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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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Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology
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在精准医学研究中需要一个跨学科性框架.

Maya Sabatello1, Gregory Diggs-Yang2, Alicia Santiago3

  • 1Center for Precision Medicine and Genomics, Department of Medicine, New York, NY, USA; Division of Ethics, Department of Medical Humanities and Ethics, New York, NY, USA.

American journal of human genetics
|October 6, 2023
PubMed
概括

精准医学需要多样化的参与者来实现公平的健康. 考虑种族和种族之外的多个因素的交叉方法对于包容性研究和改善结果至关重要.

关键词:
(反) 不信任的人.社区参与 社区参与跨学科的跨学科性精准医学研究研究精准医学研究.翻译基因组学是翻译的基因组学.

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科学领域:

  • 生物医学研究生物医学研究
  • 健康 公平 卫生 公平
  • 精准医学是一门精准的医学.

背景情况:

  • 精准医学旨在通过增加代表性不足的群体的参与来提高普遍性和健康公平性.
  • 主要挑战包括不信任,代表性问题,翻译困难和社区参与不足.
  • 目前的努力往往只集中在单一的人口因素上,忽视交叉的身份.

研究的目的:

  • 倡导在精准医学研究中建立一个更细微,跨部门的框架.
  • 了解重叠的边缘化如何影响研究参与和研究成果的差距.
  • 为不同的社区提出准确医学带来的公平利益的策略.

主要方法:

  • 关于精准医学研究参与和公平的现有文献的审查.
  • 分析当前以人口为中心的方法的局限性.
  • 开发一个数据收集,分析和实施的跨部门框架.

主要成果:

  • 边缘化的重叠层 (例如种族,残疾,性别认同,社会经济地位) 显著影响研究参与.
  • 单维的人口统计标准不足以捕捉代表性不足的复杂性.
  • 需要采用跨部门的方法来增强队列多样性和研究响应能力.

结论:

  • 超越单一的人口因素对于推进精准医学研究至关重要.
  • 一个跨部门的框架促进了更具包容性和公平的研究实践.
  • 实施这样的框架可以帮助确保精准医学有利于多样化和异质化的社区.