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机器学习增强的石墨烯晶体管生物传感:E型肝炎的定量平台验证和免疫测试.

Sofia Albesa1,2, Ezequiel Giménez1,2, Jose M Piccinini2

  • 1Institute of Theoretical and Applied Physical Chemistry Research (INIFTA), Department of Chemistry, Faculty of Exact Sciences, National University of La Plata (UNLP), CONICET. Street 64 and 113, La Plata 1900, Buenos Aires, Argentina.

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概括

与石墨烯场效应晶体管 (GFET) 集成的机器学习 (ML) 克服了传感器变异性,提供准确,无校准的分析. 这一进步提高了诸如囊性纤维化和肝炎E病毒感染等疾病的诊断准确度.

关键词:
乙型肝炎E病毒的感染.基于石墨烯的生物感应生物感应.免疫检测 免疫检测 免疫检测机器学习算法机器学习算法纳米体功能化的功能化

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科学领域:

  • 材料科学 材料科学 材料科学
  • 生物技术是生物技术.
  • 数据科学数据科学数据科学

背景情况:

  • 基于石墨烯的芯片表现出来自制造缺陷和污染的传感器变异性,阻碍了可靠的医疗保健诊断.
  • 现有的分析方法通常需要校准,并与各种分析物进行斗争.

研究的目的:

  • 开发一个与石墨烯场效应晶体管 (GFET) 集成的机器学习 (ML) 模型,用于定量,无校准的传感.
  • 提高基于石墨烯的传感器的分析可靠性和诊断能力.

主要方法:

  • 利用随机森林回归和现场效应指标来构建GFET的ML模型.
  • 通过使用pH传感作为参考来验证ML增强平台.
  • 应用ML集成的GFET用于化物检测和使用拉玛纳米体检测乙型肝炎病毒 (HEV) 抗原.

主要成果:

  • 实现了显著的准确性提高 (93%至97%) 和降低了pH传感变化系数 (14%至3%).
  • 证明了HEV抗原检测的免疫测试灵敏度-特异性从89-69%提高到100-100%.
  • 能够在没有预处理的情况下在毛细血管血液样本中定量预测HEV抗原度.

结论:

  • 与GFET集成的ML提供了一个强大的解决方案,用于无校准的定量传感.
  • 开发的平台显示了准确的临床诊断的巨大潜力,包括囊性纤维化和病毒感染.
  • 这种方法提高了GFET的性能,使复杂样本中生物标志物的敏感和特定检测成为可能.