基于光环模式和深度学习分析的核酸目标的简单和多重检测
Juhee Lee1, Taegu Lee2, Ha Neul Lee1,3
1Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
ACS applied materials & interfaces
|November 16, 2023
概括
我们开发了一种基于异热光环的新型辐射流量试验 (iFluor-RFA),用于快速,多重核酸检测. 这种方法使用深度学习来分析光模式,以识别敏感的病原体.
科学领域:
- 生物技术是生物技术.
- 分子诊断学 分子诊断
- 生物分析化学 生物分析化学
背景情况:
- 简单的核酸诊断测试对于快速检测病原体至关重要.
- 现有的方法可能缺乏灵敏度,多重复合能力,或需要复杂的仪器仪表.
研究的目的:
- 为多重核酸检测开发一种新的基于膜的测定方法.
- 将异热放大与光模式可视化集成为增强诊断.
- 应用深度学习来自动分析和定量测试结果.
主要方法:
- 开发了基于同热光环的辐射流量试验 (iFluor-RFA).
- 使用纤维素酸盐膜用于辐射色谱流和溶剂蒸发.
- 采用异热放大和光寡核酸探针,用于在单反应中实现目标杂交.
- 应用深度学习算法来分析光图像以进行模式分类和量化.
主要成果:
- 已证明在亚皮科莫尔范围内的核酸标的特定和敏感检测.
- 实现了SARS-CoV-2受体结合域 (RBD) 和依赖RNA的RNA聚合酶 (RdRp) 基因的多重检测.
- 在使用深度学习分析对正/负结果进行分类时表现出高准确度.
- 从光模式成功预测了目标核酸的定量量.
结论:
- iFluor-RFA方法为核酸检测提供了一种敏感,特定和多重的方法.
- 与深度学习的整合为自动诊断数据分析提供了强大的工具.
- 这种新的平台在快速感染性病原体检测和诊断开发中具有多功能应用.
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