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Publicações

2019

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

Autores
Paiva, JS; Jorge, PAS; Ribeiro, RSR; Sampaio, P; Rosa, CC; Cunha, JPS;

Publicação
International Journal of Nanomedicine

Abstract

2019

Optical fiber-based sensing method for nanoparticles detection through back-scattering signal analysis

Autores
Paiva, JS; Ribeiro, RSR; Jorge, PAS; Rosa, CC; Sampaio, P; Cunha, JPS;

Publicação
Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Abstract
In view of the growing importance of nanotechnologies, the detection of nanoparticles type in several contexts has been considered a relevant topic. Several organisms, including the National Institutes of Health, have been highlighting the urge of developing nanoparticles exposure risk assessment assays, since very little is known about their physiological responses. Although the identification/characterization of synthetically produced nanoparticles is considered a priority, there are many examples of "naturally" generated nanostructures that provide useful information about food components or human physiology. In fact, several nanoscale extracellular vesicles are present in physiological fluids with high potential as cancer biomarkers. However, scientists have struggled to find a simple and rapid method to accurately detect/identify nanoparticles, since their majority have diameters between 100-150 nm-far below the diffraction limit. Currently, there is a lack of instruments for nanoparticles detection and the few instrumentation that is commonly used is costly, bulky, complex and time consuming. Thus, considering our recent studies on particles identification through back-scattering, we examined if the time/frequency-domain features of the back-scattered signal provided from a 100 nm polystyrene nanoparticles suspension are able to detect their presence only by dipping a polymeric lensed optical fiber in the solution. This novel technique allowed the detection of synthetic nanoparticles in distilled water versus "blank solutions" (only distilled water) through Multivariate Statistics and Artificial Intelligence (AI)-based techniques. While the state-of-The-Art methods do not offer affordable and simple approaches for nanoparticles detection, our technique can contribute for the development of a device with innovative characteristics. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

2019

A Novel Method for Scatterers Type Enumeration in Polydisperse Suspensions through Fiber Trapping and Unsupervised Scattering Analysis

Autores
Paiva, JS; Ribeiro, RSR; Jorge, PAS; Rosa, CC; Sampaio, P; Cunha, JPS;

Publicação
IMAGING, MANIPULATION, AND ANALYSIS OF BIOMOLECULES, CELLS, AND TISSUES XVII

Abstract
Colloids and suspensions are part of our daily routines. Even the blood is considered a "naturally" occurring colloid. However, the majority of colloids are complex and composed by a diversity of nano to microparticles. The characterization of both synthetic and physiological fluids in terms of particulate types, size and surface characteristics plays a vital role in products formulation, and in the early diagnosis through the identification of abnormal scatterers in physiological fluids, respectively. Several methods have been proposed for characterizing suspensions, including imaging, electrical sensing counters, hydrodynamic or field flow fractionation. However, the Dynamic Light Scattering (DLS) has evolved as the most convenient method from these. Based also on the scattering signal, we propose a novel, simple and fast method able to determine the number of different scatterers type present in a suspension, without any previous information about its composition (in terms of particle classes). This is achieved by collecting features from a 980 nm laser back-scattered signal acquired through a polymeric lensed optical fiber tip dipped into the solution. Unlike DLS, this technique allows the trapping of particles whose diameter >= 1 mu m. For smaller particles, despite not guaranteeing their immobilization, it is also able to determine the number of different nanoparticles classes in an ensemble. The number of particle types was correctly determined for suspensions of synthetic particles and yeasts; different bacteria; and 100 nm nanoparticles types, using both Principal Component Analysis and K-means algorithms. This method could be a valuable alternative to complex and time-consuming methods for particles separation, such as field flow fractionation.

2018

Optical Fiber Tips for Biological Applications: from Light Confinement, Biosensing to Bioparticles Manipulation

Autores
Paiva, JS; Jorge, PAS; Rosa, CC; Cunha, JPS;

Publicação
Biochimica et Biophysica Acta (BBA) - General Subjects

Abstract

2018

Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach

Autores
Paiva, JS; Cardoso, J; Pereira, T;

Publicação
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS

Abstract
Objective: The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. Materials and methods: The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39 pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). Results and discussion: SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917 +/- 0.0024 and a F-Measure of 0.9925 +/- 0.0019, in comparison with ANN, which reached the values of 0.9847 +/- 0.0032 and 0.9852 +/- 0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. Conclusion: The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW.