Issue 9, 2021

An ultrasensitive colorimetric assay based on a multi-amplification strategy employing Pt/IrO2@SA@HRP nanoflowers for the detection of progesterone in saliva samples

Abstract

Progesterone (P4) belongs to a factor that affects stress response and is a potential carcinogen, and saliva levels are expected to be a standard measurement for clinical diagnosis. In this study, a new type of nanoflower with both recognition functionality and catalytic substrate ability was prepared by copper phosphate, Pt/IrO2 nanocomposites (Pt/IrO2 NPs), streptavidin (SA) and horseradish peroxidase (HRP) via a one-pot co-precipitation strategy. Due to the enhanced catalytic activity and stability of Pt/IrO2@SA@HRP nanoflowers, we developed a powerful and sensitive multiple-catalysis ELISA to monitor progesterone in saliva. Multiple-catalysis ELISA based on a specific antibody and Pt/IrO2@SA@HRP nanoflowers exhibited a linear interval range from 0.217 ng mL−1 to 7.934 ng mL−1. The median inhibitory concentration (IC50) for progesterone is 1.311 ng mL−1 and the limit of detection (LOD = IC10) is 0.076 ng mL−1 in the proposed method. Satisfactory recoveries were in a range of 79.6–107% with an acceptable coefficient of variation (below 10.6%). Results of the multiple-catalysis ELISA and LC-MS/MS had a good coincidence. Our result unraveled that multiple-catalysis ELISA is a potentially serviceable tool for the detection of progesterone in saliva.

Graphical abstract: An ultrasensitive colorimetric assay based on a multi-amplification strategy employing Pt/IrO2@SA@HRP nanoflowers for the detection of progesterone in saliva samples

Supplementary files

Article information

Article type
Paper
Submitted
10 Jan 2021
Accepted
05 Feb 2021
First published
05 Feb 2021

Anal. Methods, 2021,13, 1164-1171

An ultrasensitive colorimetric assay based on a multi-amplification strategy employing Pt/IrO2@SA@HRP nanoflowers for the detection of progesterone in saliva samples

M. Lu, Q. He, Y. Zhong, J. Pan, Z. Lao, M. Lin, T. Wang, X. Cui, J. Ding and S. Zhao, Anal. Methods, 2021, 13, 1164 DOI: 10.1039/D1AY00053E

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