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A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning
The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefiel...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9496380/ https://www.ncbi.nlm.nih.gov/pubmed/36140093 http://dx.doi.org/10.3390/bios12090710 |
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author | Arano-Martinez, Jose Alberto Martínez-González, Claudia Lizbeth Salazar, Ma Isabel Torres-Torres, Carlos |
author_facet | Arano-Martinez, Jose Alberto Martínez-González, Claudia Lizbeth Salazar, Ma Isabel Torres-Torres, Carlos |
author_sort | Arano-Martinez, Jose Alberto |
collection | PubMed |
description | The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells. |
format | Online Article Text |
id | pubmed-9496380 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94963802022-09-23 A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning Arano-Martinez, Jose Alberto Martínez-González, Claudia Lizbeth Salazar, Ma Isabel Torres-Torres, Carlos Biosensors (Basel) Review The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells. MDPI 2022-09-01 /pmc/articles/PMC9496380/ /pubmed/36140093 http://dx.doi.org/10.3390/bios12090710 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Arano-Martinez, Jose Alberto Martínez-González, Claudia Lizbeth Salazar, Ma Isabel Torres-Torres, Carlos A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning |
title | A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning |
title_full | A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning |
title_fullStr | A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning |
title_full_unstemmed | A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning |
title_short | A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning |
title_sort | framework for biosensors assisted by multiphoton effects and machine learning |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9496380/ https://www.ncbi.nlm.nih.gov/pubmed/36140093 http://dx.doi.org/10.3390/bios12090710 |
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