Abstract
The McDevitt group has sustained efforts to develop a programmable sensing platform that offers advanced, multiplexed/multiclass chem-/bio-detection capabilities. This scalable chip-based platform has been optimized to service real-world biological specimens and validated for analytical performance. Fashioned as a sensor that learns, the platform can host new content for the application at hand. Identification of biomarker-based fingerprints from complex mixtures has a direct linkage to e-nose and e-tongue research. Recently, we have moved to the point of big data acquisition alongside the linkage to machine learning and artificial intelligence. Here, exciting opportunities are afforded by multiparameter sensing that mimics the sense of taste, overcoming the limitations of salty, sweet, sour, bitter, and glutamate sensing and moving into fingerprints of health and wellness. This article summarizes developments related to the electronic taste chip system evolving into a platform that digitizes biology and affords clinical decision support tools. A dynamic body of literature and key review articles that have contributed to the shaping of these activities are also highlighted. This fully integrated sensor promises more rapid transition of biomarker panels into wide-spread clinical practice yielding valuable new insights into health diagnostics, benefiting early disease detection.
Highlights
Biomarker measurements are increasingly essential for assessing patient health and guiding clinical practice
Between 1995 and 2005, there were >26,000 publications for both cancers and cardiovascular diseases; the Food and Drug Administration (FDA) has approved only ~1 protein biomarker per year [1]. This gap in translating academic/commercial biomarker efforts to widespread clinical use may be bridged by new technologies, such as the programmable Bio-Nano-Chip (p-BNC) developed by the McDevitt group and reviewed here
The multi-biomarker heart failure (HF) diagnosis ScoreCard demonstrated improved discrimination performance over the single-marker BNP test (AUC of 0.94 and 0.93, respectively). These results suggest that the consolidation of information-rich biomarkers and risk factors into statistical learning algorithms may enhance the diagnosis of CVDs and the quantitation of overall cardio-wellness
Summary
Biomarker measurements are increasingly essential for assessing patient health and guiding clinical practice. Between 1995 and 2005, there were >26,000 publications for both cancers and cardiovascular diseases; the Food and Drug Administration (FDA) has approved only ~1 protein biomarker per year [1] This gap in translating academic/commercial biomarker efforts to widespread clinical use may be bridged by new technologies, such as the programmable Bio-Nano-Chip (p-BNC) developed by the McDevitt group and reviewed here. Another challenge that remains unresolved for the laboratory testing community is the lack of a suitable platform on which high-fidelity multiplexed and multiclass assays may be completed. These efforts define a pathway to ‘sensors that learn’
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
