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Published on: November 10, 2016
Cu-Trp nanoparticle mimetic enzyme-based colorimetric immunoassay for CA19-9 detection
Ling Zhang1, Ying Zhan1, Bing Zhang1
1College of Chemistry and Chemical Engineering, Taiyuan University of Technology, Taiyuan 030024, China. zhangbing01@tyut.edu.cn.
Analytical Methods : Advancing Methods and Applications
|August 14, 2026
Summary
A new colorimetric immunoassay uses Cu-Trp nanoparticles as a nanozyme for highly sensitive detection of Carbohydrate Antigen 19-9 (CA19-9). This method offers accurate cancer biomarker detection for early screening and monitoring.
Area of Science:
- Biochemistry
- Nanotechnology
- Analytical Chemistry
Background:
- Sensitive detection of Carbohydrate Antigen 19-9 (CA19-9) is crucial for cancer screening, monitoring treatment efficacy, and assessing prognosis.
- Existing methods for CA19-9 detection require improvement in sensitivity and accuracy for early cancer diagnosis.
Purpose of the Study:
- To develop a novel, highly sensitive colorimetric immunoassay for CA19-9 detection.
- To utilize copper-tryptophan (Cu-Trp) nanoparticles as a nanozyme for enhanced assay performance.
Main Methods:
- A sandwich-type colorimetric immunoassay was designed using Cu-Trp nanoparticles with ascorbic acid oxidase-like activity.
- Cu-Trp nanoparticles catalyze the oxidation of ascorbic acid, influencing a colorimetric reaction involving Fe(BPT)3.
- The assay measures changes in UV absorbance at 534 nm, correlating with CA19-9 concentration.
Main Results:
- The developed immunoassay demonstrated high sensitivity for CA19-9 detection.
- A linear correlation was established between UV absorbance and the logarithm of CA19-9 concentration (4–1000 U mL⁻¹).
- A low detection limit of 0.38 U mL⁻¹ was achieved under optimized conditions.
Conclusions:
- The Cu-Trp nanoparticle-based colorimetric immunoassay provides a sensitive and accurate method for CA19-9 detection.
- This analytical strategy shows significant potential for early cancer screening and monitoring of other tumor biomarkers.

