Rapid detection of cancer DNA in human blood using cysteamine-capped AuNPs and a machine learning-enabled smartphone

Koowattanasuchat, Sireemas; Ngernpimai, Sawinee; Matulakul, Piyaporn; Thonghlueng, Janpen; Phanchai, Witthawat; Chompoosor, Apiwat; Panitanarak, Uthumporn; Wanna, Yupaporn; Intharah, Thanapong; Chootawiriyasakul, Kanokon; Anata, Pimjai; Chaimnee, Prajuab; Thanan, Raynoo; Sakonsinsiri, Chadamas; Puangmali, Theerapong

doi:10.1039/d2ra05725e

Cited by 9 publications

(7 citation statements)

References 57 publications

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“…S1a, it can be observed that the curve response reached its maximum when the AchE loading was 5 ml, as the enzyme loading continued to increase, the current response decreased, indicating that an excessive amount of AchE could impede electron transfer. As reported, an excessive amount of rGo and AuNPs 35,36 may hinder the active sites of AchE. The increase of rGo can effectively increase the electrode surface area, and the peak current becomes larger.…”

Section: Resultsmentioning

confidence: 78%

Wearable Electrochemical Biosensors for In Situ Pesticide Analysis from Crops

Chen,

Zhou,

et al. 2023

J. Electrochem. Soc.

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On-site monitoring of pesticide presence on the surface of crops is a growing trend in precision and intelligent agriculture. In this study, we prepared a wearable biosensor for in-situ detection of pesticides on surfaces of crops. Here, a flexible fiber membrane substrate was prepared via the electrospinning technology. After the three-electrode system was transferred onto the membrane substrate use screen printing, resulting in a screen printing electrode (SPE) that can effectively adapt to the irregular surface of crops or fruits. By modified the SPE with acetylcholinesterase (AchE), the biosensor showed excellent selectively and recognize for the methyl parathion. To further enhance the electrochemical performance, the surface of the work electrode was modified with gold nanoparticles (AuNPs) and reduced graphene oxide (rGo). The developed wearable sensor successfully detected methyl parathion, showing a good linear relationship within the range of 1 to 2 ppm. The detection limit was measured to be 0.48 ppb, enabling on-site monitoring of pesticide levels in plants. This work presents a straightforward, sensitive, and efficient biosensors to analysis pesticide, which holds potential for application in detecting other agrochemicals. Moreover, this advancement could significantly contribute to the progress of precision agriculture in the future.

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Section: Resultsmentioning

confidence: 78%

Wearable Electrochemical Biosensors for In Situ Pesticide Analysis from Crops

Chen,

Zhou,

et al. 2023

J. Electrochem. Soc.

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“…Even though, there are numerous studies on DNA detection 23 , 24 and disease severity predictions as stated above, to best of our knowledge this current study, in our knowledge, is the first study which explored SPR-based dataset to detect and classify DNA—which open a new horizon at the field of biosensor development. The major contribution of the current study is to explore reflective angles on different gold surfaces of biosensors and utilize these parameters to classify DNA detection and classification tasks.…”

Section: Introductionmentioning

confidence: 97%

Machine learning for detecting DNA attachment on SPR biosensor

Mondal

Ahmed

Birbilis

et al. 2023

Sci Rep

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Optoelectric biosensors measure the conformational changes of biomolecules and their molecular interactions, allowing researchers to use them in different biomedical diagnostics and analysis activities. Among different biosensors, surface plasmon resonance (SPR)-based biosensors utilize label-free and gold-based plasmonic principles with high precision and accuracy, allowing these gold-based biosensors as one of the preferred methods. The dataset generated from these biosensors are being used in different machine learning (ML) models for disease diagnosis and prognosis, but there is a scarcity of models to develop or assess the accuracy of SPR-based biosensors and ensure a reliable dataset for downstream model development. Current study proposed innovative ML-based DNA detection and classification models from the reflective light angles on different gold surfaces of biosensors and associated properties. We have conducted several statistical analyses and different visualization techniques to evaluate the SPR-based dataset and applied t-SNE feature extraction and min-max normalization to differentiate classifiers of low-variances. We experimented with several ML classifiers, namely support vector machine (SVM), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbors (KNN), logistic regression (LR) and random forest (RF) and evaluated our findings in terms of different evaluation metrics. Our analysis showed the best accuracy of 0.94 by RF, DT and KNN for DNA classification and 0.96 by RF and KNN for DNA detection tasks. Considering area under the receiver operating characteristic curve (AUC) (0.97), precision (0.96) and F1-score (0.97), we found RF performed best for both tasks. Our research shows the potentiality of ML models in the field of biosensor development, which can be expanded to develop novel disease diagnosis and prognosis tools in the future.

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“…Deoxyribonucleic acid (DNA) sequencing, which is a widely known process to provide essential information for functioning of living systems, , human genomic imprint, − , disease diagnosis, ,, and biomedical treatments, , plays a significant role in human health improvement . DNA is a one-dimensional polymer − composed of four nucleic acid bases, namely, adenine (A), cytosine (C), guanine (G), and thymine (T).…”

Section: Introductionmentioning

confidence: 99%

Selective Sensing of DNA Nucleobases with Angular Discrimination

Algharagholy,

García-Suárez,

Abaas

2024

ACS Omega

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The fast and precise selective sensing of DNA nucleobases is a longpursued method that can lead to huge advances in the field of genomics and have an impact on aspects such as the prevention of diseases, health enhancement, and, in general, all types of medical treatments. We present here a new type of nanoscale sensor based on carbon nanotubes with a specific geometry that can discriminate the type of nucleobase and also its angle of orientation. The proper differentiation of nucleobases is essential to clearly sequence DNA chains, while angular discrimination is key to improving the sensing selectivity. We perform first-principle and quantum transport simulations to calculate the transmission, conductance, and current of the nanotube-based nanoscale sensor in the presence of the four nucleotides (A, C, G, and T), each of them rotated 0, 90, 180, or 270°. Our results show that this system is able to effectively discriminate between the four nucleotides and their angle of orientation. We explain these findings in terms of the interaction between the phosphate group of the nucleotide and the nanotube wall. The phosphate specifically distorts the electronic structure of the nanotube depending on the distance and the orientation and leads to nontrivial changes in the transmission. This work provides a method for finer and more precise sequential DNA chains.

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Rapid detection of cancer DNA in human blood using cysteamine-capped AuNPs and a machine learning-enabled smartphone

Abstract: The methylation landscape (Methylscape) of normal and malignant DNAs is different, resulting in unique self-assembly patterns in solution. The dispersion of cysteamine-capped AuNPs adsorbed onto DNA clusters could be employed to identify cancer DNA.

Cited by 9 publications

References 57 publications

Wearable Electrochemical Biosensors for In Situ Pesticide Analysis from Crops

Wearable Electrochemical Biosensors for In Situ Pesticide Analysis from Crops

Machine learning for detecting DNA attachment on SPR biosensor

Selective Sensing of DNA Nucleobases with Angular Discrimination

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