下一代芯片上的肺癌:面向个性化治疗,人工智能和CRISPR驱动型号
Nanziba Sharmin Hossain1, Nishat Tasnim1, Jannatul Ferdoush2
1Department of Biological Sciences, Asian University for Women, Chittagong 4000, Bangladesh.
Drug discovery today
|January 16, 2026
概括
这次审查整合了肺癌芯片 (LCOC) 技术,包括机械应变,患者瘤,人工智能和CRISPR编辑. 统一的框架使得个性化肺癌转移预测和药物反应分析成为可能.
科学领域:
- 生物医学工程 生物医学工程
- 在瘤学瘤学.
- 基因组学就是基因组学.
背景情况:
- 肺癌在芯片 (LCOC) 技术正在进步,但通常研究的组件,如机械应变,患者衍生的瘤,多器官相互作用,AI分析,和CRISPR编辑在隔离.
- 现有的模型缺乏统一的方法来整合这些关键因素,以便全面的肺癌建模.
研究的目的:
- 通过整合新兴技术,为下一代肺癌芯片架构 (LCOC) 提供统一的框架.
- 展示这种综合方法如何能够实现肺癌进展,转移和药物反应的个性化预测.
主要方法:
- 在呼吸LCOC模型中嵌入患者衍生的肺瘤碎片到循环拉伸的微环境中.
- 将LCOC与下游器官区联系起来,根据生理力学机制绘制转移性路径.
- 利用持续的高分辨率成像来为人工智能输入管道,用于自动化药物反应预测和转移性轨迹模拟.
- 整合芯片上的CRISPR编辑,用于在动态,应变调节的微环境中调查转移驱动因素.
主要成果:
- 集成的LCOC框架使得在生理上相关的机械应变下,能够针对患者进行转移路径的特定映射.
- 高分辨率成像的AI驱动分析允许自动化药物反应预测和转移性轨迹模拟.
- 在芯片上进行CRISPR编辑,便于在动态微环境中对转移性驱动因素进行准确的调查.
结论:
- 通过合成LCOC,AI和CRISPR技术,可以开发下一代个性化的多器官芯片架构.
- 这种综合平台有可能预测个体肺癌疾病进展和治疗结果,而不会直接危及患者.
- 解决瘤脆弱性,成像领域转移和基因编辑交付等实际障碍对于临床翻译至关重要.
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