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Published on: February 16, 2018
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Sensitive electrochemical immunosensor for CYFRA21-1 detection based on AuNPs@MoS2@Ti3C2Tx composites
Kai Hu1, Jiamin Cheng1, Kangbin Wang2
1Henan University of Chinese Medicine, Zhengzhou, 450046, PR China.
Talanta
|December 3, 2021
Summary
A new biosensor detects Cytokeratin fragment antigen 21-1 (CYFRA21-1), a marker for non-small cell lung cancer (NSCLC). This sensitive method offers potential for early NSCLC diagnosis and monitoring using patient serum.
Area of Science:
- Biomarker Detection
- Nanomaterials
- Biosensor Technology
Background:
- Cytokeratin fragment antigen 21-1 (CYFRA21-1) is a key biomarker for non-small cell lung cancer (NSCLC).
- Existing detection methods require improvement in sensitivity and speed for early diagnosis.
- Novel nanomaterials offer enhanced properties for biosensor development.
Purpose of the Study:
- To develop a novel electrochemical immunoassay for sensitive CYFRA21-1 detection.
- To synthesize and characterize novel AuNPs@MoS2@Ti3C2Tx composite materials.
- To evaluate the biosensor's performance in detecting CYFRA21-1 in complex biological samples.
Main Methods:
- Synthesis of AuNPs@MoS2@Ti3C2Tx composite nanomaterials.
- Fabrication of an electrochemical biosensor using the composite material, secondary antibodies (Ab2), and toluidine blue (TB).
- Electrochemical detection of CYFRA21-1 in standard solutions and patient serum samples.
Main Results:
- The AuNPs@MoS2@Ti3C2Tx composite exhibited a large specific surface area and good electrocatalytic properties.
- The developed electrochemical immunoassay demonstrated a linear detection range for CYFRA21-1 from 0.5 pg mL−1 to 50 ng mL−1.
- A low detection limit of 0.03 pg mL−1 was achieved, with potential for rapid detection in patient serum.
Conclusions:
- The novel AuNPs@MoS2@Ti3C2Tx composite is effective for developing a highly sensitive electrochemical immunoassay.
- The biosensor shows significant potential for the early diagnosis and monitoring of non-small cell lung cancer.
- This approach offers a promising tool for clinical applications in cancer diagnostics.

