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

被引:2
|
作者
Chhillar, Monika [1 ,2 ]
Kukkar, Deepak [1 ,2 ]
Deep, Akash [3 ]
Yadav, Ashok Kumar [4 ]
Kim, Ki-Hyun [5 ]
机构
[1] Chandigarh Univ, Dept Biotechnol, Mohali 140413, Punjab, India
[2] Chandigarh Univ, Univ Ctr Res & Dev, Mohali 140413, Punjab, India
[3] Inst Nanosci & Nanotechnol, Energy & Environm Unit, Sect 81, SAS Nagar 140306, Punjab, India
[4] Post Grad Inst Med Educ & Res, Dept Expt Med & Biotechnol, Sect 12, Chandigarh, India
[5] Hanyang Univ, Dept Civil & Environm Engn, Seoul 04763, South Korea
基金
新加坡国家研究基金会;
关键词
Biosensor; Bland Altman analysis; Chronic kidney disease; Gold nanoparticles; Surface enhanced Raman spectroscopy; Urine; CHRONIC KIDNEY-DISEASE; SERUM CREATININE; NANOPARTICLES; INTERFERENCE; PROBE; JAFFE;
D O I
10.1016/j.inoche.2023.111618
中图分类号
O61 [无机化学];
学科分类号
070301 ; 081704 ;
摘要
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 (lambda(absorption) = 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 mu g<middle dot>mL(-1) (R-2 = 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 similar to 9 %). Finally, the great potential of our method for CR recognition was also confirmed by good agreement (R-2 = 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.
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页数:8
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