About this Research Topic
Selective and sensitive methods for (early) detection of critical biomarkers are pivotal to identifying and treating adverse health-conditions. Recent global health afflictions such as COVID-19 and more widespread, yet underwhelmingly acknowledged conditions such as diabetes have reiterated the importance of early recognition of relevant biomarkers for early-treatment and preemptive care efforts. Health-related biomarkers are typically found in bodily fluids (e.g., blood, sweat, saliva) and breath. The plethora of matrix molecules present a significant challenge for selective biomarker detection and often lead to fouling of sensors. Thus, sensors tailored for biomarker detection not only have to operate in uncontrolled and chaotic environments but must also have to be resistant to off-target responses. Developing sensors that are low-cost and easy-to-operate (e.g., rapid antigen tests), to combat emerging and existing global health threats is essential to improve the quality of life across all resource settings.
For early detection of analytes (in this case, health biomarkers), the sensor in question must ideally be able to detect the target analyte at or below the clinically relevant concentration. Furthermore, it should be highly selective to the target analyte to minimize false readouts (both false positives and negatives), especially those originating from interactions with matrix components. While the high selectivity is typically achieved using specific antibodies and aptamers, challenges exist in non-specific bindings and limitations associated with detection methods. Although high selectivity and sensitivity are hallmarks of a good sensor, the design and operating cost dictates its global adoption potential. Thus, low-cost, easy-to-operate, selective, and sensitive sensors for biomarker detection is essential to tackle afflictions on a global scale. Not only the chemistry of sensing, but the electronics also embedded in the sensing platform plays a vital role to meet these criteria. Apart from experimental design, machine learning and artificial intelligence could be leveraged to further improve the performance of a sensor by recognizing readout patterns that are otherwise inaccessible through conventional analysis metrics and methods. Such methods can potentially uncover analyte-specific readout signatures to enable the selective recognition of the target analyte in a complex and chaotic sensing environment.
Biomarker detection involves selective chemistries for target capture, detection methods (e.g., optical and, electrical), electronics and data analytics. The synergistic cohesion of these is essential to develop low-cost, easy-to-operate, selective, and sensitive sensors for biomarker detection. Thus, this edition would focus on the development of such sensing platforms, and we welcome the submission of Original Research, Review, Mini Review, and Perspective articles on themes including, but not limited to:
• Nanopore Sensing (solid-state and biological)
• Chemiresistive breath sensing
• Electrochemical Sensing
• Machine Learning and Artificial Intelligence for Sensing
• Electronic designs for low-cost sensing
• Surface Enhanced Raman Spectroscopy
• Surface Plasmon Resonance
For early detection of analytes (in this case, health biomarkers), the sensor in question must ideally be able to detect the target analyte at or below the clinically relevant concentration. Furthermore, it should be highly selective to the target analyte to minimize false readouts (both false positives and negatives), especially those originating from interactions with matrix components. While the high selectivity is typically achieved using specific antibodies and aptamers, challenges exist in non-specific bindings and limitations associated with detection methods. Although high selectivity and sensitivity are hallmarks of a good sensor, the design and operating cost dictates its global adoption potential. Thus, low-cost, easy-to-operate, selective, and sensitive sensors for biomarker detection is essential to tackle afflictions on a global scale. Not only the chemistry of sensing, but the electronics also embedded in the sensing platform plays a vital role to meet these criteria. Apart from experimental design, machine learning and artificial intelligence could be leveraged to further improve the performance of a sensor by recognizing readout patterns that are otherwise inaccessible through conventional analysis metrics and methods. Such methods can potentially uncover analyte-specific readout signatures to enable the selective recognition of the target analyte in a complex and chaotic sensing environment.
Biomarker detection involves selective chemistries for target capture, detection methods (e.g., optical and, electrical), electronics and data analytics. The synergistic cohesion of these is essential to develop low-cost, easy-to-operate, selective, and sensitive sensors for biomarker detection. Thus, this edition would focus on the development of such sensing platforms, and we welcome the submission of Original Research, Review, Mini Review, and Perspective articles on themes including, but not limited to:
• Nanopore Sensing (solid-state and biological)
• Chemiresistive breath sensing
• Electrochemical Sensing
• Machine Learning and Artificial Intelligence for Sensing
• Electronic designs for low-cost sensing
• Surface Enhanced Raman Spectroscopy
• Surface Plasmon Resonance
Keywords: Biomarkers, Selectivity, Limit of Detection, Low-Cost, Sensors, Machine Learning and Artificial Intelligence, Electronics
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