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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.
  • Arano-Martinez JA; Sección de Estudios de Posgrado e Investigación, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Zacatenco, Instituto Politécnico Nacional, Mexico City 07738, Mexico.
  • Martínez-González CL; Sección de Estudios de Posgrado e Investigación, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Zacatenco, Instituto Politécnico Nacional, Mexico City 07738, Mexico.
  • Salazar MI; Departamento de Microbiología, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City 11340, Mexico.
  • Torres-Torres C; Sección de Estudios de Posgrado e Investigación, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Zacatenco, Instituto Politécnico Nacional, Mexico City 07738, Mexico.
Biosensors (Basel) ; 12(9)2022 Sep 01.
Article in English | MEDLINE | ID: covidwho-2009948
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Viruses / Biosensing Techniques / COVID-19 Type of study: Diagnostic study / Experimental Studies / Prognostic study Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: Bios12090710

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Viruses / Biosensing Techniques / COVID-19 Type of study: Diagnostic study / Experimental Studies / Prognostic study Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: Bios12090710