Optics and Photonics Group, Faculty of Engineering, University of Nottingham, Nottingham, UK.
Institute for Advanced Manufacturing, Faculty of Engineering, University of Nottingham, Nottingham, UK.
Sci Rep. 2023 Sep 27;13(1):16228. doi: 10.1038/s41598-023-42793-9.
There is a consensus about the strong correlation between the elasticity of cells and tissue and their normal, dysplastic, and cancerous states. However, developments in cell mechanics have not seen significant progress in clinical applications. In this work, we explore the possibility of using phonon acoustics for this purpose. We used phonon microscopy to obtain a measure of the elastic properties between cancerous and normal breast cells. Utilising the raw time-resolved phonon-derived data (300 k individual inputs), we employed a deep learning technique to differentiate between MDA-MB-231 and MCF10a cell lines. We achieved a 93% accuracy using a single phonon measurement in a volume of approximately 2.5 μm. We also investigated means for classification based on a physical model that suggest the presence of unidentified mechanical markers. We have successfully created a compact sensor design as a proof of principle, demonstrating its compatibility for use with needles and endoscopes, opening up exciting possibilities for future applications.
人们普遍认为细胞和组织的弹性与其正常、异常和癌变状态密切相关。然而，细胞力学的发展在临床应用中并没有取得显著的进展。在这项工作中，我们探索了利用声子声学来实现这一目标的可能性。我们使用声子显微镜来测量乳腺癌细胞中癌变和正常细胞之间的弹性特性。利用原始的时间分辨声子衍生数据（300k 个单独的输入），我们采用深度学习技术来区分 MDA-MB-231 和 MCF10a 细胞系。我们使用大约 2.5μm3 的单个声子测量实现了 93%的准确率。我们还研究了基于物理模型的分类方法，这些方法表明存在未被识别的机械标志物。我们已经成功地创建了一个紧凑的传感器设计作为原理验证，证明了它与针和内窥镜兼容的可能性，为未来的应用开辟了令人兴奋的可能性。
