Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring

Leong, Shi Xuan; Leong, Yong Xiang; Koh, Charlynn Sher Lin; Tan, Emily Xi; Nguyen, Lam Bang Thanh; Chen, Jaslyn Ru Ting; Chong, Carice; Pang, Desmond Wei Cheng; Liang, Xiaochen; Tan, Nguan Soon; Ling, Xing Yi

doi:10.1039/d2sc02981b

Cited by 14 publications

(10 citation statements)

References 130 publications

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“… 47 In addition to the manipulation and use of nanosensors, the concept of signal transduction is another fundamental principle of nanosensors. 48 Nanosensors consist of an analyte, transducer, signal processing, and display system, for example, ( Figure 1 ). 49 Recent advances in technology have produced nanosensors that can revolutionize medical technology by providing real-time quantitative measurements of the human biochemical signaling network, enabling new diagnostics and treatment methods.…”

Section: Fundamental Principles Of Nanosensorsmentioning

confidence: 99%

“… 39 To optimize the functionality and performance of nanosensors, researchers must first determine the target analyte, then choose appropriate, transducer elements, and signal detection methods based on the application’s specific requirements. 55 High surface-to-volume ratio nanosensors, like quantum dots or metallic nanoparticles, are chosen for sensitivity and selectivity. 56 The size and shape of the nanosensor must be optimized for efficient capture and detection.…”

Section: Design and Fabrication Of Nanosensorsmentioning

confidence: 99%

See 1 more Smart Citation

Advances in nano sensors for monitoring and optimal performance enhancement in photovoltaic cells

Dhahi,

Yousif Dafhalla,

Tayfour

et al. 2024

iScience

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Section: Fundamental Principles Of Nanosensorsmentioning

confidence: 99%

Section: Design and Fabrication Of Nanosensorsmentioning

confidence: 99%

Advances in nano sensors for monitoring and optimal performance enhancement in photovoltaic cells

Dhahi,

Yousif Dafhalla,

Tayfour

et al. 2024

iScience

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“…In order to overcome these challenges, machine learning and deep learning approaches for accurate fluorescent signal analysis of optical nanosensors have started to be developed recently. − Representative methods have been explored such as discrimination of fluorometric images of various target analytes using convolutional neural network (CNN) models and anomaly detection of the fluorescence signals using Isolation Forest (iForest) or one-class support sector machine (SVM), among others. − These attempts were successful in precisely distinguishing the stimulation source of the fluorescence signal of the sensors and effectively calculating the biochemical fingerprints from the multivariate data. Significant improvements for the fluorescent sensor signal processing have been achieved; however, there has been still no technique on discrimination of fluorescence signal changes near the noise fluctuation level .…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Enhanced Fluorescent Nanosensor Based on Carbon Quantum Dots for Heavy Metal Detection

Tian,

Lee,

Song

et al. 2024

ACS Appl. Nano Mater.

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Fluorescent nanomaterials such as carbon quantum dots (CQDs) have been widely utilized as optical nanosensors for the detection and imaging of chemical or biological species with their superior sensitivity and spatiotemporal monitoring capabilities. Even though the nanosensors originally provide high sensitivity, the potential limit of detection (LOD) could not be accurately utilized for real-world applications due to the absence of signal extraction near the noise level, which is significantly influenced by various environmental factors and conventional statistical methods. To address this issue, we have developed an optical signal processing technique based on machine learning to enhance the practical sensing performance of CQD nanosensors for heavy metal ion detection. Fluorescence spectra of the nanosensor are acquired under both normal conditions (absence of analytes, n = 786) and analyte conditions (presence of analytes with varying concentrations, n = 288). Seven different types of machine learning algorithms are systematically applied to a training data set of fluorescence features (n = 200). Our best model could distinguish the spectra of 10 nM and 100 pM Cu 2+ from just noise signals without analytes, which are 10 4 and 10 6 times enhanced LOD compared to the original detection limit (95 μM) providing accuracies of 90.69 and 70.41%, respectively. We further demonstrate that this technique could be universally applied for different types of analytes such as Hg 2+ , Pb 2+ , Cr 6+ , and Fe 3+ ions with distinct sensing performances. This technique can be up to 10 6 times lower than the LOD of the fluorescent nanosensors. Our approach is not limited to CQD nanosensors or metal ions but can also be directly applied to various types of fluorescent nanosensors and biochemical analytes.

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“…Small-molecule metabolites (SMMs) have been increasingly recognized as useful biomarkers for cancer diagnosis because they are involved in various metabolic pathways in the body and their levels can change in response to cancer or other physiological changes. The use of metabolites as biomarkers has several advantages over traditional methods, including their ability to provide real-time information about cancer status and their noninvasive nature. − Among the wide variety of SMMs, aldehyde is an important type of metabolite and a potential biomarker for cancer. , …”

Section: Introductionmentioning

confidence: 99%

Simultaneous Detection of Aldehyde Metabolites by Light-Assisted Ambient Ionization Mass Spectrometry

Wang,

Yang,

Chen

et al. 2024

Anal. Chem.

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In the clinic, small-molecule metabolites (SMMs) in blood are highly convincing indicators for disease diagnosis, such as cancer. However, challenges still exist for detection of SMMs due to their low concentration and complicated components in blood. In this work, we report the design of a novel "selenium signature" nanoprobe (Se nanoprobe) for efficient identification of multiple aldehyde metabolites in blood. This Se nanoprobe consists of magnetic nanoparticles that can enrich aldehyde metabolites from a complex environment, functionalized with photosensitive "selenium signature" hydrazide molecules that can react with aldehyde metabolites. Upon irradiation with UV, the aldehyde derivatives can be released from the Se nanoprobe and further sprayed by mass spectrometry through ambient ionization (AIMS). By quantifying the selenium isotope distribution (MS/MS) from the derivatization product, accurate detection of several aldehyde metabolites, including valeraldehyde (Val), heptaldehyde (Hep), 2-furaldehyde (2-Fur), 10-undecenal aldehyde (10-Und), and benzaldehyde (Ben), is realized. This strategy reveals a new solution for quick and accurate cancer diagnosis in the clinic.

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Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring

Abstract: Overview of the current status on emerging, multi-faceted nanosensor platform designs and data analysis strategies for rapid, point-of-need detection and monitoring of small-molecule metabolites.

Cited by 14 publications

References 130 publications

Advances in nano sensors for monitoring and optimal performance enhancement in photovoltaic cells

Advances in nano sensors for monitoring and optimal performance enhancement in photovoltaic cells

Machine-Learning-Enhanced Fluorescent Nanosensor Based on Carbon Quantum Dots for Heavy Metal Detection

Simultaneous Detection of Aldehyde Metabolites by Light-Assisted Ambient Ionization Mass Spectrometry

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