学函数来评估所构建的曲线与一系列数据点的最佳拟合。开发的模型基于其预测未来值的能力进行探索,同时在设计的分类问题中利用模型参数的重要性。这里提出的结果是令人鼓舞的,并且有可能将这项研究的范围扩展到其他研究领域,例如开发能够抑制致癌的有效营养补充剂,
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