Facile and Label-Free Detection of Lung Cancer Biomarker in Urine by Magnetically Assisted Surface-Enhanced Raman Scattering

Citations Over TimeTop 10% of 2014 papers

Abstract

Adenosine plays a crucial role in the regulation of physiological activity in various tissues and organs. As adenosine is a possible biomarker for cancer, the determination of its level presents a demanding task for deeply monitoring progress of diseases. Through the synthesis of Fe3O4/Au/Ag nanocomposites weaved and stabilized by phytic acid and its salt, we develop a magnetically assisted surface-enhanced Raman scattering (SERS) protocol to determine trace level adenosine in urine samples from both lung cancer patients and health human. The magnetic properties of the nanocomposites enable to realize the simple separation of targeted molecules from a complex matrix and the Au/Ag nanoparticles moieties act as the SERS platform. This label-free Fe3O4/Au/Ag-nanocomposites-based SERS protocol shows a good stability, reproducibility, time efficiency (less than 20 min for one sample test), and huge sensitivity down to 1 × 10(-10) M. The protocol also has high selectivity because SERS signal of adenosine provides the molecular fingerprint information as well as an azo coupling pretreatment is performed to remove the interference of urea. Furthermore, a SERS array is designed for on-site screening adenosine in urine samples in a massive way using a portable Raman. Such a magnetically assisted SERS method as a powerful alternative can be expected as a smart and promising tool for effective assessment of healthcare.

Related Papers

• Handbook of Raman Spectroscopy: From the Research Laboratory to the Process Line(2001)
• → Defects in Individual Semiconducting Single Wall Carbon Nanotubes: Raman Spectroscopic and in Situ Raman Spectroelectrochemical Study(2010)103 cited
• → Swept-source Raman spectroscopy of chemical and biological materials(2024)9 cited
• → The effect of aqueous solution in Raman spectroscopy(2009)3 cited
• → Advanced Raman techniques for real time cancer diagnostics(2016)1 cited