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Use of Enzymatic Biosensors to Quantify Endogenous ATP or H2O2 in the Kidney
Published on: October 12, 2015
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Advances in Biosensor Technologies for Acute Kidney Injury
Esma Derin1,2, Fatih Inci1,2
1UNAM-National Nanotechnology Research Center, Bilkent University, 06800 Ankara, Turkey.
ACS Sensors
|December 20, 2021
Summary
Novel biosensors offer rapid and sensitive detection of acute kidney injury (AKI) biomarkers. These point-of-care devices improve diagnostic accuracy and accelerate treatment for better patient outcomes.
Area of Science:
- Biomedical Engineering
- Clinical Diagnostics
- Nanotechnology
Background:
- Acute kidney injury (AKI) is a critical clinical syndrome with significant morbidity and mortality.
- Current diagnostic parameters for AKI lack sufficient specificity and sensitivity.
- There is a pressing need for novel biomarkers and rapid diagnostic tools for AKI.
Purpose of the Study:
- To review novel biosensing platforms for the detection of AKI biomarkers.
- To present manufacturing processes and strategies for enhancing biosensor effectiveness.
- To compare various biosensing approaches and biorecognition elements for AKI diagnostics.
Main Methods:
- Exploration of biosensors based on electrochemistry, plasmonics, molecular probes, and nanoparticles.
- Integration of efficient surface chemistry strategies for point-of-care device development.
- Review of biorecognition elements including antibodies, aptamers, and molecular imprinting molecules.
Main Results:
- Biosensing platforms are pivotal for detecting and quantifying novel AKI biomarkers.
- These platforms can significantly improve diagnostic interventions and clinical management.
- Various biosensing approaches demonstrate potential for robust, stable, and specific AKI detection.
Conclusions:
- Novel biosensing platforms offer a promising avenue for early and accurate AKI diagnosis.
- Advancements in biosensor technology can accelerate clinical management and improve patient outcomes.
- Further research into biorecognition elements and platform optimization is crucial for clinical translation.
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