Precise cancer detection via the combination of functionalized SERS surfaces and convolutional neural network with independent inputs

Erzina, Mariia; Trelin, Andrii; Guselnikova, Olga; Dvorankova, Barbara; Strnadová, Karolína; Perminova, A. A.; Ulbrich, Pavel; Martin, David; Jeřábek, V.; Elashnikov, Roman; Švorčı́k, Václav; Lyutakov, Oleksiy

doi:10.1016/j.snb.2020.127660

Cited by 72 publications

(57 citation statements)

References 46 publications

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“…This means that they seek to determine whether a patient is prone to suffer from a specific pathology, using SERS analysis of selected biofluids, and even including additional predictions such as the stage of the disease and its origin. 201 − 206 …”

Section: Hitting the Target With The Aid Of Artificial Intelligencementioning

confidence: 99%

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

et al. 2022

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Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.

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Section: Hitting the Target With The Aid Of Artificial Intelligencementioning

confidence: 99%

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

et al. 2022

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“…Erzina et al. reported Au multi-branched SERS NPs (AuM) for the accurate identification of normal cells and tumor cells [ 95 ]. These NPs were modified with different functional groups for partially-selective capture of molecules from the cell culture medium.…”

Section: Sers Detection Of Cellular Metabolitesmentioning

confidence: 99%

Human metabolite detection by surface-enhanced Raman spectroscopy

Liu

2022

Materials Today Bio

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Metabolites are important biomarkers in human body fluids, conveying direct information of cellular activities and physical conditions. Metabolite detection has long been a research hotspot in the field of biology and medicine. Surface-enhanced Raman spectroscopy (SERS), based on the molecular “fingerprint” of Raman spectrum and the enormous signal enhancement (down to a single-molecule level) by plasmonic nanomaterials, has proven to be a novel and powerful tool for metabolite detection. SERS provides favorable properties such as ultra-sensitive, label-free, rapid, specific, and non-destructive detection processes. In this review, we summarized the progress in recent 10 years on SERS-based sensing of endogenous metabolites at the cellular level, in tissues, and in biofluids, as well as drug metabolites in biofluids. We made detailed discussions on the challenges and optimization methods of SERS technique in metabolite detection. The combination of SERS with modern biomedical technology were also anticipated.

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“…Using binary stochastic filtering techniques, very small DNA damage was detected. A similar technique was used to classify between cancerous and noncancerous cell-groups using AuNPs [ 238 ]. Using neural networks, the achieved accuracy of classification was >98%.…”

Section: Nano-biosensors For Cancer Detection and Future Prospectimentioning

confidence: 99%

Nanostructures for Biosensing, with a Brief Overview on Cancer Detection, IoT, and the Role of Machine Learning in Smart Biosensors

Banerjee

Maity

Mastrangelo

2021

Sensors

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Biosensors are essential tools which have been traditionally used to monitor environmental pollution and detect the presence of toxic elements and biohazardous bacteria or virus in organic matter and biomolecules for clinical diagnostics. In the last couple of decades, the scientific community has witnessed their widespread application in the fields of military, health care, industrial process control, environmental monitoring, food-quality control, and microbiology. Biosensor technology has greatly evolved from in vitro studies based on the biosensing ability of organic beings to the highly sophisticated world of nanofabrication-enabled miniaturized biosensors. The incorporation of nanotechnology in the vast field of biosensing has led to the development of novel sensors and sensing mechanisms, as well as an increase in the sensitivity and performance of the existing biosensors. Additionally, the nanoscale dimension further assists the development of sensors for rapid and simple detection in vivo as well as the ability to probe single biomolecules and obtain critical information for their detection and analysis. However, the major drawbacks of this include, but are not limited to, potential toxicities associated with the unavoidable release of nanoparticles into the environment, miniaturization-induced unreliability, lack of automation, and difficulty of integrating the nanostructured-based biosensors, as well as unreliable transduction signals from these devices. Although the field of biosensors is vast, we intend to explore various nanotechnology-enabled biosensors as part of this review article and provide a brief description of their fundamental working principles and potential applications. The article aims to provide the reader a holistic overview of different nanostructures which have been used for biosensing purposes along with some specific applications in the field of cancer detection and the Internet of things (IoT), as well as a brief overview of machine-learning-based biosensing.

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Precise cancer detection via the combination of functionalized SERS surfaces and convolutional neural network with independent inputs

Cited by 72 publications

References 46 publications

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

Human metabolite detection by surface-enhanced Raman spectroscopy

Nanostructures for Biosensing, with a Brief Overview on Cancer Detection, IoT, and the Role of Machine Learning in Smart Biosensors

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