基于聚合物的电子舌头用于乳腺癌歧视:从人工尿样到临床尿样
Parastoo Vahdatiyekta1, Nicodemus Kraufvelin1, Lucie Legrand1
1Chemistry and Chemical Technology, Faculty of Science and Engineering, Åbo Akademi University, 20500 Turku, Finland.
Analytical chemistry
|February 2, 2026
概括
这项研究引入了一种电子舌头,用于使用尿液代谢学来检测乳腺癌. 该设备在临床样本中识别癌症时达到97%的准确性,提供了一个有前途的非侵入性查工具.
科学领域:
- 生物医学工程 生物医学工程
- 分析化学 分析化学
- 在瘤学瘤学.
背景情况:
- 早期发现乳腺癌至关重要,但由于当前查方法的局限性而受到挑战,包括密集乳腺组织的敏感性降低和高错误阳性率.
- 尿液的非侵入性代谢分析为癌症检测提供了一个有希望的补充方法.
- 目前乳腺癌查的局限性需要新的,准确的,非侵入性的诊断工具.
研究的目的:
- 开发和评估一个电压电子舌头,通过尿路代谢概况来检测乳腺癌.
- 用人工和临床尿样来评估电子舌头的性能.
- 为了比较不同机器学习算法对癌症分类和基于电化学指纹的对照组的有效性.
主要方法:
- 一个12个传感器的电压电子舌头,修改与合聚合物,被用来产生电化学指纹从尿液样本.
- 处理了微分脉冲电量计信号,并使用主要组件分析 (PCA) 减少了维度.
- 模式识别和分类使用局部最小方程差异分析 (PLS-DA) 和各种监督学习模型进行,包括梯度增强.
主要成果:
- 电子舌头成功地将尿路代谢学差异转化为明显的电化学指纹.
- 在人造尿液上,主要成分分析 (PCA) 显示出明显的分离,梯度增强达到96%的准确性.
- 在临床尿样中,虽然PCA分离不那么明显,但部分最小平方区分分析 (PLS-DA) 和监督模型,特别是渐变增强,改善了区分,产生了97%的准确性.
结论:
- 开发的电子舌头有效地捕获临床相关的尿液信号,用于乳腺癌的检测.
- 监督机器学习方法在从人工到现实世界的临床样本过渡时显著提高了诊断准确性.
- 这种非侵入性电子舌头技术显示出作为早期乳腺癌检测的补充查工具的潜力.
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