Machine learning and its applications for plasmonics in biology

Moon, Gwiyeong; Lee, Jongha; Lee, Hyunwoong; Yoo, Hajun; Ko, Kwanhwi; Im, Sang Hee; Kim, Donghyun

doi:10.1016/j.xcrp.2022.101042

Cited by 17 publications

(10 citation statements)

References 100 publications

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“…Specifically, in the field of biophotonics, machine learning models using SERS can be efficiently classified into three domains: identification, classification, and quantification, with interests such as disease and molecular diagnosis [ 367 , 368 ]; microorganism classification, identification, etc. [ 369 , 370 , 371 , 372 ]; and cancer diagnosis [ 373 ], as shown in Figure 7 . In addition, machine learning was also used to improve data collection to overcome signal fluctuations and enhance the usability on site [ 374 ], to estimate the effect of scattering [ 375 ] and for the SERS signal enhancement itself [ 376 ].…”

Section: Machine Learning In Sers-based Biosensingmentioning

confidence: 99%

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

2023

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Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS’s full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

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Section: Machine Learning In Sers-based Biosensingmentioning

confidence: 99%

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

2023

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“…Pada pembelajaran biologi, pembelajaran proyek merupakan pendekatan yang cukup efektif menunjang pembelajaran saat ini, karena fokus pendekatan ini ada pada berpikir kreatif, memecahkan masalah dan interaksi antar siswa. Khusus pendekatan ini dapat dilakukan dalam pembelajaran secara aktif dan dialog ilmiah, dimana hal ini ada pada pembelajaran biologi (Moon et al, 2022). Pendekatan pembelajaran berbasis proyek dapat diaplikasikan oleh seorang guru dalam proses belajar mengajar yaitu dengan cara menggunakan model pembelajaran PJBL (Project Based Learning) ketika merancang suatu rencana pembelajaran.…”

Section: Pendahuluanunclassified

Effect of Project Based Learning (PJBL) Learning Model on Creative Thinking Ability of High School Biology Students: A Literature Review

Deria

Fadilah

Nisa

et al. 2023

PAKAR

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Education is an effort made by the government to create an atmosphere in the learning process for students to be active in building their potential. In the educational process, students are expected to have religious values, be able to control themselves, personality, intelligent, intellectual, moral, and skilled. Students in schools are asked to have 21st-century skills, namely the appearance known as "4C", one of which is creative thinking skills. To realize this, the role of teachers in schools is needed as educators. In the learning process, the learning model needs to be considered by a teacher for the learning objectives to be achieved. The learning model is (Project Based Learning) which reveals whether the learning model influences the creative thinking of high school biology students, researchers conduct literature reviews related to this. The method in this study is a literature review method, namely analyzing relevant articles with a descriptive type qualitative approach by including the keywords "Project Based Learning" and "creative thinking ability". The result of this literature review is that the PJBL (Project Based Learning) learning model has a significant effect on the ability to think in high school biology class students. In addition to the syntax of the PJBL (Project Based Learning) model which can require students to solve problems in the form of projects so that students can think creatively, other things also support the use of the PJBL (Project Based Learning) learning model such as the use of interesting media-assisted or the use of supporting student worksheets.

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“…Recently, approaches like 4-beam structured illumination microscopy (SIM) have emerged, which employ mirrors alongside a single objective lens in 3D SIM, effectively confining axial excitation volume and contributing to optical sectioning for improved resolution 8,9 . In addition to this, there have been efforts to address issues of resource efficiency by utilizing various AI models and striving to enhance axial resolution to a level comparable to lateral resolution [10][11][12][13][14][15] . Addressing the need for a more accessible and cost-effective solution, this study proposes nanoscale fluctuation-enhanced axial localization microscopy as a promising technique for improving axial resolution.…”

Section: Introductionmentioning

confidence: 99%

Nanoscale fluctuation-enhanced axial localization microscopy integrated with metallic structure-assisted fluidic chips

Yoo,

Ko,

et al. 2024

Nanoscale Imaging, Sensing, and Actuation for Biomedical Applications XXI

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Optical super-resolution microscopy has revolutionized imaging in the lateral axis, enabling nanoscale structure visualization with unprecedented detail. However, achieving high axial resolution along the z-axis remains challenging. In this study, nanoscale fluctuation-enhanced axial localization microscopy addresses this issue by incorporating metallic structures, specifically reflective optical devices, into dynamic speckle illumination microscopy. By controlling light waves within the fluidic chip, these metallic devices enable super-resolution to be achieved not only in the lateral direction but also along the z-axis, all in a cost-effective manner. Experimental investigation using 100-nm fluorescent beads and the U-87 MG cell membrane demonstrates axial-resolving performance of fluctuation-enhanced imaging compared to conventional methods. The application of an optical fluctuation-based reconstruction algorithm further allows the extraction of 4-fold enhanced axial information over diffraction-limited system.

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Machine learning and its applications for plasmonics in biology

Cited by 17 publications

References 100 publications

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Effect of Project Based Learning (PJBL) Learning Model on Creative Thinking Ability of High School Biology Students: A Literature Review

Nanoscale fluctuation-enhanced axial localization microscopy integrated with metallic structure-assisted fluidic chips

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