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通过机器学习辅助/MXene生物电子阵列的嗅觉癌症.

Jiawang Hu1, Nanlin Hu2, Donglei Pan1

  • 1Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China; Key Laboratory of Industrial Biocatalysis, Ministry of Education, Tsinghua University, Beijing, 100084, China.

Biosensors & bioelectronics
|July 17, 2024
PubMed
概括

这项研究提出了一种新的生物传感器阵列,用于使用呼气分析进行非侵入性瘤检测. 机器学习增强了系统的功能.

关键词:
机器学习是机器学习.仿真生物传感器阵列模拟器对癌症进行非侵入性诊断.这是一种/MXene生物复合物.实时测试平台实时测试平台

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科学领域:

  • 生物医学工程 生物医学工程
  • 材料科学 材料科学 材料科学
  • 分析化学 分析化学

背景情况:

  • 非侵入性瘤检测对于早期诊断和改善患者结果至关重要.
  • 对挥发性有机化合物 (VOC) 的呼气分析是一个有希望的,非侵入性的诊断方法.
  • 开发用于复杂的呼吸气体混合物的敏感和特定传感器仍然存在挑战.

研究的目的:

  • 开发先进的生物传感器,以增强气体传感特性.
  • 创建一个模仿生物传感器阵列 (MBA),与实时测试平台 (RTP) 集成.
  • 利用机器学习 (ML) 算法来准确检测和识别出口气体信号,用于瘤诊断.

主要方法:

  • 制造具有特定气体结合能力的MXene自组装生物传感器.
  • 构建模拟生物传感器阵列 (MBA) 并集成到实时测试平台 (RTP).
  • 模式识别和机器学习算法的应用用于信号分析和疾病分类.

主要成果:

  • 与原始的MXene相比,Peptide-MXene生物传感器的气体感应性能显著提高 (响应高达150%以上).
  • 该MBA成功地使用模式识别识别在五个类别 (酒精,,化物, Ester,酸) 中识别了15种气味分子.
  • ML辅助的RTP在检测健康人群 (100%) 和患有肺癌 (94.1%),上消化道癌 (90%),下消化道癌 (95.2%) 的患者的呼吸样本方面取得了很高的准确性.

结论:

  • 通过使用-MXene生物传感器和ML开发了一种非侵入性瘤诊断的经济有效和精确模型.
  • 开发的平台通过呼吸分析证明了用于诊断其他疾病的多功能性,包括病和糖尿病.
  • 这项研究在通过呼气分析进行非侵入性疾病诊断方面取得了重大进展.