Label-free surface-enhanced Raman spectroscopy with artificial neural network technique for recognition photoinduced DNA damage

Guselnikova, Olga; Trelin, Andrii; Skvortsova, A.; Ulbrich, Pavel; Postnikov, Pavel; Pershina, Alexandra G.; Sýkora, David; Švorčı́k, Václav; Lyutakov, Oleksiy

doi:10.1016/j.bios.2019.111718

Cited by 45 publications

(25 citation statements)

References 45 publications

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“…To overcome these limitations, the enhanced effect of Raman scattering on metallic substrates or scanning probe microscopy (SPM) probes, has been explored. The effect of surface enhancement of Raman signal near metallic nanoparticles or nano-patterned metallic surfaces is known as the Surface-enhanced Raman spectroscopy (SERS) [77,78]. SERS offers the possibility for ultrasensitive detection of the rotational and vibrational spectra within biological materials, even for low analyte concentrations [77,79].…”

Section: Nanospectroscopy Of Dna Damagementioning

confidence: 99%

“…The marker Raman bands indicating DNA damage following PDT treatment were detected in the spectra acquired from the isolated mitochondria, as summarized in Table 2. SERS technique has also been exploited to reveal marker bands specific for UV induced DNA damage [78]. In this study, Guselnikova et al have incorporated an artificial neural network (ANN) to analyze the SERS spectra.…”

Section: Sersmentioning

confidence: 99%

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Molecular Spectroscopic Markers of DNA Damage

Sofińska¹,

Wilkosz²,

Szymoński³

et al. 2020

Molecules

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Every cell in a living organism is constantly exposed to physical and chemical factors which damage the molecular structure of proteins, lipids, and nucleic acids. Cellular DNA lesions are the most dangerous because the genetic information, critical for the identity and function of each eukaryotic cell, is stored in the DNA. In this review, we describe spectroscopic markers of DNA damage, which can be detected by infrared, Raman, surface-enhanced Raman, and tip-enhanced Raman spectroscopies, using data acquired from DNA solutions and mammalian cells. Various physical and chemical DNA damaging factors are taken into consideration, including ionizing and non-ionizing radiation, chemicals, and chemotherapeutic compounds. All major spectral markers of DNA damage are presented in several tables, to give the reader a possibility of fast identification of the spectral signature related to a particular type of DNA damage.

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Section: Nanospectroscopy Of Dna Damagementioning

confidence: 99%

Section: Sersmentioning

confidence: 99%

Molecular Spectroscopic Markers of DNA Damage

Sofińska¹,

Wilkosz²,

Szymoński³

et al. 2020

Molecules

View full text Add to dashboard Cite

show abstract

“…Guselnikova et al demonstrated the use of convolutional neural networks for surface-enhanced Raman spectroscopy for detection of oligonucleotide damage on gold gratings [237]. Using binary stochastic filtering techniques, very small DNA damage was detected.…”

Section: Machine Learning For Nano-biosensorsmentioning

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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“…Once the spectra are remapped using PCA, a classifier or regression model is employed such as an extreme learning machine (ELM) [71], LDA [68], SVM [60,64,73], PLSR [65], or ANN [70]. An alternative to dimension reduction is utilizing the high dimensionality spectral data directly with a node-based algorithm such as ANN [72,158,159] and CNN [160,161]. Although bioreceptors may be used to allow for specific binding of the target analyte to the SERS sensing surface [155,156], direct detection is also possible.…”

Section: Spectroscopymentioning

confidence: 99%

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Schackart

Yoon

2021

Sensors

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Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor’s signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.

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Label-free surface-enhanced Raman spectroscopy with artificial neural network technique for recognition photoinduced DNA damage

Cited by 45 publications

References 45 publications

Molecular Spectroscopic Markers of DNA Damage

Molecular Spectroscopic Markers of DNA Damage

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

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

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