Optical fiber-based sensing method for nanoparticle detection through supervised back-scattering analysis: a potential contributor for biomedicine
Received 11 October 2018
Accepted for publication 10 January 2019
Published 2 April 2019 Volume 2019:14 Pages 2349—2369
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Cristina Weinberg
Peer reviewer comments 2
Editor who approved publication: Prof. Dr. Anderson Oliveira Lobo
Joana S Paiva,1–3 Pedro AS Jorge,1,2 Rita SR Ribeiro,1 Paula Sampaio,4 Carla C Rosa,1,2 João PS Cunha1,3
1INESC Technology and Science, Porto, Portugal; 2Physics and Astronomy Department, Faculty of Sciences, University of Porto, Porto, Portugal; 3Faculty of Engineering, University of Porto, Porto, Portugal; 4Institute for Molecular and Cell Biology, i3S – Institute for Innovation and Research in Health, Porto, Portugal
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 µg/mL (8.74E+10 particles/mL), and a performance of 90% for concentrations below this value and higher than 1.22E03 µg/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.
Keywords: optical fiber sensors, light scattering effects, nanoparticles detection, extracellular vesicles (EVs) detection, lipoproteins detection, virus detection, nanoparticles, Brownian motion, diffusive analysis
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