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相关概念视频

Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Combination Therapies and Personalized Medicine02:50

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Targeted Cancer Therapies

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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
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Analysis of Combinatorial miRNA Treatments to Regulate Cell Cycle and Angiogenesis
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基于分数二氧化模糊Muirhead平均操作者的TOPSIS方法的抗癌治疗方法的分析

Abbas Qadir1, Saleem Abdullah1, Ariana Abdul Rahimzai2

  • 1Department of Mathematics, Abdul Wali Khan University, Mardan, KP, 23200, Pakistan.

Scientific reports
|August 27, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种使用分数二氧化模糊集 (FDFS) 和分析层次过程 (AHP) 选择最佳,负担得起的癌症治疗方法的新方法,改善了医疗决策.

关键词:
分析层次过程分数二氧化模糊集穆尔赫德表示操作员托普西斯方法

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Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
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科学领域:

  • 决策科学
  • 医疗信息学
  • 应用数学

背景情况:

  • 选择最佳的癌症治疗是复杂的,因为有很多因素和不确定性.
  • 现有的决策方法可能无法充分解决隐藏的权重信息的标准和专家.

研究的目的:

  • 开发一种新的方法来选择最合适和负担得起的癌症治疗方法.
  • 整合分数二氧化模糊集 (FDFS) 和分析层次过程 (AHP) 以增强医疗决策支持.

主要方法:

  • 引入了分数二定模糊集 (FDFS) 和Muirhead平均运算符来处理不确定性.
  • 使用分析层次流程 (AHP) 来确定标准和决策者的权重.
  • 为治疗评估提出并应用了分数二氨酸模糊TOPSIS (FDF-TOPSIS) 技术.

主要成果:

  • FDF-TOPSIS技术已成功应用于现实世界癌症治疗选择问题.
  • 与现有方法相比,拟议的方法显示出可靠性,准确性和可行性.

结论:

  • 开发的FDF-TOPSIS方法为癌症治疗选择提供了强大的框架.
  • 这种方法有可能显著改善瘤医疗专业人员的决策过程.