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Microbial Biosensors01:17

Microbial Biosensors

Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...

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A hybrid framework of statistical, machine learning, and explainable AI methods for school dropout prediction.

PloS one·2025
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Design and Analysis for Fall Detection System Simplification
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使用机器学习和可解释的人工智能设计优化高灵敏度PCF-SPR生物传感器.

Mst Rokeya Khatun1, Md Saiful Islam1

  • 1Institute of Information and Communication Technology (IICT), Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh.

PloS one
|September 15, 2025
PubMed
概括

这项研究介绍了一种新的光子晶体纤维表面等离子体共振 (PCF-SPR) 生物传感器,用于敏感的,无标签的检测. 机器学习和可解释的AI优化了设计,提高了医疗诊断和化学传感的效率.

科学领域:

  • 光子学和光学 在光子学和光学.
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 基于光子晶纤维的表面等离子体共振 (PCF-SPR) 生物传感器可以精确检测折射率变化.
  • 现有的传感器优化方法可能是计算密集且耗时的.

研究的目的:

  • 引入一种高灵敏,低损耗的PCF-SPR生物传感器,用于无标签的分析物检测.
  • 整合机器学习 (ML) 和可解释AI (XAI) 以加速传感器设计和优化.
  • 评估生物传感器的性能,用于医学诊断和化学传感.

主要方法:

  • 一个PCF-SPR生物传感器的设计和模拟.
  • 应用ML回归技术来预测光学特性 (有效指数,限制损失,振幅灵敏度).
  • 利用沙普利增材扩展 (SHAP) 识别关键设计参数.

主要成果:

  • 实现了高性能指标:波长灵敏度 (125,000 nm/RIU),振幅灵敏度 (-1422.34 RIU−1),分辨率 (8×10−7 RIU) 和FOM (2112.15).
  • 机器学习模型显示了关键光学属性的高预测精度.
  • SHAP分析确定波长,分析物RI,黄金厚度和度是关键的设计因素.

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结论:

  • 混合ML / XAI方法显著提高了传感器设计效率,并降低了计算成本.
  • 拟议的PCF-SPR生物传感器展示了用于癌细胞检测和化学传感等高精度应用的潜力.
  • 简单而有效的设计与人工智能驱动的优化相结合,为先进的生物传感提供了一个有希望的道路.