Simultaneous detection of human neutrophil elastase and cathepsin G on a single substrate using a fluorometric quantum dots probe and chemometric models
Human neutrophil elastase (HNE) and cathepsin G (CatG) are crucial proteolytic enzymes involved in the pathophysiology of chronic wounds. High levels of these enzymes indicate prolonged inflammation and impaired healing processes, making their discrimination and quantification essential for effectiv...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Biosensors and Bioelectronics: X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590137024001353 |
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| Summary: | Human neutrophil elastase (HNE) and cathepsin G (CatG) are crucial proteolytic enzymes involved in the pathophysiology of chronic wounds. High levels of these enzymes indicate prolonged inflammation and impaired healing processes, making their discrimination and quantification essential for effective wound management and treatment strategies. In this study, we propose a novel method combining distinctly sized CdTe quantum dots (QDs) as a fluorescent probe to implement a platform for simultaneous discrimination and quantification of HNE and CatG, applying chemometric analysis.The fluorometric response was acquired using two different methods: kinetic and excitation/emission matrices (EEM). These second-order data were processed using various chemometric models, including unfolded partial least-squares with residual bilinearization (U-PLS/RBL), radial basis function artificial neural network (RBF-ANN), and partial least squares-discriminant analysis (PLS-DA), to guarantee a detailed and precise analysis. The results showed that the kinetic method, when processed with the aforementioned models, accurately quantified CatG in the presence of HNE with a REP of around 20%. This method also successfully discriminated the two enzymes both together and individually, achieving a sensitivity and specificity of 1. In contrast, the EEM method only allowed for the discrimination of the two enzymes both together and individually.Our groundbreaking approach proved to be accurate for both the discrimination and quantification of one of the enzymes, offering the advantage of being simpler and faster than other reference procedures. This method paves the way for more effective therapeutic interventions and could initiate a path toward the simultaneous discrimination of multiple enzymes. |
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| ISSN: | 2590-1370 |