Department of Biomedical Engineering, College of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea.
Department of Neurosurgery, College of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea.
Biosens Bioelectron. 2018 Jul 15;111:59-65. doi: 10.1016/j.bios.2018.04.003. Epub 2018 Apr 4.
It is very difficult to predict some complications after subarachnoid hemorrhage (SAH), despite rapid advances in medical science. Herein, we introduce a label-free cellulose surface-enhanced Raman spectroscopy (SERS) biosensor chip with pH-functionalized, gold nanoparticle (AuNP)-enhanced localized surface plasmon resonance (LSPR) effects for identification of SAH-induced cerebral vasospasm and hydrocephalus caused by cerebrospinal fluid (CSF). The SERS biosensor chip was implemented by the synthesis reaction of the AuNPs, which were charged positively through pH level adjustment, onto a negatively-charged cellulose substrate with ξ = -30.7 mV. The zeta potential, nanostructural properties, nanocrystallinity, and computational calculation-based electric field distributions of the cellulose-originated AuNPs were optimized to maximize LSPR phenomena and then characterized. Additionally, the performance of the SERS biosensor was compared under two representative excitation laser sources in the visible region (532 nm) and near-infrared region (785 nm). The Raman activities of our SERS biosensor chip were evaluated by trace small molecules (crystal violet, 2 µL), and the biosensor achieved an enhancement factor of 3.29 × 10 for the analytic concept with an excellent reproducibility of 8.5% relative standard deviation and a detection limit of 0.74 pM. Furthermore, the experimental results revealed that the five proposed SERS-based biomarkers could provide important information for identifying and predicting SAH-induced cerebral vasospasm and hydrocephalus complications (91.1% reliability and 19.3% reproducibility). Therefore, this facile and effective principle of our SERS biosensor chip may inspire the basis and strategies for the development of sensing platforms to predict critical complications in various neurosurgical diagnoses.
尽管医学科学取得了快速进步,但仍很难预测蛛网膜下腔出血 (SAH) 后的一些并发症。在此,我们引入了一种无标记的纤维素表面增强拉曼光谱 (SERS) 生物传感器芯片,该芯片具有 pH 功能化、金纳米粒子 (AuNP) 增强的局域表面等离子体共振 (LSPR) 效应,用于识别 SAH 引起的脑血管痉挛和由脑脊液 (CSF) 引起的脑积水。SERS 生物传感器芯片通过 AuNP 的合成反应来实现,通过调整 pH 值使 AuNP 带正电荷,并与带负电荷的纤维素底物结合,ξ = -30.7 mV。优化了纤维素衍生的 AuNP 的 ζ 电位、纳米结构特性、纳米结晶度和基于计算的电场分布,以最大化 LSPR 现象,然后对其进行了表征。此外,在可见区域 (532nm) 和近红外区域 (785nm) 的两个代表性激发激光源下比较了 SERS 生物传感器的性能。通过痕量小分子(结晶紫,2 µL)评估了我们的 SERS 生物传感器芯片的拉曼活性,并且该生物传感器达到了 3.29×10 的分析概念的增强因子,具有 8.5%相对标准偏差的出色重现性和 0.74 pM 的检测限。此外,实验结果表明,提出的五种基于 SERS 的生物标志物可以提供用于识别和预测 SAH 引起的脑血管痉挛和脑积水并发症的重要信息(可靠性为 91.1%,重现性为 19.3%)。因此,我们的 SERS 生物传感器芯片的这种简单有效的原理可能为各种神经外科诊断中预测关键并发症的传感平台的发展提供基础和策略。