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

Abstract

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 1.22E−03 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.

Particle Classification through the Analysis of the Forward Scattered Signal in Optical Tweezers

“…It is proposed an alternative pre-processing strategy based on the Fourier transform of the acquired signal and subsequent principal component analysis, a methodology with a significantly lower computational load when compared with other previously reported feature extraction methods used in this topic of research [19]. The results suggest the possibility of correctly identifying the particle size and type with an accuracy around 90% with a signal of 500 milliseconds of duration which, to the best of our knowledge, is significantly smaller compared with previous time-based approaches [19,21].…”

The pro‐proliferative effect of insulin in human breast epithelial DMBA‐transformed and non‐transformed cell lines is PI3K‐, mTOR‐ and GLUT1‐dependent

“…The iLoF platform is a label‐free, real‐time and low‐cost technology that agnostically analyses nanostructures in complex fluids through a highly focused laser beam, guided by a micro‐lensed optical fibre, to obtain nanostructures' back‐scattered signal signature 19‐21 . This signal captures the specific combination of the Brownian motion of particles present in the sample under the influence of a harmonic electromagnetic potential generated by the electromagnetic field propagated by the polymeric micro‐lensed fibre tip 19,20,27 .…”

“…The iLoF platform is a label-free, real-time and low-cost technology that agnostically analyses nanostructures in complex fluids through a highly focused laser beam, guided by a micro-lensed optical fibre, to obtain nanostructures' back-scattered signal signature. [19][20][21] This signal captures the specific combination of the Brownian motion of particles present in the sample under the influence of a harmonic electromagnetic potential generated by the electromagnetic field propagated by the polymeric micro-lensed fibre tip. 19,20,27 Through the application of artificial intelligence (AI) techniques to time and frequency-derived features extracted from the back-scattered signal, relevant information about the optical and biophysical properties of the bioparticles under analysis (refractive index, optical polarizability, size, shape) can be gathered in 'optical fingerprints' characterizing biosamples.…”

Advances in Fiber‐Optic Technology for Point‐of‐Care Diagnosis and In Vivo Biosensing

“…Nine time-domain and 36 frequency-domain features of back-scattered signal from an OT were analyzed and a feature extraction technique was applied to project the information into a single feature to differentiate synthetic particles (poly-methyl-methacrylate, PMMA and polystyrene) from living yeast cells. [162] This was the first time when a back-scattered signal analysis was performed for an OT for microparticle differentiation. Such an analysis has substantial potential in healthcare for simultaneous trapping, detection and classification of microparticles.…”