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The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Acoustic separation of circulating tumor cells

Open access
Li, Peng, Mao, Zhangming, Peng, Zhangli, Zhou, Lanlan, Chen, Yuchao, Huang, Po-Hsun, Truica, Cristina I., Drabick, Joseph J., El-Deiry, Wafik S., Dao, Ming, Suresh, Subra, Huang, Tony Jun·2015·Proceedings of the National Academy of Sciences

Abstract

Significance: The separation and analysis of circulating tumor cells (CTCs) provides physicians a minimally invasive way to monitor the response of cancer patients to various treatments. Among the existing cell-separation methods, acoustic-based approaches provide significant potential to preserve the phenotypic and genotypic characteristics of sorted cells, owing to their safe, label-free, and contactless nature. In this work, we report the development of an acoustic-based device that successfully demonstrates the isolation of rare CTCs from the clinical blood samples of cancer patients. Our work thus provides a unique means to obtain viable and undamaged CTCs, which can subsequently be cultured. The results presented here offer unique pathways for better cancer diagnosis, prognosis, therapy monitoring, and metastasis research.

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Metadata

Authors
Li, P., Mao, Z., Peng, Z., Zhou, L., Chen, Y., Huang, P.-H., Truica, C. I., Drabick, J. J., El-Deiry, W. S., Dao, M., Suresh, S., & Huang, T. J.
Venue
Proceedings of the National Academy of Sciences
Published
2015-04-06
Volume
112
Issue
16
Pages
4970-4975
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2066400670
Cited by
782
References
4
FWCI
42.78
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Acoustic separation of circulating tumor cells. https://app.cochat.ai/papers/10.1073/pnas.1504484112
The paper
Li, P., Mao, Z., Peng, Z., Zhou, L., Chen, Y., Huang, P.-H., Truica, C. I., Drabick, J. J., El-Deiry, W. S., Dao, M., Suresh, S., & Huang, T. J. (2015). Acoustic separation of circulating tumor cells. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1504484112

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.