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用于监测头颈部癌症患者吞咽活动的石墨烯上的金属纳米岛。

Metallic Nanoislands on Graphene for Monitoring Swallowing Activity in Head and Neck Cancer Patients.

机构信息

Department of NanoEngineering , University of California, San Diego , 9500 Gilman Drive, Mail Code 0448 , La Jolla , California 92093-0448 , United States.

Department of Computer Science and Engineering , University of California, San Diego , 9500 Gilman Drive, Mail Code 0404 , La Jolla , California 92093-0404 , United States.

出版信息

ACS Nano. 2018 Jun 26;12(6):5913-5922. doi: 10.1021/acsnano.8b02133. Epub 2018 Jun 8.

Abstract

There is a need to monitor patients with cancer of the head and neck postradiation therapy, as diminished swallowing activity can result in disuse atrophy and fibrosis of the swallowing muscles. This paper describes a flexible strain sensor comprising palladium nanoislands on single-layer graphene. These piezoresistive sensors were tested on 14 disease-free head and neck cancer patients with various levels of swallowing function: from nondysphagic to severely dysphagic. The patch-like devices detected differences in (1) the consistencies of food boluses when swallowed and (2) dysphagic and nondysphagic swallows. When surface electromyography (sEMG) is obtained simultaneously with strain data, it is also possible to differentiate swallowing vs nonswallowing events. The plots of resistance vs time are correlated to specific events recorded by video X-ray fluoroscopy. Finally, we developed a machine-learning algorithm to automate the identification of bolus type being swallowed by a healthy subject (86.4%. accuracy). The algorithm was also able to discriminate between swallows of the same bolus from either the healthy subject or a dysphagic patient (94.7% accuracy). Taken together, these results may lead to noninvasive and home-based systems for monitoring of swallowing function and improved quality of life.

摘要

需要对接受过放射治疗的头颈部癌症患者进行监测,因为吞咽活动减少会导致吞咽肌肉的废用性萎缩和纤维化。本文介绍了一种由单层石墨烯上的钯纳米岛组成的柔性应变传感器。这些压阻式传感器在 14 名无吞咽困难和各种吞咽功能障碍的头颈部癌症患者身上进行了测试:从无吞咽困难到严重吞咽困难。这些片状设备检测到吞咽时食物团块的(1)一致性以及(2)吞咽和非吞咽的差异。当同时获得表面肌电图 (sEMG) 和应变数据时,也可以区分吞咽和非吞咽事件。电阻随时间的变化曲线与视频 X 射线荧光检查记录的特定事件相关。最后,我们开发了一种机器学习算法来自动识别健康受试者吞咽的食团类型(准确率为 86.4%)。该算法还能够区分健康受试者或吞咽困难患者吞咽相同食团的情况(准确率为 94.7%)。总之,这些结果可能会导致用于监测吞咽功能和提高生活质量的非侵入性和家庭为基础的系统。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3425/6286678/f24be6527672/nihms-990649-f0002.jpg

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