Label-free multiplex detection of miRNA-assayed hydrogel barcode using a low-aspect-ratio micropore sensor

Song, Cholho; Bae, Su Hyeon; Bong, Ki Wan; Han, Chang-Soo

doi:10.1016/j.snb.2023.133376

Cited by 3 publications

(2 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The automatic microparticle analysis platform can minimize the detection duration and fatigue during analysis. Accordingly, several automatic detection and decoding processes of graphically encoded HMPs have been developed using coulter counter, flow cytometry, and computer vision technology . Although these techniques facilitate automatic and high-throughput particle decoding processes, they have issues in terms of decoding capacity and accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Although these techniques facilitate automatic and high-throughput particle decoding processes, they have issues in terms of decoding capacity and accuracy. Coulter counter and flow cytometry techniques generally exploit unidirectionally encoded particles to be analyzed, since particle analysis is progressed in situ in one-way microfluidic channels. , Thus, the morphological design and encoding capacity of particles are limited. Even though algorithm-based computer vision systems can be applied to decoding diverse types of graphical codes, they are vulnerable to erroneous outputs for flawed or damaged particles …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Highly Flexible Deep-Learning-Based Automatic Analysis for Graphically Encoded Hydrogel Microparticles

Choi,

Jang,

Lim

et al. 2023

ACS Sens.

Self Cite

View full text Add to dashboard Cite

Graphically encoded hydrogel microparticle (HMP)-based bioassay is a diagnostic tool characterized by exceptional multiplex detectability and robust sensitivity and specificity. Specifically, deep learning enables highly fast and accurate analyses of HMPs with diverse graphical codes. However, previous related studies have found the use of plain particles as data to be disadvantageous for accurate analyses of HMPs loaded with functional nanomaterials. Furthermore, the manual data annotation method used in existing approaches is highly labor-intensive and time-consuming. In this study, we present an efficient deeplearning-based analysis of encoded HMPs with diverse graphical codes and functional nanomaterials, utilizing the auto-annotation and synthetic data mixing methods for model training. The autoannotation enhanced the throughput of dataset preparation up to 0.11 s/image. Using synthetic data mixing, a mean average precision of 0.88 was achieved in the analysis of encoded HMPs with magnetic nanoparticles, representing an approximately twofold improvement over the standard method. To evaluate the practical applicability of the proposed automatic analysis strategy, a singleimage analysis was performed after the triplex immunoassay for the preeclampsia-related protein biomarkers. Finally, we accomplished a processing throughput of 0.353 s per sample for analyzing the result image.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Highly Flexible Deep-Learning-Based Automatic Analysis for Graphically Encoded Hydrogel Microparticles

Choi,

Jang,

Lim

et al. 2023

ACS Sens.

Self Cite

View full text Add to dashboard Cite

show abstract

Nanogenosensors based on aptamers and peptides for bioelectrochemical cancer detection: an overview of recent advances in emerging materials and technologies

Kangarshahi,

Naghib

2024

Discov Appl Sci

View full text Add to dashboard Cite

Electrochemical biosensors fabricated based on nucleic acids have shown great potential for cancer recognition because of their low cost, fast feedback, high sensitivity, and easy operation. This review will demonstrate the impression of recent advances and applications of electrochemical biosensors that are nucleic acid-based for cancer detection. We compare electrochemical biosensors formulated on nucleic acids with those formed on antibodies and highlight some examples of electrochemical biosensors developed on nucleic acids for cancer detection, such as biosensors that use DNA or RNA aptamers to detect prostate-specific antigens, microRNA-21, or carcinoembryonic antigens. We discuss the rewards and drawbacks of these biosensors and the challenges they face, such as stability, reproducibility, interference, and standardization. We also suggest some possible directions and opportunities for future research and development, such as developing novel nucleic acid recognition elements, exploring new transducer materials and configurations, designing new signal amplification strategies, integrating electrochemical biosensors with microfluidic devices or portable instruments, and evaluating electrochemical biosensors in clinical settings with actual samples from cancer patients or healthy donors. Overall, we believe that electrochemical biosensors that are nucleic acid-based offer an auspicious alternative to conventional methods for cancer detection and have great potential to contribute to early diagnosis and effective cancer treatment. Graphical Abstract

show abstract

Application of smart-responsive hydrogels in nucleic acid and nucleic acid-based target sensing: A review

Song,

Zhang,

Shen

et al. 2025

Biosensors and Bioelectronics

View full text Add to dashboard Cite

Label-free multiplex detection of miRNA-assayed hydrogel barcode using a low-aspect-ratio micropore sensor

Cited by 3 publications

References 56 publications

Highly Flexible Deep-Learning-Based Automatic Analysis for Graphically Encoded Hydrogel Microparticles

Highly Flexible Deep-Learning-Based Automatic Analysis for Graphically Encoded Hydrogel Microparticles

Nanogenosensors based on aptamers and peptides for bioelectrochemical cancer detection: an overview of recent advances in emerging materials and technologies

Application of smart-responsive hydrogels in nucleic acid and nucleic acid-based target sensing: A review

Contact Info

Product

Resources

About