Acute Kidney Injury Detection using Real Human Urine NGAL Biomarker Sensor based on 3D Graphene

Sittihakote, Netnapa; Anutrakulchai, Sirirat; Tuantranont, Adisorn; Danvirutai, Pobporn; Srichan, Chavis

doi:10.1109/bmeicon56653.2022.10012085

Cited by 1 publication

(1 citation statement)

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“… a CV: cyclic voltammograms; DPV: differential pulse voltammetry; G: graphene; PANI: polyaniline; LA2: rabbit polygonal lipocalin-2 antibody; AuNPs: gold nanoparticles; PAMAM: generation-1 polyamidoamine; NGAL: neutrophil gelatinase-associated lipocalin; P(ATT)-GO: poly-3-amino-1,2,4-triazole-5-thiol/graphene oxide composite; GSPE: graphite screen-printed electrode; LCN2: lipocalin-2; APT 1: thiol-tethered DNA aptamer; APT2: biotinylated secondary aptamer; SAM: self-assembled-monolayer; ITO: indium tin oxide, TIO 2 : titanium dioxide; COPC: streptavidin-coated cobalt 2,9,16,23-tetraaminophthalocyanine; CS: chitosan; SA: streptavidin; BSA: bovine serum albumin; PEC: photoelectrochemical; EIS: electrochemical impedance spectroscopy. b Adapted from Sittihakote et al 22 …”

Section: Resultsmentioning

confidence: 99%

Empowering an Acute Kidney Injury 3D Graphene-Based Sensor Using Extreme Learning Machine

Sittihakote,

Danvirutai,

Anutrakulchai

et al. 2024

ACS Omega

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This study reports on the application of an extreme learning machine (ELM) in near-real-time kidney monitoring via urine neutrophil gelatinase-associated lipocalin (NGAL) detection with a 3D graphene electrode. This integration marks the first instance of combining a graphene-based electrode with machine learning to enhance the NGAL detection accuracy, building on our group's 2020 research. The methodology involves two key components: a graphene electrode functionalized with a lipocalin-2 antibody for NGAL detection and the ELM application for improved prediction accuracy by using urine analysis data. The results show a significant 15% increase in the area under the curve (AUC) for NGAL determination, with error reduction from ±6 to 0.54 ng/mL within a linear range of 2.7−140 ng/mL. The ELM also lowered the detection limit from 14.8 to 0.89 ng/mL and increased accuracy, precision, sensitivity, specificity, and F1 score for AKI prediction by 8.89, 30.69, 6.78, 9.94, and 19.07%, respectively. These findings underscore the efficacy of simple neural networks in enhancing graphene-based electrochemical sensors for AKI biomarkers. ELM was chosen for its optimal performance-resource balance, with a comparative analysis of ELM, support vector machines, multilayer perceptron, and random forest algorithms also included. This research suggests the potential for miniaturizing AI-enhanced sensors for practical applications.

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