Optical fiber-based sensing method for nanoparticle detection through supervised back-scattering analysis: a potential contributor for biomedicine

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Joana S. Paiva (Author) Other; Pedro A. S. Jorge (Author) FCUP; Rita S. R. Ribeiro (Author) Other; Paula Sampaio (Author) Other; Carla C. Rosa (Author) FCUP; João P. S. Cunha (Author) FEUP

Journal

Title: International Journal of Nanomedicine

Vol. 14

Pages: 2349-2369

ISSN: 1176-9114

Publisher: Dove Medical Press

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 11 Citations

Other information

Authenticus ID: P-00Q-D4J

DOI: 10.2147/ijn.s174358

Abstract (EN): Background: In view of the growing importance of nanotechnologies, the detection/identification of nanoparticles type has been considered of utmost importance. Although the characterization of synthetic/organic nanoparticles is currently considered a priority (eg, drug delivery devices, nanotextiles, theranostic nanoparticles), there are many examples of "naturally" generated nanostructures - for example, extracellular vesicles (EVs), lipoproteins, and virus - that provide useful information about human physiology or clinical conditions. For example, the detection of tumor-related exosomes, a specific type of EVs, in circulating fluids has been contributing to the diagnosis of cancer in an early stage. However, scientists have struggled to find a simple, fast, and low-cost method to accurately detect/identify these nanoparticles, since the majority of them have diameters between 100 and 150 nm, thus being far below the diffraction limit. Methods: This study investigated if, by projecting the information provided from short-term portions of the back-scattered laser light signal collected by a polymeric lensed optical fiber tip dipped into a solution of synthetic nanoparticles into a lower features dimensional space, a discriminant function is able to correctly detect the presence of 100 nm synthetic nanoparticles in distilled water, in different concentration values. Results and discussion: This technique ensured an optimal performance (100% accuracy) in detecting nanoparticles for a concentration above or equal to 3.89 mu g/mL (8.74E+10 particles/mL), and a performance of 90% for concentrations below this value and higher than 1.22E-03 mu g/mL (2.74E+07 particles/mL), values that are compatible with human plasmatic levels of tumor-derived and other types of EVs, as well as lipoproteins currently used as potential biomarkers of cardiovascular diseases. Conclusion: The proposed technique is able to detect synthetic nanoparticles whose dimensions are similar to EVs and other "clinically" relevant nanostructures, and in concentrations equivalent to the majority of cell-derived, platelet-derived EVs and lipoproteins physiological levels. This study can, therefore, provide valuable insights towards the future development of a device for EVs and other biological nanoparticles detection with innovative characteristics.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 21