Evidence map›Paper›PMID 39535619›Full record

ArticleMikrochimica acta2024

Highly sensitive detection of MMP-2 using an electrochemiluminescent biosensor enhanced by ladder-branch hybridization chain reaction.

Yuanxun Gong, Jiayi Zhang, Yunzhan Xie, Dingxi Jian, Chenyi Zhuo, Qianli Tang, Kai Zhang, Xianjiu Liao

Erratum issuedAbstract read
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In one paragraph

Article in Mikrochimica acta, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Yuanxun GongGuangxi Key Laboratory for Preclinical and Translational Research on Bone and Joint Degenerative Diseases, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China.
Jiayi ZhangWest Guangxi Key Laboratory for Prevention and Treatment of High-Incidence Diseases, Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China.
Yunzhan XieGuangxi Key Laboratory for Preclinical and Translational Research on Bone and Joint Degenerative Diseases, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China.
Dingxi JianGuangxi Key Laboratory for Preclinical and Translational Research on Bone and Joint Degenerative Diseases, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China.
Chenyi ZhuoGuangxi Key Laboratory for Preclinical and Translational Research on Bone and Joint Degenerative Diseases, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China.
Qianli TangGuangxi Key Laboratory for Preclinical and Translational Research on Bone and Joint Degenerative Diseases, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China. htmgx919@163.com.
Kai ZhangSchool of Chemistry and Materials Science, Nanjing University of Information Science and Technology, Nanjing, 210044, P. R. China. kaizhang@nuist.edu.cn.
Xianjiu LiaoWest Guangxi Key Laboratory for Prevention and Treatment of High-Incidence Diseases, Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China. lxj2006910@163.com.

Funding

National Natural Science Foundation of China 21964018Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology 2023r129
6 · The paper itself

Abstract

An advanced electrochemiluminescence (ECL) biosensor was developed that integrates T7 RNA polymerase amplification, ladder-branch hybridization chain reaction (HCR), and the precise targeting capabilities of CRISPR/Cas13a technology. The novelty of this research lies in the unique combination of these three cutting-edge technologies, which has not been previously utilized together in biosensing platforms, enabling highly sensitive and specific detection of biomolecules with exceptional precision. This innovative biosensor addresses the critical need for sensitive and specific detection of matrix metalloproteinase-2 (MMP-2), a key biomarker in cancer diagnostics. Through meticulous optimization of amplification and reaction conditions, the biosensor demonstrated remarkable sensitivity and specificity, achieving a detection limit as low as 6.34 aM, surpassing existing methodologies. The biosensor also exhibited excellent stability and reproducibility across multiple scans and maintained consistent functionality over an extended period, highlighting its reliability for practical applications. The effectiveness of the biosensor was validated using real samples, demonstrating its capability to accurately quantify MMP-2 in complex biological matrices with high recovery and minimal interference. The integration of isothermal amplification and CRISPR/Cas13a within the ECL biosensor platform represents a significant advancement in molecular diagnostics, offering a powerful tool for real-time monitoring of MMP-2. This combination of technologies sets the platform apart from traditional methods, marking a significant step forward in biosensor innovation. This biosensor holds substantial promise for revolutionizing cancer diagnostics and facilitating personalized treatment strategies, ultimately aiming to improve patient outcomes in cancer care. Future research may explore further enhancements and applications of this biosensor in various clinical settings.

Indexed as

Biosensing TechniquesElectrochemical TechniquesLimit of DetectionLuminescent MeasurementsMatrix Metalloproteinase 2Nucleic Acid HybridizationCRISPR-Cas SystemsHumansNucleic Acid Amplification TechniquesMatrix Metalloproteinase 2MMP2 protein, humanCancer diagnosticCRISPR/Cas13aElectrochemiluminescence biosensorHCRHybridization chain reactionMetalloproteinaseMMP-2

Identifiers

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.