Zn2+/GNPs nanocomposite for highly selective colorimetric detection of creatinine in urine samples of CKD patients

• Published in: Inorganic chemistry communications Vol. 158; p. 111618
• Main Authors: Chhillar, Monika, Kukkar, Deepak, Deep, Akash, Yadav, Ashok Kumar, Kim, Ki-Hyun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2023
• Subjects: Biosensor; Bland Altman analysis; Chronic kidney disease; Gold nanoparticles; Surface enhanced Raman spectroscopy; Urine; Bland Altman analysis; Urine; Chronic kidney disease; Biosensor; Gold nanoparticles; Surface enhanced Raman spectroscopy
• ISSN: 1387-7003; 1879-0259
• DOI: 10.1016/j.inoche.2023.111618

[Display omitted] •GNPs-based fast and colorimetric biosensor for CR detection has been reported.•A nanocomposite was prepared between GNPs and Zn2+ ions.•The biosensor exhibited good selectivity to CR over interfering biochemicals.•The biosensor was able to selectively detect CR in artificial urine and clinical samples.•The LOD for CR detection was determined to be 2 µg·mL−1. This research reports the fabrication of Zn2+/gold nanoparticles (GNPs) nanocomposite for colorimetric detection of creatinine (CR) in diverse media (e.g., water, artificial urine, and urine samples) between healthy subjects and chronic kidney disease (CKD) patients. The color of GNPs in suspension changed from characteristic wine-red (λabsorption = 545 ± 5 nm) to colorless or black upon the formation of nanocomposite with Zn2+ ions. However, upon addition of CR to Zn2+/GNPs nanocomposite suspension, the characteristic wine-red color of GNPs was restored. Our experimental analysis evidently validated the hypothesis on Zn2+ induced agglomeration of GNPs and their subsequent anti-agglomeration upon the addition of CR to the Zn2+/GNPs. Overall, our approach offered highly sensitive recognition of CR with a limit of detection of 2 µg·mL−1 (R2 = 0.95) along with excellent stability (>three months), selectivity (in the presence of interfering biochemicals (e.g., urea,ascorbic acid, and glucose, glutathione, Na+, K+, Ca2+, Mg2+, PO43−, and SO4 2−)), and reproducibility (relative standard deviation ∼ 9 %). Finally, the great potential of our method for CR recognition was also confirmed by good agreement (R2 = 0.95) with the gold standard ‘Jaffe’ method along with the Bland-Altman analysis for urine samples between health subjects (n = 15) and CKD patients (n = 11). In near future, a quantitative lateral flow biosensor is expected to be developed for non-invasive detection of CR through the integration of the proposed approach with the machine learning tools.