Optical Biosensors for the Diagnosis of COVID-19 and Other Viruses—A Review

John, Pauline; Vasa, Nilesh J.; Zam, Azhar

doi:10.3390/diagnostics13142418

Cited by 7 publications

(2 citation statements)

References 148 publications

(171 reference statements)

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“…There are few reports on optical biosensors for the diagnosis of COVID-19 [ 26 , 36 – 38 ]. However, these reports either present the optical biosensors without nanomaterial modifications or do not cover recent studies published within the last 2 years.…”

Section: Introductionmentioning

confidence: 99%

Optical biosensors for diagnosis of COVID-19: nanomaterial-enabled particle strategies for post pandemic era

Tekin,

Kul,

Sagdic

et al. 2024

Microchim Acta

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The COVID-19 pandemic underlines the need for effective strategies for controlling virus spread and ensuring sensitive detection of SARS-CoV-2. This review presents the potential of nanomaterial-enabled optical biosensors for rapid and low-cost detection of SARS-CoV-2 biomarkers, demonstrating a comprehensive analysis including colorimetric, fluorescence, surface-enhanced Raman scattering, and surface plasmon resonance detection methods. Nanomaterials including metal-based nanomaterials, metal–organic frame–based nanoparticles, nanorods, nanoporous materials, nanoshell materials, and magnetic nanoparticles employed in the production of optical biosensors are presented in detail. This review also discusses the detection principles, fabrication methods, nanomaterial synthesis, and their applications for the detection of SARS-CoV-2 in four categories: antibody-based, antigen-based, nucleic acid–based, and aptamer-based biosensors. This critical review includes reports published in the literature between the years 2021 and 2024. In addition, the review offers critical insights into optical nanobiosensors for the diagnosis of COVID-19. The integration of artificial intelligence and machine learning technologies with optical nanomaterial-enabled biosensors is proposed to improve the efficiency of optical diagnostic systems for future pandemic scenarios. Graphical Abstract

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Section: Introductionmentioning

confidence: 99%

Optical biosensors for diagnosis of COVID-19: nanomaterial-enabled particle strategies for post pandemic era

Tekin,

Kul,

Sagdic

et al. 2024

Microchim Acta

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“…6,7 Certain optical biosensors are of particular interest as they implement a light source to probe changes within cellular environments that can cause shifts in fluorescence wavelength, intensity, and/or spectral bandwidth which can be attributed to distinct cellular microenvironments. 8,9,10,11…”

mentioning

confidence: 99%

Machine Learning Assisted Spectral Fingerprinting for Immune Cell Phenotyping

Nadeem,

Lyons,

Kindopp

et al. 2024

Preprint

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Spectral fingerprinting has emerged as a powerful tool, adept at identifying chemical compounds and deciphering complex interactions within cells and engineered nanomaterials. Using near-infrared (NIR) fluorescence spectral fingerprinting coupled with machine learning techniques, we uncover complex interactions between DNA-functionalized single-walled carbon nanotubes (DNA-SWCNTs) and live macrophage cells, enabling in situ phenotype discrimination. Through the use of Raman microscopy, we showcase statistically higher DNA-SWCNT uptake and a significantly lower defect ratio in M1 macrophages as compared to M2 and naive phenotypes. NIR fluorescence data also indicate that distinctive intra-endosomal environments of these cell types give rise to significant differences in many optical features such as emission peak intensities, center wavelengths, and peak intensity ratios. Such features serve as distinctive markers for identifying different macrophage phenotypes. We further use a support vector machine (SVM) model trained on SWCNT fluorescence data to identify M1 and M2 macrophages, achieving an impressive accuracy of > 95%. Finally, we observe that the stability of DNA-SWCNT complexes, influenced by DNA sequence length, is a crucial consideration for applications such as cell phenotyping or mapping intra-endosomal microenvironments using AI techniques. Our findings suggest that shorter DNA-sequences like GT6 give rise to more improved model accuracy (> 87%) due to increased active interactions of SWCNTs with biomolecules in the endosomal microenvironment. Implications of this research extend to the development of nanomaterial-based platforms for cellular identification, holding promise for potential applications in real time monitoring of in vivo cellular differentiation.

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