DNA Nanotechnology-Enabled Interfacial Engineering for Biosensor Development

Ye, Dekai; Zuo, Xiaolei; Chen, Fan

doi:10.1146/annurev-anchem-061417-010007

Cited by 107 publications

(76 citation statements)

References 145 publications

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“…Currently, the ability to collect data from internal and external environmental factors is still very limited. It is believed that with the development of nanotechnology [148], wearable devices [149], the Internet of things [150], smartphone applications [151], and other technologies [152], these dynamic databases will also be established. In addition, knowledge bases, including AlzGene and UniProt knowledge base, also play a vital role.…”

Section: Perspective For the Futurementioning

confidence: 99%

The Application of Artificial Intelligence in the Genetic Study of Alzheimer’s Disease

Mishra¹,

Li²

2020

Aging and disease

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Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects. Studies have found many significant genetic and environmental factors, but the pathogenesis of AD is still unclear. With the application of microarray and next-generation sequencing technologies, research using genetic data has shown explosive growth. In addition to conventional statistical methods for the processing of these data, artificial intelligence (AI) technology shows obvious advantages in analyzing such complex projects. This article first briefly reviews the application of AI technology in medicine and the current status of genetic research in AD. Then, a comprehensive review is focused on the application of AI in the genetic research of AD, including the diagnosis and prognosis of AD based on genetic data, the analysis of genetic variation, gene expression profile, gene-gene interaction in AD, and genetic analysis of AD based on a knowledge base. Although many studies have yielded some meaningful results, they are still in a preliminary stage. The main shortcomings include the limitations of the databases, failing to take advantage of AI to conduct a systematic biology analysis of multilevel databases, and lack of a theoretical framework for the analysis results. Finally, we outlook the direction of future development. It is crucial to develop high quality, comprehensive, large sample size, data sharing resources; a multi-level system biology AI analysis strategy is one of the development directions, and computational creativity may play a role in theory model building, verification, and designing new intervention protocols for AD.

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Section: Perspective For the Futurementioning

confidence: 99%

The Application of Artificial Intelligence in the Genetic Study of Alzheimer’s Disease

Mishra¹,

Li²

2020

Aging and disease

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“…Wherein, interfacial molecular reconfiguration induced by specific interactions with solvation effects, electrostatic forces, and covalent bonds or non‐covalent bonds (i.e., hydrogen bonds, ionic interactions, van der Waals forces, and hydrophobic bonds), can be reflected by the chemical or physical properties of the substance, for instance, spectral features,, conductance, surface tension/energy, and reactive activity . Recently, considerable efforts have been invested in developing chemical detection applications with interfacial molecular reconfiguration, and however, the demands for such detection mechanism with features such as operational simplicity, accessibility, visual markers and no requirements for input electrical energy are still not fully met.…”

Section: Figurementioning

confidence: 99%

Visual Chemical Detection Mechanism by a Liquid Gating System with Dipole‐Induced Interfacial Molecular Reconfiguration

et al. 2019

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Chemical detection has awide range of applications. The detection of ac ertain substance is so vital that new detection mechanisms with features such as low-cost, accessibility,a nd readily available visual markers are in demand. Herein, aliquid-gating-based chemical-detection mechanism is reported, whichh as ad ynamic gas/liquid interface due to dipole-induced interfacial molecular reconfiguration. The mechanism exhibits as ensitive relationship between the dipole-force-induced rearrangement of interfacial molecules and transmembrane gating behavior.T hese features can be utilized to create visual markers for detection by converting the analyte-mediated interfacial interaction to ap ressure-driven marker movement.This "green" detection mechanism requires no electrical energy input and has readily available markers for anyone to observe directly.T his new mechanism opens aw indow for am ore in-depth exploration of combining liquid-gating mechanisms with detection mechanisms.The liquid-gating system, [1] which uses ac apillary-stabilized functional liquid to form reversible gates inside the pores, shows prominent properties in controlling complex, selective, multiphase substance transport. Although the system has already been applied to membrane separations, [2] research and development of the liquid-gating system with aresponsive dynamic molecular layer (the layer of molecular reconfiguration) on its gas/liquid or liquid/liquid interface is still at an infant stage.M ore possibilities are expected to arise when integrating specific chemical, biological or physical interactions into liquid-gating systems.H ere,w er eport ac hemical detection mechanism based on liquid-gating systems,inspired by the differential transmembrane-gating behaviors that respond to interfacial molecular reconfiguration.Molecular configuration is vital for applications on interfaces, [3] ranging from heterogeneous catalysis, [4] electron-transport electrodes, [5] superhydrophobic surface design, [6] as well as interfacial chemical interactions. [7] Wherein, interfacial molecular reconfiguration induced by specific interactions with solvation effects,electrostatic forces, and covalent bonds or non-covalent bonds (i.e., hydrogen bonds,i onic interactions,v an der Waals forces,a nd hydrophobic bonds), can be reflected by the chemical or physical properties of the substance,f or instance,s pectral features, [6a,b, 8] conductance, [9] surface tension/energy, [6a, 10] and reactive activity. [4] Recently,c onsiderable efforts have been invested in developing chemical detection applications with interfacial molecular reconfiguration, [7b,9a, 11] and however,the demands for such detection mechanism with features such as operational simplicity,a ccessibility,v isual markers and no requirements for input electrical energy are still not fully met.Herein, ad ipole-induced liquid-gating-based porous membrane system for detection (D-LGD) of specific analytes is reported, in which interfacial reconfigurable molecules are introduced into ag ating liqui...

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“…One area of opportunity is in detection of microRNAs, which are small regulatory RNAs important for many aspects of human health. Since nucleic acids are the natural biosensors for microRNAs, it is perhaps not surprising that microRNA detection has in some ways served as a ‘gateway’ into biosensing applications for DNA nanotechnology (31,32).…”

Section: Introduction To Dna Nanotechnologymentioning

confidence: 99%

DNA nanotechnology approaches for microRNA detection and diagnosis

Chandrasekaran

Punnoose

Zhou

et al. 2019

Nucleic Acids Research

104

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MicroRNAs are involved in the crucial processes of development and diseases and have emerged as a new class of biomarkers. The field of DNA nanotechnology has shown great promise in the creation of novel microRNA biosensors that have utility in lab-based biosensing and potential for disease diagnostics. In this Survey and Summary, we explore and review DNA nanotechnology approaches for microRNA detection, surveying the literature for microRNA detection in three main areas of DNA nanostructures: DNA tetrahedra, DNA origami, and DNA devices and motifs. We take a critical look at the reviewed approaches, advantages and disadvantages of these methods in general, and a critical comparison of specific approaches. We conclude with a brief outlook on the future of DNA nanotechnology in biosensing for microRNA and beyond.

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DNA Nanotechnology-Enabled Interfacial Engineering for Biosensor Development

Cited by 107 publications

References 145 publications

The Application of Artificial Intelligence in the Genetic Study of Alzheimer’s Disease

The Application of Artificial Intelligence in the Genetic Study of Alzheimer’s Disease

Visual Chemical Detection Mechanism by a Liquid Gating System with Dipole‐Induced Interfacial Molecular Reconfiguration

DNA nanotechnology approaches for microRNA detection and diagnosis

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