Accurate in vivo tumor detection using plasmonic-enhanced shifted-excitation Raman difference spectroscopy (SERDS).
Accurate in vivo tumor detection using plasmonic-enhanced shifted-excitation Raman difference spectroscopy (SERDS).
复制标题
使用等离子体增强移位激发拉曼差分光谱（SERDS）进行精确的体内肿瘤检测。
DOI：
10.7150/thno.53101
复制
发表时间：
2021
期刊：
影响因子：
12.4
通讯作者：
Vo-Dinh T
中科院分区：
文献类型：
--
作者：
Strobbia P;Cupil-Garcia V;Crawford BM;Fales AM;Pfefer TJ;Liu Y;Maiwald M;Sumpf B;Vo-Dinh T
对于大多数癌症患者来说，手术是主要的治疗方法。在这些情况下，准确地切除整个肿瘤而不伤害周围组织是至关重要的;然而，由于缺乏术中成像技术，外科医生依赖于视觉和物理检查来识别肿瘤。表面增强拉曼散射（Sers）技术是一种新兴的非侵入性光学技术，可用于术中肿瘤识别，具有高准确性和稳定性。但拉曼检测需要暗室才能工作，这与手术设置不一致。研究方法：本文中，我们使用Sers纳米探针结合移位激发拉曼差分光谱（SERDS）检测，以准确地检测异种移植小鼠模型中的肿瘤。结果：我们首次证明了在环境光条件下，在小鼠模型中使用SERDS进行体内肿瘤检测。我们比较了传统的拉曼检测与SERDS，表明我们的方法可以提高灵敏度和准确性，这项任务。结论：我们的研究结果表明，这种方法可以用来提高体内拉曼/Sers医学应用的准确性和鲁棒性，辅助这些技术的临床转化过程。
For the majority of cancer patients, surgery is the primary method of treatment. In these cases, accurately removing the entire tumor without harming surrounding tissue is critical; however, due to the lack of intraoperative imaging techniques, surgeons rely on visual and physical inspection to identify tumors. Surface-enhanced Raman scattering (SERS) is emerging as a non-invasive optical alternative for intraoperative tumor identification, with high accuracy and stability. However, Raman detection requires dark rooms to work, which is not consistent with surgical settings. Methods: Herein, we used SERS nanoprobes combined with shifted-excitation Raman difference spectroscopy (SERDS) detection, to accurately detect tumors in xenograft murine model. Results: We demonstrate for the first time the use of SERDS for in vivo tumor detection in a murine model under ambient light conditions. We compare traditional Raman detection with SERDS, showing that our method can improve sensitivity and accuracy for this task. Conclusion: Our results show that this method can be used to improve the accuracy and robustness of in vivo Raman/SERS biomedical application, aiding the process of clinical translation of these technologies.