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Toward point-of-care assessment of patient response: a portable tool for rapidly assessing cancer drug efficacy using multifrequency impedance cytometry and supervised machine learning
Microsystems & Nanoengineering ( IF 7.9 ) Pub Date : 2019-07-15 , DOI: 10.1038/s41378-019-0073-2
Karan Ahuja 1 , Gulam M Rather 2 , Zhongtian Lin 1 , Jianye Sui 1 , Pengfei Xie 1 , Tuan Le 1 , Joseph R Bertino 2 , Mehdi Javanmard 1
Affiliation  

We present a novel method to rapidly assess drug efficacy in targeted cancer therapy, where antineoplastic agents are conjugated to antibodies targeting surface markers on tumor cells. We have fabricated and characterized a device capable of rapidly assessing tumor cell sensitivity to drugs using multifrequency impedance spectroscopy in combination with supervised machine learning for enhanced classification accuracy. Currently commercially available devices for the automated analysis of cell viability are based on staining, which fundamentally limits the subsequent characterization of these cells as well as downstream molecular analysis. Our approach requires as little as 20 μL of volume and avoids staining allowing for further downstream molecular analysis. To the best of our knowledge, this manuscript presents the first comprehensive attempt to using high-dimensional data and supervised machine learning, particularly phase change spectra obtained from multi-frequency impedance cytometry as features for the support vector machine classifier, to assess viability of cells without staining or labelling.



中文翻译:

对患者反应进行现场护理评估:使用多频阻抗细胞术和监督机器学习快速评估癌症药物疗效的便携式工具

我们提出了一种快速评估靶向癌症治疗中药物疗效的新方法,其中抗肿瘤药物与靶向肿瘤细胞表面标记的抗体结合。我们制造并表征了一种装置,能够使用多频阻抗谱与监督机器学习相结合来快速评估肿瘤细胞对药物的敏感性,以提高分类准确性。目前用于细胞活力自动分析的市售设备基于染色,这从根本上限制了这些细胞的后续表征以及下游分子分析。我们的方法只需要 20 μL 的体积,并且避免染色,从而可以进行进一步的下游分子分析。据我们所知,本手稿首次全面尝试使用高维数据和监督机器学习,特别是从多频阻抗细胞仪获得的相变谱作为支持向量机分类器的特征,以评估细胞的活力无需染色或标记。

更新日期:2019-11-18
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