Aptamers: The Powerful Molecular Tools for Virus Detection

Han, Cong; Li, Qian; Ji, Haishuo; Xing, Wenping; Zhang, Limin; Zhang, Liyun

doi:10.1002/asia.202100242

Cited by 16 publications

(4 citation statements)

References 48 publications

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“…After analyzing the data with hardware chips and equipment or nucleic acid analysis, the signals are transferred to the mobile app for visualization through various means, such as IoT and Bluetooth. In these studies, blood, serology, and saliva data analysis results, after DNA and amino acid analysis, were sent by biosensors to mobile apps for visualization [ 27 , 28 , 47 , 52 ].…”

Section: Resultsmentioning

confidence: 99%

Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review

Gheisari,

Ghaderzadeh,

et al. 2024

JMIR Mhealth Uhealth

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Background In the modern world, mobile apps are essential for human advancement, and pandemic control is no exception. The use of mobile apps and technology for the detection and diagnosis of COVID-19 has been the subject of numerous investigations, although no thorough analysis of COVID-19 pandemic prevention has been conducted using mobile apps, creating a gap. Objective With the intention of helping software companies and clinical researchers, this study provides comprehensive information regarding the different fields in which mobile apps were used to diagnose COVID-19 during the pandemic. Methods In this systematic review, 535 studies were found after searching 5 major research databases (ScienceDirect, Scopus, PubMed, Web of Science, and IEEE). Of these, only 42 (7.9%) studies concerned with diagnosing and detecting COVID-19 were chosen after applying inclusion and exclusion criteria using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. Results Mobile apps were categorized into 6 areas based on the content of these 42 studies: contact tracing, data gathering, data visualization, artificial intelligence (AI)–based diagnosis, rule- and guideline-based diagnosis, and data transformation. Patients with COVID-19 were identified via mobile apps using a variety of clinical, geographic, demographic, radiological, serological, and laboratory data. Most studies concentrated on using AI methods to identify people who might have COVID-19. Additionally, symptoms, cough sounds, and radiological images were used more frequently compared to other data types. Deep learning techniques, such as convolutional neural networks, performed comparatively better in the processing of health care data than other types of AI techniques, which improved the diagnosis of COVID-19. Conclusions Mobile apps could soon play a significant role as a powerful tool for data collection, epidemic health data analysis, and the early identification of suspected cases. These technologies can work with the internet of things, cloud storage, 5th-generation technology, and cloud computing. Processing pipelines can be moved to mobile device processing cores using new deep learning methods, such as lightweight neural networks. In the event of future pandemics, mobile apps will play a critical role in rapid diagnosis using various image data and clinical symptoms. Consequently, the rapid diagnosis of these diseases can improve the management of their effects and obtain excellent results in treating patients.

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Section: Resultsmentioning

confidence: 99%

Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review

Gheisari,

Ghaderzadeh,

et al. 2024

JMIR Mhealth Uhealth

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“…With the amalgamation among the peroxide reductase-viz property of nanoparticles of Au. By the developed colorimetrical nanoparticle, the MNV was identified to LOD for twenty viruses/ assessment near to two hundreds viruses/mL [66].…”

Section: Norovirus (Nov)mentioning

confidence: 99%

Use of Nanomaterials-Based Enzymes in the Food Industry

Hussain

2022

Nanomaterial-Supported Enzymes

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Natural enzymes perform pivotal role in all biological reactions in living things. But their practical operations are restricted due to difficulty in synthesis, reprocessing, cost, and easy denaturation. To combat these hurdles, blistering exertion is dedicated for improving these enzymes to other enzymes known “artificial enzymes.” The man-made enzymes, which possess enzyme mimicking properties, have fascinated researchers’ attentions. From last decade, nanozymes have attained tremendous progression. Nanomaterials-based enzyme elucidates expressive features like distinct preparative protocols, low cost, long duration for storage, and high stability towards environment than natural enzymes. This draft carries survey on 1) nanozymes literature, which is considerably explored by a diverse class of nanocomposites such as composites of halogens, carbon-based nanostructured materials etc.; 2) the recent progresses made in the fabrication of nanozymes for enzyme mimicking activity; 3) the mechanism of action, schemes to increase enzymatic activities, catalytic property and recent trends of using nanomaterials-based enzymes in the food industries.

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“…Aptamers are short single-stranded oligonucleotide sequences (RNA or DNA) artificially in vitro screened by Systematic Evolution of Ligands by Exponential Enrichment, and they have superior properties to antibodies in terms of high thermal stability, easy chemical modifications, and batch preparation. 9 More recently, many OTA aptamer-based detection methods have been developed due to their capacity to be easily modified and synthesized. Colorimetry, 10−13 electrochemistry, 14−17 surface-enhanced Raman, 3,18,19 and fluorescence 18,20−23 are examples of these typical methods, which have the advantages of simplicity, efficiency, and high sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recognition-Activated Primer-Mediated Exponential Rolling Circle Amplification for Signal Probe Production and Ultrasensitive Visual Detection of Ochratoxin A with Nucleic Acid Lateral Flow Strips

Wang,

Zong,

et al. 2023

Anal. Chem.

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We proposed a visual strategy for rapid and ultrasensitive detection of ochratoxin A (OTA) by integration of primer-mediated exponential rolling circle amplification (P-ERCA) with a designed nucleic acid lateral flow strip (LFS). The recognition component was preimmobilized in the tube by hybridization between the immobilized functionalized aptamer and complementary ssDNA. Recognition of OTA induces the release of complementary ssDNA from the tube, which will also act as the primer of the designed P-ERCA. Three nicking sites on the template P-ERCA could contribute to the production of enormous signal probes based on the simultaneous amplification-nicking model, which can be visually measured directly with the constructed nucleic acid LFS. Importantly, the nicked signal probe can also act as the trigger of the new-round RCA, achieving exponential growth of signal probes for measurement and signal enhancement. Taking advantage of the extraordinary amplification efficiency of P-ERCA and the simplicity of LFS, this P-ERCA-LFS method demonstrates ultrasensitive detection of OTA with a visual limit of detection as low as 100 fg/mL for qualitative screening and a limit of detection of 35 fg/mL for semiquantitative analysis. This designed strategy could also be utilized as a universal method for detection of other chemical analytes with the replacement of the aptamer for recognition, and the nucleic acid LFS unit could also be a useful protocol for direct ssDNA analysis.

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Aptamers: The Powerful Molecular Tools for Virus Detection

Cited by 16 publications

References 48 publications

Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review

Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review

Use of Nanomaterials-Based Enzymes in the Food Industry

Recognition-Activated Primer-Mediated Exponential Rolling Circle Amplification for Signal Probe Production and Ultrasensitive Visual Detection of Ochratoxin A with Nucleic Acid Lateral Flow Strips

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