ZnO-Pd nanobolts-based immunoanalytical detection of a pancreatic cancer biomarker and its software-assisted digital quantification.

Authors

Akanksha Deshwal, Rajesh, Sang J Chung, Prashant Mishra, Ravi Mani Tripathi

Published in

Nanoscale horizons. Oct 12, 2026. Epub Oct 12, 2026.

Abstract

Early and reliable detection of pancreatic cancer (PC) remains a formidable clinical challenge, largely owing to the limited sensitivity, operational unreliability, and enzyme dependence of existing carbohydrate antigen 19-9 (CA19-9) immunoassays. In this work, we determine for the first time that ZnO-Pd nanobolts show intrinsic oxidase-like activity. Furthermore, this attribute was used to fabricate a colorimetric immunoanalytical sensor based on nanozymes for the sensitive and specific detection of CA19-9, a biomarker for PC. In this research, immunoreagent optimization, smartphone-assisted signal analysis, and kinetic analysis of nanozyme catalytic activity were employed to enable highly sensitive detection of CA19-9. Enzyme-kinetic studies showed low Michaelis-Menten constants (K_M) of 0.99 mM, as well as high turnover numbers (k_cat) and catalytic efficiencies (k_cat/K_M) of 4.48 × 10^-4 s^-1 and 0.453 M^-1 s^-1, respectively, indicating high substrate affinity. The optimized nanozyme was functionalized with Goat anti-mouse IgG (GAM IgG), thereby facilitating antigen recognition and signal transduction. The proposed immunoassay demonstrated high analytical sensitivity (LOD of 0.000159 U mL^-1 and LOQ of 0.000482 U mL^-1) and exceptional selectivity even in complex biological matrices. Software-assisted signal processing enabled precise assessment of the limit of detection (LOD) of 0.000359 U mL^-1 and the limit of quantification (LOQ) of 0.00108 U mL^-1. The practical utility of the fabricated immunoassay was further examined using human serum, with recovery rates ranging from 100.6% to 102%, indicating high accuracy and reproducibility. Furthermore, the successful digitalization of assay results into software-based readable data demonstrates the sensing approach's potential for portable, cost-effective, and point-of-care PC diagnostics.

PMID:
42861634
Bibliographic data and abstract were imported from PubMed on 12 Oct 2026.

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