Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Arano-Martinez, Jose Alberto, Martínez-González, Claudia Lizbeth, Salazar, Ma Isabel, Torres-Torres, Carlos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784794254580645888
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
work_keys_str_mv AT aranomartinezjosealberto aframeworkforbiosensorsassistedbymultiphotoneffectsandmachinelearning
AT martinezgonzalezclaudializbeth aframeworkforbiosensorsassistedbymultiphotoneffectsandmachinelearning
AT salazarmaisabel aframeworkforbiosensorsassistedbymultiphotoneffectsandmachinelearning
AT torrestorrescarlos aframeworkforbiosensorsassistedbymultiphotoneffectsandmachinelearning
AT aranomartinezjosealberto frameworkforbiosensorsassistedbymultiphotoneffectsandmachinelearning
AT martinezgonzalezclaudializbeth frameworkforbiosensorsassistedbymultiphotoneffectsandmachinelearning
AT salazarmaisabel frameworkforbiosensorsassistedbymultiphotoneffectsandmachinelearning
AT torrestorrescarlos frameworkforbiosensorsassistedbymultiphotoneffectsandmachinelearning