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相关概念视频

Tumor Progression02:07

Tumor Progression

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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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Metastasis02:30

Metastasis

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Metastasis is the spread of cancer cells from the original site to distant locations in the body. Cancer cells can spread via blood vessels (hematogenous) as well as lymph vessels in the body.
Epithelial-to-Mesenchymal Transition
The epithelial-to-mesenchymal transition or EMT is a developmental process commonly observed in wound healing, embryogenesis, and cancer metastasis. EMT is induced by transforming growth factor-beta (TGF-β) or receptor tyrosine kinase (RTK) ligands, which further...
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相关实验视频

Updated: May 15, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

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一个基于网络的模型,用于预测转移的空间进展.

Khimeer Singh1, Byron A Jacobs2

  • 1School of Computational and Applied Mathematics, University of the Witwatersrand, 1 Jan Smuts Avenue, Johannesburg, 2017, Gauteng, South Africa. khimeer.singh@gmail.com.

Bulletin of mathematical biology
|April 9, 2025
PubMed
概括

这项研究使用数学方程来模拟癌症转移,以预测二次部位. 该模型将血液流动和扩散与癌症扩散相关联,为治疗策略提供了洞察力.

关键词:
癌症生物学 癌症生物学计算建模计算建模数学瘤学数学瘤学网络建模 网络建模

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Last Updated: May 15, 2025

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科学领域:

  • 在瘤学瘤学.
  • 数学生物学 数学生物学
  • 计算医学是一种计算医学.

背景情况:

  • 转移性癌症的死亡率为90%,需要对其机制有更深入的了解.
  • 数学建模提供了一种定量方法来研究转移,并为治疗策略提供信息.

研究的目的:

  • 根据器官网络和血液流动,开发一个数学模型,预测二次转移部位.
  • 探索转移,血液流动动态和癌细胞扩散之间的关系.
  • 调查异型扩散对转移效率的影响.

主要方法:

  • 利用基于部分微分方程的数学模型.
  • 将模型嵌入到代表器官和血管系统的网络中.
  • 分析了各种癌症类型的模型预测和临床数据之间的相关性.

主要成果:

  • 模型预测与肠道和肝癌的临床数据有很好的相关性.
  • 在二级器官中的血液速度和癌细胞度之间观察到一个反向关系.
  • 异型扩散,以方向扩散性为特征,转移效率降低,与质瘤观察结果一致.

结论:

  • 开发的模型为模拟癌症进展和转移提供了一个有价值的框架.
  • 它澄清了血液流动和扩散对全球癌症传播的影响.
  • 为研究癌症转移和进展的临床从业者和研究人员提供见解.