Aptamer-Based Paper Strip Sensor for Detecting <i>Vibrio fischeri</i>

Shin, Woo‐Ri; Sekhon, Simranjeet Singh; Rhee, Sung‐Keun; Ko, Jung Ho; Ahn, Ji‐Young; Min, Jiho; Kim, Yang‐Hoon

doi:10.1021/acscombsci.7b00190

Cited by 48 publications

(17 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DNA aptamers against infectious pathogens including Haemophilus influenzae type b [11], Mycobacterium tuberculosis [40], Escherichia coli (E. coli) [9,17], Streptococcus (Streptococcus pyogenes [12], Streptococcus pneumoniae [15], Staphylococcus aureus [18,41], Staphylococcus epidermidis [42]), and Vibrio (Vibrio parahaemolyticus [43], Vibrio vulnificus [10], Vibrio alginolyticus [14]) were selected. Moreover, DNA aptamers for nonpathogenic bacteria such as Bifidobacterium [13] and Vibrio fischeri [16] were selected. For virus-based SELEXs, DNA aptamers targeting H1N1 virus [25] and RNA aptamers targeting hepatitis C virus [34], dengue virus (DENV) [31], vaccinia virus [44,45], etc.…”

Section: Bacterial/virus-based Selexsmentioning

confidence: 99%

“…Visual detection Visual detection of pathogens can translate complex signals from a series of biochemistry reactions into the ones that can be observed directly by naked eyes, such as color changes [16], therefore eliminating the need for complex instruments for signal readout and saving the cost [71]. Specifically, aptamer-based methods provide a simple, precise, and universal way for a rapid screening of pathogens [72], and serve as powerful tools for point-of-care testing (POCT).…”

Section: Bacterial Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Selection and applications of functional nucleic acids for infectious disease detection and prevention

et al. 2021

View full text Add to dashboard Cite

Infectious diseases caused by pathogenic microorganisms such as viruses and bacteria pose a great threat to human health. Although a significant progress has been obtained in the diagnosis and prevention of infectious diseases, it still remains challenging to develop rapid and cost-effective detection approaches and overcome the side effects of therapeutic agents and pathogen resistance. Functional nucleic acids (FNAs), especially the most widely used aptamers and DNAzymes, hold the advantages of high stability and flexible design, which make them ideal molecular recognition tools for bacteria and viruses, as well as potential therapeutic drugs for infectious diseases. This review summarizes important advances in the selection and detection of bacterial-and virus-associated FNAs, along with their potential prevention ability of infectious disease in recent years. Finally, the challenges and future development directions are concluded.

show abstract

Section: Bacterial/virus-based Selexsmentioning

confidence: 99%

Section: Bacterial Detectionmentioning

confidence: 99%

Selection and applications of functional nucleic acids for infectious disease detection and prevention

et al. 2021

View full text Add to dashboard Cite

show abstract

“…e strip method mainly uses immune or competitive reactions to initiate or prevent the aggregation of colloidal gold nanoparticles on the paper strip detection line. e detection line shows red or colorless to indicate the presence or absence of the target molecules [12,13]. e computer-aided discriminant analysis is mainly based on the data information of spectroscopy, chromatography, mass spectrometry, and so on, of the samples and combined with the chemometrics algorithm [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Rapid Discrimination of Cheese Products Based on Probabilistic Neural Network and Raman Spectroscopy

Zhang

2020

Journal of Spectroscopy

View full text Add to dashboard Cite

The aim of this work is to solve the practical problem that there are relatively few fast, intelligent, and objective methods to distinguish dairy products and to further improve the quality control methods of them. Therefore, an approach of cheese product brand discrimination method based on Raman spectroscopy and probabilistic neural network algorithm was developed. The experimental results show that the spectrum contains abundant molecular vibration information of carbohydrates, fats, proteins, and other components, and the Raman spectral data collection time of a single sample is only 100 s. Due to the high spectral similarity between samples, it is impossible to identify them with naked eyes. Characteristic peak intensity combined with statistical process control method was employed to study the fluctuation characteristics of samples. The results show that the characteristic peak of experimental samples fluctuates within a certain control limit. However, due to the high similarity between the Raman spectra of different brand samples, they cannot be effectively identified as well. This paper further studied and established the analytical approach based on Raman spectroscopy, including wavelet denoising, normalization, principal component analysis, and probabilistic neural network discrimination. In db1 wavelet processing, [−1, 1] normalization, 74 principal components (cumulative contribution rate of 100%) can realize the effective discrimination of different brands of cheese products in 1 s, with the average recognition accuracy of 96%. The discriminant method established in this work has the advantages of simple operation, rapid analysis, and accurate results. It provides a technical reference for the fight against counterfeit products and has a broad application prospect.

show abstract

“…In addition, easy chemical modifications, such as 5′ amine modification and Cy5-labeling, as well as the stability of aptamers, make them attractive agents [ 18 ]. Due to these advantages, aptamers are used in various fields, including imaging, drug delivery, and as biosensors [ 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Docking Simulation and Sandwich Assay for Aptamer-Based Botulinum Neurotoxin Type C Detection

Park

Cha

et al. 2020

Biosensors

Self Cite

View full text Add to dashboard Cite

Aptamers are biomaterials that bind to a target molecule through a unique structure, and have high applicability in the diagnostic and medical fields. To effectively utilize aptamers, it is important to analyze the structure of the aptamer binding to the target molecule; however, there are difficulties in experimentally identifying this structure. In the modern pharmaceutical industry, computer-driven docking simulations that predict intermolecular binding models are used to select candidates that effectively bind target molecules. Botulinum toxin (BoNT) is the most poisonous neurotoxin produced from the Clostridium botulinum bacteria, and BoNT/C, one of the eight serotypes, causes paralysis in livestock. In this study, the aptamers that bound to BoNT/C were screened via the systematic evolution of ligands by exponential enrichment, and the binding affinity analysis and binding model were evaluated to select optimal aptamers. Based on surface plasmon resonance analysis and molecular operating environment docking simulation, a pair of aptamers that had high binding affinity to BoNT/C and were bound to different BoNT/C sites were selected. A sandwich assay based on this aptamer pair detected the BoNT/C protein to a concentration as low as ~0.2 ng Ml−1. These results show that docking simulations are a useful strategy for screening aptamers that bind to specific targets.

show abstract

Aptamer-Based Paper Strip Sensor for Detecting Vibrio fischeri

Cited by 48 publications

References 42 publications

Selection and applications of functional nucleic acids for infectious disease detection and prevention

Selection and applications of functional nucleic acids for infectious disease detection and prevention

Rapid Discrimination of Cheese Products Based on Probabilistic Neural Network and Raman Spectroscopy

Docking Simulation and Sandwich Assay for Aptamer-Based Botulinum Neurotoxin Type C Detection

Contact Info

Product

Resources

About